Research Publications




Financial Orphan Therapies Looking For Adoption, Health Affairs Blog (2014)
There exist scientifically promising treatments not being tested further because of insufficient financial incentives. Many of these therapies involve off-label uses of drugs approved by the Food and Drug Administration that are readily available and often inexpensive. Pharmaceutical companies—largely responsible for clinical drug development—cannot justify investing in such clinical trials because they cannot recoup the costs of these studies.  However, without prospective data demonstrating efficacy, such treatments will never be adopted as standard of care.

In an era of increasing health care costs and the need for effective therapies in many diseases, it is essential that society finds ways to adopt these “financial orphans.” We propose several potential solutions for the non-profit sector, pharmaceutical companies, health insurers, patient driven research, and others to accomplish this goal.

Available online
New Financing Methods in the Biopharma Industry: A Case Study of Royalty Pharma, Inc. The biotechnology and pharmaceutical industries are facing significant challenges to their existing business models because of expiring drug patents, declining risk tolerance of venture capitalists and other investors, and increasing complexity in translational medicine. In response to these challenges, new alternative investment companies have emerged to bridge the biopharma funding gap by purchasing economic interests in drug royalty streams. Such purchases allow universities and biopharma companies to monetize their intellectual property, creating greater financial flexibility for them while giving investors an opportunity to participate in the life sciences industry at lower risk. Royalty Pharma is the largest of these drug royalty investment companies, and in this case study, we profile its business model and show how its unique financing structure greatly enhances the impact it has had on the biopharma industry and biomedical innovation download pdf (1228kb
Financing Drug Discovery for Orphan Diseases, Drug Discovery Today (2013)
Recently proposed ‘megafund’ financing methods for funding translational medicine and drug development require billions of dollars in capital per megafund to de-risk the drug discovery process enough to issue long-term bonds. Here, we demonstrate that the same financing methods can be applied to orphan drug development but, because of the unique nature of orphan diseases and therapeutics (lower development costs, faster FDA approval times, lower failure rates and lower correlation of failures among disease targets) the amount of capital needed to de-risk such portfolios is much lower in this field. Numerical simulations suggest that an orphan disease megafund of only US$575 million can yield double-digit expected rates of return with only 10–20 projects in the portfolio. download pdf (192kb)
Can Financial Engineering Cure Cancer?, American Economic Review 103(3), 406-411
Traditional financing sources such as private and public equity may not be ideal for investment projects with low probabilities of success, long time horizons, and large capital requirements. Nevertheless, such projects, if not too highly correlated, may yield attractive risk-adjusted returns when combined into a single portfolio. Such "megafund" portfolios may be too large to finance through private for public equity alone. But with sufficient diversification and risk analytics, debt financing via securitization may be feasible. Credit enhancements (i.e., derivatives and government guarantees) can also improve megafund economics. We present an analytical framework and illustrative empirical examples involving cancer research.
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Learning Connections in Financial Time Series, Proceedings of the 30th International Conference on Machine Learning (ICML-13) 30, 109-117
To reduce risk, investors seek assets that have high expected return and are unlikely to move in tandem. Correlation measures are generally used to quantify the connections between equities. The 2008 nancial crisis, and its aftermath, demonstrated the need for a better way to quantify these connections. We present a machine learning-based method to build a connectedness matrix to address the shortcomings of correlation in capturing events such as large losses. Our method uses an unconstrained optimization to learn this matrix, while ensuring that the resulting matrix is positive semi-de nite. We show that this matrix can be used to build portfolios that not only beat the market," but also outperform optimal (i.e., minimum variance) portfolios. download pdf (478kb)
When Do Stop-Loss Rules Stop Losses?, Journal of Financial Markets (2013)
We propose a simple analytical framework to measure the value added or subtracted by stoploss rules—predetermined policies that reduce a portfolio’s exposure after reaching a certain threshold of cumulative losses—on the expected return and volatility of an arbitrary portfolio strategy. Using daily futures price data, we provide an empirical analysis of stop-loss policies applied to a buy-and-hold strategy using index futures contracts. At longer sampling frequencies, certain stop-loss policies can increase expected return while substantially reducing volatility, consistent with their objectives in practical applications. download pdf (418kb)
Using Algorithmic Attribution Techniques To Determine Authorship in Unsigned Judicial Opinions, Stanford Technology Law Review 16(2013)
This Article proposes a novel and provocative analysis of judicial opinions that are published without indicating individual authorship. Our approach provides an unbiased, quantitative, and computer scientific answer to a problem that has long plagued legal commentators. download pdf (702kb)
Can Hedge Funds Time Market Liquidity?, Journal of Financial Economics 109(2013), 493-516
We explore a new dimension of fund managers' timing ability by examining whether they can time market liquidity through adjusting their portfolios' market exposure as aggregate liquidity conditions change. Using a large sample of hedge funds, we find strong evidence of liquidity timing. A bootstrap analysis suggests that top-ranked liquidity timers cannot be attributed to pure luck. In out-of-sample tests, top liquidity timers outperform bottom timers by 4.0–5.5% annually on a risk-adjusted basis. We also find that it is important to distinguish liquidity timing from liquidity reaction, which primarily relies on public information. Our results are robust to alternative explanations, hedge fund data biases, and the use of alternative timing models, risk factors, and liquidity measures. The findings highlight the importance of understanding and incorporating market liquidity conditions in investment decision  making. download pdf (890kb)
On A New Approach for Analyzing and Managing Macrofinancial Risks, Financial Analysts Journal 69(2013), 22-33
At the fifth annual CFA Institute European Investment Conference on 19 October 2012 in Prague, Robert C. Merton gave a presentation on analyzing and managing macrofinancial risk. This article is based on his talk and on research he carried out with his coauthors. download pdf (659kb)
Moore’s Law vs. Murphy’s Law:
Algorithmic Trading and Its Discontents
Financial markets have undergone a remarkable transformation over the past two decades due to advances in technology. These advances include faster and cheaper computers, greater connectivity among market participants, and perhaps most important of all, more sophisticated trading algorithms. The benefits of such financial technology are evident: lower transactions costs, faster executions, and greater volume of trades. However, like any technology, trading technology has unintended consequences. In this paper, we review key innovations in trading technology starting with portfolio optimization in the 1950s and ending with high-frequency trading in the late 2000s, as well as opportunities, challenges, and economic incentives that accompanied these developments. We also discuss potential threats to financial stability created or facilitated by algorithmic trading and propose “Financial Regulation 2.0,” a set of design principles for bringing the current financial regulatory framework into the Digital Age. download
pdf (309kb)

