Philip Maymin

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Academic Papers and Books

Accepted and Published Papers

Maymin, Philip Z.; Maymin, Zakhar G.
Any Regulation of Risk Increases Risk
Financial Markets and Portfolio Management
2012, forthcoming.
 
Maymin, Allan Z.; Maymin, Philip Z.; Shen, Eugene
How Much Trouble is Early Foul Trouble?
International Journal of Sport Finance
2012, vol. 7, no. 4, forthcoming.
 
Maymin, Philip Z.; Maymin, Zakhar G.
Constructing the Best Trading Strategy
Journal of Investment Strategies
2011, vol. 1, no. 1, pp.1-22.
 
Maymin, Philip Z.
Music and the Market: Song and Stock Volatility
North American Journal of Economics and Finance
2012, vol. 23, no. 1, pp.70-85.
 
Maymin, Philip Z.
Markets are Efficient If and Only If P=NP
Algorithmic Finance
2011, vol. 1, no. 1, pp.1-11.
 
Maymin, Philip Z.
Self-Imposed Limits to Arbitrage
Journal of Applied Finance
2011, vol. 21, no. 2, pp.88-105.
 
Maymin, Philip Z.; Fisher, Gregg S.
Past Performance is Indicative of Future Beliefs
Risk and Decision Analysis
2011, vol. 2, no. 3, pp.145-150.
 
Maymin, Philip Z.
The Minimal Model of Financial Complexity
Quantitative Finance
2011, vol. 11, no. 9, pp.1371-1378.
 
Maymin, Philip Z.; Fisher, Gregg S.
Preventing Emotional Investing
Journal of Wealth Management
Spring 2011, vol. 13, no. 4, pp.34-43.
 
Maymin, Philip Z. and Lim, Tai Wei
The Iron Fist vs. the Invisible Hand
World Review of Entrepreneurship, Management and
Sustainable Development, 2012, vol. 8, forthcoming.
 
Maymin, Philip Z.
Metanoia and the Market
Advances in Behavioral Finance and Economics
Winter 2011, vol. 1, no. 1, pp.27-42.
 
Maymin, Philip Z.
Regulation Simulation
European Journal of Finance and Banking Research
2009, vol. 2, no. 2, pp.1-12.
 
Maymin, Philip Z.
The Hazards of Propping Up: Bubbles and Chaos
International Journal of Business and Finance Research
2009, vol. 3, no. 2, pp.83-93.
 
Maymin, Philip Z.
Prospect Theory and Fat Tails
Risk and Decision Analysis
2009, vol. 1, no. 3, pp.187-195.
 

Published Academic Editorials

Maymin, Philip Z.
Behavioral Finance Has Come of Age
Risk and Decision Analysis
2011, vol. 2, no. 3, p.125.
 
Maymin, Philip Z.
Why Financial Regulation is Doomed to Fail
Library of Economics and Liberty
March 2011.
 

Textbooks

1. Financial Hacking
   World Scientific Publishing forthcoming. Scheduled Summer 2012.
   Pre-order on Amazon.com.

Working Papers

1. Schizophrenic Representative Investors »
2. The Five Factors of Optimal Free Throw Shooting (with Allan Maymin and Eugene Shen) »
3. NBA Chemistry: Positive and Negative Synergies in Basketball (with Allan Maymin and Eugene Shen) »
4. Momentum's Hidden Sensitivity to the Starting Day (with Zak Maymin and Gregg Fisher) »

The Lambda-Q Calculus for Quantum Computation

1. Extending the Lambda Calculus to Express Quantumized Algorithms »
2. The Lambda-Q Calculus Can Efficiently Simulate Quantum Computers »
3. Programming Complex Systems »

Author research page on SSRN and arXiv. Profile on Google Scholar.

Any Regulation of Risk Increases Risk (with Zak Maymin)

We show that any objective risk measurement algorithm mandated by central banks for regulated financial entities will result in more risk being taken on by those financial entities than would otherwise be the case. Furthermore, the risks taken on by the regulated financial entities are far more systemically concentrated than they would have been otherwise, making the entire financial system more fragile. This result leaves three directions for the future of financial regulation: continue regulating by enforcing risk measurement algorithms at the cost of occasional severe crises, regulate more severely and subjectively by fully nationalizing all financial entities, or abolish all central banking regulations including deposit insurance to let risk be determined by the entities themselves and, ultimately, by their depositors through voluntary market transactions rather than by the taxpayers through enforced government participation.

