Jessica A. Wachter

Portfolio and Consumption Decisions under Mean-Reverting Returns: An Exact Solution for Complete Markets

This paper solves, in closed form, the optimal portfolio choice problem for an investor with utility over consumption under mean-reverting returns. Previous solutions either require approximations, numerical methods, or the assumption that the investor does not consume over his lifetime. This paper breaks the impasse by assuming that markets are complete. The solution leads to a new understanding of hedging demand and of the behavior of the approximate log-linear solution. The portfolio allocation takes the form of a weighted average and is shown to be analogous to duration for coupon bonds. Through this analogy, the notion of investment horizon is extended to that of an investor who consumes at multiple points in time.

Should Investors Avoid All Actively Managed Mutual Funds? A Study in Bayesian Performance Evaluation

This paper analyzes mutual-fund performance from an investors perspective. We study the portfolio-choice problem for a mean-variance investor choosing among a risk-free asset, index funds, and actively managed mutual funds. To solve this problem, we employ a Bayesian method of performance evaluation; a key innovation in our approach is the development of a flexible set of prior beliefs about managerial skill. We then apply our methodology to a sample of 1,437 mutual funds. We find that some extremely skeptical prior beliefs nevertheless lead to economically significant allocations to active managers.

Can Mutual Fund Managers Pick Stocks? Evidence from Their Trades Prior to Earnings Announcements

Recent research finds that the stocks that mutual fund managers buy outperform the stocks that they sell (e.g., Chen, Jegadeesh, and Wermers (2000)). We study the nature of this stock-picking ability. We construct measures of trading skill based on how the stocks held and traded by fund managers perform at subsequent corporate earnings announcements. This approach increases the power to detect skilled trading and sheds light on its source. We find that the average fund’s recent buys significantly outperform its recent sells around the next earnings announcement, and that this accounts for a disproportionate fraction of the total abnormal returns to fund trades estimated in prior work. We find that mutual fund trades also forecast earnings surprises. We conclude that mutual fund managers are able to trade profitably in part because they are able to forecast earnings-related fundamentals.

Why is Long-Horizon Equity Less Risky? A Duration-Based Explanation of the Value Premium

This paper proposes a dynamic risk-based model that captures the high expected returns on value stocks relative to growth stocks, and the failure of the capital asset pricing model to explain these expected returns. To model the difference between value and growth stocks, we introduce a cross-section of long-lived firms distinguished by the timing of their cash flows. Firms with cash flows weighted more to the future have high price ratios, while firms with cash flows weighted more to the present have low price ratios. We model how investors perceive the risks of these cash flows by specifying a stochastic discount factor for the economy. The stochastic discount factor implies that shocks to aggregate dividends are priced, but that shocks to the time-varying price of risk are not. As long-horizon equity, growth stocks covary more with this time-varying price of risk than value stocks, which covary more with shocks to cash flows. When the model is calibrated to explain aggregate stock market behavior, we find that it can also account for the observed value premium, the high Sharpe ratios on value stocks relative to growth stocks, and the outperformance of value (and underperformance of growth) relative to the CAPM.

Comment on: Are behavioral asset-pricing models structural?

According to Stanley Zin, financial economists on both sides of the behavioral finance debate have failed to ask the right questions. To Zin, the most important question we should ask about behavioral models is whether they make accurate predictions. Structural models at least have prediction as their goal, whether or not they achieve it. Implicit in this view is the notion that structural models somehow ‘‘explain what is going on’’, that they can offer insights into why economic phenomena occur. In most other fields of science, evaluating a structural model would not be a matter of debate. Because the goal of a structural model is prediction, the model would be evaluated directly using a controlled experiment. If in finance we could somehow change the parameters of our economy, or at least statistically detect such a change, we would also have a direct test of a structural model. The former is clearly impossible; the latter, Zin argues, is unreliable. The question of how to evaluate a structural model without experiments is clearly a difficult one. Instead of attempting an answer, Zin argues that two commonly used criteria are wrong. One of these criteria, that a model appear realistic, looks good at first glance. But, quoting Friedman and Lucas, Zin argues that we are not trying for

Do Rare Events Explain Cdx Tranche Spreads

We investigate whether a model with a time-varying probability of economic disaster can explain the pricing of collateralized debt obligations, both prior to and during the 2008-2009 financial crisis. Namely, we examine the pricing of tranches on the CDX, an index of credit default swaps on large investment-grade firms. CDX senior tranches are essentially deep out-of-the money put options because they do not incur losses until a large fraction of previously stable firms default. As such, these products clearly reflect the market’s assessment of rare-event risk. We find that the model can simultaneously explain prices on CDX senior tranches and on equity index options at parameter values that are consistent with the equity premium and with aggregate stock market volatility. Our results demonstrate the importance of beliefs about rare disasters for asset prices, even during periods of relative economic stability.

Multivariate Stochastic Volatility Modeling of Neural Data

Because multivariate autoregressive models have failed to adequately account for the complexity of neural signals, researchers have predominantly relied on non-parametric methods when studying the relations between brain and behavior. Using a database of medial temporal lobe (MTL) recordings from 96 neurosurgical patients, we show that time series models with volatility described by a multivariate stochastic latent variable process and lagged interactions between signals in different brain regions provide new insights into the dynamics of brain function. We estimate both the parameters describing the latent variable processes and the directional correlations in volatility between brain regions using Bayesian sampling techniques. The implied volatility inferred from our process positively correlates with high-frequency spectral activity, a signal that correlates with neuronal activity and is widely used to study brain function. We show that volatility features derived from our model can reliably decode good vs. poor memory states, and that this classifier performs as well as those using spectral features. Using the multivariate stochastic volatility model, we uncovered hippocampal-perirhinal bidirectional connections in the MTL regions that are associated with successful memory encoding.

Cyclical Dispersion in Expected Defaults

A growing literature shows that credit indicators forecast aggregate real outcomes. While researchers have proposed various explanations, the economic mechanism behind these results remains an open question. In this paper, we show that a simple, frictionless, model explains empirical findings commonly attributed to credit cycles. Our key assumption is that firms have heterogeneous exposures to underlying economy-wide shocks. This leads to endogenous dispersion in credit quality that varies over time and predicts future excess returns and real outcomes.

Cross-Sectional Skewness

This paper evaluates skewness in the cross-section of stock returns in light of predictions from a well-known class of models. Cross-sectional skewness in monthly returns far exceeds what the standard lognormal model of returns would predict. However, skewness in long-run returns substantially understates what the lognormal model would predict. Nonstationary share dynamics imply a breakdown in the distinction between market and idiosyncratic risk in the lognormal model. We present an alternative model that matches the skewness in the data and implies stationary wealth shares. In this model, idiosyncratic risk is the primary driver of growth in the economy.

Pricing long-lived securities in dynamic endowment economies

We solve for asset prices in a general affine representative-agent economy with isoelastic recursive utility and rare events. Our novel solution method is exact in two special cases: no preference for early resolution of uncertainty and elasticity of intertemporal substitution equal to one. Our results clarify model properties governed by the elasticity of intertemporal substitution, by risk aversion, and by the preference for early resolution of uncertainty. Finally, we show in a general setting that the linear relation between normal-times covariances and expected returns need not hold in a model with rare events.