Shared Items – March 6, 2012
March 6th, 2012
- victorian_infographics_05.jpg (710×501)
March 6, 2012
Shared Items – March 2, 2012
March 2nd, 2012
- Infographic: Is Gold A Bubble?
March 2, 2012
R short guide
January 12th, 2012
http://www.personality-project.org/R/r.guide.html
Shared Items – January 3, 2012
January 3rd, 2012
- BRIC PMI’S POINT TO GLOBAL ECONOMIC STABILIZATION
January 3, 2012
Shared Items – December 26, 2011
December 26th, 2011
- Technology Footprint: Starting Up in New York
December 26, 2011
Shared Items – November 15, 2011
November 15th, 2011
- Baroque.me: J.S. Bach – Cello Suite No. 1 – Prelude on Vimeo
November 15, 2011
Schumpeter
November 14th, 2011
Shared Items – November 13, 2011
November 13th, 2011
- Greece, Italy, and financial stability
November 13, 2011
Shared Items – November 11, 2011
November 11th, 2011
Shared Items – November 10, 2011
November 10th, 2011
- Wealth Chasm Between Old and Young
November 9, 2011
Shared Items – November 9, 2011
November 9th, 2011
- Presenting Today’s 10-Sigma Move In BTP-Bund Spreads
- Where To Invest Now? Goldman Cautiously ‘Un’-Optimistic
November 9, 2011
November 9, 2011
Shared Items – November 8, 2011
November 8th, 2011
- 沽空产业链穷途末路：卖掉雨润?
November 8, 2011
Shared Items – October 27, 2011
October 27th, 2011
Shared Items – October 26, 2011
October 26th, 2011
Shared Items – October 25, 2011
October 25th, 2011
- Remake / Daryl Seitchik
- Nickel Cobalt
- Nickel Cobalt
October 23, 2011
October 24, 2011
October 24, 2011
Shared Items – October 24, 2011
October 24th, 2011
- Below Trend Growth
- 谁搞垮了中华网 对赌协议成为击倒其最后一拳
October 24, 2011
October 19, 2011
Shared Items – October 15, 2011
October 15th, 2011
- Tokyo Hit With Fukushima Radiation
October 15, 2011
Shared Items – October 14, 2011
October 14th, 2011
- 美林：亚太区富裕人数首次超欧洲
- CREDIT UPDATE – LONG HOPE, SHORT FAITH
- IMF: THE RISKS IN ASIA ARE “DECIDEDLY TILTED TO THE DOWNSIDE”
- How Apple DDOS’d the Interweb Yesterday
October 13, 2011
October 13, 2011 – Like the pattern quote
October 13, 2011
October 13, 2011
Shared Items – October 13, 2011
October 13th, 2011
- DIAPASON COMMODITIES Q4 OUTLOOK
October 13, 2011 – hopefuls
Shared Items – October 12, 2011
October 12th, 2011
- It's All About Financials
- Bill Gross Goes Massively Short Cash As He Bets The Newport Farm On Uber Duration
October 12, 2011
October 11, 2011
Shared Items – October 11, 2011
October 11th, 2011
- The Most Disruptive Companies in Tech
- The World Close to Stall Speed
October 10, 2011
October 11, 2011
Shared Items – October 10, 2011
October 10th, 2011
- Scientists Turn iPhone Into Microscope with $30 Mod
October 10, 2011
Shared Items – October 8, 2011
October 8th, 2011
- Charlie Rose – Reid Hoffman and Jeff Weiner of LinkedIn
- Charlie Rose – Reid Hoffman and Jeff Weiner of LinkedIn
- Dmitry Maksimov
- Eye Chart
- Efficient Frontier of Buy-Hold and Tactical System
- “Your time is limited, so don’t waste it living someone…
- RIP
October 7, 2011
October 5, 2011
October 5, 2011
October 5, 2011
October 6, 2011
October 6, 2011
October 6, 2011
Shared Items – October 7, 2011
October 7th, 2011
- #thankyousteve
October 7, 2011
Shared Items – October 6, 2011
October 6th, 2011
- What Can Quant Firms Learn From Steve Jobs
October 6, 2011
Shared Items – October 5, 2011
October 5th, 2011
- Why Game Theory Blows Up in Financial Markets
- Sitting on the Valuation Mountaintop
- Guest Post: Hong Kong – Still The Cheapest Place To Buy Gold Coins
- We try not to cry – these are old-growth forests.
October 4, 2011
October 4, 2011
October 4, 2011
October 4, 2011
Shared Items – October 4, 2011
October 4th, 2011
- We take a break.
October 3, 2011
Shared Items – October 3, 2011
October 3rd, 2011
- Difficult Month for One of My Best Ideas
September 30, 2011
Shared Items – October 2, 2011
October 2nd, 2011
- Goldman’s Jim O’Neill: “Let’s Worry About Everything”
October 2, 2011
Trainspotting
September 30th, 2011
Now I’m cleaning up and I’m moving on, going straight and choosing life. I’m looking forward to it already. I’m gonna be just like you. The job, the family, the fucking big television. The washing machine, the car, the compact disc and electric tin opener, good health, low cholesterol, dental insurance, mortgage, starter home, leisure wear, luggage, three piece suite, DIY, game shows, junk food, children, walks in the park, nine to five, good at golf, washing the car, choice of sweaters, family Christmas, indexed pension, tax exemption, clearing gutters, getting by, looking ahead, the day you die
Shared Items – September 30, 2011
September 30th, 2011
- We take off.
September 27, 2011
Shared Items – September 29, 2011
September 29th, 2011
- The Fortune 500, 1955 to 2010
September 28, 2011
Shared Items – September 28, 2011
September 28th, 2011
- Are We Headed Into A Recess/Depress-ion? The Answer In 9 Simple Charts
September 27, 2011
Shared Items – September 27, 2011
September 27th, 2011
- Photo
September 22, 2011
Shared Items – September 26, 2011
September 26th, 2011
Shared Items – September 25, 2011
September 25th, 2011
- Financial Warfare
September 22, 2011
Shared Items – September 23, 2011
September 23rd, 2011
- Why Goldman Is Surprised By The Market’s Reaction To The Twist, And What’s Next For The Fed?
- How Long Can Japan Play The Endgame?
- European Liquidity Update
- Guest Post: Why The Fed’s “Silver Bullet” Won’t Kill The Beast
- Market Snapshot: Europe Down Large But End-Tone Better In Credit
September 22, 2011
September 22, 2011
September 22, 2011
September 22, 2011
September 22, 2011
Shared Items – September 22, 2011
September 22nd, 2011
- Truth and Beauty
September 21, 2011
Shared Items – September 21, 2011
September 21st, 2011
Shared Items – September 20, 2011
September 20th, 2011
Shared Items – August 23, 2011
August 23rd, 2011
- We leave.
August 22, 2011
Shared Items – August 11, 2011
August 11th, 2011
- Dow Down 500+, S&P 500 down 4.4%
August 10, 2011
Shared Items – August 10, 2011
August 10th, 2011
- We want to be a dad.
- We can’t wait to leave.
- We are getting there.
- We would like to book a room.
- We love whoever wrote this.
July 26, 2011
July 28, 2011
August 8, 2011
August 8, 2011
August 9, 2011
Gap Minder
August 10th, 2011
Shared Items – August 9, 2011
August 9th, 2011
- Consumer Credit Jumps in June
August 8, 2011
Shared Items – August 8, 2011
August 8th, 2011
- Return of the Global Savings Glut
- For The First Time, Gold Returns Surpass Stocks Since March 2009 Lows
August 8, 2011
August 8, 2011
Shared Items – August 8, 2011
August 8th, 2011
- Return of the Global Savings Glut
- For The First Time, Gold Returns Surpass Stocks Since March 2009 Lows
August 8, 2011
August 8, 2011
Shared Items – August 6, 2011
August 6th, 2011
Shared Items – August 5, 2011
August 5th, 2011
U.S. inflation rate and liquidity trap
July 29th, 2011
cause: Deflation (Japan), insufficient aggregate demand (China), expectation of major uncertainty (War)
extreme pessimism, chronically depression.
Shadow Banking System
July 28th, 2011
Measuring Worth
July 26th, 2011
Shared Items – July 22, 2011
July 22nd, 2011
Shared Items – July 22, 2011
July 22nd, 2011
Shared Items – July 21, 2011
July 21st, 2011
- Presenting The Ultimate Indicator Of Easy Money Access
- Presenting The Ultimate Indicator Of Easy Money Access
July 20, 2011 – good.
July 20, 2011
Murdoc
July 20th, 2011
Shared Items – July 20, 2011
July 20th, 2011
Shared Items – July 19, 2011
July 19th, 2011
- Has Ray Dalio Mastered the Machine?
July 19, 2011
Shared Items – July 18, 2011
July 18th, 2011
Shared Items – July 18, 2011
July 18th, 2011
Lions Gate.
July 17th, 2011
Shared Items – July 15, 2011
July 15th, 2011
Shared Items – July 14, 2011
July 14th, 2011
- Debt and Deficit: When Graphics Speak Louder Than Words
- Greece Vs. Germany: Another Look at the Great Convergence
- Italian Regulator Urges Banks To Destroy Shorts, Pull All Stock Borrow, Generate Marketwide Squeeze
- Watch Ben Bernanke’s Testimony To Congress Live, “Prepared To Respond If Stimulus Needed”
July 13, 2011
July 12, 2011
July 13, 2011
July 13, 2011
Shared Items – July 13, 2011
July 13th, 2011
- Ratio Of Insider Selling To Buying: 3,700 To 1
- The Definitive S&P-IG-VIX Time Series, Cross Asset Arb Chart
- Why The Sudden Surge In Fixed Income Vol May Have Serious Consequences For The Market
- Willem Buiter Says If ECB Does Not Intervene In Thursday’s Italian Bond Auction, It Will Likely Fail
July 11, 2011
July 12, 2011
July 12, 2011
July 12, 2011
Shared Items – July 12, 2011
July 12th, 2011
Shared Items – July 11, 2011
July 11th, 2011
- The Economy Cannot Recover As Long As Inequality Continues to Skyrocket … But Government Policy Is INCREASING Inequality
- The Coming “New World Order” Revolution: How Things Will Change In The Next 20 Years – A Kondratieff Cycle Perspective
- The World’s Biggest Hedge Fund Complete Blow By Blow On What Happens If The Debt Ceiling Is Not Raised
- Generali CDS Surges To All Time Wide
- The Birth/Death Adjustment Was Responsible For Over 50% Of The Payroll Gains In The Past Year
- Guest Post: Boiling Frog Alert: Congress Wants Automatic Wage Deductions To Pay Down The Debt
- Here’s Why Italy’s CDS Are The Biggest Risk For The Eurozone
July 7, 2011
July 7, 2011
July 7, 2011
July 8, 2011
July 8, 2011
July 8, 2011
July 8, 2011
Shared Items – July 10, 2011
July 10th, 2011
Shared Items – July 9, 2011
July 9th, 2011
- Moody’s Downgrades Operating Entities Of Belgium’s Dexia, The Bank Most Rescued By The Fed, From A1 To A3
- Labor Force Participation Rate Drops To Fresh 25 Year Low: 64.1%
- More Records: Average Duration Of Unemployment; People Not In Labor Force Who Want A Job Now Both At All Time High
July 8, 2011
July 8, 2011
July 8, 2011
Shared Items – July 8, 2011
July 8th, 2011
Shared Items – July 6, 2011
July 6th, 2011
- Here Is Why Those Who See In IEA’s SPR Release A “Shadow QE” Are Dead Wrong
- Moody’s July 4 Bomb: Rating Agency Finds 10% Of Chinese GDP Is Bad Debt, Claims “China Debt Problem Bigger Than Stated”
- PIMCO: +$50 Million; Morgan Stanley: -$50 Million
- PIMCO Sees An End To The Currency Wars
July 4, 2011
July 5, 2011
July 5, 2011
July 5, 2011
Shared Items – July 5, 2011
July 5th, 2011
- Guest Post: Making Sense Of The French Rollover Plan
July 4, 2011
Shared Items – July 4, 2011
July 4th, 2011
- Charles Plosser and the 50% Contraction in the Fed’s Balance Sheet
- Charles Plosser and the 50% Contraction in the Fed’s Balance Sheet
- Four Commission Free Portfolios for < 20 bps
- World Gold Bug Article on WSJ Front Page
- Minimum Variance Sector Rotation
- Reading List
- THE FINANCING PYRAMID
- IS INFLATION ALREADY PEAKING?
- Are We on the Brink of High Inflation?
- CREDIT SUISSE: IT’S TIME TO BUY EQUITIES AGAIN
- Greece Is Europe: The Failure Of The Euro
- ISM New Orders Less Inventories Decoupling Hits Unprecedented Levels, Implies Sub-45 ISM Composite
July 3, 2011 – reread this post later.
April 20, 2011
April 20, 2011
April 22, 2011
July 3, 2011 – reread this post.
May 1, 2011
May 9, 2011
May 19, 2011
May 19, 2011
June 15, 2011
June 23, 2011
July 1, 2011
MIT Macro Connection
July 3rd, 2011
Shared Items – July 3, 2011
July 3rd, 2011
- The #1 Investment Return Factor No One Wants to Talk About
- Organizational charts in major tech
- Predictable Surprises
- Risk Spread Compression Time
- Swiss Franc and the possibility of huge mortgage defaults in Central Europe
- T-Minus Two Months Until The $500 Billion Rolling Debt Ticking Timebomb Goes Off
- Are the credit markets getting unstable?
- So Much For That Japanese Recovery: Large Manufacturer Confidence Plummets
- Record 44.7 Million People Celebrate Geithner’s Departure And The End Of QE2 Through Foodstamps
July 3, 2011
June 30, 2011
June 30, 2011
June 30, 2011
June 30, 2011
July 1, 2011
July 1, 2011
July 1, 2011
July 1, 2011
Shared Items – July 2, 2011
July 2nd, 2011
Shared Items – June 29, 2011
June 29th, 2011
Shared Items – June 28, 2011
June 28th, 2011
- Bob Eisenbeis: QE 2 and Policy
June 28, 2011
Shared Items – June 26, 2011
June 26th, 2011
- “Hitting The Fan” – IceCap Asset Management June Presentation
- Guest Post: Illusions Versus Reality
June 25, 2011
June 26, 2011
Shared Items – June 23, 2011
June 23rd, 2011
- How to Make a Clock Run for 10,000 Years
June 23, 2011
Shared Items – June 21, 2011
June 21st, 2011
- 京城私人会所超4000家 神秘政要混迹其中
- Honoring My Humble Father
June 20, 2011 – The next bubble
June 19, 2011
Shared Items – June 20, 2011
June 20th, 2011
- Good Grief Glasses
- Good Grief Glasses
June 20, 2011
June 20, 2011
Shared Items – June 18, 2011
June 18th, 2011
- Rare Earth Metal Prices Go Parabolic
- Surplus/Deficit as a % of GNP/GDP
- Ice-9 Sightings: [Blank]-OIS Spreads Confirm Liquidity Freeze Spreading
- The Collapsing Greek Income Statement, Or Why Greece Is Doomed
- Historic Collapse In Philly Fed Which Prints At -7.7 On Expectations Of 7.0, Weakest Since July Of 2009, Biggest 3 Month Drop Ever
- FRA-OIS Spread Jumps To 2011 Wides As Greek CDS At Unprecedented Levels
- Recent Risk Spread Dispersion Closes As Algos Bid ES Higher
- Ice-Nine Spreads To Shanghai: Chinese Interbank Liquidity Evaporates
- China Discloses “Vital Self Interests” In European Bailout
June 17, 2011
June 17, 2011
June 16, 2011
June 16, 2011
June 16, 2011
June 16, 2011
June 16, 2011
June 17, 2011
June 17, 2011
Shared Items – June 17, 2011
June 17th, 2011
Shared Items – June 14, 2011
June 14th, 2011
- Sino Support Levels
- scrotumcoat:
now all i want in life is a coi pond roof… thanks…
June 13, 2011
June 11, 2011
Shared Items – June 13, 2011
June 13th, 2011
- 华尔街银行拟减少对美国国债依赖
June 13, 2011
Money Management Review.
June 12th, 2011
Shared Items – June 9, 2011
June 9th, 2011
- Ceridian-UCLA PCI Fuel Index Shows Recovery Stalled
June 8, 2011
Shared Items – June 8, 2011
June 8th, 2011
- Apple Products vs. Platform
June 8, 2011
Shared Items – June 7, 2011
June 7th, 2011
Shared Items – June 2, 2011
June 2nd, 2011
- Avellaneda Lectures: Models and Risk
May 28, 2011
东南互保
June 2nd, 2011
近代中国军阀化自此开始
千万秘密。廿三署文，勒限各使出京，至今无信，各国咸来问讯。以一敌众，理屈势穷。俄已据榆关，日本万余人已出广岛，英法德亦必发兵。瓦解即在目前，已无挽救之法。初十以后，朝政皆为拳匪把持，文告恐有非两宫所出者，将来必如咸丰十一年故事，乃能了事。今为疆臣计，各省集义团御侮，必同归于尽。欲全东南以保宗社，诸大帅须以权宜应之，以定各国之心，仍不背廿四旨，各督抚联络一气，以保疆土。乞裁示，速定办法。
examining the cause of which shows that path dependence is extremely strong in this case.
Garage band is stunning
May 29th, 2011
Chinese RTO exposed
May 29th, 2011
[News]
中国企业在美借壳上市之路 [Chinese version of this Barron article (originally out 08-2010) came out recently]
Seeking Alpha post [ 08-2010]
追杀中国造假股——中国概念股为何血溅美国资本市场 [冯禹丁 26-05-2011]
[Investigation]
Muddy Waters Research, Carson Block;
[The small short tradition]
and
[IB]
Roth Capital; William Blair; Rodman & Renshaw
Pinnacle Adviser; Barron Capital; Guerilla Capital; Barry Kitt (Kitt’s son working in Roth Shanghai office.); Andrew B. Worden; Peter Siris;
[U.S. connection]
夏梅仪; 潘小夏; Lawrence Xiao Xia Pan;
[Chinese Connection]
杜青松; ShingHoi To; Du Chenghai; Yinshing David To; AiDi Investment; Asia Pacific Securities;
徐杰; Kit Tsui; Max Time Enterprises; China Finance; China U.S. Strategy;
Benjamin Wey;
[PR & Compliance]
CCG Investor Relations; Frazer Frost; Moore Stephens; Susan Woo; Kabani & Co.; Hamid Kabani;
[Location]
洛杉矶; Newport Beach; Shanghai ; Hawaii
[Endorser]
Proskauer; Michael D.Witter;
Shared Items – May 24, 2011
May 24th, 2011
- “暗恋”这件小事
- Greek CDS-Buying Villain Hellenic Postbank To Be First Casuality Of Hellenic “No Bid” Privatization Reality
- Time-Lapses and the 10 Most-Viewed Commencement Addresses
- The Many Hats of Great Investors
- Chicago Fed: Economic activity weakened in April
- Chicago Fed National Activity Index Drops To -0.45, Lowest Since August 2010, Economy Enters "Below-Average" Growth
May 23, 2011 – this is significant.
May 23, 2011
May 23, 2011
May 22, 2011
May 23, 2011
May 23, 2011 – first time i saw the R word since a while
Shared Items – May 23, 2011
May 23rd, 2011
- 利空交织 有色金属价格继续下跌
- Rising Bearishness = Bullish Markets?
- 房地产“鬼城”再调查：复活最长或需百年
- 淘米网或成中国首家儿童概念股
- Go West, Young Lawyer
- 中国猪肉价格触及三年高位
- 日本震后衰退程度超预期
- 中国实物黄金投资冠全球
- 知情人士：Prada香港IPO计划获监管机构批准
- Fund Investors Flee Emerging Market Stocks
- TOP 5 GRAPHS OF THE WEEK
- 和住宅价格相比，商业地产价值被低估了
May 22, 2011 – tightening + mean reverting effect of high energy price. slow down for a while.
May 22, 2011 – so a few month huh?
May 22, 2011 – ….
May 22, 2011 – ??? serious????
May 22, 2011 – tech is booming . booming booming..
May 22, 2011 – well.. it happened…
May 22, 2011 – deny deny deny ..
May 20, 2011
May 22, 2011 – subscribe.
May 22, 2011
May 22, 2011
May 21, 2010
ETH Flying robot test bed
May 22nd, 2011
Trend spotting on youtube
May 22nd, 2011
A time when conspiracy theorists(?) thrive, is a time….( ) ?
May 22nd, 2011
when conclusion draw by the theory is right even though the reason is flawed/biased, revolution is the next ?
‘national inflation association’s’ promoted video 396,183 view (embedded with silver coin ads)
#4 most discussed this week in Education category on youtube leads me to dig a bit more…
who is behind National Inflation Association www.inflation.u.s. (nice web 2.0 like website). appearance on Fox news.
apparently somebody has raise similar question last october, and a few days ago
and apparently Peter Schiff,a popular financial market commentator and ct senator contender(?) is related
and the meme NIA created is not restricted to u.s. at all. and gain recognition in various anarchist website.
and hit mainstream blog zerohedge as well.
textbook, student loan, student as cash cow… pretty true.
| Date | Event | Views | |
|
A
|
05/15/11 | First view from ad | 44,850 |
|
B
|
05/15/11 | First embedded on - topdocumentaryfilms.com | 6,988 |
|
C
|
05/15/11 | First embedded on - www.zerohedge.com | 5,712 |
|
D
|
05/14/11 | First embedded on - inflation.us | 53,285 |
|
E
|
05/14/11 | First view from a mobile device | 35,188 |
|
F
|
05/14/11 | First referral from - www.facebook.com | 18,113 |
|
G
|
05/14/11 | First referral from a subscriber module | 10,723 |
|
H
|
05/14/11 | First referral from YouTube search - college conspiracy | 8,564 |
| Ratings: 6304 | Comments: 5,609 | Favorites: 4,990 | ||||||
|
Shared Items – May 22, 2011
May 22nd, 2011
- LinkedIn: Worth $27 a Share?
- 王江：人民币升值50% 教育业就面临危险
- Bespoke's Commodity Snapshot
- Worthless Stock Certificates
- Statistics on the Amazing Volume of Online Activity
- Here Is What Happens After Greece Defaults
May 20, 2011
May 21, 2011
May 20, 2011
May 22, 2011 – its not just about idea.
May 21, 2011
May 21, 2011
random thoughts
May 21st, 2011
1) pioneering asset allocation
2) liquidity management. manage systematic risk
Shared Items – May 21, 2011
May 21st, 2011
- Happy Birthday Kickstarter!
- US Debt And The Presidents Responsible
- ShareSomeCandy
- ‘shell house’ by kotaro ide
- Are We on the Brink of High Inflation?
- Are We on the Brink of High Inflation?
- Amazon: Kindle ebooks Outsell Printed Books
- The Rich Are About To Get Very, Very Rich: Study Finds Global Millionaire Wealth Set To More Than Double By 2020
- We are the Mods
- 刘明康：一些国家征收境外资本利得税启示中国
May 21, 2011 – kickstarter is taking off.
May 20, 2011
May 20, 2011
May 20, 2011
May 20, 2011 – …
May 20, 2011 – …
May 20, 2011 – really?
May 20, 2011 – uber rich singapore?
May 20, 2011
May 20, 2011
Network structural change. the rise of P2P.
May 20th, 2011
Shared Items – May 20, 2011
May 20th, 2011
- Will China Have a Labor Shortage, Sending Inflation Out of Control?
- Leading Indicators Turn Negative in April
- TIPS Breakeven Spread Falls to Three-Month Low
- Goldman Downgrades The USD
- The Art of Economic Complexity: A new way to visualize a country’s development.
- Data Visualization: The Economic Complexity Observatory
- Japanese Economy Collapses: Q1 GDP Drops At Double Consensus Rate, Epic Nominal Plunge Of -5.2%
- House prices in UK weaken
- IS INFLATION ALREADY PEAKING?
- Another commodities crash layer – the $100bn intervention
May 19, 2011 – ?
May 19, 2011 – wow, this is a burst of negative news. inflation hit first, but then it’s easing expectation build up again?
May 19, 2011 – why i’m not monitoring this?
May 19, 2011 – …
May 19, 2011 – will like into it tomorrow and write a note
May 19, 2011 – like
May 19, 2011 – classical V shape?
May 19, 2011
May 19, 2011 – justification in 3 weeks.
May 19, 2011 – justifications
Christine Lagarde the next IMF chief ?
May 19th, 2011
RMB internationalization.
May 19th, 2011
Shared Items – May 19, 2011
May 19th, 2011
- Positive Signs
- Protests Mount in Spain; Sovereign Debt Crisis to Follow
- The History Of The World's "Reserve" Currency: From Ancient Greece To Today
- Will the Federal Reserve’s Asset Purchases Lead to Higher Inflation?
- Trading as a “Massive Multiplayer Experience”
- Vancouver and Hot Money Flows
- Next Up: Emerging Markets Re-Route
- If it bleeds…
May 18, 2011
May 18, 2011 – we are young and we are jobless
May 18, 2011 – long term trend.
May 18, 2011 – good point, so the two factor is 1)size of reserve. 2) interest rate on bank reserve. plenty room for maneuver for fed. net result is m2?
May 18, 2011
May 18, 2011 – all for the same reason, then….?
May 18, 2011 – doubt about the reasoning, commodity->inflation->easing->commodity?
May 18, 2011
Thomas Heatherwick
May 18th, 2011
Heatherwick is the founder of Heatherwick Studio
hottest game in town and WB report
May 18th, 2011
- The postwar global economic structure –defined by the dominant position of advanced countries –is in the midst of a fundamental change
- Rapid globalization and expected higher growth rates in emerging market economies will translate into greater economic influence for developing countries
- The move to multipolarity will be by and large positive for developing countries, but the transition needs to be managed
Martha Graham google animation
May 18th, 2011
Google – Martha Graham from Ryan J Woodward on Vimeo.
Shared Items – May 18, 2011
May 18th, 2011
- Two Red Flags: Russell and the Hang Seng
- Industrial Production Weakens, Misses Consensus, As Capacity Utilization Peaks
- Most Profitable Q1 Investing Strategy? Identifying And Shorting Chinese Frauds
- F for Fail
- 港楼价升幅相比内地是小儿科
- Eric Sprott Discusses Silver's Prospects And Last Week's Raid With Max Keiser
- Tepper Gives Up On Fins, Cuts Stakes In Citi, Bank Of America, Wells Fargo, GM; Adds Apple, Valero, MetLife
- Capital Context Update: Weak Breadth and Rotation
- CHINA TRIES TO NAVIGATE THE ROAD TO TIGHTER POLICY
- Monday Market Madness
- Complete Paulson Q1 Holdings Breakdown, And How Soros Sold His Gold… Two Months Ago
- 掉颜色的水果和粮食是怎么回事？
- The Economics of Globalization Are Changing Fast And Are Starting to Favor Moving Back to America
- Guest Post: Some Bubble Snapshots And Schizophrenic Five Year Plans
- On The Mechanics Of Pimco's Synthetic Treasury Short, And How The Firm Extracted 9,724 Years Of Duration Out Of Cash
- DOLL: THE END OF QE2 SHOULD BE A NON-EVENT
- Great Inflation Debate: U.K. CPI Jumps to 4.5%
- DOLL: THE END OF QE2 SHOULD BE A NON-EVENT
- China’s Real-Estate Developers Struggle with Debt; Servicing China’s Total Debt Load is Problematic; Dramatic Slowdown in China Coming
- 资金紧张致短端收益率全线上升
May 17, 2011
May 17, 2011 – IP weaken, u.s. france, germany., evidence is accumulating fast…?