Available on SSRN and on arXiv.

Citation: Maymin, Philip Z.; Maymin, Zakhar G. (2012), "Any Regulation of Risk Increases Risk," Financial Markets and Portfolio Management, forthcoming.

Media and Press

• Library of Economics and Liberty: Why Financial Regulation is Doomed to Fail.
• American Banker: Viewpoint: An Experiment in Securities Risk. Download PDF.
• LewRockwell.com: The War on Risk.
• Fairfield County Weekly: Listen Up, Chris Dodd.

Blogs and Discussions

• Portfolio Wizards
• Marginal Revolution(5 comments)
• Modeled Behavior (1 comment)
• Reddit Main Site (8 comments)
• Reddit Economics (2010) (26 comments)

Presentations

• Oliver Wyman, November 19, 2010
• Stanford University Workshop on Capitalism's Crises, October 14, 2010
• NYU-Poly Alumni Day, May 16, 2010

  

• Society of Actuaries, October 18, 2011

SSRN Top Ten Download Lists

• CGN: Risk Management, Including Hedging and Derivatives (Topic)
• Risk Management eJournal
• ERN: Uncertainty and Risk Modeling (Topic)
• ERN: Regulation (IO) (Topic)
• Microeconomics: Decision-Making under Risk and Uncertainty eJournal
• IO: Regulation, Antitrust and Privatization eJournal
• Regulation of Financial Institutions eJournal
• Risk, Regulation, and Policy eJournal
• Banking & Financial Institutions eJournal
• Corporate Governance: Disclosure, Internal Control, and Risk-Management eJournal

How Much Trouble is Early Foul Trouble?
(with Allan Maymin and Eugene Shen)

We analyze a large and comprehensive play-by-play dataset of professional games in the National Basketball Association using tools from financial economics to explore the optimality of strategically idling resources in the face of uncertain future demand. We find that starters ought to be idled by the coach on a "Q+1" basis, meaning that a starter has one more foul than the current quarter, when the future option value is high or the value of the replacement player is high. We use a novel win-probability approach that can be easily extended to other applications.

Available on SSRN. Data source: BasketballGeek. Download PDF.

Citation: Maymin, Allan; Maymin, Philip Z.; Shen, Eugene (2012), "How Much Trouble is Early Foul Trouble?", International Journal of Sport Finance, 7:4, forthcoming.

Citation: Maymin, Allan; Maymin, Philip Z.; Shen, Eugene (2011), "How Much Trouble is Early Foul Trouble?", Proceedings of the 5th Annual MIT Sloan Sports Analytics Conference,.

Presentations

• 5th Annual MIT Sloan Sports Analytics Conference, March 5, 2011
  
• Southern Economic Association, November 19, 2011

Blogs and Discussions

• Paul Kedrosky
• YCombinator Hacker News (15 comments)
• Baylor Fans (3 comments)
• Fighting Illini Basketball (12 comments)
• APBRmetrics
• Marginal Revolution (1 comment)

Media and Press

• Basketball Prospectus: "Rethinking Foul Trouble" by Kevin Pelton
• ESPN TrueHoop: "How much trouble is early foul trouble?" by Brian Robb
• ESPN TrueHoop: "Research: Bench starters with fouls because they play poorly" by Henry Abbott
• The Atlantic: "NBA Coaches Should Yank Starters in Foul Trouble, Say Economists" by Derek Thompson

SSRN Top Ten Download Lists

• Behavioral & Experimental Finance (Editor's Choice) eJournal
• Behavioral & Experimental Finance eJournal
• FEN: Behavioral Finance (Topic)
• Economics Research Network
• Financial Economics Network
• ERN Subject Matter eJournals
• FEN Subject Matter eJournals
• ERN: Other Microeconomics: Decision-Making under Risk & Uncertainty (Topic)
• Microeconomic Theory eJournals
• Microeconomics: Decision-Making under Risk & Uncertainty eJournal

Constructing the Best Trading Strategy:
A New General Framework
(with Zak Maymin)

We introduce a new general framework for constructing the best trading strategy for a given historical indicator. We construct the unique trading strategy with the highest expected return. This optimal strategy may be implemented directly, or its expected return may be used as a benchmark to evaluate how far away from the optimal other proposed strategies for the given indicators are. Separately, we also construct the unique trading strategy with the highest information ratio. In the normal case, when the traded security return is near zero, and for reasonable correlations, the performance differences are economically insignificant. However, when the correlation approaches one, the trading strategy with the highest expected return approaches its maximum information ratio of 1.32 while the trading strategy with the highest information ratio goes to infinity.

