Journal of Monetary Economics 58 (2011), 471-479
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 too amplify their results and place them in a broader context in my comments.
with Jiang Wang, Handbook of Financial Econometrics, Volume 2, 2010, North-Holland
If price and quantity are the fundamental building blocks of any theory of market interactions, the importance of trading volume in understanding the behavior of financial markets is clear. However, while many economic models of financial markets have been developed to explain the behavior of prices—predictability, variability, and information content—far less attention has been devoted to explaining the behavior of trading volume. In this article, we hope to expand our understanding of trading volume by developing well-articulated economic models of asset prices and volume and empirically estimating them using recently available daily volume data for individual securities from the University of Chicago's Center for Research in Securities Prices. Our theoretical contributions include: (1) an economic definition of volume that is most consistent with theoretical models of trading activity; (2) the derivation of volume implications of basic portfolio theory; and (3) the development of an intertemporal equilibrium model of asset market in which the trading process is determined endogenously by liquidity needs and risk sharing motives. Our empirical contributions include: (1) the construction of a volume/returns database extract of the CRSP volume data; (2) comprehensive exploratory data analysis of both the time-series and cross-sectional properties of trading volume; (3) estimation and inference for price/volume relations implied by asset-pricing models; and (4) a new approach for empirically identifying factors to be included in a linear-factor model of asset returns using volume data.
The Financial Times
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.”
with Jasmina Hasanhodzic and Emanuele Viola
We construct a financial "Turing test" to determine whether human subjects can differentiate between actual vs. randomized financial returns. The experiment consists of an online videogame where players are challenged to distinguish actual financial market returns from random temporal permutations of those returns. We find overwhelming statistical evidence (p-values no greater than 0.5%) that subjects can consistently distinguish between the two types of time series, thereby refuting the widespread belief that financial markets "look random". A key feature of the experiment is that subjects are given immediate feedback regarding the validity of their choices, allowing them to learn and adapt. We suggest that such novel interfaces can harness human capabilities to process and extract information from financial data in ways that computers cannot
PBS Newshour, The Business Desk
with Mark Mueller, Journal of Investment Management 8 (2010), 13-63.
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.
with Amir E. Khandani and Adlar J. Kim, Journal of Banking & Finance 34 (2010), 2767-2787.
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.
with Thomas J. Brennan, Management Science 56 (2010), 905-923.
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.
This document is the written testimony submitted to the House Financial Services Committe for its hearing on systemic risk regulation, held October 29, 2009, and it is not a formal academic research paper, but is intended for a broader audience of policymakers and regulators. Academic readers may be alarmed by the lack of comprehensive citations and literature review, the imprecise and qualitative nature of certain arguments, and the abundance of illustrative examples, analogies, and metaphors. Accordingly, such readers are hereby forewarned—this paper is not research, but is instead a summary of the policy implications that I have drawn from my interpretation of that research. This testimony focuses on three themes: (1) Establishing the means to measure and monitor systemic risk on an ongoing basis is the single-highest priority for financial regulation reform; (2) Systemic risk measurement and regulation will likely require new legislation compelling systemically important entities to provide more transparency on a confidential basis to regulators, e.g., information regarding their assets, liabilities, holdings, leverage, collateral, liquidity, counterparties, and aggregate exposures to key financial variables and other risks; and (3) Because systemic risk cuts across multiple regulatory bodies that do not necessarily share the same objectives and constraints, it may be more efficient to create an independent agency patterned after the National Transportation Safety Board (NTSB), solely devoted to measuring, tracking, and investigating systemic risk events in support of—not in competition with—all regulatory agencies.
The New York Times, Freakonomics Blog
In this guest post, MIT Sloan Prof. Andrew Lo provides an insightful look at how "extended periods of prosperity act as an anesthetic in the human brain," lulling everyone involved into "a drug-induced stupor that causes us to take risks that we know we should avoid."