May 17, 2011 – Chinese Fraud Hunting… ouch
May 17, 2011 – interest piece of research.
May 17, 2011 – consensus is forming fast….
May 17, 2011 – interesting point about risk of comex short squeeze.
May 17, 2011 – a common theme is, funds has been playing defensive since Q1, cut financial, cut commodity etc. etc.
May 17, 2011 – bet goes where the mouth is. QE rebalance, Asia crash.
May 17, 2011 – well, another concentrated positioning has been spotted. The ‘rebalance’ is indeed a multi-dimensional one and risk for mild missteps is very high.
May 17, 2011 – debt ceiling will create distortion in the market e.g. short term yield goes down, probably last till aug? so hint/news of debt ceiling will trigger a sudden reverse of this distortion?
May 17, 2011 – when will paulson sell his 4 billion dollar worth of gold, or he’s expecting 8 billion?
May 17, 2011 – we will sex it up if the premium/risk-reward for sex it up is high enough
May 17, 2011 – setting stage for what i’m expecting…
May 17, 2011 – warning shot, not the first one. let’s monitor the banking stocks.
May 17, 2011 – justification.
May 17, 2011 – first of those view. but not so convince, the fact is people perceive tightening and act upon that expectation therefore realize what they expected.
May 17, 2011
May 17, 2011
May 17, 2011
May 17, 2011
QE2 effect, or self fulfilling prophecy
May 17th, 2011
Risk spectrum exposed:
Energy>Industrial>Consumer>Material>Financial
Utility<Consumer(?)<Health Care<Consumer(?)
Risk Spectrum exposed:
Financial>Consumer>Industrial>Material>Energy>Consumer(?)>Health Care >Utility
In that order, than it is working?
housing and banking
May 17th, 2011
bankers-> house ->bankers-house.
Random thoughts
May 17th, 2011
portfolio optimization is about maximize the embeded optionality in the overall portfolio. The idea is quite general, even budda said there is a middle way…
Shared Items – May 17, 2011
May 17th, 2011
- SocGen On A Chinese Slowdown: Buy China CDS, Sell Hard Commodities
- Third Point Closes To All New Investment
- Economic Bust in Australia:Near-Record Corporate Bankruptcies, Employment Drops Unexpectedly; Rise in Bad Home Loans;Record Low Property Transactions
- Consumer Sentiment Remains Gloomy
- Consumer Sentiment Remains Gloomy
- Fun Fact of the Day: California Edition
- BEARISH SENTIMENT HITS 8 WEEK HIGH
- 茅于轼：中国房产泡沫将会在一两年内破灭
May 16, 2011
May 16, 2011 – why so fast?
May 16, 2011 – some to watch during the summer.
May 16, 2011 – seems more negative news hitting market at moment.
May 12, 2011
May 16, 2011 – serious?
May 12, 2011
May 16, 2011 – real rate. real rate. real rate.
Shared Items – May 16, 2011
May 16th, 2011
- Central Banks Purchase 127 Tons Of Gold In Q1
- THE PERPETUAL WALL OF WORRY
- THE PERPETUAL WALL OF WORRY
- Access To iPad App Flipboard Compromised In China
- Guest Post: Q2 Economic Contraction Highly Probable
- 银价波动后的中国身影
- 美财政部敦促国会尽快提高债务上限
- 报导：中国计划尽快推出小QFII机制
- Inflation Fears Are Still Largely Unfounded
- For Many Items, Prices Are Falling Not Rising
- Summary for Week Ending May 13th
- TOP 5 GRAPHS OF THE WEEK
May 16, 2011 – reserve management. 100 ton
May 16, 2011 – demography + structure + optimism triumph. disaster is so easy to forget.
May 12, 2011
May 15, 2011
May 15, 2011
May 13, 2011
May 15, 2011
May 16, 2011
May 15, 2011 – non-core inflation v.s. core inflation, QE2 v.s. M2.
May 15, 2011 – when house, food, commodity price deflates, where is the ‘inflation’?
May 14, 2011
May 15, 2011
florida, california fall first.. east coast is still the most resilient and it exceeds expectation by nature.
May 15th, 2011
job data visualization by TIP strategy.
More interesting stuff from Nanex
May 15th, 2011
1) how to halt trading using non-firm order to bypass NBBO.
what is quote stuffing for…
May 15th, 2011
Nanex rebuff SEC investigation -continued
May 15th, 2011
the rebuff ( 5000 quote/sec data = 5000*4000 = 200,000,000/sec )
Nanex’ further investigation following SEC report ( 1 min data ? seriously?)
0) quote stuffing happened cross NYSE, ARCA, NQEX 400 ms before. [this is suspicious]
However, approximately 400ms before the eMini sale, the quote traffic rate for all NYSE, NYSE Arca, and Nasdaq stocks surged to saturation levels within 75ms. This is a new and surprising discovery. Previously, when we looked at time frames below 1 second, we thought the increase in quote traffic coincided with the heavy sales, but we now know that the surge in quotes preceded the trades by about 400ms. The discovery is surprising, because nearly all the trades in the eMini and ETFs occurred at prevailing bid prices (a liquidity removing event).
1) buyer of Waddel & Reed CBOT e-mini ($50 x s&p), 2,000 contract ( 200 m notional, 4 m VAR), slam 10 level at once (50~100 millisecond) after accumulation.
2) readjust in the system, e-mini-> ETFs ->ETFs components->option chains explosion of quote/trade/update (20 millisecond (CBOT->NYSE) +how long?) [shock in index is the most vulnerable]
It appears that the event that sparked the rapid sell off at 14:42:44:075 was an immediate sale of approximately $125 million worth of June 2010 CME eMini futures contracts (not originating from Waddell & Reed) followed 25ms later by the immediate sale of over $100 million worth of the top ETF’s such as SPY, DIA, QQQQ, IVV, IWM, SDS, XLE, and EEM. Both the eMini and ETF sales were sudden and executed at prevailing bid prices. The orders appeared to hit the bids. The volume in these sales are not considered to be extreme.
3) surge in information saturated every system, information dissemination slow down (worst in NYSE)
The quote traffic surged again during the ETF sell event and remained at saturation levels for nearly 500ms. Additional selling waves began seconds later sending quote traffic rates back to saturation levels. This tidal wave of data caused delays in many feed processing systems and networks. We discovered two notable delays: the NYSE network that feeds into CQS (the “NYSE-CQS Delay”), and the calculation and dissemination of the Dow Jones Indexes (DOW Delay).
4) another two round of e-mini slam 4 sec later -> overwhelm the system further (especially NYSE) [a good tactic if it is an attack with a purpose to overwhelm the system and create psychological impact]
5) NYSE quote queue jammed, affects CQS. maybe not premium quotes.
6) arb BOTs acted on delayed NBBO information (mainly due to NYSE wrong timestamp) and routes order to NYSE -> overwhelm the system further
7) MM BOTs detects CQS v.s. premium data discrepancy, data integrity flag up, pull off the system, this is evident by BATS stub order increase.
7) momentum chasing…
7) flash crash began.
Its seems
0) it is a cascaded failure
1) nobody can detect NYSE delay when it pass 20s. probably passing a inflection point in signal/noise ratio where fool-proof mechanism can make sense itself.
2) system shock -> overwhelmed infrastructure -> data integrity deteriorate -> liquidity dry up -> panic selling is a generic pattern of a crisis in a system characterized by reflectivity, in various time scales.
3) seems the difference in CQT/CQS v.s. Prem data feed , and subsequently data feed into DJI, ETFs creates problem and opportunities.
Flash Crash Analysis by Nanex
May 15th, 2011
loop 1) delay in NYSE quote dissemination -> mismatch between ‘real’ quote time stamp v.s. ‘disseminated’ time stamp -> arb flood NYSE with selling order on delayed signal -> arb orders hit real quote which is lower than what they received against DMM order book. [problem seems too much quote overwhelming NYSE infrastructure, and a loophole in quote queue and dissemination time stamp]
loop2) trade signal(CQS v.s. CTS) from DMM execution disseminated without delay -> trade signal trigger short-term momentum chasing/ front running algo -> more selling order hit NYSE
loop3) arb/mmk algo detects anomaly and pulled out -> reduced liquidity -> panic ensue.
observation:
0)this most delayed seems to be NYSE, the most updated seems to be NQSE, BATS always managed to be inbetween.
1)quote stuffing burst on NYSE and PACF at the first 2 min of the crash triggered further delay on NYSE quote dissemination, DDOS like attack on NYSE ??
2) liquidity, measured by price move between trades, worsen. seems all crisis start and ends as a liquidity crisis, even in high frequency scale.
3) 5~15 mins delay in small TICK, TD. 1 min for GE
It is important to note that we understand 5,000 quotes in one second on any given issue would pose no problem. However, consider that there are approx. 4,000 stocks listed on the NYSE and 9 reporting exchanges. If each reporting exchange for each stock quoted at 5,000 per second this would work out to 180,000,000 quotes per second. Furthermore 5,000 quotes per second is 5 changes per millisecond. At those rates you’d have to abandon the concept of market orders entirely. In fact, at rates exceeding even 50 quotes per second/stock you’d have to abandon market orders entirely. Some would also point out that you couldn’t change the prices at those rates due to the bid/ask spread being so narrow. However there are plenty of cases where the price remains fixed and the sizes flutter.
Furthermore, a CQS quote is 58 or 102 bytes depending if it’s a short or long quote respectively. If the bbo is affected, then add 28 or 58 bytes for a short or long appendage respectively (if the goal is to stuff everyone, you can be sure it would require a long quote and affect the bbo). This equates to a minimum size of (5,000 x 58) 290,000 bytes per second and a maximum size of ((102+58) x 5,000) 800,000 bytes per second for 1 exchange for 1 stock. As a T1 line is 150,000 bytes/sec, one stock ticking away at the minimum 290,000 bytes per second would jam two T1 lines.
4) BATS stub quote (with its own definition) percentage is a good indication of market liquidity.
and more of crop circle, quote stuffing and strange sequence.
Shared Items – May 15, 2011
May 15th, 2011
- Just how out of line are house prices?
- Just how out of line are house prices?
- Just how out of line are house prices?
- 'In Syria we have been scared all our lives. Now at least we have hope, too'
- ‘In Syria we have been scared all our lives. Now at least we have hope, too’
- Coach计划大幅减少在华生产
- Chart Of The Day: Currency Devaluation, Old School Style
- As Denmark Reintroduces Border Controls, Is The Heart Of Europe – Its Customs Union – About To Flatline?
- Dylan Grice On The Coming Japanese Hyperinflation
- GOLD STUCK IN CONSOLIDATION
- Commodities Drubbing Follows Chinese Reserve Requirement Hike, Even As Japan Boosts Capital, Prepares To Print
- 2 emails and zero calls, Street still bullish
- Civil Rights
- convoy
- Agata No. 14 by *franekchrzonszcz on deviantART
- Wade – Creative Studio | Silver Black Cycle
- Piccsy :: Image Bookmarking :: just do it
- We See What We Want
- i can read
- i can read
May 13, 2011 – so expect another 20% to 30% adjustment from now to some time in 2012~2013…. how about the chinese number?? the housing boom lasted for …. 10 years in U.S.
May 13, 2011
May 13, 2011 – house is utility, not investment. equity is, bond is, house ???? remember this, this has been the biggest factor, and will be the biggest factor again.
May 13, 2011 – Hope is what drove growth, there is cause and effect yes. But hope could be a good indication
May 12, 2011
May 13, 2011 – World will slowly changw
May 12, 2011 – hmmm…
May 12, 2011 – yes, talk it up baby, talk it up.
May 12, 2011 – one of the main risk factor going fwd , it seems.
May 12, 2011 – have to wait until talk about end of bull market? hmm.. what kind of level would trigger that type of comment… what kind of fundamental?
May 12, 2011 – japanese QE, so tokyo should be buying?
May 12, 2011 – it’s going to turn around then. since more mind to change….?
May 12, 2011 – ?
May 12, 2011 – think twice, cut once.
May 12, 2011 – there is something
May 12, 2011 – remeber this
May 12, 2011 – just do it.
May 12, 2011 – yes. especially when we really want to see it…
May 12, 2011 – what if tomorrow is the last day, why the procrastination?
May 12, 2011 – put idea into action, get hands dirty.
Random thoughts
May 13th, 2011
House is like gold, capital allocation to house is not productive, capital allocation to gold is not productive, spending on research and education is investment, capital allocation on forest is productive, capital allocation to bond and equity market is productive. House is not, Gold is not. Owning those ‘desire driven’ asset class is a hedge, not a business model, owning infrastructure is a producer business model, owning investment conduit is a financial business model.
In a stone age society, the cost of building a house, or the cost of producing a bushal of wheat or the cost of pumping a barrel of oil would determine the how much a caveman have to pay for a house or a bushal of wheat or a barrel of oil. The concept of ownership and rights draws an artificial boundary and therefore created scarcity and therefore a desire for ownership. This desire could stem from a few causes, 1) utility 2) status 3) scarcity expectation. 1) is on the fundamental level(applies to house, land, oil, wheat) 2) is more affiliated food, energy, base metal, forestry not so much in precious metals. 3) is ultimate premium and most pronounced in gold (it has the self-fullfilling property, i.e. scarcity leads to price appreciation which leads to further scarcity).
I suspect the magnitude of 3) is also a function of accessibility to the asset and how interconnected the network is. e.g. one of the reason why HongKong is the place most susceptible to speculation mania and housing bubble is the sheer density of the city, which created an scarcity but also increase the speed a mass hysteria spread cross the population. The fundamental of both factors has been changed dramatically in the past five years, e.g. ETF, various commodity indices, zerohedge, internet etc.. which itself could be the shift in the fundamental in additional to traditional considerations, inflation, interest rate, production cost etc. etc. etc. The impact is hard to quantify though.
it seems we are going through a implicit tightening phase again, and as one hedgie said, how the market and central bank game out in through the summer will determine the course.
Surviving the Storm
May 12th, 2011
case of real-time data
May 10th, 2011
In pursuit of timeless.
April 18th, 2011
Gallup Daily: Economic Indexes
April 17th, 2011
art and artist
April 14th, 2011
precious metal market search index
April 10th, 2011
search engine index
April 9th, 2011
http://index.baidu.com/
http://www.google.com/insights/search
Pavane – Gabriel Faure
February 14th, 2011
毛匪与周主任
December 6th, 2010
没事偷着乐
November 26th, 2010
据英国《每日电讯报》26日报道，4年前41岁的法国摄影师戈德伯格(Sacha Goldberger)发现91岁高龄的奶奶意志消沉，为了让奶奶振作精神，他建议为奶奶拍摄一组不同寻常的照片。他将奶奶扮成各种“超级英雄”，此后这位奶奶的脸上总是挂着笑容。
“超人奶奶”雷德里卡(Frederika)出生在匈牙利布达佩斯，她在二战期间冒着生命危险拯救了11条性命，而她的丈夫也帮助一些犹太人躲避纳粹的追杀，可谓是一位“现实英雄”。后来她作为二战的幸存者从匈牙利移居到了法国，但年老后倍感孤独，渐渐开始出现忧郁症状。
戈德伯格提议为奶奶拍摄具有想象力的照片，照片中的奶奶穿着超人服，戴着头盔，摆出各种姿势，戈德伯格最后用计算机技术为这些照片制造超现实场景。随着这些照片的意外成功，戈德伯格还特意为奶奶开设了一个MySpace网页，之后在网上引起了轰动。
目前这位“超级奶奶”的网页上已经积累了数千名粉丝，有网友这么评价道：“这就是我希望拥有的那种奶奶”、“我很开心看到这些照片，希望我老的时候也可以像你一样。”
Volatility Trading – Euan Sinclair
September 20th, 2010
This book is going to be a classic next to baird, taleb & natenerg in option trading literature. There is a few key points I’d like to highlight and investigate.
CHAPTER 2 (basics)
1) volatility measurement, its bias and convergence, and what EXACTLY is each estimator estimating.
2) high frequency data, vol, volume, liquidity measure, optimal trading strategy.
3) HF estimator and GARCH family estimator (looks problematic in chp. 9)
4) contextualize vol estimation with vol cone, and vix forecast.
CHAPTER 3 (alpha: mr, rv, bias)
1) principle risk (prediction) , curve (2 mr plays), surface(2 mr plays). volatility regime. short cut: 20*|R|
2) behavioral bias in pre-post announcement over bet/ under bet. automated execution at announcement could provide an edge.
3) RV tools: normalized variance, term structure of skew and kurtosis(Corrado & Su), normalized RR FLY(v.s. delta)
CHAPTER 4 (dynamics hedging, source of slow bleeding if not take care of)
1) transaction cost and optimal hedging, HN, WW, Zakamouline, Leland’s adjustment.
2) the edge of MM is in its access to liquidity through out the surface (legging, Baird).
3) transaction cost estimation, market impact function.
4) proxy hedge, index hedge.
CHAPTER 5 (path dependence & discrete hedging)
1) path dependence of pnl due to discrete hedging. estimation and pnl variance.
2) choice of vol for delta hedging. bias of vol for trending and range bound market.
3) breakeven vol skew (dupire 06).
4) close form & simulation of pnl var v.s. hedging vol.
CHAPTER 6 (Money Management)
1) problem of kelly, kelly extend to continuous case return/var
2) kelly drawdown probability, kelly mean time, kelly pnl dynamics.
3) dynamic strategy.
4) mean reverting kelly, optimal stop time (there is a paper on this topic), logistic innovation, backtesting
CHAPTER 7 ( post trade)
VERY IMPORTANT, should spend a lot of time doing this.
CHAPTER 8 ( psychology)
1) point estimation is not that useful
2) wrong estimation of complexed probability
3) framing, stereotype, ignore the details
4) bleeding v.s. blast
5) antidote: rigorous framework, critical, self-critical
Anna May Wong
September 18th, 2010
conversation with yale vice president and dean of admission
September 16th, 2010
three points:
1) admission should be considered in local context and recognize the true human potential that cannot be measure through ordinary ways.
2) play the long game, reflect on the past 300 years, and think forward about next 100 years. and address the risk (land lock, obsolete, irrelevant) from very early on. [this is the most impressive point, reminded me of some of swenson’s points stated in PPM)
3) the object is to mold character through liberal arts education, produce more ‘interesting’ people.
The Awakening of Spring
September 14th, 2010
mega city population growth.
September 13th, 2010
Martha Argerich play Bach Partita
August 28th, 2010
sometimes caress, sometimes kiss, sometimes kill.
a man can’t just sit around?
August 12th, 2010
Larry Walter, an American truck driver who, in 1982, attached 45 helium balloons to a standard lawn chair and then floated from his home in San Pedro, California, to an altitude of 16,000 feet, before eventually shooting a few of the balloons with a pellet gun and drifting into the controlled federal air space of Long Beach airport, where he crashed into a power line that caused a 20-minute blackout
Add routing to Simulation
July 8th, 2010
ORS (order routing service?) is the core for marketcera, I intend to add a similar Order Routing Service into the Swarm simulation, this is one more step towards a arbitrarily realistic market simulation, structural wise. Of course the real tough work should be in the modelling of agent behavior, but I will deal with it later…
Randomize everything
July 7th, 2010
I amended the code to randomize three things
- time between each agent do evaluation and send order
- time between agent send order and exchange process order
- size of order
The dynamics scheduling framework provide great flexibility to make the simulation as close to reality as possible. Here is an example, instead of synchronize and prearrange the step of WHEN traders send out orders, and WHEN the exchange process order by setting:
this.scheduleTraderEvaluation$ForTrader(Globals.env.getCurrentTime()+generator.nextInt(10), trader);
and
this.scheduleTraderSendOrder$ForExchange(Globals.env.getCurrentTime()+generator.nextInt(10), modelSwarm.exchange, trader);
the sequence of events happened can be totally mixed up according to desirable distribution(e.g. Poisson), while the relative causal relationship(e.g. send order -> process order) being maintained. This is very close to reality, i.e. random order arrival, random delay time.
Random order size is handled by wrapping every Order in OrderWrapper, each incoming ‘market’ order can be matched with multiple limit order on the other side of various size. TradeRecord would record the trade information properly.
Next step:
- add OrderHeap.cancel() and implement OrderHeap using sorted Linklists instead of PriorityQueue, so that I can make the whole orderbook information available to Traders and Exchange…
- add expiration time to Order class, and OrderHeap.expire() to clean up the orderbook from time to time. This should eliminate some bias due to permanent nature of current orders.
Swarm Limit Order Book Simulation
July 7th, 2010
Finished implementing the Swarm version of Limit Order Book simulation… This one is built in java and using Swarming’s dynamic scheduling engine. It’s much more flexible then the Matlab time driven implementation and runs bleeding fast compare to matlab. Order book and order crossing engine is using the exact same design as before(two heaps, order wrapper, pure limit order, etc.), strictly speaking I can emulate the time driven version in this implementation, so this one is a more general purpose simulator. Here some market dynamics generate by 100 zero-intelligence agents (time as price, price as time).
0.5/0.5 limit v.s. mkt
0.4/0.6 limit v.s. mkt
LOB Multi-Agent simulation using dynamic scheduling
July 5th, 2010
I’m still thinking about the difference between using Swarm’s dynamic scheduling architecture v.s. using Java multi-threading. Dynamic scheduling clearly gives me more control over the simulation, and processing time/connection delay can be modeled explicitly. here is the schematics:
LOB simulation different architectures
July 5th, 2010
The core for any multi-agent simulation engine is scheduling, which can be dealt in two ways: explicit(with control over instruction) or implicit(give up control to some library). So far I have tried two implicit architectures and trying one explicit.
1. in matlab implementation, the scheduling is achieve by attach a local timer to each agent the sequence of action would be: [action]->[wait]->[action]->… waiting time is explicitly defined, but each agents’ sequence action is on programming level independent of each other(not on run-time level, I really don’t know how matlab handle multiple timer running together, it is definitely multi-threading though). agents are linked by buffers which can be read and write for the state information. This is what Shovrob called ‘time driven’ implementation.
2. Time driven implementation sortof realized parallel simulation of each agent(i.e. each agent’s timer is running independent of each other), but this parallel process is implemented on programming logic level. We have no idea on process level how this parallelism is implemented(need to look into timer in Matlab doc for this, but I’m sure it’s not implemented in multi-thread). What we really want is to simulate the true parallel processes on processor level. This could be implemented in parallel computing fashion, or single process but multi-thread fashion. parallel processing is the most ideal and most close to reality implementation (like those protein simulation), but I havn’t figured out how to do parallel computing on Matlab for a loop in object, there is some routine available but mostly exploiting parallelism in a for loop itself, but not multiple for loops. The java multi-thread architecture is more feasible and is being coded right now.
3. The above two architectures does not control the sequence of instructions send to processor for execution explicitly. The dirty job is passed to a Matlab timer function in the first case, and multi-threads java classes in the second to handle it. Swarm provide a explicit way to schedule this sequence of events and dynamically generate and kill instructions in a schedule queue of instructions IDed by a time stamp. Concurrent instruction is resolved by randomize order of execution of instructions with same time stamp. each trader-exchange can be considered as a state machine, which generate its owe sequence of instructions. This is a beautiful architecture, although not really realistic ? or is it? maybe it’s should not be a statement but a question of how?
p.s. I’m modelling each trader/exchange pair as two state finite machine,trader send order triggers exchange action, which will have instantaneous response(place,match,update etc), then pass control of instruction back to trader. This design clearly defined the causality between trader and exchange, i.e. trader send order cause exchange to take action then pass control back. In reality exchange and trader could overlap in time… this means exchange finite states model is actually not an accurate representation, the system could exists in multiple states simultaneously.
p.s. find a solution! The good thing about having control over a dynamic schedule is that you can simulate concurrent process. instead model the system as two state finite machine. trader evaluation can be self-referenced at each time step, while exchange state stacked over when condition met! finite state machine is just a subset of what is possible with dynamic scheduling engine like swarm. Εὕρηκα!
The interesting thing is the Swarm dynamic scheduling engine runs very much like a order book in abstraction, the difference is scheduling engine do a a simple pull()->execute() while order book do a more complicated match()->pull()->execute(). concurrent scheduling is resolved by randomize the order of execution while order book resolve this ‘conflict’, i.e. orders at same price level by execute them according to order’s time of arrival.
Swarm resources
July 3rd, 2010
Other fella in town
July 2nd, 2010
LOB
July 2nd, 2010
Order Book Dynamics
June 30th, 2010
I managed to get some non-trivial market dynamics out of my LBO model. Exactly as Farmer pointed out, if order is generated disregarding the status of order book, market will turn either stable or unstable, i.e. limit order eventually accumulates around current price, or mkt order wipe out everything. What’s interesting is to see how market could remain in critical states. There are several directions this problem could be studied:
- incorporate each trader’s inventory into her strategy
- maslov’s toy model
- …
this study should yield some useful insight into better understanding of market liquidity.
p.s. 1. the crossing engine could be improved, some kind of tree structure to sort new order, which should be implemented in PriorityQueue, right now the engine slows down significant as heap size increase, as can be seen from the plot above.
p.s.2. price increase should be discrete in reality, what’s the effect?
p.s.3 order elapse time
coded Maslov’s toy model in Strategy, looks like the critical states still depends on P(Mrk_order/Limit_order), fixed number definitely doesn’t work so I set P = rand(there should be some dependency in reality, something to think about), at lease the time series looks better this time. remain to do reality check on its statistical properties. improvement/next step:
- order auto cancellation …tricky
- faster queue in crossing engine … improved to log(n), but seems the speed problem is due to memory not sort.
- write archives into file instead of memory.
- investigate the odd synchronization…after putting some thoughts this seems to be realistic.
- run mm algo
- investigate impact of latency (data feed, order queue, exchange congestion ..)
MATLAB Limit Order Book crossing engine
June 29th, 2010
Instead of sort each order into 4 categories and try to cross between LIMIT and MKT (as in Shovrob’s) , here every order is treated as LIMIT order by consider MKT order as special LIMIT with infinite Limit price. This solved the problem in Dimitri’s original implementation, i.e. no limit order would cross each other. here is a sketch of the crossing algorithm.
put new order W1 into BUY/SELL HeapW2 = othrSide.peak()While (W1.price*W1.Heap_id<=W2.price*W2.Heap_id)match order, send confo, broadcast tradeupdate W1, W2if W1 == 0if W2 == 0Heap(W1).get()Heap(W2).get()breakendHeap(W1).get()breakelseHeap(W2).get()W2=othrSide.peek()endend
The next step is to incorporate the engine into the overall Trader/Exchange/DataVendor frame work and add the feedback loop from OrderBook through DataVendor back to Trader, and see how the simulated market mechanism could remain critical.
download file.
Maradona
June 24th, 2010
No.9
June 21st, 2010
Be embraced, millions!
This kiss is for the whole world.
Toscanini 3/7
Toscanini 6/7
Toscanini 7/7
veritas et lux
June 6th, 2010
This is what sets Marlon Brando apart from the rest.