Available on SSRN and JIS. Download PDF.

Citation: Maymin, Philip Z.; Maymin, Zakhar G. (2011), "Constructing the Best Trading Strategy: A New General Framework," Journal of Investment Strategies, 1:1, pp.1-22.

SSRN Top Ten Download Lists

• ERN: Optimization Techniques; Programming Models; Dynamic Analysis (Topic)
• ERN: Speculation in Economic Markets (Topic)
• Econometrics: Mathematical Methods & Programming eJournal

Music and the Market: Song and Stock Volatility

Popular music may presage market conditions because people contemplating complex future economic behavior prefer simpler music, and vice versa. In comparing the annual average beat variance of the songs in the US Billboard Top 100 since its inception in 1958 through 2007 to the standard deviation of returns of the S&P 500 for the same or the subsequent year, a significant negative correlation is observed. Furthermore, the beat variance appears able to predict future market volatility, producing 2.5 volatility points of profit per year on average.

Available on SSRN and on NAJEF.

Citation: Maymin, Philip Z. (2012), "Music and the Market: Song and Stock Volatility," North American Journal of Economics and Finance, 23:1, 70-85.

Presentations

• 3rd Annual Meeting of the Academy of Behavioral Finance and Economics, September 22, 2011

Media and Press

• Article on SmartMoney.com: Can Music Predict the Stock Market's Volatility?
• Slideshow on SmartMoney.com: Music to Buy, Sell, or Hold By.
• Interview with The Takeaway on WNYC: Music to Invest By. Download MP3.
• Article in the Guardian (UK): Beyonce's new single spells economic doom.
• Interview on BBC Radio Ulster: Arts Extra (from 25:12 on).
• Article in the German-language Swiss daily Tages-Anzeiger.
• Interview with NYU-Poly website: Rickrolling Explained.
• Washington Square News: Prof. links music with poor economy.
• Interview on NPR's All Things Considered: Volatile Markets? Try Lada GaGa to Calm Down. Download MP3.
• Newsweek Poland: Muzyczne spadki.
• Financial Times Deutschland: Britney als schlechtes Omen. Print edition PDF.
• Boston Globe: Pop Goes the Market. Interactive graphic.
• Interview with Studio 360: Recession Pop. Download MP3. More recent blog post by interviewer Jocelyn Gonzales.
• Interview with NPR's Here and Now: Do Billboard Hits Reflect the Economy? Download MP3.
• Slideshow on CNBC: Eleven Surprising Stock Market Indicators.
• Interview with Laptop Rockers: Michael Jackson's Music Good for the Economy?
• Story on USA Today: Could the pop-culture mood mirror stock market swings?
  Download cover (PDF, teaser at middle left).
  Download front page of Money section (PDF, teaser at top).
  Download main article (PDF, page B3).
• Pop quiz on USA Today: Test your music knowledge.
• The Rachel Maddow Show on MSNBC.
• WABC
• CJAD
• Sirius/XM.
• Article in the Belgian business and economics newspaper De Tijd.
• New York Post: Flop Culture: How Music, Skirts, and the Weather Move Markets.

Can you perceive the relationship? Suggestive video on YouTube:

SSRN Top Ten Download Lists

• Banking & Financial Institutions Journals
• Capital Markets Journals
• Behavioral & Experimental Finance
• Cognition and the Arts eJournal
• CSN: Genre and Media (Topic)
• CSN: Music (Sub-Topic)
• CSN: Subject Matter eJournals

Markets are Efficient If and Only If P = NP

I prove that if markets are weak-form efficient, meaning current prices fully reflect all information available in past prices, then P = NP, meaning every computational problem whose solution can be verified in polynomial time can also be solved in polynomial time. I also prove the converse by showing how we can "program" the market to solve NP-complete problems. Since P probably does not equal NP, markets are probably not efficient. Specifically, markets become increasingly inefficient as the time series lengthens or becomes more frequent. An illustration by way of partitioning the excess returns to momentum strategies based on data availability confirms this prediction.

Available on SSRN, on arXiv, and on AF.