Adaptive Markets and the New World Order, Financial Analysts Journal, 68 (2012), 18-29
In the Adaptive Markets Hypothesis (AMH) intelligent but fallible investors learn from and adapt to changing economic environments. This implies that markets are not always efficient, but are usually competitive and adaptive, varying in their degree of efficiency as the environment and investor population change over time. The AMH has several implications including the possibility of negative risk premia, alpha converging to beta, and the importance of macro factors and risk budgeting in asset-allocation policies. download pdf (438kb)
An Evolutionary Model of Bounded Rationality and Intelligence, with Thomas J. Brennan, PLOS One, 7 (2012)
Most economic theories are based on the premise that individuals maximize their own self-interest and correctly incorporate the structure of their environment into all decisions, thanks to human intelligence. The influence of this paradigm goes far beyond academia–it underlies current macroeconomic and monetary policies, and is also an integral part of existing financial regulations. However, there is mounting empirical and experimental evidence, including the recent financial crisis, suggesting that humans do not always behave rationally, but often make seemingly random and suboptimal decisions.
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Commercializing biomedical research through securitization techniques, with Jose-Maria Fernandez and Roger M. Stein, Nauture Biotechnology, 30 (2012), 964-975. Biomedical innovation has become riskier, more expensive and more difficult to finance with traditional sources such as private and public equity. Here we propose a financial structure in which a large number of biomedical programs at various stages of development are funded by a single entity to substantially reduce the portfolio's risk. The portfolio entity can finance its activities by issuing debt, a critical advantage because a much large pool of capital is available for investment in debt versus equity. By employing financial engineering techniques such as securitization, it can raise even greater amounts of more-patient capital. In a simulation using historical data for new molecular entities in oncology from 1990 to 2011, we find that megafunds of $5-15 billion may yield average investment returns of 8.9-11.4% for equity holders and 5-8% for 'research-backed obligation' holders, which are lower than typical venture-capital hurdle rates by attractive to pension funds, insurance companies and other large institutional investors. download pdf
Systemic Risk and the Refinancing Ratchet Effect, with Amir E. Khandani and Robert C. Merton.  The combination of rising home prices, declining interest rates, and near-frictionless refinancing opportunities can create unintentional synchronization of home owner leverage, leading to a ‘‘ratchet’’ effect on leverage because homes are indivisible and owner-occupants cannot raise equity to reduce leverage when home prices fall. Our simulation of the U.S. housing market yields potential losses of $1.7 trillion from June2006 to December2008 with cash-out refinancing vs. only $330 billion in the absence of cash-out refinancing. The refinancing ratchet effect is a new type of systemic risk in the financial system and does not rely on any dysfunctional behaviors. download pdf (738kb)
Estimating the NIH Efficient Frontier, with Dimitrios Bisias and Jamie Wakins, PLOS One, 7 (2012) The National Institutes of Health (NIH) is among the world’s largest investors in biomedical research, with a mandate to: “…lengthen life, and reduce the burdens of illness and disability.” Its funding decisions have been criticized as insufficiently focused on disease burden. We hypothesize that modern portfolio theory can create a closer link between basic research and outcome, and offer insight into basic-science related improvements in public health. We propose portfolio theory as a systematic framework for making biomedical funding allocation decisions–one that is directly tied to the risk/reward trade-off of burden-of-disease outcomes. download pdf (1.0Mb)
Complexity, Concentration and Contagion: A Comment, to appear in Journal of Monetary Economics.  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.  download pdf (670Kb) 
Illiquidity Premia in Asset Returns: An Empirical Analysis of Hedge Funds, Mutual Funds, and US Equity Portfolios, with Amir Khandani, Quarterly Journal of Finance, 1(2011), 1-59.  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. download pdf (377Kb) 
The Origin of Behavior, with Thomas Brennan, Quarterly Journal of Finance, 1(2011), 55-108.  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. download pdf (504Kb) 