Google Talk – Computing
May 29th, 2010
Computing:
- [interesting] the parallel revolution has started, are you part of the solution or part of the problem link
- [weird/interesting] Computer science unplugged link
- GPU link
- Erlang link
- turning’s cathedral link
- [interesting] statistical virtualization link
- use physics to design algorithm link
- differential synchronization real-time link
- consumer programing, link
- [interesting, promising] new approach for modeling and control complex system link
- [algorithmic game theory] Nash bargain link
- An new approach for massive parallel system link
- multi-touch interface link
- [Game/Auction] generating trading agent strategy link
- natural programming project link
- NASA HPC visualization link
- industrial control link
Google Talk – Learning
May 29th, 2010
Learning:
- machine learning for malicious url classification. link
- decaying MCMC link
- [promising stuff] new stuff on deep learning, walking style link
- large graph algo bleeding edge link
- [very interesting] low dimensional manifold link
- online PCA, work fast and well(convergence) on low intrinsic dimension data,
- discover intrinsic dimension, v.s. k-mean (NP-complete), very good approximation of k-mean
- application : learn micphone array location, link to control theory + reinforcement learning, Russ Tedrake et al optimal control for a walking robot, MIT Locomotive group, Russ showed some pretty good bird robots.
- futuristic product
- [very interesting] visual thinking with graph network link
- [interesting] generative algo application link
- [wired] cat detection link
- people recognition in video link
- what is people doing link
- motion as graph
- [deep learning] visual perception with deep learning link
- use evolution to design: polyworld link
- Hirachical learning model link
- [important] challenges in causality link causality discovery
- statistical aspect of dataming link
- Hidden topic markov model link
Andrew Ng’s Machine Learning Course
May 28th, 2010
[3] parametric, e.g. LR v.s. non-parametric, e.g. locally weighted liner regression (KD-trees)::: MLE -> maximizing likelihood is equal to minimizing Mean Square Root.::: logistic regression
[4] newton’s method, Hessian operation ::: generalized linear model(GLM), exponential family
[5] discriminative v.s. generative. GDA(stronger data assumption than logistic (whole exponential family), Naive Bayes, Laplace smoothing P(sum rise tomorrow =1)
[6] Naive Bayes(events model), NN, SVM. maximize geometric margine
[7] convex optimization, KKT , Primary/Dual optimization problem. Lagrange multiplier. , SVM dual, kernal
[8] kernal, soft margin, coordinate ascend, SMO(a variation of coordinate ascend to hold constrain)
[9] learning theory : high bias v.s. high variance. ERM non convex NP hard<- SVM/logistic regression convex optimization.
- uniform convergence, given gamma, function set, training set, what’s the training erro v.s generative error bound?
- variance, bias trade off. -> model selection
[10] VC, sampling complexity bound. Model selection: hold-out cross validation/K-fold CV/leak-one-out. Feature selection(fwd selection/backward selection/filter method(corr, mutual information)) NP hard to find the best.
[11] regularization. bayesian stats, prevent overfitting, online learning.
develop learning algo – > high classification error -> normal approach
- more training set {variance}
- smaller feature, more feature, better features, {bias}
- longer descent, or use newton. {error v.s. iteration}
- regulation right? {C for SVM, lamda for gradian decent, Bayesian logistic regression}
- try svm
–> luck.-> better approach
- diagnosis ( what’s the problem? high bias/high variance (training error, fitting error), fix where problem is)
helicopter problem
- better simulator
- learning algorithm
- better cost function
design diagnosis to figure out the problem, then fix the problem.
Error analysis/ablative analysis
e.g 15% error rate. what’s the contribution?
avoid over theorizing, but do diagnosis first and address the dominant issue first.
[12] K mean, mix of Gaussian, EM
[13] Jensen inequality, derive EM(smart idea).
[14] mix of gaussian, mix of naive bayes, condition of sigma, marginal/condition property of gaussain, EM for factor analysis( i.e. linear regression on steroid)
[15]PCA application (visualization, learning(dimension reduction), compression, anomaly detection. matching)
[16] latent component indexing(clustering the words given context), single value decomposition, independent component analysis(source recovery), e.g. EEG data pre-processing example
[17] MDP, markov decision process, bellman function, value iteration, policy iteration. unkonwn transition probability. Value functionn approximation etc. credit assignment problem(what made reinforcement learning hard),
[18] State action reward, Finite horizon MDP, Linear Dynamic system(Linear Quadratic regulization, Riccati equation/++optimal control, time dependent++)
[19] debugging RL algorithm, noise in the model doesn’t affect optimal policy. DDP(differential dynamic programming, tracking problem) for LQR, Kalman Filter, Linear Quadratic Gaussian
[20] POMDP (partially observable MDP). Policy search. (PEGUSUS)
Student project 09{stock trading}, 08{social network}
QC & AI
next 5 years?
Net generation NN —> google talk/ Geoffery Hinton/ Deep Belief Net(RBM) better result than SVM, Backprop, K-mean, no lable
PCA, linear, 30 dimensional supermarket, mean field for lateral propagation.
[1]perception: classification (perception/generation/compression) <==>
[2]reasoning: Bayesian network <==>
[3]decision: game, optimization.
—————-
[1] map high dimensional data to lower dimension manifold, intrinsic dimension
[1.5] temporal?
[2] concept of environment, but still low dimension map to high dimension, forming concept in bayesian framework.
[3] assigned name to the object, environment, temporal pattern, logic operator => language.
NYSE handheld Interface
May 28th, 2010
didi white paper.
Market surveilance
May 28th, 2010
I’d like to bring this up a separate topic:
market surveillance strive to identify the following market activity in real-time:
- Trade-Through;
- Locked Markets;
- Crossed Markets;
- Trading Ahead of customers;
- Prearranged trading;
- Wash trading;
- Shredding Orders;
- Money Passing;
- Counterparty trade percentages;
- Top order fishing;
- Marking the close;
- Error account;
- Transfer trade activity and
- Exchange transactions.
wrt to regulation framework that prohibits/enforce:
- Insider trading
- Market manipulation
- Breaches of fiduciary duties
- Violation of agency responsibility and investor protection rules
- Failure by specialists to maintain fair and orderly markets in listed securities and products, and
- Violation of rules governing members’ on-Floor trading and auction market procedures.
literature:
……
Atelier HFT
May 28th, 2010
[0] Charles-Albert Lahall’s presentation,
sell side, not particularly exciting. almgren, optimal trading curve, avellaneda optimal market making, + some of his own optimization doodles.
[1] Thierry Faucault‘s very interesting presentation. + IBM brochure.
working paper : Liquidity cycle and make/take fee in electronic market. <== important and interesting
paper: competition for order flow & smart routing system <==
He’s making very good points about anonymity, reputation, cost of monitoring in previous publications.
[2] ultra high frequency vol estimation, presentation
paper: ultra-high frequency volatility and co-volatility estimation. Christian Y. Robert, Mathieu Rosenbaum
[3] market simulation : two levels architecture: mean field game approach to LOB simulation, paper, Oliver Gueant. presentation
also refer to early work of LeBaron ==> notes, code || swarm project wiki
[4] Colliard & Faucault ‘s new presentation, Inter market competition, trading fee, and making/taking decision.
Bloomfield, O’Hara 02 on make/take problem ==> this.
[5] optimal splitting order cross liquidity pool,
modeling/optimization :: data/estimation :: market simulation :: market structure/market making :: connection/routing/latency :: what else?
========================================
paper: The role of time in price discovery: ultra high frequency data, Sita 06
press: how great is the need for speed, algorithmics 06
NYSE Technology
Apama UHFCEPAADPT (Ultra High Frequency Complex Event Processing Adaptive Algorithmic Development Platform)
DMA +RTN /CEP +Empirical stats ->
Market Microstructure Literature Review III Market Design
May 24th, 2010
paper transparency and liquidity, a comparison between auction and dealer market, Pagano
paper order flow and liquidity around NYSE trading halt, Corwin
paper dealership market, Amihud
paper volatility efficiency and trading, Amihud
paper stock market microstructure and return volatility evidence from italy, Amihud
paper the anatomy of a call market, Kehr
paper the specialist’s discretion, Ready
paper stock market structure and volatility, Stoll
paper price discovery in auction market, Madhaven
paper what’s special about specialist, Beveniste
paper automated versus floor trading, Venkataraman
paper quote disclosure and price discovery in multi-dealer financial market, Flood
paper transparency and liquidity a study of LSE, Gemmill
paper can transparent market survive, Bloomfield
paper an order prohibited analysis of transaction stock price, Hausman & Lo
paper eighth, sixteenth and market depth
paper sixteenth, direct evidence on institutional trade
paper minimum price variation, Cordella
paper does large minimum variation encourage order exposure, Harris
paper multimarket trading and market liquidity, Chaudhry
paper quote order flow and price discovery, Bloom
paper a cross exchange comparison of execution cost and information, Bessenbinder
paper third market broker-dealer, price competitor or cream-skimmer, Batallio
paper order preference and market quality in United States Stock market, Lightfoot
paper potential competition and actual competition on equity option, Neal
paper competition and collusion in dealer market, Dutta & Madhaven
paper the effect of market reform on trading cost and market depth, Barclay
paper the effect of market reform on trading cost of public investor, Naike
this is probably the most interesting part of microstructure research
paper market maker quotation behavior and penetration transparency, Simman & Whitcomb
paper entry exit market maker and bid ask spread, Wahal
paper the making of a dealer market, Ellis & O’Hara
paper order flow composition and trading flow in a dynamic limit order market, Foucault
paper market making with costly monitoring, Foucault
paper market order versus limit evidence from SuperDOT, Harris & Hasbrouk
paper an empirical analysis of limit order book and order flow in Paris bourse, Biais
paper an empirical analysis of NYSE specialist trading, Madhaven
paper timing of orders, order aggressiveness and the order book at paris bourse, Bisiere
paper econometrics model of limit order execution, Lo
paper the econometrics of ultra high frequency data, Engle
paper liquidity based competition for order flow, Parlour
paper price dynamics in limit order market, Parlour
paper split orders, Bernhardt
paper strategic liquidity supply and security design, Biais
paper informed speculation and imperfect competition, Klyes
paper continuous auction and insider trading
paper competitive bidding and proprietary information, Englebecht
paper bid-ask price competition with asymmetric information, Calcagno
paper life in the pit
paper strategic trading and welfare in a dynamic market, Voyanas
paper inventory information, Cao
paper inter dealer trading in financial market, Wang
Finished reading Baird’s option market making today, what they said? what can be said has been said?
paper high frequency trading in limit order book, Marco Avellaneda 06.
- market order arrive frequency modeled as power law f_Q(x) = x^(-1-a) a~ 1.5
- impact function using Bauchard’s result as ln(Q)
- a combination of above two gives the frequency limit order get filled as a function of spread to mid
- indifference mid price as a function of the inventory, vol and hold period(? c’est bizarre), do the optimization, or learn the parameter.
- intuitively optimal bid offer is a function of inventory (reduce vol) and market order arrival rate, and risk preference.
- vol model/intra-day seasonality can be incorporated in this to account for heteroskedasticity. this could be an edge over plain vanilla market maker, as most money is probably made when vol collapse and your algo offer the tightest spread in the market, vice versa during vol explosion.
- correct prediction of order arrive rate and size distribution can provide extra edge as well, e.g. detect iceberg order, incorporate detect abnormal flow, regular flow, intraday pattern etc.
paper the microstructure of stock market, Bruno Biais
- inventory control
- information asymmetry
- strategical liquidity provision
- design(competition between exchange, tick size etc.)
paper statistical property of stock order books, Bouchard
- limit order arrival power law with exponent 0.5, universal
- volume, uniform distribution up to 20 ticks, then flowing power law.
- average shape of order book, gamma distribution, most sensitive to market order arrival rate
- analytical model for average order book given out, similar to Daniel&Farmer’s.
paper how strong supply and demand affects price diffusion, Daniel
paper theory of large fluctuation in stock market
- price tail exponent =3(equity), fx 3.4
- volume exponent =3/2
- number of trade in given time interval exponent =3.4 empirical 3 theoretical
- fund size exponent = 1, price impact 1/2 (an alternative proposal is log function but this complicate the matter)
- price imapact exponent 1/2, link can be easily established between size of trade 3/2, impact 1/2 and price fluctuation 3.
- main result is to establish between size of fund and size of trade, through optimal trading strategy perspective.
paper statistical properties of share volume traded in financial markets
- statistical property of trade volume, number of trade, size.
- long memory in volume.
paper more statistical properties of order books and price impact, Bauchard
- interesting result limit order arrival follow power law with exponent 0.5 for paris bourse( pure limit order, more large movement) while 1.5 for LSE.
- SPY follows normal within 20 tick, then follows power low with exponent ~1, QQQ follows power law more closely with exponent ~1
- cancel rate is not uniform, itself again follows power law wrt distance from mid price.
- log response function is proposed R(t)ln(V) v.s. V^1/2, with R(t) increase from few seconds to few hundred seconds then decrease to permanent impact, variation 50%.
- implication of ln(V): 1)large trade wrt to order book size is cost effective than smaller trade. caused partially by hump shape in order book, partially by correlation between size of limit order and market order (Trading Strategy: show large limit order few tick away from best bid/ask to attract large market order while honor only a partial fill, open for optimization, a) how large is the bail order, b)how far away from mid, c) response strategy wrt to market move)
- implication of R(t), the steady state part of R(t) represents the market estimation of information content of each new trade.
paper dealer bid ask quote and transaction price, Ho
- break down transaction price into three components using Stoll’s model: ‘true price’, inventory effect, spread effect
- OLS test for the above hypothesis, bid-ask bounce accounted for.
- this paper basically applied Stoll’s model to AMEX option specialist data (monopolistic MM)
paper the dynamics of dealer market under competition
paper optimal dealer pricing under transactions and return uncertainty, Ho
- Ho & Stoll’s seminal work, the same period when Whitcomb start ATD.
- dynamics programing problem postulated in this paper. i.e. adjust bid ask to maximize terminal utility
- the easy part is the reserve spread which depends on probability of fill (assume to be linear), this could be refined using Avellaneda 06
- risk premium in bid ask spread depends on: risk preference, size of transaction, vol. independent of inventory level
- inventory response function has same risk parameter as spread function.
the optimization problem is formulated in this paper. max(E(X_T+q*S_T)) subject to bid/offer
paper on dealer market under competition, Ho & Stoll
- single period, two competitior
paper competition and collusion in a dealer market, Madhaven
paper a steady state model of the continuous double auction, Luckock (close form)
- assumption A4, submitted order independent of current order book, improvement?
- assumption A2, temporal dependence of order arrive, improvement?
- assumption A3, in avellaneda 06′s context, this could be another parameter (external) for order arrival rate besides temporal dependence, which affects P(a/b), probability an order being executed.
- assumption A1~A5 means market microstructure is a) markov process 2) zero intelligence a)&b) together means agent with no memory.
- model works well with no informed large order flow & external information, with either of which close form solution is hard to attain. refer to the next 4 papers.
paper analyzing and modeling 1+1d market, Challet
- stats of Island ECN data, 15 nearest order bid-ask, ex size larger than 10,000 share
- volume clustering, life time (-1.5fill, -2.1cxl with peak at 60,100, etc)
- asymmetric impact function, largely due to shape of order book??
- lattice model, take into account of super diffusion in small time scale compare to maslov’s
paper statistical theory of continuous double auction, Smith & Farmer
- seminal paper. robust model
- order cancellation fine tune the balance between market order and limit order to remain critical
- explain well for scale dependent behavior, but insist power law tail behavior depend on non-Poisson order arrival
- correlation of order flow, its depends on other parameters need further investigation
- so most promising future direction is condition order flow to some external parameters, since everything depends on order flow rate in this model.
paper an simulation analysis of double auction markets, Chiarella
- agent based model, noise, technical, fundamental
- advantage of this model is effect of informed and uninformed agent could be studied
paper an integrated model of market making and limit order book, Chakravaty
paper the information content of limit order book, Cao
- Trading Strategy: 0) test for temporal stability/correlation in order book info content, 1)naive 2) active, could be a game here.
- error correction function to estimate information content, Hasbourk 97, Granger & Engle
paper price fluctuation from order book perspective, Maslov
- nonlinear impact function
- information content of order book imbalance
- hinted delay & strategy
- claim to be the first to study order book stats
- market order size x^-1-1.5, limit order size x^-1-1
- concave ‘virtual impact’ with exponent 2 i.e. delta_P=N^2, while real impact is convex function, N^0.5 or ln(N)
- latency come into play when causal correlation time span between order book imbalance and short term price movement is less than network delay, 30 seconds to a few minutes 02, this stats could be much short nowadays.
- Trading idea Figure 7 & Figure 8.
paper simple model of limit order driven market, Maslov
- sergei’s toy model 99.
- Hurst exponent 1/4, due to self-reinforcing trap. this is discussed in Smith & Farmer’s
- log log plot of delta_P for such a simple model is AMAZING!
paper order book approach to price impact
- real impact is 1/4 of virtual impact
- 0.76 exponent for mid point, 0.5 for transaction price.
- exponent tends to decrease wrt to time. ln(Q) in small scale
- reason for for concave function:1) discretion trading (the causal relationship is from trading -> empirical Q^0.5, not vice versa) 2) anticorr between market order imbalance and limit order
- correlation between return and market/limit order.
paper option market making under inventory risk, Skoikov
- MS equity microstructure grant
- analysis under broadly speaking three scenarios, complete, inventory risk, stoch vol overnight risk.
- interesting feature of this paper is that it approached the optimal market making problem through mean variance frame work as oppose to maximize utility, fast calculation.
- optimal spread for vanna & gamma depend on maturity of option under stoch vol.
paper paper why do NASDAQ market maker avoid odd eighth rule, Christie
paper split order, Bernhardt
- given fixed cost, the equilibrium number of market maker
paper trading machenism and stock return, Animhud
- open vs close
paper the only game in the town, Bagehot
paper continuous auction and insider’s trading, Kyle
paper bid ask and transactional price in a specialist market with heterogeneous information, Glosten
paper is electronic open limit order book inevitable, Glosten
- the framework can be used to analyze relationship between EBS, CME, and Dealer market
- this is a very important issue.
paper insider’s trading, liquidity and role of monopolistic specialist, Glosten
- dealer market or specialist market.
paper estimates components of bid ask spread, Glosten
- unbiased estimation of spread and its components, adverse selection account for 10% in this result.
paper specialist gross trading revenue on NYSE, Sophiano
paper liquidity provision with limit order and a strategic specialist, Seppi
paper market microstructure and stock return prediction, Huang
- this type of model should be at least incorporated into market making. e.g. lagged index
- Trading ideas. test of causal relationship/structure (Tsay, multivariate, previous post)?
paper incentive compatible contact for sales of information, Bias
paper insider, outsider and market breakdown
- why market breakdown.
paper insider’s trading without normality, Rochet
paper selling and trading information in financial market, Admati
paper a theory of intraday pattern, Admati
paper a theory of intraday variation, Foster
paper trading and returns under periodical market closure, Hong
paper the effect of sequential information arrival on asset price, Copeland
- Human experiment.
paper order flow composition and trading flow in dynamic limit order, Faucault
- equilibrium between submitting limit order or market order
- very important question but has been left open
- along with glosten, seppi, parlour’s model, but addressed the pick off question of limit order
paper insider and liquidity trading in stock and option market, Biais
- this group of people, biais, seppi, glosten, klye who publish on journal of finance, is OBSESSED with informed trader, insider…
paper the trades of market maker, empirical study of NYSE, Hasbrouk
- first empirical study of inventory(amihud 80, ho 83, o’hara 86, madhaven & smidt 91, 93) and asymmetric information problem
- classical dealer behavior in illiquid market, mix mm with prop. spectral decomposition of specialist profit.
- specialist can predict short term price movement e.g. next 100 trades, but this is a small component compare to profit from bid-ask
- speculation profit for specialist is, random
paper liquidity in futures pit, inferring market dynamics from incomplete data, Hasbrouk
- Hasbrouk made a good point which is: regression of signed order flow and price change, i.e. impact function is actually – trading ideas: the def of liquidity
- Bayesian (MCMC/Gibbs sampling) estimation of latent variables -> estimation of liquidity || while conventional estimator estimate spread & vol through variance & cross-variance of delta_P (patent) GMM
paper trades quotes inventory and information, Hasbrouk
paper an analysis of changes in specialist inventories and quotation, Madhaven
- combined inventory theory with asymmetric information theory.
- patch-up? for little empirical evidence for inventory effect
paper price discovery in auction
- NYSE open
paper life in the pit, competitive market making and inventory control, Manaster
paper does risk sharing motivates inter-dealer trading
paper does inventory matter in dealership market
paper price impact of block trading on NYSE, Kraus & Stoll
paper large block transaction, the speed of response…, Holthaulsen
paper time and process of security price adjustment, Easley
paper cream skimming or profit sharing, Easley
- trading idea: estimate information content in order flow and compete/separate uninformed order flow from informed order flow.
- likehood estimation given for informed trader.
paper test of microstructure hypothesis in forex market, Lyon
paper adverse selection and bid ask spread evidence from close end fund, Neal
paper adverse selection and competitive market making, Sandas
- state dependent order flow.
- an alternative inventory paradigm differs from Stoll & Ho’s with no dealer risk preference
and O’Hara, Madhaven, Hasbrouk
JHU Microstructure course
Ted
May 21st, 2010
review on Iran
May 12th, 2010
Shah of Iran: American military support, suppress grassroots movement, advocate western value.
Khomeini: exiled to Iraq, Saddam offered help to Shah to eliminate/exile Khomeini, gathered a group of young intellects around him, exiled to Paris. Khomeini suceeded in stage a revolution from Paris. Khomeini’s words spread like wild fire in Tehran. Strike by the leftist.
American: view Iran as its outpost against communism and anti-western movement in middle east.
Shah’s court: generals and admirals wear fancy costumes, no mention of disorder on the street or worry or anything, the party goes on. a bit like Havana before its down fall. American want Shah to take hard line position, but try to avoid express this opinion explicitly. Shah refused(?) to suppress the revolution, and announce to take an extended holiday(?) aboard.
American: decided to surpass the Shah by directly support the military to stage a coup to foil the revolution(general huyser), while the Shah is away. This was a deja vu, in 1953 the Shah fled a popular uprise, then American and British staged a coup which put him back to his throne. This time American decided to abandon the Shan and support the new prime minister.
Khomeini: ask supporter to send flower to the military in Tehran, the military dissolved in droves. Kohmeini decided to return to Iran, took journalist on board as insurance. Kohmeini was welcomed as a hero.
Revolutionary Guard: attack police station, military bases, seize weapons. Military stay neutral, prime minister fled, Shah’s inner circle executed. American reduce embassy from 2000 to 100.
Shah: Shah ask for refugee in U.S., got refused(Carter refused, Brzezinski argued for the Shah). Shah was refused entry by every countries he asked for refugee. Shah revealed his terminal cancer which as been kept secret for 4 years, Carter agreed to admit Shah.
Tehran university students: some propose to occupy american embassy and take hostage to force the Shah back. Mahmoud Ahmadinejad opposed the plan and point out that this would strengthen the russian influence of the revolution which is the next enemy to the revolution rather than the Shah or American. Ahmadinejah fear the communist gaining ground through this act.
Khomeini: student report this plan to one clerk close to Khomeini, who replied ‘go ahead but don’t let the Iman Know it.’ Khomeini retreated to some rural holy city far away from Tehran. what is it Khomeini want to bargain with the american? or to exterminate american influence in post revolution Iranian politics totally? moderate was swept out of the government.
American: replied with sanction, freeze 5.5 billion asset , ban oil import. Shah left America. Carter was forced to take action. rescue mission failed. Carter admitted the mission the next morning.
Khomeini: show the world Carter administration’s incompetence, worst still in an election year.
Saddam: Shah’s general fled or executed. sanction started to work in the military, which became preeminent when Saddam threaten war.
Deal: frozen asset, shah’s personal fortune 20 billion-> constitution ground.
Saddam: bombarded Tehran airport, 70,000 cross the border, 8 years war, 1 million dead. American pass Iran’s defense information to Saddam through Saudi and Jordan. Iran is desperately short of arm. Saddam used chemical weapons. Iran demand UN Security council to take action, but no action was taken.
Iran: Parliament voted to resume talks with America. Carter defeated in election, Regan got elected. American offered 7.9 billion (BOE hold 2 billion bullion) to settle before Carter leaving the office. hostage freed 20 seconds after Regan sworn in.
Israel: invaded Lebanon in 82, Iran see chance to spread revolution. revolutionary guard plan to entering Lebanon, Khomeini cautioned that it could be a trap that the western attempt to start a second front on Iran. Khomeini instructed revolutionary guard to intervene in Lebanon through organizing and training.
Hezbollah: was created. American send marine to stabilize the region. marine barrack bombed by suicide bomber, 243 us marine killed. 53 French killed. Bush senior visited Lebanon. U.S. and French troop moved out. Hezbollah claimed this as a great victory, Islamic Jihad claimed responsible for both attack, and started to take western as hostage to hijack western leaders around election.
Francois Mitterrand: sent Le Mond journalist to deal with revolutionary guard, who in exchange wanted France to stop selling arm to Saddam, France refused and counter-offered 1 billion.
Jaque Chirac: send message direct to Lebanon to oppose the release of hostage. Mitterrand’ deal was called off. Iranian told Mitterrand, you offered 10, your opponent(Jaque Chirac?) offered 100.
Hashemi Rafsanjani: told Khomeini he would announce ceasefire as deputy command-in-chief, then Khomeini can have him tried for disobeying his order, it will prove to the people it was not your decision. Khomeini said, no it is not just, I will do it myself.
Khomeini: died of heart attack, Rafsanjani became president, Bush senior became president as well. In his inauguration speech Bush senior specifically mentioned about american hostages, ‘assistance can be shown here, and will be long remember. goodwill begets goodwill’. Rafsanjani picked up the hint and responded saying Iran is willing to work with western country, but only if they approach us in the right way, that means equal partner with no colonial attitude’.
Opposition of Rafsanjani: executed one american hostage in Lebanon, show the video to the world.
Bush: enlisted the De Cuellar to broker a deal with Hezbollah and Iran to release hostages. Secretary General’s trouble shooter, Gianni Picco was sent to Rafsanjani to bring Bush’s personal message. Rafsanjani’s term is request neutral review of Iraq-Iran war, and demand Iraq to pay war damage.
Saddam: Seize Kuwait, American force intervened, and stayed in Saudi after the war within reach of Iran.
Rafsanjani: released the hostages. American refuse to left the sanction. UN lost its credibility in the Middle East. Bush administration betrayed Rafsanjani. no attempt for reconciliation is attempted.
Mohammad Khatami: a liberal cleric who promotes a version of Islam coexists with democracy, free expression and modern world. This got into trouble and he resigned as minister of culture. Khatami has the support of students and intellectuals, but his position angered the supreme leaders. Khatami toured the whole nation for his presidential campaign, and won the election by a landslide. interview with CNN, acknowledge hostage taken(hurt feeling), acknowledge terrorism (condemn any form by anyone anywhere), dialogue of civilization (wrestling).
Khobar Bombing: Saudi->U.S. intelligence->Clinton->Khatami implies Iran is responsible, Iran denied responsibility and pointed to Al Qaeda. after Rjafsanjani, Khatami again attempted to normalize the relationship but failed. Taliban is the only point where U.S. and Iranian interest coincides. Taliban killed Iranian diplomats, Iran prepared to invade Afghanistan. Iran double dealt albright in UN summit.