Citation: Maymin, Philip Z. (2011), "Markets are Efficient If and Only If P=NP," Algorithmic Finance, 1:1, 1-11.

Blogs and Discussions

• Andart
• Barry Ritholtz (8 comments)
• Discourse.net (8 comments)
• Maniagnosis
• Marginal Revolution (2011) (14 comments)
• Marginal Revolution (2010) (22 comments)
• Reddit Compsci (2011) (157 comments)
• Reddit Economics (2011) (41 comments)
• Reddit Math (2011) (16 comments)
• Reddit Economics (2010) (106 comments)
• Reddit Programming (2010) (23 comments)
• Reddit Math (2010) (11 comments)
• YCombinator Hacker News (2011) (83 comments)
• YCombinator Hacker News (2010) (58 comments)

Presentations

• NYU-Poly Alumni Day, May 22, 2011
• NYU Stern IOMS-IS Research Seminar, February 10, 2011
• Kent State University, September 24, 2010
• TEDxNSIT, "The Nature of Genius", March 30, 2010

SSRN Top Ten Download Lists

• Behavioral & Experimental Finance eJournal
• Capital Markets: Market Efficiency eJournal
• FEN: Behavioral Finance (Topic)
• ISN Subject Matter eJournals
• Capital Markets eJournal
• Information Systems and Economics eJournal
• Information Systems and eBusiness Network
• Information Systems: Behavioral & Social Methods eJournal

Self-Imposed Limits to Arbitrage

A multi-billion-dollar, multi-year discrepancy between two identical share classes of HSBC did not suffer from traditional external limits to arbitrage such as transactions costs and risk measures. One possible explanation is that self-imposed limits to arbitrage (SILTA) such as internal restrictions on position size allowed persistent mispricings. SILTA predicts a novel negative relation between relative volume and relative price. This prediction from SILTA holds not only for HSBC, but also other large mispriced pairs such as 3Com/Palm and Royal Dutch-Shell. Indeed, the implied overall maximum position size of arbitrageurs is roughly constant at one hundred days of trading volume for various mispriced pairs spanning different time periods and countries, suggesting SILTA as a possible explanation for all of them.

Available on SSRN and JAF. Download PDF.

Citation: Maymin, Philip Z. (2011), "Self-Imposed Limits to Arbitrage," Journal of Applied Finance, 21:2, 88-105.

Presentations

• 9th Annual Columbia-JAFEE Conference on Quantitative Finance, March 10, 2010

Behavioral Finance Has Come of Age (Editorial)

Did the recent financial crisis vindicate behavioral finance and vitiate rational finance? Perhaps the history of general relativity could guide the answer.

Available on IOS Press.

Citation: Maymin, Philip Z. (2011), "Behavioral Finance Has Come of Age", Risk and Decision Analysis, 2:3, 125. Editorial.

Presentations

(General behavioral finance presentations)

• Gerstein Fisher, July 12, 2011
• Columbia University Portfolio Management Seminar Program, May 26, 2011

Past Performance is Indicative of Future Beliefs
(with Gregg S. Fisher)

The performance of the average investor in an asset class lags the average performance of the asset class itself by an average of one percent per year over the past fifteen years, based on net investor mutual fund cash flows. We present a model in which a representative behavioral investor believes next year's returns will exactly match last year's returns and show that this leads to price adjustments on what would otherwise be random walk securities that effectively lower the future return of high performers and raise the future return of poor performers. The average predicted behavioral lag indeed matches the observed lag when asset returns are normally distributed with a mean and standard deviation equivalent to historical fifteen year averages of six percent and eighteen percent, respectively, and when the representative investor increases his allocation by 25 percent more than the return itself. In other words, investors chase returns and in doing so create the conditions of their own demise.

Available on SSRN and on IOS Press. Download PDF.

Citation: Maymin, Philip Z.; Fisher, Gregg S. (2011), "Past Performance is Indicative of Future Beliefs", Risk and Decision Analysis, 2:3, 145-150.

Non-technical summary: When a fund performs well, investors pile in. Because they buy so much of it, it has a further increase in price. But this increase in price is not based on fundamentals, and will eventually reverse. That resulting drop in prices will cause losses for the investors who rushed in, especially when compared to other funds that did well. So those investors move on to the next performer, and again bump up its price and take the subsequent loss. In other words, investors chase performance, and by doing so, lose money, not because they are necessarily bad pickers, but because there are so many investors all making decisions the same way.

