The dramatic growth in institutionally managed assets, coupled with the advent of internet trading and electronic brokerage for retail investors, has led to a surge in the size and volume of trading. At the same time, competition in the asset management industry has increased to the point where fractions of a percent in performance can separate the top funds from those in the next tier. In this environment, portfolio managers have begun to explore active management of trading costs as a means of boosting returns. Controlling execution cost can be viewed as a stochastic dynamic optimization problem because trading takes time, stock prices exhibit random fluctuations, and execution prices depend on trade size, order flow, and market conditions. In this paper, we apply stochastic dynamic programming to derive trading strategies that minimize the expected cost of executing a portfolio of securities over a fixed period of time, i.e., we derive the optimal sequence of trades as a function of prices, quantitites, and other market conditions. To illustrate the practical relevance of our methods, we apply them to a hypothetical portfolio of 25 stocks by estimating their price-impact functions using historical trade data from 1996 and deriving the optimal execution strategies. We also perform several Monte Carlo simulation experiments to compare the performance of the optimal strategy to several alternatives.

Typical value-at-risk (VAR) calculations involve the probabilities of extreme dollar losses, based on the statistical distributions of market prices. Such quantities do not account for the fact that the same dollar loss can have two very different economic valuations, depending on business conditions. We propose a nonparametric VAR measure that incorporates economic valuation according to the state-price density associated with the underlying price processes. The state-price density yields VAR values that are adjusted for risk aversion, time preferences, and other variations in economic valuation. In the context of a representative agent equilibrium model, we construct an estimator of the risk-aversion coefficient that is implied by the joint observations on option prices and underlying asset value.

In this review article, we explore several recent advances in the quantitative modeling of financial markets. We begin with the Efficient Markets Hypothesis and describe how this controversial idea has stimulated a number of new directions of research, some focusing on more elaborate mathematical models that are capable of rationalizing the empirical facts, others taking a completely different tack in rejecting rationality altogether. One of the most promising directions is to view financial markets from a biological perspective and, specifically, within an evolutionary framework in which markets, instruments, institutions, and investors interact and evolve dynamically according to the "law" of economic selection. Under this view, financial agents compete and adapt, but they do not necessarily do so in an optimal fashion. Evolutionary and ecological models of financial markets is truly a new frontier whose exploration has just begun.

Current risk-management practices are based on probabilities of extreme dollar losses (e.g., measures like Value at Risk), but these measures capture only part of the story. Any complete risk-management system must address two other important factors: prices and preferences. Together with probabilities, these comprise the three P's of Total Risk Management. This article describes how the three Ps interact to determine sensible risk profiles for corporations and for individuals, guidelines for how much risk to bear and how much to hedge. By synthesizing existing research in economics, psychology, and decision sciences, and through an ambitious research agenda to extend this synthesis into other disciplines, a complete and systematic approach to rational decision-making in an uncertain world is within reach.

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.

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.

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.

We propose the National Transportation Safety Board (NTSB) as a model organization for addressing systemic risk in industries and contexts other than transportation. When adopted by regulatory agencies and the transportation industry, the safety recommendations of the NTSB have been remarkably effective in reducing the number of fatalities in various modes of transportation since the NTSB’s inception in 1967 as an independent agency. The NTSB has no regulatory authority and is solely focused on conducting forensic investigations of transportation accidents and proposing safety recommendations. With only 400 full-time employees, the NTSB has a much larger network of experts drawn from other government agencies and the private sector who are on call to assist in accident investigations on an as-needed basis. By allowing the participation in its investigations of all interested parties who can provide technical assistance to the investigations, the NTSB produces definitive analyses of even the most complex accidents and provides actionable measures for reducing the chances of future accidents. It is possible to create more efficient and effective systemic-risk management processes in many other industries, including financial services, by studying the organizational structure and functions of the NTSB.

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 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.