Al Qaeda pulled off an very complexed suicide attack on Taliban’s last opponent in Afghanistan who has been supported by Iran. Massoud’s other ally is Russia. both Iran and Russia is alerted by this attack. Bush junior was informed. the next day is September 11.
Khatami: government quickly made public statement, public allowed to express sorrow. Iran offer cooperation with U.S. against Al Qaeda and Taliban. 6+2 group, share intelligence with U.S. government on northern alliance.
Bush Administration: claimed Iran constitutes axis of evil, and turned the course of war on terror against Iraq. Iran offer to help, Jack Staw pass the message, State department got excited, hit a brick wall when the message was passed to the white house.
Sadeq Kharrazi: foreign minister’s nephew, in-law of supereme leader, proposed a road map for U.S. Iran relationship normalization to swiss ambassador. Iranian demands 1)abolish of regime change policy 2)life all sanction. bureaucrat at state department ignored it.
Bush and Bush administration: the problem of terrorism is because the existence of evil regimes in the middle east, the solution of the problem is to eliminate them and promote freedom and democracy in the middle east. get those monkey civilized. Bush made clear that all option is on the table regarding Iran nuclear weapon development.
Europeans: start their own Middle East peace process, the objective is to avoid a second iraq, E3 offered to lift sanction in exchange for an Iranian guarantee for not pursuing nuclear weapon. It was a big gamble, both by the west and the supreme leader. They really believe american is crazy? IAEA inspected Iranian site, and no sanction is left as a result. Iman is betrayed again by the westerners.
Supreme Leader: moderate was cleared out of parliament, this signaled a change of Iranian foreign policy against the west. E3 felt the pressure to offer Khatami regime some carrot, the message is passed to Powell but again blocked by John Bolton, who represented Dick Cheney. Clearly in Cheney’s mind there is only two possibility to the Iranian problem, nuclear disarmament, or face total annihilation, any deal only prolong the problem which is not in U.S. interest.
Ahmadinejad: got elected. pop star of Iranian politics. Powell is sacked. Rice became new secretary of states. Rice received ‘a convergence of’ intelligence indicates Iran is behind most terrorist plot against the west in middle east, and it is developing a nuclear weapon.
New Iranian deal: we stop killing american troops in Iraq, you let us develop nuke. Ahmadinejad announced Iran would restart the enrichment program.
Rice: American is pushed into a even more isolated position by preparing (at least pretend to) attack Iran. British decided to draw a line in the sand and said no to Rice. In fact jack straw even dare not hint the possibility of invading iran. meanwhile, american media started to drum up about military action against iran.
Bush Administration: Yield to pursue a diplomatic solution. accept Iran into international community, lift sanction, stop enrichment etc. instead of sending Larijani, the supereme leader sent Ahmadinejad to spoke at UN. This time Iranian double dealt American.
Obama: we are willing to extend our hands, if you are willing unclench your fist.
88~08 中国改革史
April 28th, 2010
88 莫干山会议->市场经济->价格双轨制->倒买倒卖->副食品价格放开->粮票重出江湖->北戴河会议->通货膨胀->海南立省->宏观调控->三角债问题->东北，重工，钢铁，煤矿流动性不足->发放国债，打击投机倒把->停止价格闯关，整顿经济秩序
89 农民工失业->外资静观其变->个体户减少三百万户，私营企业减少一半，捐献，私转公->流动性崩溃，信用崩溃-> 易货博览会，逃债讨债->五大行组织清理三角债->地方保护主义限制流动性，流动性问题已发生了质变->学运->邓大人辞中央军委主席->冷战结束->希望工程
90 安排朱江入主上海, 邓大人连续三年在上海过春节。 开发浦东-> 建立证券市场、引进外资银行-> 以资本市场带动国有企业改制，上海退二进三，浦东开发 -> 企业开始快速成长 -> 资本市场开始冲击计划经济的基石->从广东时代进入上海时代
91 朱镕基调北京东北解决三角债问题，现场清欠->全国电话会议，电报传真电话规定时间前直接汇报三角债问题. -> 营销，策划，竞争，眼球经济，efficient market, 中国从生产者的世界过渡到消费者的世界 -> 苏联解体，反和平演变
92 小平南巡，三角债问题得到本质解决 -> 公务员下海 -> 制造/营销/贸易 -> 点子公司，高科技公司-> 牟其中-> Clinton got elected.
93 ..
94 计算机进入家庭, 李阳英语. 世界制造业中心由亚洲周边转到中国.
95 心中充满狂热, 眼中只有对手. 走向世界, 2005 进入世界五百强. 海尔管理模式。Natscape 上市，互联网高速公路， 马云，丁磊创业。
96 彩电关税， 价格战，产业整合，打民族工业旗号。 Yahoo listed on Nasdaq. 国有企业破产达到高潮，中央实施抓大放小的策略。 买断，收购。
97 亚洲金融危机，winner’s curse: 三株，秦池，亚细亚，太阳神多元化失败，史玉柱。巨人大厦， 农村包围城市。
98 朱镕基当选总理，企业改革的重点从经营转向所有权。国退民进，科达垄断中国胶卷市场。 西安 重庆出让国有资产 03。7000家国有企业， 30 B non-performing bank loan, 150 B 股份制改造。 福利分房结束，商品房时代开始。香港禽流感。
99 tech bubble, 全球新兴经济受冲击，外国资本大规模进入中国。 中粮，中化，工行，中行等进入世界五百强。 证券法开始实施，股市大涨。 财富中国论坛。宣传中国，宣传上海。 请帖，新闻考察团(喀什到宜昌到上海)。 oicq, 协程网，当当网， 盛大。 华纳在浦东开董事会。 会议热，APEC, 搏敖论坛，世界经济论坛，奥运会，世博会。 普京上台，北约轰炸南联盟， 破译DNA，
00 财经杂志刊发基金黑幕，时任证监会主席的周小川表态支持。吴敬琏在经济半小时节目发表评论，释放出整顿证券市场的信号。蓝天事件，财经刊发银广夏陷阱，中国证券市场监管开始走上群众运动之路。 小布什，小灵通。 时代华纳并购，中国门户网站上市，tech bubble bust.
01 IT 公司烧钱，ipod，汽车工业民营准入，互联网企业过冬，坚定乐观者取得最后胜利。 中国加入WTO, made in China.
02 buyout, 美的，四通，联想，玉溪，健力宝，华晨汽车，春兰，海尔，长虹，ＴＣＬ(96开始, 广东惠州), 美菱，荣事达 (安排退休). 改制 v.s. 国有资产流失．建设银行（王雪冰，张恩照). soho长城脚下的公社。仇富，郎咸平．ENRON, WorldCom, Xerox．
03 固定资产投资，房地产，出口加工．温州炒访团出现．房价从两千到四千．十万人每人一百万．流动性将仍然原自温州．山西炒访团．海龟炒访团．商品房批发市场出现．新大炼钢铁运动，上海复新郭广昌1.2b 宁波, 戴国芳铁本1.5b，刘永行1.5b 铝电一体化项目．运输行业繁荣，超载现象严重．卡车，造船，港口建设．水泥钢材能源等原材料发生短缺．电荒．房地产总投资达一万亿，中国成为全世界最大的建筑工地．土地出让金３０b each year. 征地卖地成为地方政府最重要的财政收入来源．房地产及其相关产业成为国民经济主导．朱镕基退出政坛．成立国资委，对国有企业开始重组．央企诞生．
04 宏观调控，德隆: IPO炒家, 产业整合(湘火炬：大汽配 亿亿亿)，00 LBO 37 million, 农村超市集团，中国重型汽车集团，畜牧业旅游业集团，航母公园. 房地产，水泥，电解铝投资过热，股市市盈率达到50~200倍，宏调导致中国民企再一次大规模完蛋．
05 超级女声．华为 (质量，服务，耐心，坚持)． 狼文化，偏执．中海油收购Unicol受阻，但大规模海外并购开始．百度美国上市，阿里巴巴收购雅虎总国．连战访问大陆．京都议定书签订，人民币升值，青藏铁路开通．股权分置改革启动新一轮牛市．
07 made in China危机, 生态问题’凸现’，卖啥啥贵，卖啥啥便宜．原材料成本上升，中国产品出现大面积质量问题，玩具，宠物食品，牙膏，牛奶，水产品，以高能耗，低劳动力成本，环境污染为代价的产业需要提升．钉子户，物权法．碧桂园中国首富，股票新开户25倍，600只股票当日跌停．
08 和平崛起，奥运会，社会福利，贫富差距，王益，黄松有，官宦社会向真正商业社会转型．海峡两岸实现三通，雪灾，地震，志愿者，公益，推进改革．．．
Relevant concepts in distressed debt
April 19th, 2010
10Q (30 days after end of quarter) – 10K
Senior Debt, Subordinated Debt, Mezzanine capital, High yield debt, Preferred share
Poison Pill etc.
Chapter 11, 13, 7
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what’s interesting in the current greek debt crisis is how similar it is to a typical corporate distress scenario. economic distress -> management reshuffle -> previous accounting loophole exposed in order to blame the previous management -> downgrade -> liquidity dry up -> problem with rolling over a large debt maturity -> creditor worried-> draconian distress financing plan proposed and debated -> compromise reached.
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the only thing necessary for the evil to triumph is for the good to do nothing about it.
Natural Monopoly
April 10th, 2010
Coca Cola, Barbie, Lego.
1) product -> brand -> part of the culture.
2) it appeal to a small niche market (kids, young people, people seeking identity?)
3) the name of product became the name of this particular category of product, a word entered into dictionary.
4) stealth is the key to success. it is a new concept but must be a simple, low key concept, something easy to dismiss as a serious business subjects to power struggle, only this type of product is able to quietly gain a foothold as element of culture before it enter into business school’s case study. the product is so well entrenched before any serious competitor enters the game and finds it is hard to compete with, because the selling point is no longer functionality, quality or brand recognition but the product per se.
5) scalability
6) legal concern. it can only be a natural monopoly not a coercive monopoly. people still have a choice but the choice
how can you build something like that?
Natural Monopoly and Its Regulation Richard Posner
here is the more orthodox version of Natural Monopoly, e.g. 1) high fix cost infrastructure: utility, high way etc. 2) high economy of scale industry(idea size v.s. market size), first move overwhelm new comer before reach bureaucratic stage 3) few competing standards, due to positive feedback, one standard eventually dominates
another interesting episode of municipalization and monopolistic competition between water companies in 1890s.
another example: Common Carriage Competition, power market deregulation e.g. California Electricity Crisis(TBR)
Anti-Competition Tactics
April 8th, 2010
The court used the efficiency argument, i.e. forbid unlawful maintenance of advantage, but encourage competition on the merit. the doctrine has been relax recently in exclusive contract (burden of proof is on showing alternative to achieve similar lawful objective for plaintiff). The court is trying to draw a line between antitrust and anti-meritocracy. However, system robustness of financial sector cannot be addressed along this line of reasoning, i.e. a bank gain dominant position through meritocracy, yet the result, although increased the efficiency, reduced the robustness of the system. this type of short-term v.s. long-term trade off is not addressed here.
explicit collusion e.g. chip dump, 1) money laundry possibility 2) unfair game combined odds v.s. single hand.
implicit collusion, stag hunt. 1) increase complexity of price (hidden tax, fee, negotiable pricing) 2) risk based pricing (credit rating etc. ) which make price comparison more difficult. 3) equity analyst conference (this is rather ambiguous).
implicit collusion can lead to cheating and price war, so how to determine the Nash equilibrium? anti-collusion regulation should focus on regulating the payoff, but how? my feeling is when opportunity is abound, player are more likely to collude, but when the pie shrinks to a threshold level, cheating become advantageous(prison dilemma), and implicit collusion break down to become Bertrand/Cournot competition. This idea need to be formalized.
- Cartel (OPEC, Fed, Coal, German economy during interwar etc. mean life time 8 years, extra profit 25%)
- Asia Racing Federation( HK Jokey Club contribute 12% of government Tax revenue, which is 13.8% of GDP(300b))
- Seven sister(standard oil (esso(SO NJ) + mobile(SO NY)) +Chevron(SO Cal))+ Royal Dutch Shell+BP(APOC->AIOC->BPAMACO->BP/SO indiana)+Amarco+Gulf oil(BP+Kuwait JV->Chevron) 50~70 -> OPEC
- New seven: ARMCO+GAZPROM+CNPC+NIOC+PDVSA(venezuela)+Petrobras+Petronas
- Zaibatsu
- Bidding Rig (Sam Zell used this in EOP deal): [1]sub-contract bidding,[2]bidding suppressoin [3] complimentary bidding [4] bidding rotation.
- Tying generate ‘lock-in’ in consumer behavior (MS’ defense is probably IE & file explorer is different use of same piece of software, while file explorer is part of its main product). related to freebie marketing (legal, e.g. Rockfeller’s give away 8 million kerosene lamps in China; Comcast give away DVR; Prince give away CD The Mail on Sunday(this is a brilliant case!); printer; game console; bundle with addictive materials; sample, affiliate marketing):: lose leader(supermarket sell sugar milk at lower price to attract customer, auto dealer/RE agent bait and switch strategy)
- Contract Tying: section 106 of Bank Holding Company Act, measure against tying loan practice (loan bundle with an issuance deal?)
- Product Churning cinema selling drink(small cup at $5, big cup at $6), wine dealer sell wine, RE agent sell house (overpriced second choice v.s. fairly priced but more expensive property).
- Vendor lock-in (product specification->industrial standard setting) car stereo vender use special design to affect dashboard design of car to achieve lock-in, membership card, gift card, rebate card in bookstore, vacuum cleaner and specially designed dust bag. Flash(95% online video). IBM: product specification in development line punch card->reader->tabulator->printer->mainframe->OS->application. MS: windows API, file format, outlook data format, etc. Apple: bundle iTune with ipod through .ACC (creative? sony? only device manufacturers)
- Essential facility: AP v.s. United States; Lorain Journal v.s. United States; Otter Tail Power v.s. United States. a boundary case, but IP related product could be deemed as ‘essential facility’.
- Third Line Forcing: per se prohibited. supply on condition that purchaser acquire from a third party.
- pattern misuse.
Standard Oil NJ v.s. United States This is an interesting case because the court is specifically addressing my concern for point 1) I pointed out in the previous post, i.e. is size and power the reason a company is guilty of (an implied doctrine of Sherman 1890), therefore should subject to antitrust lawsuit? the court concluded that ‘a contract offended Sherman 1890(basically a reinterpretation of Sherman 1890 IMO) only if the contract restrained trade ‘unduly’…’ despite the ambiguity of what constitutes ‘unduly’ restrain, this opinion is more inline with ‘common good’ reasoning that I advocates.
Copper
April 8th, 2010
Imagine the inconceivable, as much as most people would not believe that copper would lost 70% of its peak value in mid 2008, it had been inconceivable for most for imagine copper would rise 300% at beginning of 2009. This is the nature of risk, a minority game.
Bust the Trust
April 8th, 2010
the issue of competition law can be viewed from two perspectives. 1) The protection for the unprivileged 2) the competitiveness and efficiency of market and national economy. related to 1) is consumer protection, e.g. regulation against predatory sales/lending practices. The legal argument is similar to the one that a certain drug need to be controlled and criminal should be incarcerated, the boundary of ambiguity is in determine what constitute predatory practice, and what is normal fair dealing. Clear line could be drew on extreme cases,e.g ban heroine, imprison murderer etc. However, the tricky point is competition by definition is an activity with certain predatory nature. so it seems to me that in essence antitrust law is about penalize the successful and dominant much less about the practice in competition. anti-competition degenerates itself to be anti-meritocracy For 2) I found it more appealing an argument which based one the principal that ‘common good’ should be looked after. The need for this type of regulation can be demonstrated in game theory setting. My preference of 2) over Schumpeter argument for lassei-faire is that the creative destruction process he described, which is essentially an uncoordinated activity, is not the most efficient way for the transition. History has told us that many wars, violence, destruction is unnecessary in retrospect to achieve the goal. Regulation is necessary to bring the social and human cost down in the inevitable creative-destruction precess.
Sherman Act 1890(make it illegal),
Clayton Act 1914(kill it at inception, M&A, Board of Director),
Robinson-Patson Act 1934(FTC jurisdiction, anti-price discrimination),
Celler-Kefauver 1950(cross shareholding to reduce competition, Japanese failed to address this issue)
Hart-Scott-Rodino 1974(very detailed code concerning pre M&A announcement)
shift of legal doctrine in 70s e.g. prove conspiracy is plausible not merely conceivable (Bell Atlanta corp v.s. Tombly) antitrust backfire to labor movement as well, i.e. cartel of labor
Standard Oil v.s United States
[Theodore Roosevelt 45: William Howard Taft 74]
European Union competition law
Anti-Competition Practice:
Case Study: Take EOP Private -continue
April 5th, 2010
Judging from two analyst reports, one from prudential equity research and one from Stifel Nicolaus. according to Predential’s logic, FFO (fund from operation) estimated to be 2.2+/- which is not to far away amongst analysts, times an REITs wide multiple which is in the range of 8.5~15 with EOP at high range of 14(predential estimiated around 12). The share price of EOP should be, accordingly, 25 dollars. Stifel Nicolaus took a different approach, given the recent Bacon Fund II deal, with estimated EV of 500 dollars per Sqrft, with current EOP price, the implied EV is only 300 dollars per sqrft. Going forward, if the REITs market is to be driven by Privatization deals instead of Real Estate fundamentals, EOP had at 50% premium in any situation when PE get interested. Market and valuation would anchoring itself according to the previous benchmark deals rather to the fundamental discounted cash flow analytics. And this is exactly what happened, the final deal price is 50% higher than the price at time when analyst publish those reports. Tishman/KKR + CalPERS rumor has been circulating way before Norando/BS submit any serious bid. Question remained open that how could the high bids from PE being justified, but if one trace the flow of asset all the way down to the final end, i.e. pension, endowment etc. and dig deep enough into their thinking process & analysis framework, you would see all those compelling arguments about diversification/alternative investment, with complexed, but essentially a dress-up mean-variance asset allocation engine(with historical performance + adjustment as input), the conclusion is not so difficult to draw. – the logic above has its flaw, EV would depend on quality of portfolio, a comparison between EOP property and Bacon II is needed to draw a sound conclusion.
implied EP trading around replacement cost, despite of high quality portfolio. replacement cost 360/sqrft (similar situation across REITs).
Here is what happened to EOP share price during the bidding war.
lesson can be learned from this:
1. valuation doesn’t really generate much prediction power, it obscure the game/dynamics/strategical aspect of the playout.
2. know who is involved, who could potentially get involved, what’s their relationships, what’s their motive (desire/fear) is the first step. this including buyer, seller, facilitator, financier etc. etc.
3. a historical perspective is important, recent deals in the same domain, recent deals in similar domain. recent trend in the market, overall economy. recent activity of bidder, seller, entrepreneur, capitalist, financier, investor, regulator etc. then it’s boarder historical perspectives (RJR case definitely defined BS’ strategic movement)
4. focus on immediate but broader implication of the deal, whose interest is infringed(RE fund in this case), and predict their movement along this line. Norford/CSX railway deal is an good example.
5. focus on details of the terms designed, e.g. break-up fee, competition clause (zell’s focus) etc. those micro-structure would dictate the direction of the deal at later stage.
6. focus on small deals (previous REITs privatization deals), small deals herald a beginning, big deal conclude an end.
7. rumor rumor rumor. certain type you should ignore it, certain type you should absolutely on top of it.
8. focus on catalyst and see the ramifications, in this case its the PE fund, the abundance of liquidity, the alternative investment movement. The real world is a small world.
9. analyze character and reputation is important in order to predict the behavior and the response.
Case study: take EOP private
April 5th, 2010
John Schreiber: (black stone RE founder, worked at JWB, backed by GS, MS etc. )
John Snow: CSX + U.S. Treasury+ Cerberus
Steven Roth: Vornado (backed by JP, UBS, Lehman, Barcap, RBS
Barry Sternlicht: Starwood Capital(worked for Bluhm at JWB)
Neil Bluhm: Walton Street(backed Sternlicht to setup Starwood)
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what each party wants:
BS: significant foothold in RE portfolio, fee, avoid RJR situation
EOP: godfather price, auction
Vornado: complimentary of RE portfolio from EOP’s coastal property.
Starwood/Walton Street: compeition with BS, feeding institutional appetite for Commercial RE
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bargain chips
BS: finance, access to capital market, all cash offer, timing
EOP: unique portfolio, biggest opportunity. even out exposure to national market, no dominant position, effectively an index. not much EOP can do wrt to development and growth, option for strategic maneuver exhausted(therefore EV trading around replacement value).
Vornado et al.: the only contender from EOP’s stand point.
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contention point:
BS: break up fee, but at initial stage BS have no choice but to accept 200m, due to the uniqueness of the portfolio.
EOP: 1) avoid BS tires up capital market(esp JPM et al.), 2) avoid BS serve as a conduit(given BS’ agenda not likely to sell all of them, but BS is able use part of it to neuralize say starwood/walton street or other alliance) 3) lower break up feed for initial break up fee. send clear signal that a second suitor is possible.
Vornado: unable to offer all cash(this is a fatal blow), financing(cerberus then syndicate loan), timing, safty. the second offer send mixed signal with no actual improvement and make me doubt about the true intention of vornado.
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Sam Zell:
1. send out intention early. godfather price, public responsibility. lack of suitor because market don’t believe BS is a long term player( until jonathan gray gobble up 200m sqft property they start to realize. Capital market deal triumphs RE deal).
2. planning and constant valuation of opportunities and offering. allow world to spread, allow market to go up(Commercial RE market condition in favor of EOP during the bidding process), set condition for other participant in the auction. reduce debt ahead of time, to create opportunity. play hard on the advantage(uniqueness) to bargain for most important factor(not price, but measures to encourage participation). monitoring. take time to know BS and the rest. make time/plan for condition to mature and market sentiment to develop.
3. timing in orchestrating the bidding, Varnado’s bid is accepted only towards the end of BS-EOP deadline, which created much tension in the bidding war.
4. not sure if its a deliberated move by EOP, but in Sep 06, EOP tightened the national control by consolidating the management to Chicago. also the share buy back in 05~06 1.5b, issue of new debt 1b+.
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Schwarzman
perspective on RE: largely private owned by entrepreneurs. no committees, no lawyers special lawyers to protect interest in corp world, which make dealing making almost impossible. more opportunities, more imperfections, more possibilities to combine which is PE’s specialty(arbitrage: capital + combination). market condition favorable(vacancy rate, building speed etc.) the lastest frontier for institutional capital to play with.
EOP:
1. cap rates dropping rapidly(office building), vacancy rate 50% -> 11% rents going to spike.
2. sam visit BS in 1985! the very first day of BS. (no secretary, no telephone). delivery person with leather. sister worked with schwarzman in lehman, sister suggest sam to see schwarzman.
3. BS came in after BS learned EOP had a unsatisfactory flirtation with Varnado, BS did the deal with EOP in ten days (valuation, financing, raising equity), this is a significant advantage. BS has done another two REIT deal with brookfield 4b +10b early that year. mechanics is mastered, skills learned, channel established, broker relationship in place. 24b asset sold out of previous REITs privatization with decent spread. all is setup for EOP 39b deal.
4. BS in 2 month moved close to 50b RE, the spread for taking REITs private should be around 25% (60-47)/47. how to find that kind of spread(valuation, capital structure?, public/private, management, sum/divide)? and how to monetize and capture that kind of spread(access to finance, distribution)?
5. Steven Roth bid came in January, which is able to create maximum tension between the competing bidder given the short time frame to response.
6. BS insists on matching right, which is an important tactics(how?).
7. in response to vonardo’s first bid(52), BS chose to ignore its matching right but proposed 54(with condition to pump up breakup fee 720m). vonardo forced to go 56. (50% stock means vonardo’s stock would go much lower if the bid get through, hence trade lower). the preference of sam is clear, perhaps clear to vonardo as well, the only party get hurt in the bidding war is BS, hence BS insist to increase breakup fee every time they increase the price, or else we are out. the breakup fee structure worked for BS and EOP but not for vonardo.
8. why no other people jump in during nov to feb? RE industry is used to buy asset cheap, EOP deal fowards 50 is certainly not cheap, therefore on Vonardo et. al. jumped out but nobody else. it’s a market where institution money jump into RE, but distress/grave dancer type.
9. towards the end of the negotiation, BS insisted on the valuation and demand that unless BS is allowed for channel out part of the acquired portfolio, BS is not able to pay up. initially BS depends on Sam, as price build up, now BS has more negotiation power as sam’s 50+ deal depend on BS doesn’t pull out, and the key is Vonardo is only able to pay upto 40% cash. what schwarzman termed as ‘riskless increases of price‘ which is in the interest of both EOP and BS.
Nature of technology -Arthur Brian
April 4th, 2010
1. Structure of Technology
- Modularity: engine, german general stuff, cerebellum.
- Recursive: aircraft->carrier->theater of battle.
2.Phenomena
- Technology is what capture a phenomena(physical/behavioral), put it to use and serve a purpose. e.g. burning + control->engine/power plant. wobble+doppler effect+filtering->identification->discover more phenomena.
- Subsystem is need to refine the main system to render a phenomenon usable.
- Combination of different phenomena drive technology forward.
- Science and technology share similar structure, one exists in theory, the other exists in physical world. one discover, the other exploit.
3. Domain
- new domain define future wealth and political power. 1. re-domain, but still serve the old purpose, 2. re-domain and generate new needs. e.g. mechanical, vacuum tube, transistor. stream->piston->jet
- good design contains some unexpected combination that shocks with appropriateness. rightness of choice given constrain to full-fill a purpose.
- re-domain redefine economics, canal -> railroad->express way->aviation.
4. Engineering
- a engineering design is a set of compromise, especially for one the edge project.
- the solution of compromise becomes the new building blocks: e.g.tort law, trade union, monetary system
5. Origin
- solution for a problem: economic opportunity, market; change of circumstances, social challenge; military
- subconscious search, conceptual, wide, obsessive. key revelation came in a rash and in a simple, appropriate and elegant form. embodiment of concept is a long process
- invention proceeds from a new phenomenon. refinement is as crucial as conceptualization itself.
6. Structure deepening
- initial version of invention is crude.
- complexity increase as technology becomes mature, supporting system to enhance performance and control. e.g. turbojet prototype of 1936 had a few hundred parts, modern version had 22,000 parts. so is tax code, custom, legal system, political system
- lock-in effect, in system design, supporting system, infrastructure, economics, and cognitive level
- the elaborated system is pushed to its limit, and eventually manifest the tension as cognitive dissonance and emotional mismatch, lack of security of human adapted to the technology.
- when fundamental limitation has been reached and realized, a burst of simplicity cut through stretched elaboration, the process repeat itself.
- scientific theory process in the same fashion, with accumulation of anomalies, eventually leads to paradigm shift.
7. Revolution and redomain
- early innovation is used sparsely in the system, e.g. horse carriage railway, steam engine backup
- scientific calculation->mainframe(big corp commercial)->PC->network
- journalist begin to promote it-> capitalist get interested->attract lunatics maniacs and they should prevail!-> crush(financially)->new domain survives, but become stable one of sobriety and hard work-> embedded into the economy became the real engine of the economy as predicted(embedded, mandate, ordinary, unnoticed, take for granted, IBMish)
- concentration of technology development(textile&stream&engine in Britain, chemistry industry in German, computer&bio in U.S.)