Blogs and Discussions

• CXO Advisory
• Bogleheads (24 comments)
• Falkenblog (1 comment)
• Abnormal Returns

Media and Press

• Globe and Mail
• Financial Times
• Forbes Online (2010)
• Forbes Online (2011)

SSRN Top Ten Download Lists

• ERN: Behavioral Finance (Topic)
• FEN: Behavioral Finance (Topic)
• Behavioral & Experimental Finance (Editor's Choice) eJournal
• Mutual Funds, Hedge Funds, and Investment Industry eJournal
• ERN: Econometric Modeling in Financial Economics (Topic)
• Household Finance eJournal
• Behavioral & Experimental Finance eJournal
• Microeconomics: General Equilibrium and Disequilibrium Models of Financial Markets eJournal

The Minimal Model of Financial Complexity

A representative investor generates realistic and complex security price paths by following this trading strategy: if, a few ticks ago, the market asset had two consecutive upticks or two consecutive downticks, then sell, and otherwise buy. This simple, unique, and robust model is the smallest possible deterministic model of financial complexity, and its generalization leads to complex variety. Compared to a random walk, the minimal model generates time series with fatter tails and more frequent crashes, thus more closely matching the real world. It does all this without any parameter fitting.

Available on SSRN and on arXiv. Also see this introductory poster.

Citation: Maymin, Philip Z. (2011), "The Minimal Model of Financial Complexity," Quantitative Finance, 11:9, 1371-1378.

Download PDF but please note:

Author Posting. (c) Taylor & Francis, 2010.
This is the author's version of the work. It is posted here by permission of Taylor & Francis for personal use, not for redistribution.
The definitive version was published in Quantitative Finance, 2010.
doi:10.1080/14697681003709447.

Presentations

• Cornell Financial Engineering Manhattan, September 28, 2011

Demonstrations

The minimal models can best be understood through live demonstrations.

Step-by-step trader dynamics

Prices Generated by Rule 54

Explore All Rules Price Series... Histograms... Diagrams... ...And More!

See some videos of these demonstrations:

SSRN Top Ten Download Lists

• Financial Engineering
• ERN: Behavioral Finance (Topic)
• FEN Risk Journals

Preventing Emotional Investing: An Added Value of an Investment Advisor (with Gregg S. Fisher)

We analyze a unique, comprehensive, multi-decade dataset of all communications with clients by a boutique investment advisory and investment management firm to explore the behavior of individuals involved in financial decision making. We propose and test a theory of self-regulation to explain both the appeal and the value of investment managers to individual investors, and we find that all of the predictions of the theory are borne out by the data. In short, our unique dataset allows us to provide evidence that an important service provided by investment advisors, and apparently desired by individual investors, is the barrier the advisor provides to prevent the individual from aggressively trading and thereby losing money.

Available on SSRN. Link to publisher.

Citation: Maymin, Philip Z.; Fisher, Gregg S. (2011), "Preventing Emotional Investing: An Added Value of an Investment Advisor," Journal of Wealth Management, 13:4, 34-43.

Download PDF. The license for the PDF allows it to be printed once per person.

Presentations

• International Research Forum at the Hong Kong Polytechnic University and International Conference in Applied Statistics & Financial Mathematics, December 17, 2010

Blogs and Discussions

• AdvisorOne (1 comment)
• ActiveRain
• Truepoint Investor

Media and Press

• CBS MoneyWatch

• New York Times

  

SSRN Top Ten Download Lists

• CGN: Financial/Investment Practice (Topic)
• CGN: Gatekeepers (Topic)
• Household Finance eJournal
• Pension Risk Management eJournal
• Corporate Governance Practice Series eJournal

The Iron Fist vs. the Invisible Hand: Interventionism and libertarianism in environmental economic discourses
(with Tai Wei Lim)

Drawing from a broad range of sources, we define and discuss the two primary ways of contemplating issues related to environmental economics, namely, interventionism and libertarianism. We then interpret a cellular automaton as a model that allows for either approach, as well as anarchy, and show that interventionism exponentially reduces the number of possibilities while libertarianism, even when only probabilistically applied, tends to retain rather than destroy the underlying economic complexity. Thus, the libertarian, ex-post, remuneration approach may deserve more than the scant consideration it typically receives in such discourse, while the interventionist, ex-ante, regulation approach may have hidden long-term dangers not previously recognized. More generally, the approach outlined here may prove useful as a mechanism by which various regulatory proposals may be tested and compared.