Security Trading of Concepts (STOC), with Ely Dahan, Adlar J. Kim, Tomaso Poggio, and Nicholas T. Chan, Journal of Marketing Research, 48 (2011), 497-517.

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.     download pdf (6.8Mb)
The National Transportation Safety Board: A Model for Systemic Risk Management, with Eric Fielding and Jian Helen Yang, Journal of Investment Management, 9(2011), 17-49. 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 reductin the number of fatalities in various modes of transportation since the NTSB's inception in 1967 as an independent agency. Formerly part of the Civil Aeronautics Board (now the Federal Aviation Administration), 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 and encouraging the participation of all interested parties in its investigations, the NTSB is able to produce definitive analyses of even the most complex accidents and provide genuinely actionable measures for reducing the chances of future accidents. We believe it is possible to create more efficient and effective systemic-risk management processes in many other industries, including the financial services industry, by studying the organizational structure and functions of the NTSB.  download pdf (842Kb)

What Happened To The Quants In August 2007?: Evidence from Factors and Transactions Data, with Amir Khandani, Journal of Financial Markets 14(2011), 1-46.
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.
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Consumer Credit Risk Models via Machine-Learning Algorithms, 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. download pdf (6.7Mb) 

WARNING!: Physics Envy May Be Hazardous To Your Wealth, 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.

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Impossible Frontiers, 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.

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Jumping the Gates: Using Beta-Overlay Strategies to Hedge Liquidity Constraints, with A. Healy, Journal of Investment Management 7(2009), 1–20. In response to the current financial crisis, a number of hedge funds have implemented "gates" on their funds that restrict withdrawals when the sum of redemption requests exceeds a certain percentage of the fund's total assets. To reduce the investor's risk exposures during these periods, we propose a futures overlay strategy designed to hedge out or control the common factor exposures of gated assets. By taking countervailing positions in stock, bond, currency, and commodity exposures, an investor can greatly reduce the systematic risks of their gated assets while still enjoying the benefits of manager-specific alpha. Such overlay strategies can also be used to reposition the betas of an investor's entire portfolio, effectively rebalancing asset-class exposures without having to trade the less liquid underlying assets during periods of market dislocation. To illustrate the costs and benefits of such overlay, we simulate the impact of a simple beta-hedging strategy applied to long/short equity hedge funds in the TASS database.