- real technology, on the edge development depends on ‘deep craft‘, anything cannot be transmitted except through person-to-person interaction, which is what needed to push the development to the edge. (Cavendish Lab, Hartford, Stanford(wireless telegraphy->electronics->bio->nano), Akron(Tire->polymer related), 上海轻工金融，兰州军工，天津工业. Deep social network, generation after generation, not liquid at all. less so nowadays but still-‘DEEP CRAFT’
- creativity is a misnomer for outsiders. craft, combination, mutual stimulation is the essence of innovation.
8. Mechanism of Evolution
- combination
- clustering of innovation along the time line, when key innovation came about.
9. Economy and Technology
- Economics as expression of technology
- cotton->labor->urbanization->victorian industrial economy
- Dickensian condition ->legal system responded ->unionization->political force
10. Conflict between nature and technology
- we trust nature, but we place our hope in technology
- this insecurity turns people to tradition, environmentalism, family value, fundamentalism.
多元，矛盾，进化，统一
April 1st, 2010
对立的两派存在于任何体制当中，但外部局限能够尽可能满足体制内的需求时(即对增长的需求), 此一矛盾不易被激化，系统呈现多元趋势(micro-trends), 当系统发展与外部条件发生冲撞时，各个多元之间的矛盾被激化，经过争夺之后多元趋向统一，最终以两股势力的交锋为终结。最代表更先进的生产力及组织方式的集团将胜出。在这一过程后新的体制代替旧的体制，新的认识和技术随之诞生，整体的外部条件得到改善，系统重新回归多元。
ABCP, SPV, Limited Limited Liability Company
March 31st, 2010
SPV is a ridiculously brilliant legal concept.
if the main advantage the Corporation is to separate liability form ‘legal person’ to ‘natural person’, which is a structure that encourage risk taking. Then SPV is trying to separate liability from ‘legal person’ to ‘legal person of higher order”. A structure perfectly suits for extreme risk taking and main source and sign of ‘natural person’ irresponsibility. I wonder on what ground is this second order separation of liability tenable, and how to defend it if I was challenged.
ABCP is the main source of funding for SPVs to hold high yield asset, which needs to be rolled every 60 or 120 days. The first coupling in the setup is that ABCP is backed by assets (or closely correlated assets) those paper is used to finance, when the asset price goes up, SPV faces no problems in roll over the paper; however when price start to decline, with the expectation of further decline, the supply of ABCP at roll over would be less than needed to finance SPV’s asset priced at current price. Therefore the sponsor had two choice, either order the SPV to sell the asset and quickly destroy itself (this will disturb the ABCP market and destroy everybody else using ABCP to fund SPV, the sponsor will be cursed as well), or bring it back into the balance sheet and let this little blood sucker drain on its own funding(Citi etc) . When everybody realizes that everybody else will realize this, it turns out to be a fire sales and arm race to shore up the balance sheet. Punters whose who set up SPV and make market is at the core of this panic.
here is more raw material for analysis.
Clearing House Interbank Payment System (CHIPS)
March 30th, 2010
difference between CHIPS and FEDwire:
- privately owned: 47 members (70% foreign banks, BOC, BOComm is the only two Chinese banks)
- cheaper
- netting engine, not real time gross settlement system(RTGS) as FEDwire
- mainly used for cross-boarder transaction: 1.5 trillion daily.
- for credit efficient than FEDwire, 1:500(CHIPS) v.s. 1:100(FEDwire)
- less time critical than Fedwire, but more prone to settlement risk as well.
- CHIPS’ solution providers list
Longford 06, The White Countness 05, Running with Scissors 06, Lars & the Real Girl 07, the red belt 08
March 30th, 2010
Longford 06: what’s interesting is the psychological profile of Myra Hindley, Moors murders case
Samantha Morton as Myra Hindley in the 06 movie.
The White Countness 05: what’s interesting is the vision Todd Jackson & Matsuda had for the bar. And the conversation between Jackson & Matsuda, imply the intention but never state it.
Running with Scissors 06: ‘i got rejection from virginia quarterly, it kinda worried me…’, message delivered through surprise.
Lars & the Real Girl 07: the way the doctor talked to Lars. the tone, the pauses, the leads, the listening , the hints, the effortlessness.
Fedwire payment system in 08- Lattice model
March 30th, 2010
this paper turned its focus from infrastructure risk to risks arise from endogenous, mutually reinforced response from constituents of payment system itself. as a high though-put system, 1:100 buffer/traffic ratio, this system is highly sensitive to the risk of second type. where is the boundary between virtuous circle and vicious circle?
the author then set out to study two senarios: 1) one bank start to horde liquidity 2) one bank being identified as vulnerable. this two senarios is highly relevant to the banking crisis in 08, as the bank where I worked claimed to build a ‘fortress’ balance sheet, while Bear Stern, Lehman, MorganStanley(a long list…) subjected to rumor of liquidity shortage.
the author stipulated two payment regimes:
- under normal condition, payment is made upto 80% of previously accumulated incoming payment + 100% of daylight OD
- under cautious condition, payment is made upto 20% of previously accumulated incoming payment + 20% of the daylight OD
condition shift from normal to cautious when daylight OD exceed 50% of debit cap(only Fed knows, but rules has be relaxed to accept collateral); condition shift back from cautious to normal when balance is replenished to above zero. Notice the path depend nature of bank decision here, a barrier which is of 50% size of daylight OD is required here to recover from cautious to normal for each individual bank in the system.
a few observations:
- the first mover advantage is obvious in large volume, low liquidity period(after 4pm). The first bank start to play cautious would be able to build up a large pool of liquidity, the first bank to access daylight OD would have an advantage as well.
- the game in payment system prefer to concentrate the payment towards the end of day in a short period of burst of trading(this is similar to most other markets, but much more pronounced), this is to reduce the uncertainty from late day payment and to achieve maximum benefit of netting. what surprise me is the fact that even with the presence of Fed fund market to dissipate payment liquidity, this pattern is still the case, maybe to reduce the work load & coordination between internal funding desk and settlement/payment operation?
- another possible explanation of this tendency to settle towards the end is probably due to the netting and payment in CHIPS which happens later afternoon. To mitigate intra-day credit risk among 47 banks. But this tendency dramatically increased the contagious settlement risk arise from one bank trying to act cautiously through out the day, and defied the purpose of FEDwire as RTGS system.
- when interbank market credit becomes a concern, the Fed fund market(bilateral, unsecured) might frozen up, which would have devastating effects in payment processing and further call banks’ creditworthiness into question. (another positive feedback here!), so why don’t we make Fed fund market a collateral one?
as banks varies their guideline in payment decision, i.e. to give out instructions to settlement to pay out 20% 40% 60% etc. in cautious states, intuitively the settlement congestion should ease accordingly. The simulation result showed similar effect. The conclusion can be drew from this is that gradualism in changing credit control policies, especially for money center banks is conducive to the robustness of the system. Sudden, drastic shift of policy is more likely to trigger cascade effects cross the network. This principle is parallel to the fact that smooth real-time payment/settlement through out the day increases the robustness of payment system. The current concentrated settlement towards the end of the day pose a systematic risk. figure (d) is able to settle all payment because the reduced incoming payment instruction towards the very end(after 5:30) had enabled banks to settle the outstanding orders, this is not the case when payment instruction is overly conservative.
The first point, i.e. single bank policy shift triggers payment congestion, would be compounded by the fact that the remaining bank shift towards more conservative payment policy, this would expedites the congestion in payment system. Luckily liquidity can be transfered through Fed fund market, but as credit-worthiness of interbank player was in doubt, things can deteriorates very fast.
The power of rumor is even more devastating than Goldilocks in the system. While goldilocks in the system is temporary and can be addressed through measures such as discount windows, and more importantly, Fed has the explicit mandate from congress to do so, it is not the case for Fed to rescue individual bank subjects to rumors and suffers a resulting liquidity crisis. Unless:
- an entity with enough bilateral credit line in Fed fund market, who is willing to channel credit back to the specific bank (CEO would not be able to make such a bet, merge is normally the only choice)
- a fund willing to inject liquidity (the only fund manager able to make such decision would be the ultra rich (buffet), sovereign wealth (dictatorship countries, ADIA, CIC, GIC, never expect it from Japan or Scandinavia), size should be in billions (2 trillion flow, 6000 members, 3% accounts for 75%, 1:100 money speed reduce to 1:10 in crisis).
- if Dick Fuld understand all these, he would be much aggressive in seeking white knight investor, which could turn out to be an advantage for Lehman towards the end of the crisis. Bob Dimond
One last point, the stag hunt analysis in the previous post pointed out that by adjusting the daylight OD, it is possible to create two synchronization in FEDwire market, one in the morning, one in the afternoon. This would be a more economized use of FEDwire system and reduce the risk of system wide congestion.
Deciphering the 20078 credit liquidity crunch
Federal Reserve Bank response during 911
March 29th, 2010
- Open Market Operation
- Discount Windows
- Payment Service
- Regulation, Supervision
- Communication, Coordination.
9:44 broadcast in Fedwire to assure Fedwire will be operational till order close is achieved
11:25 broadcast in Fedwire to announce discount window is available for liquidity demand
Press release at noon
Regional Fed Chairman press release and assurance: ‘Fed as lender of last resort’
Critical Fed function remained in NYFed building, relocation followed the next day.
Extend credit line to bridge check clearing, 47b on Thursday and 44 b on Friday.
Reach out banks and armored carriers to assure ample availability of cash reserve, extend working hours, make special delivery available (157b v.s. 583b in circulation) this turn out to be less a concern as the cash surge is only 5b.
settlement issue is mainly caused by scarcity of a particular issue, Fed relaxed security lending guidance of SOMA’s inventory and hold an usual 10 year notes auction.
two important ramifications in the days following the attack, Fed’s response is focused on anticipatory management of expectation and avoiding credit frozen up:
1. settlement backlog cause significant increase in bank’s portfolio, security & payment obligation inevitably showed up in bank’s balance sheet and reduce bank’s capital ratio. (the concern of Fed is equity market expectation and bank’s credit rating expectation, Déjà vu?)
- fed took preemptive action by issuing statement that fed note many bank may TEMPORALLY experience balance sheet growth, and urge banks to contact fed should they anticipate an resulting decline in their regulatory reserve ratio.
- fed later issued an Supervisory Letter allow banks some flexibility in calculating their reserve ratio
2. Operational difficulty caused in rolling over commercial paper cause many firm to rely on bank credit lines.
- fed’s concern here is the deterioration of banks’ capital ratio, couple with their survivor instinct and the glitch/froze up in CP market, which cause even more firm to demand on banking credit, would cause a credit crisis like the one in 08. Fed dodged bullet after 911, but this scenario eventually played out in 2008.
3. large than normal liquidity injection is necessary because:
- to alleviate the operation constrains experience by banks due to glitch in settlement and payment
- banks’ precautionary measure to hold larger than normal reserves due to uncertainty in clearing & settlement
- the increasing reliance of peripheral non-banking entities on bank credit-lines for day-to-day operation
4. Open Market Operation
NYFed add or drain through Repo via auction, operates early in the morning according to estimation of balance/u.s. treasury balance/check floating etc. bank balance requirement arise from: reserve requirement/balance commitment/precautionary reverse for late day settlement. Settlement of repo took place on BoNY&JPMC’s book. on Wednesday, NYFed shift focus to satisfy dealer financing needs (previously address through discount windows (secured, end of the day, exhausted other source of funding, prohibited to finance FF lending, stigma attached to it)), rather than control money supply in the banking system, any bid above FF target rate is accepted. on next Monday, FOMC cut FF rate by 50 bps.
5. Federal Credit
OMO(term and overnight)<<Check float<<Daylight OD(Fee)<<Discount Windows(+50bps? secured)<<Overnight OD(+400bps, unsecured)
OMO/Check float are designed to address majority of the liquidity demand, DW/Overnight OD are designed to mop up the remaining residue at EOD. DW/Overnight OD served as a leading indicator for next day OMO during 911.
Fee on Daylight OD was waived on all members, penalty on Overnight OD was waived on depository institution(with collateral agreement in place with Fed). Non-depository institution was charged on ‘extended credit’ 4% + 55bps.
6. Swap line with foreign central bank
- foreign bank experience difficulty in positioning collateral to U.S. branch in order to access discount windows with Fed
- Fed set up swap line with ECB(50b), BOE(30b), BOCanada(10b)
Disruption of financial system after 911
March 29th, 2010
‘a market event originates from payment system disruption is merely a financial crisis, a market event causes disruption in payment system is an economic crisis’ – I said that.
Disruption:
1. physical damage
- casualty in WTC, 74% civilian financial industry related.
- Cantor Fitzgerald, key interbank dealer in government security is wiped out, 600+
- Verizon telecommunication center at 140 west street(isn’t that a bit far away for I bean to travel?) knock out 40% of land line, 20% of NY stock exchange. main cause of telecommunication break down is from this single site.
2. close of exchange/broker
- NY stock exchange, Nasdaq, NYBot (destroyed), CME, CBOT.
- Broker Cantor, ICAP voice service down, electronic platform operates out of London.
3. clearing/settlement disruption
- break down of communication between government security dealer and Clearing house is the key to the disruption
- two major settlement bank, BoNY JPMC. JPMC resume operation from Florida, BoNY both main and backup site has to be evacuated. Settlement/Clearing/Fund Transfer operation of BoNY is just one block away from WTC.
- Government Security Clearing Corp(GSCC), which provides clearing service to Fedwire, lost connection with BoNY, resulted in a surge of ‘fail to match’, ‘fail to deliver’ up to 190 billion from 3 billion.
- BoNY, JPMC posed as two poles in custodian for fund and security in the system:
- disruption of payment lead to accumulation of fund upto 100 billion at one point at BoNY and many other payment flow banks, the remaining part of network suffered a liquidity shortage, JPMC?
- the build up of congested payment is not only caused by disrupted topology of Fed payment system, but by disrupted Fed fund market. (this point has been made in the model mentioned in previous post), as several broker was out of market for the rest of the week.
- as settlement of payment becomes uncertain, coupled with more difficulty to locate lender in Fed fund market, bank had incentive to withhold payment. This caused a classical Goldilocks and break down of coordination in Fed fund market.
- majority of CP due on 11th, 12th was rolled.
- vulnerable FX market, e.g. MICEX suspended due to settlement difficulty related to BoNY issue.
4. ATM, Cash, Check etc.
- ground of airline caused check clearing problem
- on 11th, most retail branches closed in NY and Washington. CT bank commission ordered the close of all Credit Union and Bank. BOA, Bank One, Wachovia closed head quarters.
- ATM withdraw was limited, NY city 500;Citibank 5,000;Wells Fargo 1,000~5,000 depends on location;WaMu 2,500.
- Transportation of paper currency from D.C. and Fort Worth to regional Fed is hampered. supply to Hawaii and Alaska is delayed.
- surge of ATM withdraw, but mostly from gas station 30%. rush for cash is observable but not large.
- sharp drop in retail sales, which lasted much longer. (need more explanation for this)
- sharp drop in air traffic, 20% lower post 911.
- property damage & life insurance claim is well absorbed
- banking sector is fundamentally sound (as they always say, but it’s true this time)
Fed fund market congestion
March 29th, 2010
This model studied fed fund market dynamics in details.
- system wide liquidity parameter (L1 v.s. L2 in canadian LVTS, 0.30 collateral requirement for L2).
- liquidity market conductance (trading cost, fed fund market, discount windows), dissipates the payment liquidity in central bank and dramatically reduce the length of cascade.
both serve to control the payment queue size of individual bank in processing payment and the size of average cascade (one payment triggers lease of fund of a number of bank in the downstream), and hence the speed of money flow through the system and wider economy. At low liquidity regime, the instruction arrival greatly exceeds the payment settlement, topology/internal dynamics of payment network start to dominates. The shift towards congestion is more likely to be a sudden cardiac arrest then a smooth continuous function as suggested by the stag hunt game analysis mentioned in last post( so is the recovery).
Prison dilemma v.s. Stag hunt
March 29th, 2010
Illiquity in interbank payment system following large scale disruption -> NYFed staff report 06
during disruption, when price of intraday liquidity exceeds a threshold level, the unique nash equilibrium morphs into two nash equilibria, worst still the inefficient equibrium is coupled with scarcity of intraday liquity, without coordination, volunteering the efficient strategy could be prohibitively expensive, which would be exacerbated by the resulted hording behavior. the solution for Fed is either to coordinate the settlement, or more realistically inject liquidity into payment system, e.g. provide free overdraft with/without collateral.
the paper used a potential function method to exam system sensitivity to shocks. intuitively, the higher the cost of liquidity, the smaller a disturbance is required to knock the system into less efficient equilibrium. big bank’s operation is highly important to system coordination(patience when not disrupted in events, quickly back to system when disrupted). see Lacker 04 for Fed response during 911.
assume communication of intention is possible, in Prison dilemma the declared intention to corporate will never be trusted, as whoever choose to believe and corporate would lose the game to rational player; while in stag hunt the declared intention to corporate will always be trusted, as the alternative is to nobody’s advantage. in Prison dilemma the incentive is skewed to be cunning, while in stag hunt the reward is skewed to trust and to be trustworthy.
Mutual trust doesn’t depend on the willingness to trust, but depend on the payoff for each party in the game, or the perception of payoff. To maintain the game as Stag hunt i/o Prison dilemma(where trust break down), it is essential to establish a system to penalize the defect behavior on both party, assurance from existence of such a system is the cornerstone of mutual trust. The boundary between the righteous and the evil is blurred in this framework, both of which is rather a reflection/function of the payoff rather than a predisposition.
Communication is in the interest of both parties in stag hunt game, in fact communication is a result of the very payoff structure of stag hunt. The complication in real world is probably from the fact that communication and trust building affects the perceived and gradually underlying payoff structure.
Which Bank is Central Bank?
March 28th, 2010
the main idea is network topology alone is not enough to determine bank centrality, the node behavior(specifically in this case the processing speed), is also important. Facility mutual trust through custodian, collateral is the main characteristics/function of ‘central’ bank. using a modified transit matrix, with non-zero diagonal element representing the delay in processing-> estimate diagonal element using MCMC -> calculating stationary state of transit matrix to determine center of liquidity. (tautology?)
================================
network flow & centrality(Borgatti 05)
================================
centrality measurement:
- Freeman 79{closeness, sum of geodesic distance} distribution center (CCTV, control of media)
- Freeman 79{betweenness} air traffic hub(bangkok or singapore?)
- Bonacich 72{eigen centrality,long term influence measurement} google’s page rank
- Freeman 79{degree centrality} immdiate impact, as oppose to steady state measurement such as eigen, subtle difference wrt closeness measure.
traffic central doesn’t imply gossip central. topology along is not sufficient to determine centrality, dynamics is also important.
================================
some background information Canadian LVTS
================================
BOC last resort creditor
T1 debit limit set by reserve collateralized by full amount, ‘default pay’ arrangement
T2 debit limit equal sum of total bilateral credit*factor(0.24), collateral equal to largest bilateral credit line*factor(0.24), ‘survivor pay’.
Visualization
March 28th, 2010
eigenfactor: science journal visualization
html visualization: using processing
flare: many eye’s visualization layer
one thousand painting [uniqueness + engineered expectation + publicity]
Topology of Fed Fund Market
March 27th, 2010
Morten L. Beck 08 ECB working paper.
remarks:
0) pacific northwest is unique in the map(the last frontier), interesting method for cross boarder regress
1) network reciprocity leads FF rate
2) there are clearly two banks at center of this network, potentially the vulnerable points(this has been mentioned in 06 conference), i’m going to dig out who it is. This become evident especially towards late afternoon(possibly in distressed situation as well):
3) the partition of network into GSCC, GIN, GOUT etc. GSCC account for 10%, constitute the most robust/complete part of the network. In terms of districts, San Fransisco is a notable supplier of fund(large GIN portion), Chicago is a notable sink of Fed fund(GOUT), and New York is the most robust part of the network, SanFran and Chicago being the most vulnerable parts in the system. GIN(supplier of fund) constitute 60% of the nodes. SanFran are the most independent(most tendrils) one as well. 49% of GSCC is from New York district (too much concentration!!!).
4) degree: FF market pool fund into the hands of a few. i.e. max in degree 127> max out degree 48. GSCC(20in, 10out), GOUT(6in, 0out), GIN(0in,4out).
5) economy of connection, in-degree for GSCC is 3 times larger than that of GIN, in-strength is 9 times larger. double in-degree, increase value receive by factor of 3.5; two critical banks( one borrows larger than normal with in-degree 30~60, payment 200~400, and it’s counter-party which lends larger than normal), who could this be?
6) assortivity::cluster coefficient::distance::centrality(Borgatti&Everett 05)
7) Bonacich centrality(Power and Centrality: a Family of Measure, Bonacich 87)
FF loan rate is correlated with Centrality diff, Asset diff, Loan Size etc.
some references:
Financial Contagion, Allen & Gale 2000 {Sparse network are more vulnerable}::Financial Network, Gale & Kariv 2007{Experiment results, intermediation reduce the efficiency}::The Topology of Interbank Payment Flow, Soramaki 06{Fedwire network model}
Some background knowledge about CB credit facility
March 26th, 2010
Primary Dealer Credit Facility, who’s using this? FEB balance sheet expanded from 800 billion to 2 trillion.
Grayson grilled Kohn:
Roubini’s post on this issue.
Discount windows, +100bps/50bps above FF, the stigma associated with it cause FF traded above DWR
Fed Fund Rate —> LIBOR (Eurodollar, deficit dollar){ reserve ratio, OMO(rate target(U.S.) , Fx target(Singapore), Gold price target(pre Nixon)}
FF rate market is effective in distribute the reserve concentration among deposit-taking institutions. By definition only deposit-taking institution is qualified to participate in FF rate market, as well as discount windows, the alternative source of funding, CP, Libor is where things blow out during 08~09 crisis, Prime Dealer Credit Facility is a direct response to this –> 1.2 trillion.
Repo & SOMA (direct operation when loosen money supply, tighten money supply through prime dealer(close auction with cut off for price discovery)
Prime rate: 300 bps ?!!?????
Commercial paper: used by banks & large corporation to fund ONLY short term obligation (pay roll, inventory receivable etc.).
- 1.7 trillion in size, 1,700 participant.
- half ‘asset-backed’, half naked credit.
- of the nake credit CP, majority of them is from Financials.
- Marcus Goldman start from trading CPs. guess it’s more tedious than what white shoe WASP banker would be interested.
- Traditional depository institution’s function is, to an extend, replaced by, Financials on one end, CP market in the middle, and Money market fund at the end. although the uniform commercial code stipulate that CP can only be used to finance operation expense and current asset, the spill over effect means Financials will have more resource to finance their long term asset and other expenses.
- CP is cheaper and more flexible than credit line. CP is more flexible and liquid than deposit for investor.
- Default is rare, 70, 97(300m well absorbed), 08( lehman, triggered major panic).
- Actually the Money Market Fund run has replaced traditional bank run and has much more potential to disrupt the real economy. Money Market Fund constitutes a shadow banking system deemed as a threat to commercial bank’s saving and loan operation.
- Treasury department’s insurance effectively promised a high return on MMF over bank deposit (with FDIC), but again it’s MM who’s funding the real economy operation, not the banks’ credit line, so the priority is obvious and sacrifice necessary.
- in Lehman aftermath, CP funding shoot up from 2% to 8%, treasury bill drive to 0%. the redemption in whole MM fund is 5% of 3.4 trillion, i.e. 169 b. The 500bps+ spread lasted for over a quarter, the market didn’t normalize for a year. The outstanding CP for financial shrinked 30% from its peak(800b->600b), the non-financial CP shrinked into half(200->100), and there is no sign of bounce back yet. either this part of financing is now draw on bank credit line, or this much operation is destroyed during the crisis. The is probably the most serious blow during this crisis.
- The asset backed CP is totally destroyed, and it happened early during the crisis. it would be interesting to look into what ABCP is used to finance.
NyFed conference summary Part IV 07.
Self-Organized Criticality, H.J. Jensen
The Structure and Function of Complex Networks, Newman 03, a good introductory of CN
Self-Organized Criticality: An Explanation of 1/f Noise, Bak, Tang, Wiesenfeld 87
Illiquidity in the Interbank Payment System Following Wide-Scale Disruptions, NYFed staff report 06[stag hunt, potential function, too big to fail]
Congestion and Cascades in Payment Systems, NYFed staff report 06[a parsimonious queuing model explain congestion/cascade]
Disturbance in Power Transmission System, Sachtjen 00 [cascade become unbounded]
Payment System Disruptions and the Federal Reserve Following September 11, 2001, J.M. Lacker 2004[detailed account of the events, havn't read the policy recommendation]
Liquidity Effect of the Events of September 11, 2001, NYFed staff report 02 [cursory]
The Topology of Interbank Payment Flows, NYFed staff report 06[topology impact of 911 is not much different from normal holidays]
The Topology of Fed Fund Market, NYFed staff report 08; ECB version
Systemic Risk and Liquidity in Payment Systems, NYFed staff report 09[lattice model]
Other interest NYFed staff report 08-10
[this whole block not TBR]
Fed Fund Market in Crisis :: Amplification Effect During Crisis:: Aggregate Balance Sheet During Crisis::Fed Fund rate and Target Rate during Crisis::Network Analysis of International Financial Center::Bank Reserve During Crisis::Aggregate Balance Sheet & Monetary Policy during Crisis::Liquidity and Congestion
Dynamics Hierarchical Factor Model
Use Google Page rank to study Banking Network [steady state distribution]
That’s quite a lot of papers to read, I will try to finish them during this weekend and early next week and post my remark/synthesis soon…
feedback loop in financial system
March 25th, 2010
- The definition: damage to real economy is not sufficient ground for definition. Defined ‘systematic’ risk as phase transit from one equilibrium to anther, with self-reinforced feedback and path ‘lock-in’. categorize small disturbance which is able to trigger phase shift as ‘systematic’ risk. collapse of american manufacturing is one, collapse of investment bank (irreversible?) is another. downwards risk, i.e. destructive feedback is the focus of central bank
- The first loop(liability side): concentrated exposure to volatile industry(oil, real estate etc.)->positive feedback from ‘think what other people think’ from liquidity provider(depositor, CP fund)->interbank exposure->liquidity provider panic. ->policy response is equity cushion/leverage requirement, loan concentration regulation, liquidity window(last resort depositor) -> deposit insurance(FDIC)
- The second loop(asset side): the policy guidance for CB is provide fund to bank that is solvent at penalty rate, but not so if the bank is truly insolvent. The matter is further complicated by the problem of determine the value of bank asset(especially illiquid, hard to price, OTC, determined by few asset). The feedback here is, if CB deems a certain bank as insolvent, in case of liquidation, it would cause asset to be depressed, and more bank to become insolvent; or if asset to be absorbed by another bank, increase further the systematic risk. The last option is nationalization, but that cost political capital.
- The third loop(behavioral loop): the attempt to address loop 1 & 2 created an asymmetry internally within the bank, i.e. privatize the gain, socialize the lose. The risk taking is rewarded but self-regulation is a relative penalty in the competition. Therefore banking regulation is socialized as a response.
