During the week of August 6, 2007, a number of quantitative long/short equity hedge funds experienced unprecedented losses. It has been hypothesized that a coordinated deleveraging of similarly constructed portfolios caused this temporary dislocation in the market. Using the simulated returns of long/short equity portfolios based on five specific valuation factors, we find evidence that the unwinding of these portfolios began in July 2007 and continued until the end of 2007. Using transactions data, we find that the simulated returns of a simple market-making strategy were significantly negative during the week of August 6, 2007, but positive before and after, suggesting that the Quant Meltdown of August 2007 was the combined effects of portfolio deleveraging throughout July and the first week of August, and a temporary withdrawal of market-making risk capital starting August 8th. Our simulations point to two unwinds—a mini-unwind on August 1st starting at 10:45am and ending at 11:30am, and a more sustained unwind starting at the open on August 6th and ending at 1:00pm—that began with stocks in the financial sector and long Book-to-Market and short Earnings Momentum. These conjectures have significant implications for the systemic risks posed by the hedge-fund industry.

We propose a single evolutionary explanation for the origin of several behaviors that have been observed in organisms ranging from ants to human subjects, including risk-sensitive foraging, risk aversion, loss aversion, probability matching, randomization, and diversification. Given an initial population of individuals, each assigned a purely arbitrary behavior with respect to a binary choice problem, and assuming that offspring behave identically to their parents, only those behaviors linked to reproductive success will survive, and less reproductively successful behaviors will disappear at exponential rates. This framework generates a surprisingly rich set of behaviors, and the simplicity and generality of our model suggest that these behaviors are primitive and universal.

We establish a link between illiquidity and positive autocorrelation in asset returns among a sample of hedge funds, mutual funds, and various equity portfolios. For hedge funds, this link can be confirmed by comparing the return autocorrelations of funds with shorter vs. longer redemption-notice periods. We also document significant positive return-autocorrelation in portfolios of securities that are generally considered less liquid, e.g., small-cap stocks, corporate bonds, mortgage-backed securities, and emerging-market investments. Using a sample of 2,927 hedge funds, 15,654 mutual funds, and 100 size- and book-to-market-sorted portfolios of U.S. common stocks, we construct autocorrelation-sorted long/short portfolios and conclude that illiquidity premia are generally positive and significant, ranging from 2.74% to 9.91% per year among the various hedge funds and fixed-income mutual funds. We do not find evidence for this premium among equity and asset-allocation mutual funds, or among the 100 U.S. equity portfolios. The time variation in our aggregated illiquidity premium shows that while 1998 was a difficult year for most funds with large illiquidity exposure, the following four years yielded significantly higher illiquidity premia that led to greater competition in credit markets, contributing to much lower illiquidity premia in the years leading up to the Financial Crisis of 2007–2008.

Identifying winning new product concepts can be a challenging process that requires insight into private consumer preferences. To measure consumer preferences for new product concepts, the authors apply a 'securities of trading of concepts,' or STOC, approach, in which new product concepts are traded as financial securities. The authors apply this method because market prices are known to efficiently collect and aggregate private information regarding the economic value of goods, sevices, and firms, particularly when trading financial securities. This research compares the STOC approach against stated-choice, conjoint, constant-sum, and longitudinal revealed-preference data. The authors also place STOC in the context of previous research on prediction markets and experimental economics. The authors conduct a series of experiments in multiple product categories to test whether STOC (1) is more cost efficient than other methods, (2) passes validity tests, (3) measures expectations of others, and (4) reveals individual preferences, not just those of the crowd. The results also show that traders exhibit bias on the basis of self-preferences when trading. Ultimately, STOC offers two key advantages over traditional market research methods: cost efficiency and scalability. For new product development teams deciding how to invest resources, this scalability may be especially important in the Web 2.0 world, in which customers are constantly interacting with firms and one another in suggesting numerous product design possibilities that need to be screened.

Although the precise origins of the term "complex adaptive system" are unclear, nevertheless, the hackneyed phrase is now firmly ensconced in the lexicon of biologists, physicists, mathematicians, and, most recently, economics. However, as with many important ideas that become cliches, the original meaning is often obscured and diluted by popular usage. But thanks to the fascinating article by Gai, Haldane, and Kapadia, we have a concrete and practical instantiation of a complex adaptive system in economics, one that has real relevance to current policy debates regarding financial reform. Since there is very little to criticize in their compelling article, I will seek to amplify their results and place them in a broader context in my comments.

The hedge fund industry has grown dramatically over the last two decades, with more than eight thousand funds now controlling close to two trillion dollars. Originally intended for the wealthy, these private investments have now attracted a much broader following that includes pension funds and retail investors. Because hedge funds are largely unregulated and shrouded in secrecy, they have developed a mystique and allure that can beguile even the most experienced investor. In Hedge Funds, Andrew Lo--one of the world's most respected financial economists--addresses the pressing need for a systematic framework for managing hedge fund investments.

"A movement is over when the news is out," so goes the Wall Street maxim. For thousands of years, technical analysis—marred with common misconceptions likening it to gambling or magic and dismissed by many as "voodoo finance"—has sought methods for spotting trends in what the market's done and what it's going to do. After all, if you don't learn from history, how can you profit from it?

In The Evolution of Technical Analysis, the director of MIT's Laboratory for Financial Engineering, Andrew Lo, and coauthor Jasmina Hasanhodzic present an engaging account of the origins and development of this mysterious "black art," tracing its evolution from ancient Babylon to the rise of Wall Street as the world's financial center. Along the way, the practices of Eastern technical analysts like Munehisa Homma ("the god of the markets") are compared and contrasted with those of their Western counterparts, such as Humphrey Neill, William Gann, and Charles Dow ("the father of technical analysis").