Available on SSRN.

Citation: Maymin, Philip Z.; Lim, Tai Wei (2012), "The Iron Fist vs. the Invisible Hand: Interventionism and libertarianism in environmental economic discourses", World Review of Entrepreneurship, Management and Sustainable Development 8:3, forthcoming.

The following figure from the paper shows the result of a "noisy libertarian" evolution of pollution with an 80 percent enforcement probability on a rule 110 cellular automaton.

SSRN Top Ten Download Lists

• Environmental Economics eJournal
• Environmental Justice and Sustainability eJournal
• PSN: Environment (Topic)
• Political Economy: Development eJournal
• Political Economy: Structure and Scope of Government eJournal
• ERN: Structure, Scope, and Performance of Government (Topic)
• ERN: Other Political Economy: National, State and Local Government; Intergovernmental Relations (Topic)

Why Financial Regulation is Doomed to Fail (Editorial)

One might argue that the recent financial crisis demonstrates that financial regulations have backfired: Not only did the widespread regulations fail to prevent systemic risk, but, also, the systemic risk itself was even higher than it otherwise would have been in the absence of regulations. Is this argument true? If so, can the regulations be fixed? If not, what new regulations can be introduced to prevent a similar meltdown from happening in the future?

Available on Library of Economics and Liberty. Download PDF.

Citation: Maymin, Philip Z. (2011), "Why Financial Regulation is Doomed to Fail", Library of Economics and Liberty, March 2011. Editorial.

Metanoia and the Market

If investors randomly switch between being rational and irrational, then eventually the market will be half rational and half irrational, even if all investors start off rational, no matter how low the switching probability is. Thus, mispricings can persist even with continued volume between two fundamentally identical investments. Multiple survey results for hypothetical investment scenarios support this metanoia model. In short, the law of one price will be violated so long as there is any probability of switching: identical assets will have different prices.

Available on SSRN. Download PDF. Link to publisher.

Citation: Maymin, Philip Z. (2011), "Metanoia and the Market", Advances in Behavioral Finance and Economics 1:1, Winter 2011, pp.27-42.

Presentations

• 2nd Annual Meeting of the Academy of Behavioral Finance and Economics, September 16, 2010

Media and Press

• Whitebox Selected Research: Crazy is as Crazy Does

SSRN Top Ten Download Lists

• ESMST: Survey Methods (Topic)

Regulation Simulation

A deterministic trading strategy by a representative investor on a single market asset, which generates complex and realistic returns with its first four moments similar to the empirical values of European stock indices, is used to simulate the effects of financial regulation that either pricks bubbles, props up crashes, or both. The results suggest that regulation makes the market process appear more Gaussian and less complex, with the difference more pronounced for more frequent intervention, though particular periods can be worse than the non-regulated version, and that pricking bubbles and propping up crashes are not symmetrical.

Available on SSRN and on arXiv. Download PDF. Link to publisher.

Citation: Maymin, Philip Z. (2009), "Regulation Simulation", European Journal of Finance and Banking Research 2:2, 1-12.

This paper uses the minimal model of financial complexity above to explore the effects of different regulatory environments. It also shows how the simulated price series resemble the actual price history in European equity markets.

SSRN Top Ten Download Lists

• ERN: Regulation (European) (Topic)

The Hazards of Propping Up: Bubbles and Chaos

In the current environment of financial distress, many governments are likely to soon become major holders of financial assets, but the policy debate focuses only on the likelihood and extent of short-term market stabilization. This paper shows that government intervention and propping up are likely to lead to long-term bubbles and even wildly chaotic behavior. The discontinuities occur when the committed capital reaches a critical amount that depends on just two parameters: the market impact of trading and the target exposure percentage.

Available on SSRN and on arXiv. Download PDF. Link to publisher.

Citation: Maymin, Philip Z. (2009), "The Hazards of Propping Up: Bubbles and Chaos," The International Journal of Business and Finance Research 3:2, 83-93.

See the interactive demonstration.

See a video of the demonstration:

Media and Press

• AOL Daily Finance: Bull market or bubble? History suggests brace for the 'pop'.
Excerpt:


Intuitively, it's almost too pretty a story: "This time it's different because of China."