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Regulatory Reform in the Wake of the Financial Crisis of 2007-2008, Journal of Financial Economic Policy 1(2009), 4-43.
 Financial crises are unavoidable when hardwired human behavior—fear and greed, or 'animal spirits—is combined with free enterprise, and cannot be legislated or regulated away. Like hurricanes and other forces of nature, market bubbles and crashes cannot be entirely eliminated, but their most destructive consequences can be greatly mitigated with proper preparation. In fact, the most damaging effects of financial crisis come not from loss of wealth, but rather from those who are unprepared for such losses and panic in response. This perspective has several implications for the types of regulatory reform needed in the wake of the Financial Crisis of 2007-2008, all centered around the need for greater transparency, improved measures of systemic risk, more adaptive regulations including counter-cyclical leverage constraints, and more emphasis on financial literacy starting in high school, including certifications for expertise in financial engineering for the senior management and directors of all financial institutions. download pdf (310Kb)
Where Do Alphas Come From?: A New Measure of the Value of Active Investment Management, Journal of Investment Management 6(2008), 1–29. The value of active investment management is traditionally measured by alpha, beta, tracking error, and the Sharpe and information ratios. These are essentially static characteristics of the marginal distributions of returns at a single point in time, and do not incorporate dynamic aspects of a manager's investment process. In this paper, I propose a new measure of the value of active investment management that captures both static and dynamic contributions of a portfolio manager's decisions. The measure is based on a decomposition of a portfolio's expected return into two distinct components: a static weighted-average of the individual securities' expected returns, and the sum of covariances between returns and portfolio weights. The former component measures the portion of the manager's expected return due to static investments in the underlying securities, while the latter component captures the forecast power implicit in the manager's dynamic investment choices. This measure can be computed for long-only investments, long/short portfolios, and asset allocation rules, and is particularly relevant for hedge-fund strategies where both components are significant contributors to their expected returns, but only one should garner the high fees that hedge funds typically charge. Several analytical and empirical examples are provided to illustrate the practical relevance of these new measures.

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130/30: The New Long-Only, with Pankaj Patel, Journal of Portfolio Management 34(2008), 12-38.
Long-only portfolio managers and investors have acknowledged that the long-only constraint is a potentially costly drag on performance, and loosening this constraint can add value. However, the magnitude of the performance drag is difficult to measure without a proper benchmark for a 130/30 portfolio. In this paper, we provide a passive but dynamic benchmark consisting of a 'plain-vanilla' 130/30 strategy using simple factors to rank stocks and standard methods for constructing portfolios based on these rankings. Based on this strategy, we produce two types of indexes: investable and 'look ahead' indexes, in which the former uses only prior information and the latter uses realized returns to produce an upper bound on performance. We provide historical simulations of our 130/30 benchmarks that illustrate their advantages and disadvantages under various market conditions.
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What Happened To The Quants In August 2007?, Journal of Investment Management 5(2007), 29-78.

During the week of August 6, 2007, a number of quantitative long/short equity hedge funds experienced unprecedented losses. Based on TASS hedge-fund data and simulations of a specific long/short equity strategy, we hypothesize that the losses were initiated by the rapid "unwind" of one or more sizable quantitative equity market-neutral portfolios. Given the speed and price impact with which this occurred, it was likely the result of a forced liquidation by a multi-strategy fund or proprietary-trading desk, possibly due to a margin call or a risk reduction. These initial losses then put pressure on a broader set of long/short and long-only equity portfolios, causing further losses by triggering stop/loss and de-leveraging policies. A significant rebound of these strategies occurred on August 10th, which is also consistent with the unwind hypothesis. This dislocation was apparently caused by forces outside the long/short equity sector—in a completely unrelated set of markets and instrumentssuggesting that systemic risk in the hedge-fund industry may have increased in recent years.

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Can Hedge-Fund Returns Be Replicated?: The Linear Case, with Jasmina Hasanhodzic, Journal of Investment Management 5(2007), 5–45.

In contrast to traditional investments such as stocks and bonds, hedge-fund returns have more complex risk exposures that yield additional and complementary sources of risk premia. This raises the possibility of creating passive replicating portfolios or "clones" using liquid exchange-traded instruments that provide similar risk exposures at lower cost and with greater transparency. Using monthly returns data for 1,610 hedge funds in the TASS database from 1986 to 2005, we estimate linear factor models for individual hedge funds using six common factors, and measure the proportion of the funds' expected returns and volatility that are attributable to such factors. For certain hedge-fund style categories, we find that a significant fraction of both can be captured by common factors corresponding to liquid exchange-traded instruments. While the performance of linear clones is often inferior to their hedge-fund counterparts, they perform well enough to warrant serious consideration as passive, transparent, scalable, and lower-cost alternatives to hedge funds.