- The fourth loop(game between regulator & perpetrator): the complexity in financial market is a direct result of the game between regulator and perpetrator. But in terms of man power and response time, regulator is routinely being outmaneuvered, new regulation only came in after disaster. As the complexity grew, the expert behind regulator and perpetrator increasing become one. The consequence of this continuous gaming is, regulator fail to catch up with what’s going on in the bank, but on microscopic level, internal also fail to catch up with what’s going on in individual LOBs. The regulator often came from the industry, as a result they carried their ’common sense’ from the industry into the regulatory body, potential risk become difficult to realize due to the ‘lock-in’ effect in thinking process (e.g. 20x leverage is common in the industry).
- the fifth loop(market mechanism and short term liquidity), if the market mechanism, e.g. market making/arb activity relies heavily on short-term funding, the fall in price will trigger capital to fly to short-term instrument, as more player pulls out of MM/Arb, this would exasperates/exacerbates the failure of market mechanism, hence another positive feedback loop, e.g. Salomnn Brothers close bond arb group in 98, followed by Russian default, which triggered the collapse of LTCM.
- the sixth loop(risk appetite and VAR/MTM accounting), as volatility increase, exposure tolerated under Var limited is reduced which leads to more liquidation and absent of risk appetite, and increase the volatility further.
- the seventh loop(convexity, dynamics hedging, concentrated insurance), e.g. portfolio insurance.
- an observation, the systematic risk of LTCM and Enron is much less sever than systematics risk generated by balance sheet problem of money center banks(FDIC insurance effectively stalled the traditional bank run) or security firms (CP market froze up is much severe).
financial market dis-intermediation(75~05), banks moved from liquidity arbitrage business model towards a more service oriented diversified business model (i.e. securitize loan, underwriting, market making/price matching/liquidity pooling, broker-dealer/execution service/settlement/custodian, asset management, short-term loan/trade finance etc. ), the financial system has moved from institution oriented model to market oriented model (therefore the rise of exchange, and Chicago based market makers) and will probably continue to do so. The dis-intermediation also shift to importance from banks to end users (pension/endowment/mutual/hedge funds). Short term liquidity’s reliance on deposit has been shifted to other means, i.e. CP market at outskirt, LIBOR for international, Fed Fund at core (need more clarification for short-term liquidity).
The shift of systematic risk from institution to market poses a new challenge for everybody, i.e. to understand the nature of market liquidity, with market making, arbitrage at its core (Getco, Prime Dealers, RenTec, DE Shaw, Stat Arb shops, Exchanges). Cascade failure of market infrastructure(settlement and beyond), from unwilling to buy to unable to buy until complete vacuum of liquidity which permanently ‘lock-in’ market into a new path is the typical development process of system risk.
John Kambhu: Hedge Fund, Financial Intermediation and Systematic Risk 07
NYF ED Conference 06: New Direction for Understanding Systematic Risk
part II presented three papers, 2. cascaded balance sheet and MTM accounting 3. Funding liquidity and Mkt liquidity.
part III, Ecology: robustness{heterogeneous/redundant/compartmentalized}; model: network theory,epidemic theory, spatial stochastic process. useful to answer where is the vulnerable point? how it is going to spread? which design is stable? ; infrastructure vulnerability, power grid modelling and control. inductive reasoning.
Darryll Hendricks : Systematics risk in the Financial System 06
Arthur Brian 89 essay
March 25th, 2010
so far my attention on positive feedback mainly focused on destructive process: cause of Peloponnesia War, the fall of Habsburg empire, break down of financial system, cascading failure of power network, escalation of torture that lead to the death of Sylvia Likens etc. In his 89 essay, Arthur Brian pointed out the role of positive feedback in the process of creation and growth, specifically in the economics. The development of silicon valley from the 40s, the rise of Chicago in lake district, the competition of two rivaling video standard and personal computer, the adoption of light water reactor in U.S. nuclear industry, the donimation of gasoline engine in automobile industry, the rivalry between different system of language etc. all share a common increasing marginal return paradigm as oppose to the conventional diminished marginal return paradigm in equilibrium economics.
In increasing return paradigm, initial condition, randomness, history, timing all play important roles in determine equilibrium(if there is any). The positive feedback economics not only address how the system evolve, but provided a fuller picture of multiple possible equilibrium states along different paths. The notion of reinforced growth, the lock-in in development path(an important consequence of which is ‘survivial not necessarily the fittest’, the sensitivity to small initial advantage all point to challenge the Newtonian equilibrium world view and random walk assumption. Much of the Chicago school laissez faire economics thinking is based on the previous diminishing return concept, which should be treated with skepticism and extreme caution when used to address real world problems (look how they screwed up Russia!). Path dependence in Economics
Investment and risk management activity, at its very core, should be a process to identify potential creation or destruction feedback in underlying process. With regards to all the boundary conditions in reality, increasing return paradigm is much more relevant than its diminishing return counterpart for practitioner, equilibrium is only for theorist, hide-bound academia and economics research, not for trader, investor and policy makers. On the other hand, activist fund, business venture should focused on building and nurture the creative feedback loop while avoiding the destructive loop.
Link Feast March-24-2010
March 24th, 2010
{empathy suit, loneliness, joint replacement surgery & aging–> link
{stress, physical training, novelty, aging, multisensory –> link
Cacciopo’s book, Cacciopo’s reading list
parallels between neurophysiology and intelligent system design
March 24th, 2010
The development of neocortex and cerebellum is one of the most prominent feature in the evolutionary development of Mammal esp. Homosapien. In terms of functionalities, neocortex’s role is to sense, reason and express; cerebellum is a massive parallel structure works in higher abstraction, provides general purpose models for finer control and more economical computation; basal ganglia, hippocampus, thalamus provide coordination and feedback through dopamine reward. The feed-forward/back connection between cerebral cortex and cerebellum is probably the source of human adaptability, creativity and intelligence. The enlargement of both parts, neocortex first among mammals and then the follow-up development of cerebellum among ape corroborated this point. Interestingly cerebellum provides both finer motor control and finer concept manipulation, unified both subjective world and objective world on neurophysiology level. There are also some interesting parallelism between the development of brain and other intelligent systems, e.g. nations founded on principle (theoretically, a function of cerebellum) as oppose to empire founded on ambition(a function of basal ganglia); the rise of German general stuff during WWI and its adaptation by military systems across the world etc. This provides a paradigm for designing intelligent system(e.g. a trading system, a company）and also a way to understand where an existing intelligent system is heading towards into the future.
Neurophysiology
March 24th, 2010
Basal Ganglia: role in motivation: Athymhormic Syndrome{able to feel the sensation but no desire to react}: Reinforced Learning: sensorimotor/action-reaction: agent model(?): 欲望、好坏、是非（？）
Cerebellum: fine motor/thought(ito) control: conceptual framework: general purpose{motor/language/temporal-spatial}, plasticity {Supervised Learning}: econometrics model(?): the most ‘human’ part of brain/and most distinctive evolutionary advantage : 知识、经验
Cerebral Cortex: feature extraction: grammatic : sense/motor/logic/language/conscious thought: Unsupervised Learning： 感知、语言、逻辑
Hippocampus: temporal-spatial memory formation, procedure memory(working memory seems belong to a different category): 时空记忆的形成
Thalamus: switch on you are alart, switch off you are in coma, relay point between sensor and cortex: 开关
[]complementary roles of thebasal ganglia and the cerebellum in motor control and cognitive learning, Doyan 2000
[]basal ganglia article : Parkinson: Plasticity(!!?): Dopamine Reward(in Striatun) System: good/bad(moral?):Focus(Dopamine related as well): Habit/Addiction (Plasticity of Dopamine Neurones): Sadism disposition: ADHD/OCD(positive feedback) etc.:
Giftedness-Positive feedback
March 24th, 2010
Anders Ericsson: Wiki, FSU, HBR article, HAS paper
Ericsson’s Book: Development of Professional Expertise， Road to Excellence,
Vandervert’s neurophysiology studies in giftedness, creativity, positive feedback, cerebellum and working memory.
Cerebellum, Working Memory, Cerebral Cortex unfortunately the two 09 papers are not available in library yet.
consciousness -> repeat {perdition-> correction-> expression} [working memory(WM)]
subconsciousness -> repeat{synthesize -> creation -> expression} [cerebellum/LWM]
–> excellence [speed/ precision/spontaneous] [collaboration of WM and cerebellum]
战略二十讲笔记之五 Twenty Lessons on Grand Strategy -5
March 22nd, 2010
18) The Soviet Grand Strategy: From October Revolution to Gorbachev Condoleezza Rice
冷战的大战略在凯南在四八年所预设的脚本之下展开。 以围堵，封锁，孤立为核心的美国政策激化并强化了苏联的疆化的国家策略。 从赫鲁晓夫打肿脸充胖子，到勃列日涅夫更趋向务实的做法，苏维埃政权的核心：Kremlin宫内最高领导人的大脑被成功攻陷，但当五十年独立发展后苏联决定重新突然加入国际体系，这无异是一场灭顶之灾。 苏联体制的僵硬与大战略思维的缺失体制上与军国主义日本，纳粹德国，飞利浦二世的 Habsburg Spain 有这惊人的相同之处。赫鲁晓夫的策略是防卫性的恐吓，而勃列日涅夫的策略是扩张性的缓和。戈尔巴乔夫时代经济上的封锁使苏联被迫开始经济改革，这导致对意识形态领域的控制出现松动。相信国际体系意图摧毁社会主义，接受西方援助会遭受经济盘剥，这种自闭性质的怀疑和恐惧情绪的对一个人，一个国家都是不健康的，病理上与抑郁症相似。 对于冷战的起源赖斯认为加入一方或者另外一方较好的理解了对手的恐惧和关切，那么冷战就可能避免。 但我相信实际上的原因要比这种博弈更与人意识深处的恐惧感有关，而当两种精神体系的核心价值无法调和时，冲突无法避免。 冷战如同热战一样其本质如同癌症， 有一种正反馈使得资源被不断地吸收到无异于生存的器官上，直至毁灭。
19) End of Cold War: American Grand Strategy and its Future Paul Kennedy
分析美国大战略必须从分析美国的立国之本与建国历史入手，立国之本即其对自由，平等，限制政府权力，对权力的不信任这一系列基本理念入手； 历史即美国从二十世纪初以物资出口(资源丰富和人口稀少使其必须以更快的速度大规模粗放式的进行工业化)，自由贸易，工业创新强国，加上二战后美国对全球贸易几乎垄断的地位。 这一系列事实加深了美国人对自己制度的优越性以及其在世界上所处的特殊地位的信念，也为当前一系列问题埋下了伏笔。 在对应苏联的问题上美国的处境与英国在一战前所面临的形式有相似之处。但不同的是美国汲取了历史教训而采取了‘冷’ 战的策略。 以围堵为手段，以克林姆林宫高层思想意识为目标，和平演变为最终目的将北极熊放倒。 马歇尔计划开放的贸易和投资框架，与美国的立国精神相契合，但也为对抗苏联提供了有效地手段。 好苏联时代， 防止核武器扩散，组建能够部署全球的机动化部队，维持与旧的同盟国的联系成为美国外交的主轴。 伊斯兰极端主义能够造成的最大伤害，是美国的自身反应所能造成的自伤，而不是所谓的恐怖威胁。 另一方面国内政治将要求美国将大部分尽力至于国内这包括：债务问题，制造业的崩溃， 教育，医疗，社保等等。在国际政治中美国将不得不将维护当前世界体系的义务与权力分配到世界各级。 维护并重建生产，金融，技术，教育的实力储备将是Obama及以后几届政府工作的主轴， 大政府，高税收，欧洲化社会体系不可避免。 美国人将开始被迫重新审视美国式的生活方式，自由市场，小政府等等观念。
20) American Strategical Tradition and critics of Bush’s Grand Strategy G. John Ikenberry
恐怖主义造成了一个美国大战略的两难，即在无法定义区分敌方和明确敌方所代表的意识形态及目的的情况下，报复性打击变得无效，先发制人的打击缺乏正当性。事实上美国武力的优势在应对恐怖主义的斗争中并无压倒性的优势可言，而敌方可以在任何时间任何地点发动对美国的攻击。 布什的战略对损害了多变的均势格局，分化了美国的盟国， 削弱了美国作为benign superpower 的道义上的领导地位, 同时激化了基督教世界与伊斯兰世界矛盾。 所以说恐怖主义对美国最大的损害要由美国人自己来完成。美国一定程度上陷入了帝国式国家最古来的陷阱：自招包围。 在其无视合法性和规则制约的行为下， 现存的世界秩序的公信力被大大削弱了。
战略二十讲笔记之四 Twenty Lessons on Grand Strategy -4
March 22nd, 2010
16) Grand Strategy of Winston Churchill. Elloit Cohen
Churchill 的大战略的根本特征在于全局观念，分寸感和战略平衡意识。 Churchill 主张讲一个盟友招到战场中来的谋略同赢得一场重大战役的谋略一样重要。 获取一个战略要的的谋略，也可不如安抚或者威慑一个危险的中立国的谋略那般富有价值。 他强调在战争中的任何事情，必须从观察整体的性质入手。大战略的效用是特定时期内起作用的所有力量的总和。 在具体操作上Churchill 倾向于不拘泥与小的利益的牵制，而以总体目标入手决定在局部利益上的取舍，这体现在战争初期只要能进一步将美国拖向欧洲就同美国作交换利益即使这样的利益交换就局部而言不平等，和当日本偷袭珍珠港后，Churchill 及做出英国作为一个主权国家将生存下去判断。 在作为达成主要目标的努力上，Churchill 不遗余力，这一点由他同Roosevelt 通信的频繁程度可见一斑。在大战略的把握方面， Churchill 的做法与毛泽东关于主次矛盾的论述相当的一致。
17) George Frost Kennan‘s Strategic Thinking and National Policy Design John Lewis Gaddis
Kennan 作为二战后美国国策的缔造者其思想的犀利与前瞻性令人印象深刻。 美国的政策特殊性在与历史原因造就其努力从原则而非权势为出发点看待国际社会的种种现象。 但让这一传统以确保国家安全为前提，这就造成了当美国大陆安全被挑战的时候，美国便会也更接近传统霸权主义的方式推行其大战略(古巴导弹危机及911这两起事件决定了今后很长一段时间美国的国际姿态), 以原则为出发点，以及地理上与历史上的孤立主义经验使其在二战扩张后能过有限的回归原则，这一点使美国的强大对与世界和平起到了决定性的作用。 Kennan 意识到并指出美国不能够追求征服敌对的或是不负责任的势力的策略，因为这将彻底改变美国作为立国之本的生活方式和政治体制，并且购销作为捍卫这一体制而做出的扩张的真正目的。 这里体现了克劳塞维茨式的军事服从政治目的的思想，也是美国在本质上与古代帝国最突出的不同之处(判断美国是否会像Habsburg Spain 或者古罗马帝国一样衰落，必须考虑这一特殊性). 除去当今伊斯兰极端主义的威胁， Kennan认为美国对于国家安全应寄托于国际均势，而非在全世界范围内扩散美国的体制， 这一点在今后美国相对衰弱，中国，印度，欧洲，南美甚至非洲相对崛起的世界中最有可能再次成为美国外交策略的主轴。 其目的类似于二十世纪英国队欧洲大陆的操作，即在任何必要的地方使各方相互争斗，确保它们在彼此冲突中消耗，而是美国减少维持和平的成本并从中获取经济利益。
对于苏联Kennan (美驻苏联大使) 意识到斗争的至高点不在物质领域而在意识形态上。Kennan 指出‘威胁我们的不是苏联的军事权势，而是苏联的政治权势…如果它不完全是一种军事威胁，那么我怀疑能够完全考军事手段来有效地对付他’。 苏联所推动的共产国际运动，以及德国和日本两个被摧毁但仍可恢复的工业-军事基地较苏联地理上为接近这一现实，是对美国最大的威胁。 这一特征也是中美关系同美苏关系最大的不同之处。 当大战结束后， 这两地留下的权利真空，以及民众普遍的绝望给共产国际运动很大的操作空间。 在欧洲最大的威胁不是苏联的军事扩张， 也不是共产主义，而是战争对各国所留下心理不适(由指德国)，使得均势的条件更有利于苏联趁虚而入(在亚洲日本受到的威胁要小得多)。Kennan提出必须要有一足够戏剧性的行动，在亚洲和欧洲造成新的均势，这便是后来的 Marshall Plan, Kennan深刻之处在于他意识到这一计划的关键作用是短期内迅速恢复西欧的自信，而长期的经济复苏倒是其次的目标(类似于Obama以 HOPE 为竞选平台)。同时欧洲必须作为一个整体才能抵御苏联可能采取的分而治之，逐个击破的策略， 最薄弱的德国必须在经济上被整合到整个欧洲经济的框架内，并处于较核心的地位，以求一劳永逸的解决德国作为欧洲战争策源地的可能(其方针是通过经济融合使德国人摆脱集体主义和自我中心， 鼓励他们以宽广的眼光看待世事，在欧洲其他地方和世界其他地方尤其关切，学会把自己想做世界公民而非仅仅是德国人, this is similar peach to the Chinese government nowadays, but I doubt how well Chinese government will take it)。统一的欧洲能够更好的抵抗苏联的压力，并为美国从欧洲的撤出做好前期准备。
对于日本由于其仍是美国在太平洋上的潜在对手，美国的策略是有限复兴，长期管制。目标是是日本能够在亚洲均势上起到作用但有不至于再次在太平洋上对美国造成挑战，这一思维一直是日美关系的主轴， 也是日本政治积弱，日本社会稳定的外部原因。 在中国崛起的前提下，日美，韩美关系变得尤为重要，而作为能源咽喉的亚细安-美国，尤其是新加坡美国关系将获得进一步提升。 新加坡由于可以牵制整个东亚的潜力也将变得越来越受美国重视，在这一个世纪里新加坡将很可能是这一矛盾争夺的焦点和受益者。 越南泰国和整个东南亚的整合有利于美国在这一区域实现对中国的制衡。 以同样的思维思考，朝鲜半岛的一体化进程符合美国利益，而东亚三国一体化对美国不利。 台湾与大陆的统一即使在所难免，美国也希望这一过程越长越复杂对美国最为有利。 同样的，新疆和西藏问题的复杂化也对美国有利。
Kennan 对中国内战的形式判断不拘泥其外在现象，而从本质入手。Kennan 的核心判断是在革命云运动过后， ‘权力，即使是品尝，也可能像腐化资产阶级领导人一样腐化共产党人’。 苏共的国际共产主义必定与中国的民族主义相冲突， 其结果是共产主义的中国队苏联的威胁远比对美国的威胁要小。而共产中国将在很长一段时间内无力通过两栖战和空战威胁日本，台湾，以及印度支那地区。而如果支持国民党政府，美国将以更高更直接的代价介入到中国内战以及之后中苏对抗当中去，这是Kennan 认为美国必须避免的。 显然Kennan 的分析一功利主义为主导，而非苏联, 纳粹或是Phillip II式的意识形态。
在亚欧均势框架形成之后， Kennan 定下了一个更广范的以北美大陆和能源为核心的防御圈，其包括加拿大，格陵兰，冰岛，斯堪的纳维亚，英伦三岛，西欧，伊比利亚半岛，摩洛哥和突出部的西非海岸，以及突出部对应的南美诸国；包括伊朗在内的地中海和中东诸国； 日本和菲律宾。 Kennan遵照其对意识形态之争的判断，提出在这些地方创造有利于美国的生活态度和政治制度，并认为在整个世界建立这种制度超出美国能力。根据这一圈定，Kennan 提出三个对抗苏联的步骤， 1)通过鼓励受苏联扩张威胁的国家的内部自信来恢复均势。 2)利用莫斯科与共产国家之间的紧张关系来减小苏联向境外投射势力的能力。 3)逐渐修正苏联的国际关系思维，使莫斯科主动提出通过谈判解决突出分歧。 Kennan 意识到对苏联政策的细微之处，即苏联人不能被说服，更难以被征服，只能通过包围和将其排除于体制之外来使其最终意识到美国试图说服他的观点-即邓小平强调的所谓和平演变。
战略二十讲笔记之三 Twenty Lessons on Grand Strategy -3
March 22nd, 2010
12) Napoleon and Strategy Renovation Peter Paret
在社会结构及炮兵技术发展之后，拿破仑是欧洲最先充分意识到并充分运用这一变化的人(只一点上类似微软), 与大革命一样，拿破仑的军事策略对于欧洲传统的以攻城夺地为目的的有限战来说是颠覆性的。一方观念的革新与另一方观念上的不适应使得拿破仑在短短数月之内摧毁了欧洲大陆上传统的制衡和均势的格局。 普遍兵役制和平等选拔军官制使法国拥有了欧洲军事上的绝对优势，而长驱直入的，以消灭有生力量或者夺取敌军政治经济中心为目标的歼灭战使拿破仑一直掌握着战场上和心理上的主动。拿破仑的异军突起代表了现代战争的趋势，但犹如纳斯达克指数一般最终还是跌回了历史缓慢前进的洪流当中。
13) Clausewitz Peter Paret
克劳塞维茨的战争观和战略观其核心为以下四点： 1）战争对暴力的使用其核心是恐吓与顿挫敌人使用武力的意志(恐怖主义是对战争运用的新的更有效方式) 2)战争必须要有明确的目标，其核心应是政治的 3)偶然性不可预测性是战争的重要特性(即克劳塞维茨所指的friction),战争非单纯的博弈(纸上谈兵与真实战争的区别所在)，战争作为一种暴力行为本身不存在任何逻辑限制，武力冲突无拘无束最后达到以彻底摧毁对方为目的的绝对战争的极端 4)智慧，感情，与创造性的随机应变这些人的因素往往是决定因素，即克劳塞维茨所指的talent
14) Helmuth von Moltke: the Rise of German General Stuff Hajo Holborn
Moltke 作为Bismarck 战争策略的执行者将克劳塞维茨的战略思想运用到了实践当中。 在组织结构上Moltke 强调大兵团作战战略上的中央集权，与战术上的分权和灵活机动。 Moltke 所代表的德军参谋本部强调保持部队的机动性，并认为战略必须包括计划与按照不断变化的环境形成应对这两个方面。战略不仅仅是知识，而是知识在实际生活中的运用，是在困难状况压力下行动的艺术。 总参谋长直接向国王汇报，同时形成向各部队委派参谋副官的管理使参谋本部逐渐取得对军事事务的主导，并扩大了总参谋长对全体将领的影响。 在执行上，与普鲁士军队传统的服从纪律不同，Moltke重视各级军官的独立判断力，鼓励下级指挥官的自发能动性和战斗精神， 并试图对战略与战术做出划分以避免军事体系过于僵化。 Moltke 的战略思想不拘一格，从一种谋略灵活的变为另一种谋略这是其战略的特征。 在欧洲战场上他充分利用了以时间换取地理上的优势这一策略。但在二战期间苏联以空间换取时间的策略成功的反制了这一策略。 这一变化体现了战争中的辩证演变， 而保有弹性和随机应变的能力是克劳塞维茨和Moltke思想的精华。 不幸的纳粹Philip II式的执着使其只注重战术上的机动，而忽略了战略上的应变能力。
15)Hans Delbrück: Military History and Concept of Strategy Gordon Craig
一战由德国的角度所观察的事态的发展与伯罗奔尼撒战争中雅典的处境有相同之处。尤其是战争开始以后国内的诊治气候演变使德国逐渐失去了Bismarck 式的战略运筹与自我批判的能力， 而最终成为一头为战而战的怪兽。 机构的设置：司令(战术)，参谋(战略), 政委(政治) 是保证战略实施逼必要条件，但组成人员的素质，三个部分之间的牵制与分权同样重要。 Delbruck 意识到德国在丹麦和波兰德意志化过程中的名声使其失去了道德上的支持点，其后果是德国在战役上的上的胜利反而激起而不是消灭敌人的斗志，从而在战略上得到相反的结果。 理性分析得出了结论是要使德国取得胜利，就必须克服周边国家队德国领土野心的恐惧， 但在无有效均势的前提下，加上国内好战舆论的惯性，德国这类提法几乎完全不可信。 事实证明，军队领导人的政治觉悟，或者更根本的来说如果没有有明确战略目标的政治家对军队的绝对控制，国家在战争中很可能走上歧途。 而这些政治家的判断，也应尽可能少的受国内舆论的干扰。
战略二十讲笔记之二 Twenty Lessons on Grand Strategy -2
March 22nd, 2010
6) the Grand Strategy of Roman Empire Edward Luttwak
战略的最终目标是取得对方心理上的慑服， 罗马通过外交，穷追不舍的歼灭战，以及基础设施和税收体系来维持其庞大帝国并取得巨大成功。 第三点但是罗马帝国长寿的主要原因，而穷追不舍明确表达了其战略所最求的最终目的。 Edward Luttwak 指出一个正在演化的文明有两大根本需求，即健全的物质基础及充足的安全。 罗马人清楚地意识到权势的主宰方面是心理的而不是物质的。 历史上它以极少的战役和大量的强制性外交征服了整个希腊世界，在这一过程中外交，军事，基建三者相辅相成。 在帝国的最后阶段，野蛮化这个体系丧失了它的最后支持，即人对陌生者的恐惧. (remember Cicero defended Sextus Roscius against Chrysogonus(a favorite of Sulla)? of course Chrysogonus is just a Greek, not too barbaric by Roman standard).
7) Grand Strategy of Roman Empire 阿瑟 费里尔
罗马人在军事上的优势体现在交战中军力投放的效率上，虽然其外在表象为罗马士兵的纪律与英勇，但其本质是兵团排列(罗马式层次轮换相对于希腊式的方阵, 对士兵心理有极大影响,美国人的海军陆战队不抛弃伤兵和被围困部队的传统与之有相同效果)的运用及训练。 后勤保障对维持罗马的军事优势起着决定性的作用(地方农民军无法长期脱产对抗). 在罗马帝国后期，机动化精英化的中央军降低了罗马的军事开支，但也导致了边防军的野蛮化，从而使国防过分倚重中央军(驻防在城市附近-使后期罗马军队沉溺于炫耀和奢侈;从市民中招募-逃役非常普遍)而无法发挥整个军事体系的最大效能。 过分突出精锐部队的负面影响往往被忽视而且一犯再犯(这一点类似于所有欧洲贵族国家加到一起打不过掌握全民资源的拿破仑，正规军打不过全民战争). 军队的士气与战斗力要有正确的组织结构加以保障， 而由理性的计算做出的结构上的调整必须仔细考虑其可能对心理和士气造成的后果。 在宏观上效力决定最终结果，这包括数量，运用，效率。 而非仅仅是数量。
8) Machiavelli: the Beginning of Modern Strategical Thinking Felix Gilbert
封建骑士时代的终结，帝国时代的带来。对暴力的垄断从领主手中集中到君主手中，在拿破仑时期又从君主手中掌握到议会手中。 小规模低组织程度的荣誉之战转变成大规模大消耗的屠杀活动。货币的流通，银行，债券的出现给统治者更大的操作空间。 高成本高效率的炮兵使得权利得以进一步集中。 集权使现代征兵制度成为可能。 经济与金融开始在战争中扮演重要角色。民族主义，爱国主义的出现为新一代的野心家们提供了从未有过的操作题材。
马基雅维利对于权利的思想也贯穿与其军事思想中。 理想的首领必须有以下素质：即雄心勃勃，举止难测，果断坚决， 行动迅速， 有必要的话残酷无情。 对于治军，马基雅维利的中心思想是以对严惩的恐惧来加强纪律，一对敌人的恐惧来加强部队的危机意识， 以爱国主义来鼓舞士气。
9) Grand Strategy of Habsburg Spain (Phillip II) Geoffery Parker
a mammoth empire towards its end, geographically over extended and logistically over loaded, financially exhausted with an overwhelmed and overworked ruler obsessed with an impractical religious agenda, the demand of total control, blind faith and self-justifying logic ruled out any possibility of rational thinking. The reason Philip II refused to avoid confrontation on multiple frontier is his fear of collapse of fear which will trigger a cascade effect, which turned out to be a self full-filling prophecy.