Phil Maymin, professor of finance and risk engineering at the Polytechnic Institute of New York University, doesn't buy that line for a second. "Wasn't it the early 80s when we were all enamored of Japan, the Rising Sun, the Eastern miracle? And the 90s was Taiwan and Thailand and Indonesia," Maymin says. "So now it's China. Bubbles, bubbles everywhere."

Yes, China's already a much bigger deal on a fundamental and economic basis than Japan or the Asian Tiger's ever could have hoped to be, but we wonder if investors aren't overpricing the would-be effects of the Middle Kingdom, if that is indeed the case.

We're going to have to go with Maymin -- not to mention Gluskin Sheff's David Rosenberg, star bank analyst Meredith Whitney and Nouriel "Dr. Doom" Roubini -- on this one. Like the fizzy lifting drink scene in Willy Wonka and the Chocolate Factory, we see bubbles, bubbles everywhere -- and fear they are propelling us right into the spinning blades of a fan.

SSRN Top Ten Download Lists

• IPE: International Finance and Investment (Topic)
• Political Methods Journals
• QM: Econometrics, Polimetrics, and Statistics (Topic)
• PSN: Econometrics, Polimetrics, and Statistics (Topic)
• Political Methods: Quantitative Methods eJournal
• MMFP: Comparative or Joint Analysis of Fiscal and Monetary Policy; Stabilization (Topic)
• ERN: Comparative or Joint Analysis of Fiscal and Monetary Policy; Stabilization (Topic)

Prospect Theory and Fat Tails

A behavioral representative investor who evaluates a single risky asset based on cumulative prospect theory will often induce high kurtosis, negative skewness, and persistent autocorrelation into the distribution of market returns even if the asset payoffs are merely a sequence of independent coin tosses. These findings continue to hold even when the investor is simply loss averse.

Available on SSRN. Download PDF. Link to publisher.

Citation: Maymin, Philip Z. (2009), "Prospect Theory and Fat Tails," Risk and Decision Analysis 1:3, 187-195.

Presentations

• 5th Annual CARISMA Conference, February 2, 2010
• Gerstein Fisher, November 11, 2009

Media and Press

• Forbes Magazine: How to Protect Investments from Cataclysmic 'Fat Tails'

I wrote an article for Forbes (with Gregg S. Fisher) which refers to my research here.
Excerpt:

"And get the bits together, the fat tail, every good part." Ezekiel 24:4

If you traveled back in time thousands of years to tell Abraham, Moses or Ezekiel that you had some fat tails, they would have been delighted. In ancient times, the fat tails of certain Middle Eastern sheep were considered a delicacy. Today, they're more often associated with investment cataclysms.

...

Recent research suggests that in certain cases, investors subject to these two biases--loss aversion and mental accounting--will generate fat tails via their trading activity. In trying to avoid losses and compartmentalize investment decisions, they can exacerbate moves upward and down. For example, when earnings randomly rise, investors buy more; and when earnings fall, they sell. This is not very logical, but it is very human.

Schizophrenic Representative Investors

Representative investors whose behavior is modeled by a deterministic finite automaton generate complexity both in the time series of each asset and in the cross-sectional correlation when the rule governing their behavior is schizophrenic, meaning the investor holds multiple seemingly contradictory beliefs simultaneously, either by switching between two different rules at each time step, or computing different responses to different assets.

Available on SSRN and on arXiv.

Citation: Maymin, Philip Z. (2011), "Schizophrenic Representative Investors," Working Paper.

The Five Factors of Optimal Free Throw Shooting (with Allan Maymin and Eugene Shen)

We use three-dimensional optical tracking data on the 25-frames-per-second positional data of 2,400 free throw shots by the twenty players with at least twelve makes and twelve misses over the course of the 2010-2011 NBA season, fit each trajectory to a comprehensive physics model to find the implied backspin, initial launch height, velocity, angle, and left-right deviation, and examine the differences of those five factors between makes and misses for each player with sufficient attempts in our sample. We find that usually one or two factors are most responsible for a given player's misses, but the particular factors at fault differ across players. Thus, the causes of suboptimality in free throw shooting are idiosyncratic. This framework may also be useful in analyzing jump shots taken during the game.

Available on SSRN.

Citation: Maymin, Allan; Maymin, Philip Z.; Shen, Eugene (2011), "The Five Factors of Optimal Free Throw Shooting," Working Paper.