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Trading Volume: Implications of an Intertemporal Capital Asset Pricing Model, with Jiang Wang, Journal of Finance 61(2006), 2805–2840.

We derive an intertemporal capital asset pricing model with multiple assets and heterogeneous investors, and explore its implications for the behavior of trading volume and asset returns. Assets contain two types of risks: market risk and the risk of changing market conditions. We show that investors trade only in two portfolios: the market portfolio, and a hedging portfolio, which allows them to hedge the dynamic risk. This implies that trading volume of individual assets exhibit a two-factor structure, and their factor loadings depend on their weights in the hedging portfolio. This allows us to empirically identify the hedging portfolio using volume data. We then test the two properties of the hedging portfolio: its return provides the best predictor of future market returns and its return together with the return of the market portfolio are the two risk factors determining the cross-section of asset returns.

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Systemic Risk and Hedge Funds, with Nicholas Chan, Mila Getmansky, and Shane M. Haas, in M. Carey and R. Stulz, eds., The Risks of Financial Institutions and the Financial Sector, 2007. Chicago, IL: University of Chicago Press.

Systemic risk is commonly used to describe the possibility of a series of correlated defaults among financial institutions—typically banks—that occur over a short period of time, often caused by a single major event. However, since the collapse of Long Term Capital Management in 1998, it has become clear that hedge funds are also involved in systemic risk exposures. The hedge-fund industry has a symbiotic relationship with the banking sector, and many banks now operate proprietary trading units that are organized much like hedge funds. As a result, the risk exposures of the hedge-fund industry may have a material impact on the banking sector, resulting in new sources of systemic risks. In this article, we attempt to quantify the potential impact of hedge funds on systemic risk by developing a number of new risk measures for hedge funds and applying them to individual and aggregate hedge-fund returns data. These measures include: illiquidity risk exposure, nonlinear factor models for hedge-fund and banking-sector indexes, logistic regression analysis of hedge-fund liquidation probabilities, and aggregate measures of volatility and distress based on regime-switching models. Our preliminary findings suggest that the hedge-fund industry may be heading into a challenging period of lower expected returns, and that systemic risk is currently on the rise.

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Reconciling Efficient Markets with Behavioral Finance: The Adaptive Markets Hypothesis, Journal of Investment Consulting 7(2005), 21–44.

The battle between proponents of the Efficient Markets Hypothesis and champions of behavioral finance has never been more pitched, and there is little consensus as to which side is winning or what the implications are for investment management and consulting. In this article, I review the case for and against the Efficient Markets Hypothesis, and describe a new framework—the Adaptive Markets Hypothesis—in which the traditional models of modern financial economics can co-exist alongside behavioral models in an intellectually consistent manner. Based on evolutionary principles, the Adaptive Markets Hypothesis implies that the degree of market efficiency is related to environmental factors characterizing market ecology such as the number of competitors in the market, the magnitude of profit opportunities available, and the adaptability of the market participants. Many of the examples that behavioralists cite as violations of rationality that are inconsistent with market efficiency—loss aversion, overconfidence, overreaction, mental accounting, and other behavioral biases—are, in fact, consistent with an evolutionary model of individuals adapting to a changing environment via simple heuristics. Despite the qualitative nature of this new paradigm, I show that the Adaptive Markets Hypothesis yields a number of surprisingly concrete applications for both investment managers and consultants.

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Fear and Greed in Financial Markets: A Clinical Study of Day-Traders, with Dmitry V. Repin and Brett N. Steenbarger, American Economic Review 95(2005), 352–359.

We investigate several possible links between psychological factors and trading performance in a sample of 80 anonymous day-traders. Using daily emotional-state surveys over a five-week period as well as personality inventory surveys, we construct measures of personality traits and emotional states for each subject and correlate these measures with daily normalized profits-and-losses records. We find that subjects whose emotional reaction to monetary gains and losses was more intense on both the positive and negative side exhibited significantly worse trading performance. Psychological traits derived from a standardized personality inventory survey do not reveal any specific "trader personality profile", raising the possibility that trading skills may not necessarily be innate, and that different personality types may be able to perform trading functions equally well after proper instruction and practice.

