- Ph.D. & M.A., Finance; The Wharton School, University of Pennsylvania; 2010-2015 (expected)
- M.S.E., Operations Research & Financial Engineering; Princeton University; 2005-2007
- B.A., Applied Math & M.S. IEOR; University of California, Berkeley; 1996-1998, 2001-2003
The Wharton School
The Wharton School / Imperial College
The Wharton School
The Wharton School
The Wharton School
Honors & Awards:
- Wharton Finance Fellowship, 2014-2015
- University Fellowship for Distinguished Merit; 2010-2014
- Institute for Operations Research and the Management Sciences, Best Interactive Presentation Award, 1st Place, 2006
- Princeton Graduate Fellowship, 2005-2007
- Eugene Cota-Robles Graduate Fellowship, 2001-2003
- California Alumni Leadership Scholarship, 1996-1997
- Vice President—Professional Services, DFA Capital Management; 2008-2010
- Senior Research Consultant—Predictive Modeling, Travelers; 2007
- Quantitative Analyst, BWR&B Consultants; 2003-2005
- Actuarial Analyst, AIG; 1998-2000
- Opposite Sides of a Skewed Bet: Implications and Evidence for Forecast Dispersion and Returns
(Job Market Paper)
I present theory and evidence regarding the impacts of return skewness and dispersion in analysts' forecasts on average stock returns. Prices that induce investors to take opposite sides of a skewed risk deviate from fundamental value on average in the direction of skewness. The magnitude of such deviations depends on investors' confidence in fundamentals. Dispersion in analysts' forecasts tends to reduce that confidence, requiring larger deviations to induce offsetting trades. Consistent with the theory's equilibrium implications, I show that skewness and forecast dispersion have a joint impact, yielding an average return gap of 1.61% monthly (19.3% annualized) between stocks in the 5th and 95th percentiles by skewness and dispersion. I also show that forecast dispersion has no marginal impact without skewness and that higher risk or risk aversion is associated with a deepening of their joint effect. These otherwise anomalous discoveries comprise new significant cross-sectional features of stock returns.
- Preference Irregularities and Asset Pricing Regularities (with Mark Clements) [Abstract]
We present a model of asset prices with recursive preferences and the simple consumption growth dynamics of Mehra and Prescott (1985) but relax the assumption that preference parameters are constant over time. We show that rare, temporary, and plausible fluctuations in the elasticity of inter-temporal substitution (EIS) and risk aversion (RA) can quantitatively explain numerous regularities in U.S. asset prices including: the equity premium and risk-free rate puzzles, excess return and consumption growth predictability, a counter-cyclical risk premium and an upward-sloping real yield curve. A novel implication is that time-varying EIS is more important than time-varying RA for explaining many of these regularities, suggesting a new source of risk in investors' ability to plan their consumption over long horizons. In addition, our model can accommodate a behavioral interpretation of psychological factors (e.g. fear) that drive fluctuations in asset prices beyond traditional risk factors.
- Skewness, Forecast Dispersion, and Pricing [Abstract]
I develop a new asset pricing theory that bridges two major pricing effects from separate literatures: (1) the negative relationship between return skewness and expected returns and (2) the negative relationship between dispersion in financial analysts' earnings forecasts and expected returns. I show that both effects arise intrinsically from market clearing of stochastic demand in a standard noisy rational expectations economy that incorporates skewed assets followed by financial analysts. Positive correlation between forecast dispersion and investor heterogeneity arises endogenously. The theory generates several novel testable predictions: (a) skewness and forecast dispersion have a joint impact on expected returns and (b) can yield negative average returns; (c) forecast dispersion has no marginal impact without skewness; (d) the skewness effect can operate without forecast dispersion; (e) higher risk or risk aversion deepens the effects; and (f) higher investor heterogeneity can weaken the effects.
- Detection and Identification of an Unobservable Change in the Distribution of a Markov-Modulated Random Sequence (with S. Dayanik), IEEE Transactions on Information Theory, 2009 [Abstract]
The problem of detection and diagnosis of an unobservable change in the distribution of a random sequence is studied via a hidden Markov model approach. The formulation is Bayesian, on-line, discrete-time, allowing both single- and multiple-disorder cases, dealing with both i.i.d. and dependent observations scenarios, allowing for statistical dependencies between the change-time and change-type in both the observation sequence and the risk structure, and allowing for general discrete-time disorder distributions. Several of these factors provide useful new generalizations of the sequential analysis theory for change detection and/or hypothesis testing, taken individually. In this paper, a unifying framework is provided that handles each of these considerations not only individually, but also concurrently. Optimality results and optimal decision characterizations are given as well as detailed examples that illustrate the myriad of sequential change detection and diagnosis problems that fall within this new framework.