造成Phillip这种宗教偏执的是西班牙在取得美洲贵金属资源后所得到的巨大成功。 人在成功的刺激下产生信念，在信念的驱动下产生强大力量，但偏执的信念也往往是毁灭的根本原因。 相反的，英国在对西班牙的作战上超越传统界限，海盗活动削弱了西班牙的财政收入，在与无敌舰队的对抗中充分发挥了牵制作战的灵活性。 这是大战略中经典的旧势力衰弱，新势力崛起的案例。
10) 17 Century European Strategist and Strategical Thinking Gunther Rothenberg
Gustav Adolf 30 years war. 胶着状态，有限战争。
11) Hamilton and List: Modern Strategical Economics Concept Edward Mead Earle
准备下一场战争成为列强的主要政治目的，为战争服务的扩张性的重商主义策略成为各国的基本国策。 发展战争潜力为首要目的， 其基本手段包括：政府严格管理进出口，增加贵金属储备，对军需品的生产和进口予以鼓励和补偿，扶持航运与渔业借以促进海军发展，通过海外殖民追求原材料的自己自足。民族主义与重商主义是天然的一对孪生兄弟，而最早实现国内统一的英国(Oliver Cromwell)在重商主义各国中脱颖而出。这也促使了德国(Otto von Bismarck) ，日本(明治维新），法国(Richelieu, Louis XIV, Napoleon)，美国(Civil War) 等国开始最求国内统一市场和中央集权。 美国制造业基础在这一阶段被开始奠定，而其在当时所面对的贸易保护主义，与新兴工业面临不公平竞争等一些事实，也为其一百年后推行全球化提供了历史基础。 Hamilton 意识到统一对整合整个美国经济有巨大的意义，而且可以避免在美洲大陆出现欧洲那样的威胁自由而且内耗的军事竞赛。 他还意识到在不久的将来，美国的统一将使其成为身处美洲的欧洲事务仲裁者，可以以很小的代价投射到敌对双方的天枰上将是其可以通过均势外交的手段获取有利的贸易条件。由此可见在美国形成霸权之前，其大战略的出发点是通过均势来达到商业利益的目的，这也解释了在二战前三年美国一直抱有孤立主义的想法。二战后美国致力与建立于战前重商主义所不同的全球化的国际体系，为避免世界走向下一场战争提供了保证, 而这个体系今后将如何发展将决定整个世界的和平远景。
List 为德意志民族的统一规划出了远景。在这之前作为历史上的欧洲战场，德国一直处于地缘政治上的弱势，德意志民主的振兴是从历史上的受害者的位置出发的。这一点可以解释为什么德国一直存在民族被迫害妄想症情节(与日本相似), 在奋发图强之后心里上有强烈的拓展生存空间的欲望。 List 意识到铁路网将是德国的地缘劣势变为地缘优势， 而对德国统一的最大威胁是最求欧陆均势的英国。因为一旦欧洲开始控制贸易行道，对英国这样一个岛国将是致命的。因此List 提出欧洲应建立一个抑制英国权势的欧洲同盟，这也是日后德国在欧洲的野心的经济上的原因。 而英国之后两次卷入欧陆做战(拿破仑，纳粹) 也是出于对其最关键的利益的考虑(二战之后的国际体系解决了这一根本问题）。List 还预见斯拉夫族和拉丁族将通过法俄同盟联系起来，并认为英德应带领日耳曼族制衡这样的同盟， 如果纳粹在欧洲追求均势的话可能出项这样的局面，很可惜希特勒更倾向于罗马式的彻底击溃。List 对铁路的迷恋还是他提出了连接印度和埃及和英国的欧亚铁路线和从莫斯科到中国的铁路线，这可谓是两百年前对全球化的憧憬。 而当时连接太平洋和大西洋的铁路线正在美国被讨论。 有趣的是 List 在世时的国的工业化进程换慢，但在他自杀后两年，革命浪潮席卷德意志， 民族主义者接受了List 的经济教诲，但抛弃了他的人权宪政之说。 德国从此走上了霸权之路。
战略二十讲笔记之一 Twenty Lessons on Grand Strategy -1
March 21st, 2010
1) on Strategy and Grand Strategy Liddell Hart
战略是群体性行为， 在有种族，社区，宗教，传统，价值观，国家这些划分之下，又少部分人对大部份人产生支配的前提下才会产生战略的必要。 在一个完全一个人行为为基础的社会所谓战略并无实际意义。 对战略的思考，运用赋予统治者以使命。
在专制封建时代这一使命的本质是通过暴力与非暴力的手段壮大并扩张。 在民治时代这一使命转化为满足统治者所属群体的群体需要(collective need). Liddell Hart 的核心思想是政治目的决定政治， 政治统帅战略。
2) Grand Strategy in War and Peace Paul Kennedy
大战略的范畴不仅包含战争，更包含和平。 大战略的目的为保证种族的生存，兴旺，昌盛。
3) the Making of Strategy Williamson Murray, Mark Grimsley
影响战略形成的要素，也是分析战略的入手点。 他们的观点基于两点，即 1. ‘人类在没有一个共同的权利使他们全部都慑服的情况下生活，便处于所谓的战争状态’-Thomas Hobbes. 2. 任何一方以更高效的方式投入战争就将获胜。 在分析和形成战略是必须考虑： 地理，历史，政权的性质， 宗教意识形态及文化， 经济因素， 政府和军事体制组织方式。
4) 战略缔造的方式的连续和变革 Macgregor Knox
战略的形成机制的先天不足，或是这一机制有效性的缺失是历史上灾难性决策的根本原因之一。 大众政治是一柄双刃剑，对其有效控制至关重要。对于未来，麦教授担心日本古怪的民族主义神话，以及东方民族缺乏除成功以外的中心价值和合法性源泉包含着在特定条件下突变的可能。 而西方民主国家的人口至2025将跌至全球人口的8.8%， Western Civilization as a whole is threatened (?) as prom to decline (probably true).
5) Peloponnesia War Donald Kagan 1
伯罗奔尼撒战争体现了复杂的两极体系在相互怀疑的状态下为持均势平衡而采取的防卫性方针相互激化，最终将整个体系拖入无可逆转的大战漩涡之中。互信的缺失以及冲突解决机制信用(30 years peace under Pericles) 的崩溃，使战争的过程无法逆转。其悲剧性在于相互无对抗意愿的两大强权却无可避免的走向对抗。 这一部史书的价值在于它的复杂性和所提供的细节(Thucydides 作为伯罗奔尼撒战争的参与者和史学家)。 虽然双方都已理性分析为出发点制定战争初期有限战(limit war)的策略，即守己所长，以较低的代价取得胜利。 但当战争机器开始运作，作为战争特性的混乱冲动与随机性开始引起国内政治及民意的变化,雅典国内的主战派开始占上风(Cleon)。 全面战争造成对扩张的需求(Sicily Campaign) ，最终即使斯巴达人获胜，但古希腊文明也从此终结，取而代之的是更野蛮强悍的内陆力量(Macedon)。
伯罗奔尼撒战争的几个值得借鉴的特性：
1. 战略要地加种族矛盾是必然的火药桶， 伯罗奔尼撒战争的开端是Cornith 与 Corcyra 两个小联邦国在 Cornith Isthmus 的种族矛盾。 随着危机的持续，Cornith 人说服斯巴达人使其相信雅典的本质是扩张主义。
2. 鸡毛蒜皮的小事(Athenian blockade in Megara)，如果被赋予因恐惧而让步这样一个意义的时候，也会变成冲突升级的原因。
3. Pericles 的消耗战的目的是心理上的而非物理上的。 但当斯巴达人明白之一点以后，就开始死缠烂打下去，起更本目的也是心理上的，即使对方舆论发生改变并消灭敌方首脑的斗争意志。 悲剧的是mutual bluff 很快上升为实际上的mutual destruction. 心理战的前提条件是掩盖己方真是想法， 但要做到只一点随着时间推移变得越来越不可能。 激情和对敌仇恨经常湮没关于自我利益的考虑。
4. 偶不胜奇，后Pericles时代雅典人缺乏统一意志。 而公元前430年失败的谈判时雅典内部的主和派名誉扫地-只一点对斯巴达人及双方都不利。
5. Demosthenes 的成功之处在于出奇制胜，‘奇’在对地方心理上有巨大的震撼作用，相反的例子是埃及突袭以色列。 但战术上的胜利由于缺乏战略上的审视而失去了和平机会。 斯巴达人的求和很有可能是因为国内主战派有了台阶，主和派有了借口，Cleon 浪费了一个珍贵的机会。
6. 公元前423年斯巴达人控制住雅典人的银矿 (类似于美国内战时期联邦军控制住New Orleans), 逼迫雅典人媾和。 但这样的和平是脆弱的，因为正如 Pericles所意识到得，斯巴达人并没有从心理上承认自己无力打败雅典。
7. 当Alcibiades (此人在伯罗奔尼撒战争中的角色类似中国近代的张学良)为雅典赢得有利战略优势是(通过分化伯罗奔尼撒半岛上的各族势力），雅典人的犹豫不断使他们再一次失去永久解决斯巴达人问题的机会。 力量不单单包含经济军事实力，统一的意志与决断力起到非凡的作用。 Be very cautious about Alcibiades and his kind.
8. 在末期雅典人开始豪赌西西里， 历史的讽刺源于战争状态下巨大的不确定性和非理性因素： 野蛮落后，墨守陈规和缺乏想象力的斯巴达人战胜了敏捷，积极进取，善于创新的雅典人。
Positive feedback, Cascading failure
March 21st, 2010
Crespi B. J. (2004) Vicious circles: positive feedback in major evolutionary and ecological transitions. Trends in Ecology and Evolution, 19, 627-633.
Winner, E. (1996). Gifted children: Myths and Realities. New York: Basic Books.
Deangelis, Post, Travis (1986). Positive Feedback in Natural Systems (Biomathematics)
Vandervert, L. (2009a). Working memory, the cognitive functions of the cerebellum and the child prodigy. In L.V. Shavinina (Ed.), International handbook on giftedness (pp. 295-316). The Netherlands: Springer Science.
Vandervert, L. (2009b). The emergence of the child prodigy 10,000 years ago: An evolutionary and developmental explanation. The Journal of Mind and Behavior, 30, 15-32.
Arthur Brian’s new book: The Nature of Technology :: Positive Feedbacks in the Economy, Scientific American, Feb. 1990.
Systematic risk in financial market, Cascading failure (blackout):
1. a loading-dependent model of probabilistic cascading failure
2. Cascade-based attacks on complex network
3. Cascade-based attack vulnerability on U.S. power network
4. U.S. Homeland Defense resources
5. Critical Infrastructural Protection in Homeland Security: Defending a Networked Nation, Theodore G. Lewis (Navel Postgrad School)
Behavioral Perspectives on Neurscience of Drug Addiction
Charles Lehalle’s blog
March 20th, 2010
Pelopennesia War
March 17th, 2010
Battle of Sphacteria: trap of 120 Spatiate is enough to brought the mighty Spartan to his knee.
Pericles: would be the dictator if he was born in rome.
Alcibiades: student and lover(?) of Socrates; 性格背景类似张学良in terms of the unruliness, ambitious similar to Alexander or Caesar, unfortunately he was born in Athene.
Cyrus the Great: a model ruler, Xenophon’s Cyropadia is a must read for Thomas Jefferson, along with ‘The Prince’.
Hellenistic Civilization 中国春秋至东汉时期 [caliphate, muslim consquest, golden age,
Spartan conquered Athene, Philip II of Mecedon conquered Spartan, Alexander III(tutorship under Aristotle) conquered asian minor Persian(ended the legacy of Cyprus), then rustic Latium conquered the Hellenistic world, English pirate overthrew the mighty habsberg empire, then Germanic barbarian conquered the whole latin world, civilization is a disease whoever touched it destinated to be conquered by the more ruthless(Alexander being the archetype) but that is how civilization spreads through this creative destruction cycles. Remember Godian Knot?
Legal case behind ‘Got You In My Mind’
March 13th, 2010
the couple in good days
[Allegedly] Greenberg and Hurbst misreprented ownership of GMIYM in negotiation of MP-Rounder contract, which could amount to charge of contract fraud. Rounder discovered the co-ownership of GMIYM, threat to cancel the contract with Madeleine, instruct Greenberg to ‘make the problem go away’ , Greenberg alleged attempt to buy the right of GMIYM, Galison didn’t bite; Greenberg later wrote letter to Galison to threat legal action. [according to Galison] The tactical options lay in front of Beldock’s firm was either being sued by Rounder for contract fraud or sued by Galison for libel, the choice is apparent.
Greenberg & Peyroux press copy right infringement & criminal harassment charge against Galison in attempt to stop Galison’s distribution of GMIYM CD, which is in Rounder’s interest as well Beldock’s firm. Galsion’s attempted evidence collection regarding Greenberg & Peyroux’s misrepresentation in MP-Rounder contract is obstructed.
personal letter from Galison to Peyroux jun 03
personal letter from Peyroux to Galison on 29th oct 03
personal letter from Peyroux to Galison on 23rd nov 03
letter Robinson to Greenberg 3rd dec 03 [Galison pursue commercial exploitation, ultimatum end of dec]
letter Greenberg to Robinson 17th dec 03 [ allegation of physical abuse]
letter Robinson to Greenberg 17th dec 03 [withhold of negotiation, threat of defamation litigation, demand withdraw allegation forward to Rounder & Cynthia Herbst ??!!]
letter Robinson to Greenberg 21 dec 03 [Greenberg's conduct, Peyroux legal position]
Robinson contacted Principle of Greenberg’s firm
Robinson contacted Rounder( Rounder took side, evident by the fact that all Madeleine’s album upto now is under Rounder label)
Galison wrote to Ms. Westgate( Madeleine’s mother with no avail)
Galison got a restrain order( Robinson orchestrated in recording the telephone message), Myron Beldock from Greenberg’s firm get involved in this case.
Galison got David Heilbroner(ex ass district attorney NY) to call Myron Beldock (ex ass US attorney NY), Beldock categorically refused to discuss the matter with Heilbroner-> affidavit here 2004
Galison attempted mediation with Rounder Greenberg et. al. didn’t work.
Galison offer for settlement upon condition of Madeleine’s testimony for Greenberg & Beldock et. al. wrong doing, in January 2005. Madeleine and Greenberg et. al. counter sued for 1 million dollar.
Galison’s letter to Frankfurt, Seltz, Kurnit 06
毛词二首
March 10th, 2010
浪淘沙·北戴河
大雨落幽燕，白浪滔天，秦皇岛外打鱼船。
一片汪洋都不见，知向谁边？
往事越千年，魏武挥鞭，东临碣石有遗篇。
萧瑟秋风今又是，换了人间。
忆秦娥.娄山关
西风烈，长空雁叫霜晨月。
霜晨月，马蹄声碎，喇叭声咽。
雄关漫道真如铁，
而今迈步从头越。
从头越，
苍山如海，
残阳如雪。
Cpark
March 9th, 2010
Matlab resource
March 5th, 2010
[1] matlab based environment(not really functioning).
[2.0] matlab IB integration ->here
[2.1] integration with MBTrading -> here
[2.2] yahoo data feed -> here
[2.3] real-time data handler -> here
[3.0] a simple sys implementation example -> here
[3.1] another example -> here
[3.2] pair trading implementation -> here
[3.3] monkey trading -> here
[3.4] another TA system -> here
[4.0] an implementation for TA-> here
[4.1] another TA system -> here
[5] black litterman -> here
[] looking for puzzle? -> here
[] a poker predictor -> here
[] model implementation book -> here, code
[] openquant(retail) seems attracting more attention -> here
link feast 3-3-10
March 3rd, 2010
Derman’s Volatility Laughter notes
more trading platform: Tethys, Flextrade, and Portwrare (again!)
Academia run asset management firm: INTECH(robert fernhortz ~50b AUM), research(factor model, arbitrage& probability, stoch portfolio theory)
Headline risk, hedge fund blow-up->post here
glad to see some INRIA news
here is a glimp of what Tito is doing(notes Lo, ETF rotation, Peter Stone(see below) MM, VWAP scalping, HMM, auto gamma)
Interesting finding by tito, any reason why? here is a related bb news(gotcha!). leverage etf and reverse etf WSJ article, and paper (madhaven is with blackrock, author of ‘microstructure for practitioner’, and this review. oil ETF ft article.
Trade against broker’s execution algo would be another line of attack, havn’t seen much execept for the detect iceberg paper.
Wordle ->
Rama Count ‘ order book dynamics stoch model’, I’ve recommended his ts review paper before->paper
Tobias Preis has done some interesting modelling work (order book, corr structure) using GPU
James Str and one of their talk at CMU, CAML page
regime change, multi-strategy.
avellaneda’s stat arb got referred again. and carlos alexandre’s book.
apply information theory to derivative pricing and fraud detection? paper.
you may find some interesting ideas here.
Order driven market
March 2nd, 2010
Multi-agent based order book model -> paper
Simulation of limit order driven market -> paper (JOT), (check reference as well)
double auction simulation market -> paper, and this
Matlab code -> here
Complex Event Processing(CEP), with application to P2P network, senor network, exchange etc. Event Processing Industry
真实的历史，another demystified historical figure.
Geek is the new puppet Master?
March 2nd, 2010
Matt Welsh->SEDA->Lime (messaging, concurrent system)
Harvard senor network Lab(robotBee, Mercury). There is really some interesting thoughts, hardware implementation of financial data processing application (analogue to Soundblast and GPU). and Tito Ingagiola point out this, apparently ICAP is using this. Tervala (switch technology, option MM case) is backed by GS, Sigma(database VC), Acartha(financial tech PE), Nothhill( financial tech VC). Tervala using ( Arista switch technology(cisco, stanford cs guys), Celoxica( system design, UK based, former INSTINET, IB IT, HF Operation), Fix Flyer(process automation, courant math, nasa software engineer, IB op engineer), Portware (trading app design, former trader and IT), softModule(grid computing), Volante (sys integration).
on hardware side, it’s ASICs, FPGAs, network processors, GPU etc. etc.
even valuation and risk guys has interest in GPU, for monte carlos?
this two webinar about algo trading and infrastructure.
Yahoo Pipes, graphic programming. this is an awesome application!
one stock trading agent idea came out from Peter Stone(autonomous bidding agent, RL, robotCup).
Exchange simulator JessX
Link Feast 3-1-10!
February 28th, 2010
Unscented Particle Filter Talk
Option MM Baird
General direction: Liquidity – Arbitrage – Understand details.
Elwyn Berlekamp, i’m late to the story but Misha Malyshev(ex-citadal HFT, Teza technology LLC & the guy behind Aleynikov GS saga(similar story at UBS, and this interesting post)had similar track/background with a few authors in previous post (MIPT, Princton, hedgefund etc. etc.) Tactical (Turtle trend following fund The System incorporates mathematical market models that integrate key elements of modern portfolio theory, chaos theory, and proprietary money management concepts. sounds like snake oil, no?)
Detection of security fraud would be an interesting topic. e.g. how can madoff return being detect? not just suspicsion but quantifiable measurement of likelihood.
Poker Bot, RL Go, U Alberta Grp,
bank algo: CS AES, GS GSET, JPM Kissel ’05, DB Mkt Impact ’08.
Ad Hoc inference, a lot of talk about boosting, bagging etc.
given a set of timing model space, how to separate over fit/snooping with genuine prediction, regime shift how? 1 2 3 4 5
Time Series shops ( inefficiency/prediction from time series):
AHL (ManGrp, oxford based, stats, time series),
Winston (ex AHL stats, trending follow, old people)
Lynx( Brumer grp Stockholm based, seems v good performance since 01)
Transtrend( Robeco grp. Netherland based systematic 7b AUM), QIM(2b AUM)
Amplitude (people, bunch of IBankers, investor relationship guy is SOM ’00)
Cantab Capital (ex GS quant strategy euro head, Cambridge based)
Crabel(Milwaukee based Toby Crabel had a short stint with Niederhoffer)
John Locke(french fix-income arbitrage)
Quantmatrix(nothingness)
DFarmer team up with Lillo working of order flow model, here is information content in Volumne data, from northwestern again 1.
quant prop shop.
February 28th, 2010
RGM(Austin based)
Tower Research(Lime)
QuantLab v.s. Shi Jingpu (developer), Xu Yongzhong(worked in prediction). co-founder.
Two Sigma: D.E.Shaw math + Tudor IT
EWT: ex-nymex CEO
Jump Trading / Spot Trading(Robert Merrilee trading equity options)
Getco (Chi market making), Forbes on Getco, WSJ, Bloomberg story, the prophet is being fulfilled, and market kept on evolving. with different player join the arena, the natural question is: What’s NEXT?
Sun Trading(SnP option pit trader); Matlock Trading (market maker, system sold to GS in 99); Ronin Capital(prop, affiliation to exchange)
Ikos London based HF shop. they are not even hedge fund<what’s the point to be a fund?? dilute return??>
Allston Trading: interesting JD and requiremet
Chopper Trading, Chicago based, said nothing
Trade Link, very old. option focused. TransMarket, again exchanged based, early arber.
World Quant, igor Tulchinsky, Upenn/Millennium stat arb.
Capstone, (organizer+ ex-pit trader+ finanical engineer+???)
links 2-27-10
February 27th, 2010
Klipp’s saying was “You have to love to lose money, to take small losses, and hate to make money to be successful. There’s no other way.”
The hard and stiff will be broken, the soft and supple will prevail.
In July 2009, Spitznagel opened a fund betting on hyperinflation
Clone of low turn over value focused fund actually make sense(?)
Kaching: another Clone.
SharePost: private company research and exchange.
Second Market: illiquid asset market place(?)
Portfolio Solution, diversified(?) index fund ETF portfolio charging 0.25% management fee.
IASG: many listed CTA, the strategy description could be interesting.
Global Debt problem: Forbes article, Hayman advisor letters, Bloomberg story about mortgage
An intersting presentation tools: prezi, Vimeo’s top 25 video of 2009. blublu wall painting
Investment outlook for 2010: 立此存照
Buffet letters, bridgewater, firstQ(currency)
Swensen, Ihcan, Schwarzman, Redleaf lecture.
Mebane Faber’s idea for 2009, all seems reasonably good idea with hindersight.
ETF related resources: T Herzfeld, Close End Fund Center (CEF), Claymore
link feast 2-27-10
February 27th, 2010
Financial Time Series Analysis giberrish-Tasy
February 23rd, 2010
I just finished re-reading Tasy’s Finanical Time series analysis, here is a list of points worth taking notes of:
- What time series analysis is trying to model here is f(x_t|F_t-1), so what we are trying to understand here is how mean and variance related to the past, through ACF.
- portmanteau Test Q(m) (Ljung-Box stats for testing sufficiency of model) i.e. residue of dynamics, i.e. no series correlation no conditional heteroscedasticity). Q(m) ~ X^2(m) link to p stats to reject null hypothesis( no series corr in residue, no CH in residue square etc.)
- model identification( PACF or Akaike Information Criteria) -> parameter estimation (OLS with significant level of parameter estimated)-> model checking ( check if residue series is close to white noise, using Ljung-box Q(m) ~X^2(m-order(AR model used))->conditional forcast with model(i.e. error in model is not taken into account).
- unit-root nonstationary, long memory, ARIFIMA(d), ARIMA(1), dicky fuller test for unit root. Ljung-box test, look at ACF, same effect.
- seasonality model, ACF contains information for AR and seasonality. (i.e. a tool to detect seasonality & predict shape of futures curve).
- nonlinearity test( Q, BDS, F, Threshold Test), parametric( TAR, Markov switch model), nonparametric( NN, kernel, MCMC etc.
- nonsynchronize trading, var(r_o) v.s. var(r). bid ask bounce, and high frequency dynamics(negative lag-1 AR)
- order prohibit model(lo et. al.),A Decomposition Model(ADS)* ( using partition I(i) indicative function and MLE to estimate parameters) ; duration model( using combination of quadratic function to remove duriual effect), using ACR model with exponential or generalized gamma innovation; non-linear duration model with two regime. bivariate model for both price change and duration( with method similar to ADS, this is close to what altreva is doing, i.e. simulate the market)
- cross-correlation matrix contains linear dependency information( couple, direct, independent) .
- VAR model able to absorb all dynamic dependency and concurrent dependency information into transition matrix, with cholesky decomposition. stationary condition, how to test for cointegration in real applicaton, Tasy mentioned about difficulty. And erro correction form( still fuzzy about this concept).
- PCA, FA, MCMC
If there is will, there is a way :SWM Defence System
February 23rd, 2010
SWM Defence system stands for ‘Star War Mosquitoes Defence’ System.
under which ‘there is no spot at all where the mosquito is safe’ and ‘don’t stop there. You can adjust the system to destroy all irritating elements in your surroundings. For instance the neighbor’s cat. Or the neighbor’s house. Or undesirable planets.’ here is the wired article.
Here is the force behind this new invention: Intellectual Venture Lab, the pilot project, The Venture firm behind it, and seems it is expanding. HBS idea cast: re-inventing invention.
here is the high speed camera the project was using: up to 1,000,000 frame/sec !! here is the method used to detect mosquito: Particle Image Velocimetry.
a summary
February 18th, 2010
Approaches:
- microstructure/order book/agent based modeling/market maker/double auction/gaming/liquidity-> largestly heuristic but more pragmatic
- dynamic system approach/econophysics, distribution, complexity, much on theoretical level
- hardware/latency/routing/optimal execution, institution dominates with fancy word such as ‘dark pool’
- HMM-Kalman filter/BDNetwork ML(EM etc.)/Classifier (VM, NN, TREE, BOOSTING etc) ->advanced technical analysis, advanced classifier
- RLearning, GA, evolutionary market hypothesis -> capture market dynamics(?), prediction power(?)
- pair trading, Stats Arb, –> technically simple yet basic, BREAD AND BUTTER OF algo trading.
- last, most basic and fundamental is money management and risk management. control of order flow, system monitoring.
information theory address both problem, estimation(model)/inference(observation) and optimal betting/money management(kelly/drawdown/portfolio) together on the fundamental level. MPT address the second problem for multivariate case.
let start with most basic and most fundamental proble: stat arb
- modelling dynamics of stat arb [kalman filter application, with time dependency, long winding?]-> paper
- stat arb in US equity market -> paper
- pair trading, with EM algo, model mean reversion with state space model, [clear and succinct, kalman for estimation, EM, smooth KF for model validation, interesting idea is using Ornstein-Uhlenbeck stop time as exit strategy]05-> paper
- flexible least square for data mining and stat arb apply on ETF, imperial math/bluecrest, Tesfatsion’s FLS page -> paper [ this paper discussed the implementation of an online FLS algo, and online SVD for priciple component]
- data mining for algorithmic asset management on Futures, Montana 09 -> paper
- FLS and stat arb in layman’s term ->[the is a waste of time, so much so for B-school professors] paper
- Review of Statistical Arbitrage, Cointegration, and Multivariate Ornstein-Uhlenbeck [clearly written, intuitive, my favorite so far.], Attilio Meucci BB alpha head-> link
- Engle Granger, erro correction representation and cointegral -> paper
- Engle Granger 87 -> paper did this one won them nobel? repost again for emphasis.