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Sifting Through the Wreckage: Lessons from Recent Hedge Fund Liquidations, with Mila Getmansky and Shauna X. Mei, Journal of Investment Management 2(2004), 6–38.

We document the empirical properties of a sample of 1,765 funds in the TASS Hedge Fund database from 1994 to 2004 that are no longer active. The TASS sample shows that attrition rates differ significantly across investment styles, from a low of 5.2% per year on average for convertible arbitrage funds to a high of 14.4% per year on average for managed futures funds. We relate a number of factors to these attrition rates, including past performance, volatility, and investment style, and also document differences in illiquidity risk between active and liquidated funds. We conclude with a proposal for the U.S. Securities and Exchange Commission to play a new role in promoting greater transparency and stability in the hedge-fund industry.

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An Econometric Model of Serial Correlation and Illiquidity in Hedge-Fund Returns, with Mila Getmansky and Igor Makarov, Journal of Financial Economics 74(2004), 529–609.

The returns to hedge funds and other alternative investments are often highly serially correlated in sharp contrast to the returns of more traditional investment vehicles such as long-only equity portfolios and mutual funds. In this paper, we explore several sources of such serial correlation and show that the most likely explanation is illiquidity exposure, i.e., investments in securities that are not actively traded and for which market prices are not always readily available. For portfolios of illiquid securities, reported returns will tend to be smoother than true economic returns, which will understate volatility and increase risk-adjusted performance measures such as the Sharpe ratio. We propose an econometric model of illiquidity exposure and develop estimators for the smoothing profile as well as a smoothing-adjusted Sharpe ratio. For a sample of 908 hedge funds drawn from the TASS database, we show that our estimated smoothing coefficients vary considerably across hedge-fund style categories and may be a useful proxy for quantifying illiquidity exposure.

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Asset Prices and Trading Volume Under Fixed Transactions Costs, with Harry Mamaysky and Jiang Wang, Journal of Political Economy 112(2004), 1054–1090.

We propose a dynamic equilibrium model of asset prices and trading volume with heterogeneous agents facing fixed transactions costs. We show that even small fixed costs can give rise to large "no-trade" regions for each agent's optimal trading policy and a significant illiquidity discount in asset prices. We perform a calibration exercise to illustrate the empirical relevance of our model for aggregate data. Our model also has implications for the dynamics of order flow, bid/ask spreads, market depth, the allocation of trading costs between buyers and sellers, and other aspects of market microstructure, including a square-root power law between trading volume and fixed costs which we confirm using historical US stock market data from 1993 to 1997.

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The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective, Journal of Portfolio Management 30(2004), 15–29.

One of the most influential ideas in the past 30 years of the Journal of Portfolio Management is the Efficient Markets Hypothesis, the idea that market prices incorporate all information rationally and instantaneously. However, the emerging discipline of behavioral economics and finance has challenged this hypothesis, arguing that markets are not rational, but are driven by fear and greed instead. Recent research in the cognitive neurosciences suggests that these two perspectives are opposite sides of the same coin. In this article I propose a new framework that reconciles market efficiency with behavioral alternatives by applying the principles of evolution—competition, adaptation, and natural selection—to financial interactions. By extending Herbert Simon's notion of ``satisficing'' with evolutionary dynamics, I argue that much of what behavioralists cite as counterexamples to economic rationality—loss aversion, overconfidence, overreaction, mental accounting, and other behavioral biases—are, in fact, consistent with an evolutionary model of individuals adapting to a changing environment via simple heuristics. Despite the qualitative nature of this new paradigm, the Adaptive Markets Hypothesis offers a number of surprisingly concrete implications for the practice of portfolio management.

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Trading Volume, with Jiang Wang, in Dewatripont, M., Hansen, L. and S. Turnovsky, eds., 2003, Advances in Economic Theory: Eight World Congress (Econometric Society Monograph).

We develop a dynamic equilibrium model of an asset market with multiple securities in which investors trade to share risks and smooth consumption over time, and investigate the empirical implications for the cross-sectional characteristics of trading volume and the dynamic volume-return relation. We extend the model to include fixed transactions costs, and when calibrated to aggregate data, the model implies realistic levels of trading volume. We also evaluate the efficacy of technical analysis in capturing the relation between prices and volume heuristically.