With deep roots in antiquity, technical analysis is part art and part science, seeking to divine trends, reversals, cycles, and other predictable patterns in historical market prices. While the techniques for capturing such regularities have evolved considerably over the centuries, the all-too-human predilection to extrapolate into the future using the past has been a constant driving force throughout history.

The authors chronicle the fascinating and unexpected path of charting that likely began with simple superstitions and coincidences, and has developed into widespread practices in many markets and instruments, involving sophisticated computational algorithms and visualization techniques. The Evolution of Technical Analysis is the story of how some early technicians failed miserably, how others succeeded beyond their wildest dreams, and what it means for traders today.

Trading volume is an important aspect of the economic interactions in financial markets among various investors. Both volume and prices are driven by underlying economic forces, and thus convey important information about the workings of the market. This chapter focuses on the empirical characteristics of prices and volume in stock markets. The interactions between prices and quantities in an equilibrium yield a rich set of implications for any asset pricing model, when an explicit link between economic fundamentals and the dynamic properties of asset returns and volume are derived. By exploiting the relation between prices and volume in the dynamic equilibrium model, one can identify and construct the hedging portfolio, which can be used by all investors to hedge against changes in market conditions. This hedging portfolio has considerable forecast power in predicting future returns of the market portfolio and its abilities to explain cross-sectional variation in expected returns is comparable to other popular risk factors such as market betas, the Fama and French SMB factor, and optimal forecast portfolios. The presence of market frictions, such as transactions costs, can influence the level of trading volume and serve as a bridge between the market microstructure literature and the broader equilibrium asset pricing literature.

MIT Sloan Prof. Andrew Lo authored this opinion piece supporting the creation of a “Capital Markets Safety Board’ (CMSB) patterned after the National Transportation Safety Board, dedicated to investigating, reporting, and archiving the ‘accidents’ of the financial industry.”

Andrew Lo, director of the MIT Laboratory for Financial Engineering, posted his commentary on a recent economics book in this online Q&A.

The quantitative aspirations of economists and financial analysts have for many years been based on the belief that it should be possible to build models of economic systems—and financial markets in particular—that are as predictive as those in physics. While this perspective has led to a number of important breakthroughs in economics, "physics envy" has also created a false sense of mathematical precision in some cases. We speculate on the origins of physics envy, and then describe an alternate perspective of economic behavior based on a new taxonomy of uncertainty. We illustrated the relevance of this taxonomy with two concrete examples: the classical harmonic oscillator with some new twists that make physics look more like economics, and a quantitative equity market-neutral strategy. We conclude by offering a new interpretation of tail events, proposing an 'uncertainty checklist' with which our taxonomy can be implemented, and considering the role that quants played in the current financial crisis.

We apply machine-learning techniques to construct nonlinear nonparametric forecasting models of consumer credit risk. By combining customer transactions and credit bureau data from January 2005 to April 2009 for a sample of a major commercial bank's customers, we are able to construct out-of-sample forecasts that significantly improve the classification rates of credit-card-holder delinquencies and defaults, with linear regression R-squared's of forecasted/realized delinquencies of 85%. Using conservative assumptions for the costs and benefits of cutting credit lines based on machine-learning forecasts, we estimate the cost savings to range from 6% to 25% of total losses. Moreover, the time-series patterns of estimated delinquency rates from this model over the course of the recent financial crisis suggests that aggregated consumer-credit risk analytics may have important applications in forecasting systemic risk.

A key result of the Capital Asset Pricing Model (CAPM) is that the market portfolio—the portfolio of all assets in which each asset's weight is proportional to its total market capitalization—lies on the mean-variance-efficient frontier, the set of portfolios having mean-variance characteristics that cannot be improved upon. Therefore, the CAPM cannot be consistent with efficient frontiers for which every frontier portfolio has at least one negative weight or short position. We call such efficient frontiers 'impossible', and show that impossible frontiers are difficult to avoid. In particular, as the number of assets, n, grows, we prove that the probability that a generically chosen frontier is impossible tends to one at a geometric rate. In fact, for one natural class of distributions, nearly one-eighth of all assets on a frontier is expected to have negative weights for every portfolio on the frontier. We also show that the expected minimum amount of shortselling across frontier portfolios grows linearly with n, and even when shortsales are constrained to some finite level, an impossible frontier remains impossible. Using daily and monthly U.S. stock returns, we document the impossibility of efficient frontiers in the data.

The Heretics of Finance provides extraordinary insight into both the art of technical analysis and the character of the successful trader. Distinguished MIT professor Andrew W. Lo and researcher Jasmina Hasanhodzic interviewed thirteen highly successful, award-winning market professionals who credit their substantial achievements to technical analysis. The result is the story of technical analysis in the words of the people who know it best; the lively and candid interviews with these gurus of technical analysis.

The first half of the book focuses on the technicians' careers:

• How and why they learned technical analysis
• What market conditions increase their chances of making mistakes
• What their average workday is like
• To what extent trading controls their lives
• Whether they work on their own or with a team
• How their style of technical analysis is unique

The second half concentrates on technical analysis and addresses questions such as these:

• Did the lack of validation by academics ever cause you to doubt technical analysis?
• Can technical analysis be applied to other disciplines?
• How do you prove the validity of the method?
• How has computer software influenced the craft?
• What is the role of luck in technical analysis?
• Are there laws that underlie market action?
• What traits characterize a highly successful trader?
• How you test patterns before you start using them with real money?

Interviewees include:
Ralph J. Acampora, Laszlo Birinyi, Walter Deemer, Paul Desmond, Gail Dudack, Robert J. Farrell, Ian McAvity, John Murphy, Robert Prechter, Linda Raschke, Alan R. Shaw, Anthony Tabell, Stan Weinstein.