- UCDavis Econ time series course with all the reference and list of topics -> website
- pair trading, performance of relative arbitrage rule [utility sector, average crossing, bid-ask bounce, trading cost, question left open is: a) how to choose the threshold? b) within trading period till when before next calibration you should not trade? ]-> paper, 06 IFC working paper
- here is an paper describe all SA strategy [wast of time] -> paper
- an intelligent SA system [a good outline of strategy, (moving window to estimate B, and NN-Garch for interval estimation and corresponding trading strategy]-> paper
- high frequency pairs trading with u.s. treasury securities[touched implementation issue e.g. repo financing cost in rates market ]-> paper
- statistical analysis of cointegral vector(strucuture change) -> paper
- THIS google search would yield interesting result, example here, here ( Alexandre 08), here(alexandra investment related to AP Alexandra?)here, here cointegration alpha: index tracking(carol alex 02), here (carol alex 03), here( carol alex JPM 05), here( this one came from SMU), optimal hedge cointegration (carol alex)|| here(fractional cointegration ”09), copper market efficiency, metal future efficiency
- an VERY good tutorial: NYU Courant Stat Arb course, let’s call it Dyna arb? the author Farshid Maghami Asl is a VP in GS FICC. other courant people(robert almgre, algo 08, marco avellaneda present some course presentation here), there is a good portion dedicated to vol trading and stoch vol estimation using KF, but not enough details is given out. Chpt 14 start to touch some interesting general topics as financial system, robust control, bayesian optimal control and control of balance sheet, again just touched, not enough details.
- pair trading, quantitative method and analysis -> book
- pair trading, capture profit and hedge risk with SA strategy -> book
- Tsay’s course website for Time series analysis -> 1 2
Bayesian learning and more general topic
February 15th, 2010
Fallacies; Psychological manipulation(abuse, bully, narcissism) ; Propaganda;
- pdf v.s. likihood function
- MLE, estimation
- estimation bias
- BI and court case: Howland will Forgery, Sally Clark, Lucia de B.
- Bayesian search. USS Scorpion P(here|nofind)=(P(nofind|here)*P(here)) ../(P(nofind|here)*P(here)+P(nofind|nohere)*P(nohere))
- clearly there is a strong relationship between Kalman filter and HMM, both of which belongs to Dynamic Beyesian Network, here is a tutorial on this topic. Mackey’s toturial on Beyesian network. Murphy K presentation on DBN, and his ’02 thesis. Murphy K’s publication page. Micheal Jordan’s publication and tutorials. Judea Pearl, Bayesian causality (Judea asking very fundamental and interesting questions here) his publication. this explains why RMF is interested by physicist while DMN is interested by AI community. reasoning(prediction, estimation, classification) is causal, describe nature law is non directional(parameter and variable interchangible, observational(algebra explains observation non-experiment v.s. interventional, control experiment, algebra explains observation + causality lead to prediction(?)).P(r|w) v.s. P(r|do(w)). Simpons paradox and solution; reverse regression (gender, salary, qualification). aka adjustment problem, covariate selection problem. wiki on Belief Propagation just learned that Judea Pearl is father of Denial Pearl.
- K. Murphy tutorial on: RL here, Graphical model and BN here
- Naive Bayes classifier (independent feature)
- a unified review of linear gaussian model (FA, PCA, QFA, KF, HMM unified!@_@)
- Information theory, Inference, Learning algorithm, David MacKay’s book; an intro here
Rules
February 14th, 2010
rule 1. there is always a reason if there is a profit.
rule 2. knowledge and skill is abundant (this and this when you can Google it), cutting edge knowledge & skill (means you cannot Google it) is rare, judgement/capital/organization skill is precious.
rule 3. whatever you are doing, imagine a million people are doing the same and doing better, keep on refining and researching until you can no longer find any other people sharing your idea, then you are out of mediocracy.
a summary from max dama’s blog
February 14th, 2010
- python tutorial
- max dama’s python system monitoring, IB via python
- connect IB with MATLAB post and this and this
- API: Lime (people at lime); IB (retail shop); Wolverine Ex (check their software package brokerage turn technology company? out source IT).
- the competition and information dissemination on this topic seems extremely fast on the website
- algo podcast, algo forum, algo open platform, algo human resource, algo evaluation, Suite - Alib/Aladin(!!&Suite people), algo Event processing blog, Algo empirical research
- and you have to check this resume and think about what you want to do here.
- this is what i’m looking for: how to front run iceberg order! and this, dama post on dark pool/smart routing
- related to above is GS’ thoughts on Electronic Trading
- two trading problems from dama’s class
- some DEShaw insights
- Kelly post on Dama, and this, and this, and this, information theory betting post, trader’s ruin, trading objective post, throp 07 on Kelly; simulation and optimization post; system optimization post; hill climb optimization; human crude optimization
- open source system development, including marketc
- a long and interesting post, about a whole system implemented in R, with tree bagging(random forest) and kelly etc.
- another system based on svm idea implemented in M, background and this and this and this decision tree post
- the very first system description and implementation, and this and this, commercial system design
- Dama’s SVM post, BL post; Bayersian learning video, dirichlet process post; something about statistical learning; Information theory post; SVM application to trading post; base AI and trading post; SVM basic application; SVM basics; SVM paper
- data preprocessing post; over fitting and bias 1, 2, 3; vol v.s. leverage post, and a case study; some data to play with, clarifi a capital IQ tool for portfolio management.
- Dama’s hands-on Matlab class, LDA; having fun with matlab, and this. this paper contain a literature review of attempted empirical trading strategy and history of pair trading.
- Rapid Miner SVM post, Rapid miner intro here.
- market regime indicatior
long memory
February 10th, 2010
here is a website dedicated to long memory issue in various domain of research interest. interesting articles includes under others, fractional, multifractionality. D Farmer a main contributor. an good illustration of HMM from model to metamodel: 
Rama Cont wrote a very readable overview of time series analysis here, here is someone recently updated on this issue (interesting point maybe now calendar effect weakened in recent year). he coauthored with J.P. Bauchaud who run Capital Fund Management (largest french hedge fund). here is a ’93 interview on CFM with founder J.P. Anguliar, and ’09 story on his death, moral of the story: in long term gliding will kill you. CFM’s research archive here, particularly the random matrix theory in ’99 and ’07. see also this one. French risk manager has a distinctive taste for risk see this coppula in finance, correlation is only matters in extreme negative value, therefore the conventional mean-variance method is inadequate, see matlab toolbox.
work of Andrei Leonidov: market mill dependence, non-gaussian dependant pattern, long memory, non-Markovian nature.
well, work of benoit mandelbrot, and his yale website.
Ian Kaplan (his guy worked in Prediction for a while) digged out and digested Hurst’s original paper here. quote – ‘when comes to wavelet, i’m the guy with a hammer to whom every problem is a nail’
Dynamics approach, Joint PDF approach:
1) robust structure without predictability
2) the variation of certain speculative price
3) conditional probability as a measure of volatility clustering
4) price clustering and discreteness:is there chaos behind noise
5) conditional dynamics driving financial market
A Dias: using copula model short term dynamics in high frequency.
dependent structure paper
there is this whole fund industry which focused on fee, and there is this whole prop trading shops preaching ‘make money with us’ and rack up margins see this.
microstructure review and market making model
February 8th, 2010
a review of micro-structure literature here
a limit order market survey here
some TORQ data to play with here
markovian model for sequential data: [1] bengio 99, [2], [Inria], [general review]
HMM tutorial: Rabiner 89
Alex Chekhlov & Peter Kambolin interview
Liquidity provider algo idea
February 7th, 2010
Here is the idea to utilize optimal trading strategy to extract excessive return through providing liquidity in the short term market. The basic tenet behind is that aggressive price taker demand immediate execution hence inccurs higher transaction cost. By providing liquidity to them and act to bridge the short term liquity and longer term liquidity, liquidity provider should be able to extract this part of premium impatient trader give up. using a pca factor model, the linear regression residue would be approximately reflect the idyosyncratic factor which will be dominated by erratic liquidity mismatch in the market, this could serve as an indicator to initiate market making algo.
p.s. I found this on internet: here (Doyne Farmer Andrew Lo 07)
Modeling Limit Order Removal Times from Market Data with q-Weibull Distribution <== this is the building block to estimate liquidity imbalance. actually working paper is missing(?)
Adlar J. Kim is student of Lo and Farmer at SFI, member of AI and Postdoc Sloan. very interesting combination. Adlar’s 08 thesis.
paper:
security trading of concept*?*
market making by learning liquidity imbalance ***
[]model stock order flow and mm * kim and shelton’s 02 paper, using learning in both market model and strategy model, rely on shelton’s o1 thsis.
hidden markov chain for model mm *
[]farmer’s presentation at SFI * basically double auction simulate mkt well.
Electronic market making * (CMU intelligent software agent Center here)
reenforcced learning for optimal trading * (Upenn micheal kearns another AI Market don as Farmer)
Lo’s auto MM patent and this with reference and related info worth digging
C.R. Shelton UC Riverside prof. machine learning, cowork with adler, micheal kearns et. al. publication list here. Shelton’s PhD thesis is on importance sample to improve POMDP (MDP with hidden states) with multiple objective, much close to some real world problem like MM.
an electronic market maker AI lab memo by nick chan and shelton.
Artificial market and intelligent agent, nich chan phd thesis.
here is an actual agent based forecast product, a very interesting idea Altreva.
Study of Artificial Financial Markets with Adaptive Trading Agents, nich chan & adler kim.
A learning market making in Gloston Milgrom model, by Sanmay Das (adler kim and Sanmay both from MIT learning center) Sanmay’s other publication and thesis.
O’hara’s review on market structure issue on market liquidity(BIS paper) O’hara’s publication
Columbia math course (Financial Price Analysis spring 2010) outline: with an interesting list of reference
Alex Chehkov, one who offer the above mentioned course, is a visiting AP from Systematic Alpha Management, with an interest estimating liquidity imbalance as well. alex’s drawdown paper; something else; supply chain finance.
trading and market making course, Craig Holden do research in microstructure as well.
Market Making
February 6th, 2010
Supposed you are trading gold in Asia market, daily vol is 25%. During the day, your largest source of liquidity, TOCOM trade 2 mio oz gold in 8 hours time span from 9:00 to 16:00. now a client call you asking for gold price, market is trading 1000/0.5 3X3 koz with more order on EBS 999.75/1000.75 10X10 koz. what should be you quote and how should you execute to lay off the risk? let’s say you made 999.5/1000.5 and the client lift your offer for 10k oz gold. Now what’s next? On one hand you are worried about if you execute the order immediately on exchange, the market impact will eat off your spread, and you will lose 30 cents on the trade, on the other hand if you trade two slowly, market may move around and you’re facing increasing risk linear to square root of T. The optimal strategy is to strike a balance between this two conflicting factors.
A few factors need to be taken into consideration: market volatility going fwd during the trading period, market volume during the trading period and instantaneous market impact. With respect to these parameters an optimal point, which maximize the instantaneous sharp ratio can be calculated.
In this case, given 0.5 spread(from mid) and 0.8 instantaneous impact(I). The optimal strategy is to layoff the risk in 10 minutes buy trading 1k oz per minute. Given TOCOM’s 2mio oz trading volume in 8 hours, this give you a participation ratio (trade volume/market volume) roughly around 25%, this reduce the theoretical impact k=I* (0.95*participation ratio +0.05 ) to around 27 cents. The instantaneous sharp ratio is 0.4, with profit distribution ~N(2588,5022) in dollar term. The simplified average slicing scheme aim to achieve VWAP during the execution period and with expected value of P_0. If you keep trading in this way, time will help to take care of the volatility(remember mean is linear to number of trade (N), while risk is linear wrt to sqrt(N)). If you do this kind of 10k trade one time a day for a year, your year end sharp ratio should be improved by 14 time, a nice 5.4.
a few interesting property can be observed here:
- your optimal point is always close to 50~25% participation ratio
- your spread can be pretty tight, half of instant impact you are still very profitable with acceptable PnL risk
- assuming random with drift (fwd rate) and trying to achieve VWAP is what a purist market marker will do
- never willingly cross the spread (motto of oliver weldon) will help to improve it further
- negative selection could be a problem( large distributed order, interbank), if there is no reciprocity there should be no love for this.
- MM is really fill the bathtub with teaspoon, but should be and could be automated with this framework
- Trading strategy could fine tuned using more elaborated scheme, uneven distribution in volume and time, tracing liquidty.
- better way to predict instantaneous impact, vol and volume, incorporate some correlation structure between them?
Here is the nice ‘theoretical’ result:
Instinet strategy pool
February 6th, 2010
Instinet provides an intersting strategy for openning closing auction, similar to TOCOM auction and LBMA AM PM price setting?
Benchmark:
- VWAP participation,
- Dynamic Volumn paritcipation,
- O/C Auction,
- P shortfall( balance impact with OC).
Tactic:
- seek liquidity, v.s. minimize info leak
- aggregate liquidity, anti-gaming + info leak minimization logic
- market rule (HK, JAPAN)
- liquidity through pair
cusum, structure change, idiosyncratic risk
February 5th, 2010
Northfield used an interesting example (river nile v.s. aswan dam) to explain how they identify structural change through performance time series of fund manager ( a lot of assumptions here, but on balance I mean the idea capture the essense). Using 1-factor risk model and assuem excessive risk prepresent everything else (using a statistical multifactor model will be more accurate but…) I copied the idea here and there is some interesting finding:
Google’s cumulative execess return over QQQQ has been flat since 2006, one year after its IPO, according to wiki the buying mainly came from individual investor:
while Apple’s cusum took off some time in 2004:
Interstingly, ipod sales chart indicated that its sale start to took off ( 4G and first mini(my first as well) hit the mass market) in 2004 as well.
ipod sales (250 m cumulative uptodate) has been tailing off since 2008, but this has been substituted by sales growth in iphone:
a particular inflection point in Q$ 08 in cusum data coincide with strong Q4 08 iphone sales.
the bias in idiosyncratic risk should be slow moving and more closely reflecting the underlying factors of a particular company. should this be a better bet?
the caveat of using cusum is the effect of leverage, the cusum will be positively biased in bull market and viceverse, this effect is especially pronounced in small cap. In this case, the statistically significant ‘structural’ change is more difficult to spot, taking BITSTREAM for example:
At least based on the public information reflected in price, it’s safe to say no structurally change has happend to this company yet:
in case of IBM, it’s a story of reinvent from hardware manufacturer to technology consulting. SP 500 is not a perfect proxy for systematic risk, but a little reassuring is the fact that residue show little correlation with market.
Risk Model Shops and Industrial Risk Models
February 5th, 2010
Northfield Infomation Services: Northfield risk model here, publications here
adjust for sensitivity of estimation, considering constrain(tax, cost, fee, turnover etc.), translate RR model into easily comprehensible factors. chain optimization, backtesting w rebalancing.auto identify management style(normalize leverage, ?.) CUSUM(cumulated IR for exsample, claim to detect structure change), dispersion comparison to detect(skill v.s. luck).
APT , advanced portfolio technologies. using stats factor model, robust port optimization. acquired by Sungard. webinar, whitepaper etc. here
Barra: equity risk model details (fundamental model), including a short term trading model(predict vol mainly).
Quantal: acquired by reuters, app available in capital IQ
Optimal Trading Strategy
February 5th, 2010
[2-1-10] read up to chp 8, so far nothing solid, but start touch something interesting with dealer model and timing risk.
Robert Kissell( one of the author of OTS) is actually a ED at JPM quant trading team, here is an internal paper. see also ‘expanded implementation shortfall’ below.
’05 articles about pre-trade analytics offered by citi, feature almgren
digging gold in footnote – 4
February 3rd, 2010
Barr Rosenberg, factor model ( Swanson had a few page discuss about Barr’s fund performance, except for initial success, the performance post fee is sub par).
Rosenberg, Persuasive evidence of market inefficiency.
Rosenberg, Factor related and specific returns of common stock.
BARRA on campus: kit should be available at SOM, check
BARRA webinars
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Advanced Trading, a tech trading magazine
Automated Trader, anther algo trading magazine
Andre Perold: HBS prof. one of the most important transaction cost paper: Implementation Shortfall (jpm version), (’88), article about GS new algo to address it (’08)
Andre Perold (very well connected, worked with E. Schulman, Black, Sharpe, Markowitz etc. , also the author of batterymarch case, and many other investment management/trading related case ( where can I get all these cases??), some interesting work as well.
Wayne Wagner, LA based Plexus Group founder, Stanford Alum, acquired by JPM then by ITG. ‘whole cost measurement industry originated by Plexus according to Leiweber.
Wayne Wagner, The Incredible Story of Transaction Cost Management: A Personal Recollection. Journal of Trading 3, no. 3. Summer 2008. (Go get it!)
ALL THE AI TECHNIQUES, PARTICULARLY GA/TEXT FRONT, SOCIAL MEDIA/COLLECTIVE INTELLIGENCE.
digging gold in footnote – 3
February 3rd, 2010
Ben Rosen, MS analyst, VC, first one to spreadsheet (VISICALC), his blog here
Vernon Smith, ’02 Noble Laureate for experimental economics, reflection on behavior, work, wiki / Charles plott Smith’s college at Caltech(?) foundation paper, exp econ handbook with Veron.
Ross Miller, GE quant finance head, authors of Paving Wall Street: Experimental Economics and the Quest for the Perfet Market ( ordered from amazon on 2/2/10)
Blake LeBaron, Agent based market model, work, a good summary
Terrence Hendershott, CIFT stuff, Haas a.p. some paper shares my interest: e.g. mm inventory, mm algo etc. etc. / SEE ALSO David Easley (Cornell), Taron Ramadori (Oxford)
Dimitris Bertsimas/Andew Lo: Books: cont time method and mkt microstr(Lo), stat method non standard finance(Lo). Course materials and paper could be interesting as well. The transaction cost paper is HERE.
Robert Almgren, another cost pioneer in Courant, the optimal trading paper is HERE, the idea is more close to real environment by taking risk preference into consideration.
IBM article, the trader is dead, long live the trader 06
2015 Prognostication: Bearingpoint, 1 2 Datamonitor 1,
SEC: TRW(?), General Dynamics, BAE system, Northrop Grumman, [ different kind: AICC (10b '17) , China Poly Group, China New Era Group. ]
NYSE: abasco service report in 57 to automate the trading floor. abasco acquired by Raytheon (5th defence contractor).
INQTEL: CIA’s VC arm
WOMBAT Fin Software: acquired by NYSE in 08, Adnan Khashoggi of low-latency finance(means infrastructure i guess).
Wall Street and Technology: the magazine
digging gold in footnote – 2
February 3rd, 2010
David Whitcomb
[0] papers
[1] Transaction Costs and Institutional Investor Trading Strategies, Schwartz, Whitcomb, nomoograph 1988-23
[2] The Microstructure of Securities Markets, Kalman J Cohen, Robert A. Schwartz, David Whitcomb 1986 ( available in yale social science lib)
[3] Trading and Exchange: Market Microstructure for Practitioner, Larry Harris
[4] Empirical Market Microstructure: The Institutions, Economics, Econometrics of Security Trading, Joel Hasbrouck (available in yale social science lib)
Robert A. Schwartz:
research ( a few interesting paper before ’88)
tradex ( experimental market, here offers a glimpse of what tradex is about)
digging gold in footnote – 1
February 2nd, 2010
CIFT [adviser]
Steven Skiena: Stony Brook CS faculty. interesting book on betting, and internet datamining.
Toby Segaran: author of programming collective intelligence, data mining, visualization.
Richard Rosenblatt: CEO of Rosenblatt Security. Equity market structure expert, runs NYSE floor.
William Janeway: Warburg Pincus TMT advisor, experience includes FORTENT(finance crime, surveillance merge with Actimize), O’Reiley media, Roubini Global Economics, Nuance( voice recog), NYFIX/Wallstreet System(order management, transaction service acquired by NYSE EURONEXT).
Roger Ehrenberg: ex boss of of DB hedge fund, bloggist of Information Arbitrage. now running TMT focused IA capital. founded Kinetic Trading Strategy( comp fin, textual analytics, alt data, ’08) Portfolio here.
David Whitcomb: true entrepreneur, ATD founder, research on microstructure and trading cost in 80s. top 100 economist(?). ATD focused on limit order execution. Whitcomb SEC testimony.
Evan Schuleman: father of quant trading, batterymarch autotrading system in ’70s, lattice trading (order matching, routing, VWAP execution, sold to state street in ’97).
Richard Lindsey: author of how I become a quant. former SEC director of mkt and chief economist, former president of bear security, former SOM professor. market microstructurerist.
Henry Lichstein: citibank IT chief. doing VC now.
William Hart: data communication background, Black’s team member for 4 years in developing algo trading strategy and infrastructure. Lehman, Soloman Strategic Equity B dev for trading infrastructure. EVP Nasdaq, BOA equity strategy. board of ECN, ATS.
Bill Aronin: founder of quantitative analytics acquired by Reuters.
Peter Dickson: Algo solution for Dowjones.
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CIFT [stuff]
Mahesh Krishnan – microstructurist
Max Dama - the bloggist
Jike Chong – infrastructure
Organization – Process – People
February 2nd, 2010
The last two chapter of ‘Pioneering Portfolio Management’ conveyed a clear message, one of a recurring theme of Swanson’s idea, which is a relentless emphasis of Process Process Process! The structure of an organization defined the its behavior and behavior of its people, and henceforth determines the characters and performance. Swanson outlined in his book his ideal investment management organization:
1) Investment Committee
- diversified
- judgment over professional qualification
- goal is to manage the process not the investment portfolio
This is similar to emphasize policy portfolio and asset allocation over tactical decision making and market timing. As the investment committee suppose to oversee the higher order attributes of the organization.
2) Investment stuff
- encourage honesty guarantees disclosure
- disciplined process, coherent intellectual framework guards against informal casual decision
- scheduled meeting institutionalize the decision process. Written memo encourage logical thorough thinking over oral discussion
- guard against group dynamics, limit consensus building in the process, encourage independent thinking and contrarian view. provide supportive environment for loner (this is much more like a university department than a corporate)
- constantly renewal of young professional, train through apprenticeship is much more effective than outside hiring or seek training through academic program
3) Organizational character
- high quality people are attracted to cutting edge issues, embrace global strategy and focus on mentorship
- operates on the PERIPHERY of standard institutional norm
- aware of the fact that standard selection process exclude interesting manager. embrace non-standard approach
- aware of the fact that conventional unimaginative bureaucracy ensure poor result.
- avoid assigning blame, encourage discussion of failure opening (by emphasizing the process over individual decision)
4) Decision making Challenge
- misalignment of interest between individual and institution, between agency and principal.
- fear, seek of instant gratification, peer pressure
- contrarian pitfall ( NYU too far away from norm, Boston U too concentrated, strike the right balance is the name of the art).
The source of alpha
January 23rd, 2010
illiquid (long term, less marketable)
unconventional/inconvenience (geographical IFC/OPIC, out-of-run issues, ultra low latency)
complexity (regulation, hard to price)
value (evaluation, margin of safety, patience, this words in combination with liquidity)
value creation ( improved management(forestry, corporate, urban planing, (potential)celebrity, (potential) network effect, prop technology)
size ( fill bathtub with teaspoon)
out-of-favor, contrarian ( GMO out of favor, this is not in strict sense a source of alpha, but …)
outright inefficiency (Sallie Mae stock conversion)
Information ( cap < 1b, no coverage)
Asset class
January 22nd, 2010
As a starting point, the first step for a good portfolio manager is to identify the asset class. So exactly what is asset class? According to MPT, from a numerical perspective, an asset class differentiates from one to another wrt return, volatility of return and more importantly covariance. But merely treat asset class with respect to its numerical attributes is missing the deeper meaning of the concept and hence bounded to be misguided when choice asset classes and pursuing diversification. The basic concept of diversification is to construct a basket of asset whose return does not correlate with one another hence one factor of general environment affects a limited part of the whole portfolio. Invest benefits by trying to understand the source of value creation when analyzing their portfolio. Value can be created through a variety of ways.
Taking forest land for example, the value creation process in this case is nature biological growth of tree. The asset itself does not diminish during the value creation process, and better forestry management enhance the timber yield in a sustainable way. Other auxiliary value could be created in the process, for example, better resource management might improve the environmental quality of the surrounding area and therefore increase the desirability of property near the forest land. So roughly speaking, there are three components of value creation in this case: 1) the natural growth rate, which is the reward for planting the seed and create a desirable resource 2) direct, observable value enhancement through better management 3) indirect, hard to monetize enhancement through step two.
Taking this analogy one step way into equity market. The first layer of value creation is through accumulation of capital. As a scarce resource, capital is rewarded with approximately risk free interest rate. For putting the capital in a risky position as oppose to risk free asset, a premium is demanded which should be linear wrt to the risk assumed, this can be considered as merely enhance the return on investment through leverage, which should constitutes part of the conventional equity risk premium. Equity market as a whole wrt to debt demands another type of reward, which is for the business activities private sector going through with incorporate and organization in general sense. This part of reward(1~2%) together with the reward for assuming the capital risk constitute the equity risk premiums. For U.S. market debt ( 2% return v.s. 10% risk) is consistent with equity ( 6% return v.s. 20% ; assume 2% premium for business activities), the result is consistent with this assumption. Through better management and operation (buy out in pure sense), propitiatory technology (GOOG, 70% return v.s. 50% vol, which implies, after normalize with debt vol, 2%(capital)+1%(corp)+9%(management+prop)), the equity return can be improved wrt to benchmark.
Another source of value generation is liquidity. Due to the behavioral and capital constrain, patient investor is rewarded for holding illiquid asset over long period of time. According to Yale endowment data, the adjusted liquidity premium should be around 2% in long term. Besides, absolute return provide another source of value through enforcing market efficiency. The caveat for this two strategy is that historical data might paint an overoptimistic picture for the future. Real asset vol is normally underestimated due to low observation frequency; absolute return data normally suffer from back filing and survival bias.
Applying this conceptual framework, many diversified portfolio might not be as what investor expected. For example, Private equity (buy out and VC) in purity extract value through improving management and spotting promising prop technologies. However, if, and in most case, the main component of PE’s PnL is generate through leverage and late stage IPO financing, investor will not have much true diversification compare to boarder equity market.
Also, most traditional 70/30 equity/debt portfolio actually over weighted in capital component and over concentrated on corporation activity component. a comparison of best endowment portfolios and mean portfolio illustrate the case:
Components Yale: Mean
cap 33% 50%
corp 20% 38%
mange+prop 13% 3%
liquidity 20% 3%
absolute 16% 6%
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Building blocks
January 21st, 2010
components need:
1. data extraction || DONE
2. trading rules(pnl, pos,px) || DONE
3. frequency, parameter v.s. sharp ratio Max_sharp(parameter, frequency) || DONE with parameters
4. cross validation, stability
5. money management, reinvestment, risk
6. visual, report