- Bayesian Sequential Change Diagnosis (with S. Dayanik and H. V. Poor), Mathematics of Operations Research, 2008 [Abstract]
Sequential change diagnosis is the joint problem of detection and identification of a sudden and unobservable change in the distribution of a random sequence. In this problem, the common probability law of a sequence of i.i.d. random variables suddenly changes at some disorder time to one of finitely many alternatives. This disorder time marks the start of a new regime, whose fingerprint is the new law of observations. Both the disorder time and the identity of the new regime are unknown and unobservable. The objective is to detect the regime-change as soon as possible, and, at the same time, to determine its identity as accurately as possible. Prompt and correct diagnosis is crucial for quick execution of the most appropriate measures in response to the new regime, as in fault detection and isolation in industrial processes, and target detection and identification in national defense. The problem is formulated in a Bayesian framework. An optimal sequential decision strategy is found, and an accurate numerical scheme is described for its implementation. Geometrical properties of the optimal strategy are illustrated via numerical examples. The traditional problems of Bayesian change-detection and Bayesian sequential multi-hypothesis testing are solved as special cases. In addition, a solution is obtained for the problem of detection and identification of component failure(s) in a system with suspended animation.
- Joint Detection and Identification of an Unobservable Change in a Random Sequence (with S. Dayanik and H.V. Poor), Information Science and Systems, 2007 [Abstract]
This paper examines the joint problem of detection and identification of a sudden and unobservable change in the probability distribution function (pdf) of a sequence of independent and identically distributed (i.i.d.) random variables to one of finitely many alternative pdf's. The objective is quick detection of the change and accurate inference of the ensuing pdf. Following a Bayesian approach, a new sequential decision strategy for this problem is revealed and is proven optimal. Geometrical properties of this strategy are demonstrated via numerical examples.
Research in Progress:
- Emergence of Fundamental Uncertainty in Coordination around Radical Innovation [Abstract]
I show using a global games approach that radical innovation is not just more risky than incremental innovation, but also can introduce fundamental uncertainty (in the Knightian sense) in addition to risk. A firm considers the allocation of resources between its core competencies and innovation opportunities. After learning about profitability from both private information and aggregate adoption decisions of asymmetrically informed potential adopters, the firm can curtail a bad project, limiting the expected downside. For incremental innovation, the coordination game of potential followers has a unique equilibrium that entails innovation risk alone. For radical innovation, the coordination game has multiple equilibria, a source of fundamental uncertainty above and beyond innovation risk. Yet, some of these equilibria can be attractive (relative to incremental innovation) so radical innovation is not a dominated strategy.
- Real Decisions as a Resolution of the Grossman-Stiglitz No-Equilibrium Paradox [Abstract]
With a minor departure from the financial market model in Grossman and Stiglitz (1980), I illustrate the existence of a rational expectations equilibrium in which costly information is supported even though the asset price is "fully revealing." Although noiseless asset supply renders the price of the publicly-traded asset fully revealing of collective private information with respect to asset demand decisions, when different types of private information are acquired by informed investors, such investors can gain additional information from the price—above and beyond the information conveyed by the asset price alone. Informed agents can exploit this information gain in taking other decisions, such as real decisions for which private information is useless without the financial market information mechanism, justifying the cost of becoming informed in the first place despite private information being neutralized in the market for the financial asset.
- Opposite Sides of a Skewed Bet: Implications and Evidence for Excess Volatility
- Inattention to the Stock Market, Leverage, Q Theory; Prof. Andrew B. Abel
- Corporate Finance, Feedback Effects, Synergies; Prof. Alex Edmans
- Asset Pricing, Ambiguity Aversion; Prof. Philipp Illeditsch
- Asset-Backed Securities; Prof. Luke Taylor
- Capital Markets (MBA/Undergrad); The Wharton School, University of Pennsylvania
- Corporate Finance (MBA/Undergrad); The Wharton School, University of Pennsylvania
- Regression and Applied Time Series (Grad/Undergrad); Princeton University
- Dynamic Programming (Undergrad); Princeton University
- Fundamentals of Probability and Statistics (Undergrad); Princeton University