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It's 11pm—Do You Know Where Your Liquidity Is? The Mean-Variance-Liquidity Frontier, with Constantin Petrov and Martin Wierzbicki, Journal of Investment Management 1(2003), 55–93.

We introduce liquidity into the standard mean-variance portfolio optimization framework by defining several measures of liquidity and then constructing three-dimensional mean-variance-liquidity frontiers in three ways—liquidity filtering, liquidity constraints, and a mean-variance-liquidity objective function. We show that portfolios close to each other on the traditional mean-variance efficient frontier can differ substantially in their liquidity characteristics. In a simple empirical example, the liquidity exposure of mean-variance efficient portfolios change dramatically from month to month, and even simple forms of liquidity optimization can yield significant benefits in reducing a portfolio's liquidity-risk exposure without sacrificing a great deal of expected return per unit risk.

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Bubble, Rubble, Finance In Trouble?, Journal of Psychology and Financial Markets 3(2002), 76–86.

In this talk, I review the implications of the recent rise and fall of the technology sector for traditional financial theories and their behavioral alternatives. Although critics of the Efficient Markets Hypothesis argue that markets are driven by fear and greed, not fundamentals, recent research in the cognitive neurosciences suggest that these two perspectives are opposite sides of the same coin. I propose a new paradigm for financial economics that focuses more on the evolutionary biology and ecology of markets rather than the more traditional physicists' view. By marrying the principles of evolution to Herbert Simon's notion of ``satisficing'', I argue that much of what behavioralists cite as counter-examples to economic rationality—loss aversion, overconfidence, overreaction, mental accounting, and other behavioral biases—are, in fact, consistent with an evolutionary model of rational agents learning to adapt to their environment via satisficing heuristics.

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The Psychophysiology of Real-Time Financial Risk Processing, with Dmitry V. Repin, Journal of Cognitive Neuroscience 14(2002), 323–339.

A longstanding controversy in economics and finance is whether financial markets are governed by rational forces or by emotional responses. We study the importance of emotion in the decisionmaking process of professional securities traders by measuring their physiological characteristics, e.g., skin conductance, blood volume pulse, etc., during live trading sessions while simultaneously capturing real-time prices from which market events can be detected. In a sample of 10 traders, we find significant correlation between electrodermal responses and transient market events, and between changes in cardiovascular variables and market volatility. We also observe differences in these correlations among the 10 traders which may be systematically related to the traders' levels of experience.

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Econometric Models of Limit-Order Executions, with Craig MacKinlay and June Zhang, Journal of Financial Economics 65(2002), 31–71.

Limit orders incur no price impact, however, their execution time is uncertain. We develop an econometric model of limit-order execution times using survival analysis, and estimate it with actual limit-order data. We estimate versions for time-to-first-fill and time-to-completion, and for limit-sells and limit-buys, and incorporate the effects of explanatory variables such as the limit price, the limit size, the bid/offer spread, and market volatility. We find that execution times are very sensitive to limit price and several other explanatory variables, but not sensitive to limit size. We also show that hypothetical limit-order executions, constructed either theoretically from first-passage times or empirically from transactions data, are very poor proxies for actual limit-order executions.

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The Sources and Nature of Long-Term Dependence in the Business Cycle, with Joseph Haubrich, Federal Reserve Bank of Cleveland Economic Review 37(2001), 15–30.

This paper examines the stochastic properties of aggregate macroeconomic time series from the standpoint of fractionally integrated models, and focuses on the persistence of economic shocks. We develop a simple macroeconomic model that exhibits long-term dependence, a consequence of aggregation in the presence of real business cycles. We derive the relation between properties of fractionally integrated macroeconomic time series and those of microeconomic data, and discuss how fiscal policy may alter their stochastic behavior. To implement these results empirically, we employ a test for fractionally integrated time series based on the Hurst-Mandelbrot rescaled range. This test is robust to short-term dependence, and is applied to quarterly and annual real GNP to determine the sources and nature of long-term dependence in the business cycle.

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Hedging Derivative Securities and Incomplete Markets: An Epsilon-Arbitrage Approach, with Dimitris Bertsimas and Leonid Kogan, Operations Research 49(2001), 372–397.

Given a European derivative security with an arbitrary payoff function and a corresponding set of underlying securities on which the derivative security is based, we solve the dynamic replication problem: find a self-financing dynamic portfolio strategy—involving only the underlying securities—that most closely approxim