NBA Chemistry: Positive and Negative Synergies in Basketball (with Allan Maymin and Eugene Shen)

We introduce a novel Skills Plus Minus ("SPM") framework to measure on-court chemistry in basketball. First, we evaluate each player's offense and defense in the SPM framework based on three basic categories of skills: scoring, rebounding, and ball-handling. We then simulate games using the skill ratings of the ten players on the court. The results of the simulations measure the effectiveness of individual players as well as the 5-player lineup, so we can then calculate the synergies of each NBA team by comparing their 5-player lineup's effectiveness to the "sum-of-the-parts." We find that these synergies can be large and meaningful. Because skills have different synergies with other skills, our framework predicts that a player's value is dependent on the other nine players on the court. Therefore, the desirability of a free agent depends on the players currently on the roster. Indeed, our framework is able to generate mutually beneficial trades between teams. Other ratings systems cannot generate mutually beneficial trades since one player is always rated above another. We find more than two hundred mutually beneficial trades between NBA teams, situations where the skills of the traded players fit better on their trading partner's team.

Available on SSRN or on SSAC.

Citation: Maymin, Allan; Maymin, Philip Z.; Shen, Eugene (2011), "NBA Chemistry: Positive and Negative Synergies in Basketball," Working Paper.

Presentations

• 6th Annual MIT Sloan Sports Analytics Conference, March 2-3, 2012

Media and Press

• New York Times: Off the Dribble
• ESPN TV: Numbers Never Lie
• CBS Sports
• ESPN TrueHoop (2012)
• Basketball Prospectus (2012)
• ESPN TrueHoop (2011)

Blogs and Discussions

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Momentum's Hidden Sensitivity to the Starting Day
(with Zak Maymin and Gregg Fisher)

We show that the profitability of time-series momentum strategies on commodity futures across their entire history is strongly sensitive to the starting day. Using daily returns with 252-day formation periods and 21-day holding periods, the Sharpe ratio depends on whether one starts on the first day, the second day, and so on, until the twenty first day. This sensitivity is higher for shorter trading periods. The same results also hold in simulation of independent and identically lognormally distributed returns, showing that this is not only an empirical pattern but a fundamental issue with momentum strategies. Portfolio managers should be aware of this latent risk: starting trading the same strategy on the same underlying but one day later could, even after many decades, turn a successful strategy into an unsuccessful one.

Available on SSRN.

Citation: Maymin, Philip Z.; Maymin, Zakhar G.; Fisher, Gregg S. (2011), "Momentum's Hidden Sensitivity to the Starting Day," Working Paper.

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Lambda-Q Calculus (1996-1997)

Extending the Lambda Calculus to Express Quantumized Algorithms

I introduce a formal metalanguage called the lambda-q calculus for the specification of quantum programming languages. This metalanguage is an extension of the lambda calculus, which provides a formal setting for the specification of classical programming languages.

As an intermediary step, I introduce a formal metalanguage called the lambda-p calculus for the specification of programming languages that allow true random number generation. I demonstrate how selected randomized algorithms can be programmed directly in the lambda-p calculus.

I also demonstrate how satisfiability can be solved in the lambda-q calculus.

View the complete manuscript. Available on arXiv.

The Lambda-Q Calculus Can Efficiently Simulate Quantum Computers

I show that the lambda-q calculus can efficiently simulate quantum Turing machines by showing how the lambda-q calculus can efficiently simulate a class of quantum cellular automaton that are equivalent to quantum Turing machines.

I conclude by noting that the lambda-q calculus may be strictly stronger than quantum computers because NP-complete problems such as satisfiability are efficiently solvable in the lambda-q calculus but there is a widespread doubt that they are efficiently solvable by quantum computers.

View the complete manuscript. Available on arXiv.

Programming Complex Systems

Classical programming languages cannot model essential elements of complex systems such as true random number generation. I develop a formal programming language called the lambda-q calculus that addresses the fundamental properties of complex systems. This formal language allows the expression of quantumized algorithms, which are extensions of randomized algorithms in that probabilities can be negative, and events can cancel out.

View the complete manuscript. Available on arXiv.

Citation: Maymin, Philip Z. (2003), "Programming Complex Systems," in Yaneer Bar-Yam (Ed.), Unifying Themes in Complex Systems, Vol. I, Proceedings of the First International Conference on Complex Systems (1997), Chapter 32, pp. 325-341, Colorado: Westview Press.

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