Dividend Dynamics,Learning, and Expected Stock Index Returns

We present a latent variable model of dividends that predicts, out‐of‐sample, 39.5% to 41.3% of the variation in annual dividend growth rates between 1975 and 2016. Further, when learning about dividend dynamics is incorporated into a long‐run risks model, the model predicts, out‐of‐sample, 25.3% to 27.1% of the variation in annual stock index returns over the same time horizon, with learning contributing approximately half of the predictability in returns. These findings support the view that investors' aversion to long‐run risks and their learning about these risks are important in determining stock index prices and expected returns.     

Do Hot Hands Exist among Hedge Fund Managers? An Empirical Evaluation

In measuring performance persistence, we use hedge fund style benchmarks. This allows us to identify managers with valuable skills, and also to control for option-like features inherent in returns from hedge fund strategies. We take into account the possibility that reported asset values may be based on stale prices. We develop a statistical model that relates a hedge fund's performance to its decision to liquidate or close in order to infer the performance of a hedge fund that left the database. Although we find significant performance persistence among superior funds, we find little evidence of persistence among inferior funds.     

Lazy Investors, Discretionary Consumption, and the Cross-Section of Stock Returns

When consumption betas of stocks are computed using year‐over‐year consumption growth based upon the fourth quarter, the consumption‐based asset pricing model (CCAPM) explains the cross‐section of stock returns as well as the Fama and French (1993) three‐factor model. The CCAPM's performance deteriorates substantially when consumption growth is measured based upon other quarters. For the CCAPM to hold at any given point in time, investors must make their consumption and investment decisions simultaneously at that point in time. We suspect that this is more likely to happen during the fourth quarter, given investors' tax year ends in December.     

An Investigation of Commodity Futures Prices Using the Consumption-Based Intertemporal Capital Asset Pricing Model

In this paper we extend the multigood futures pricing model of Grauer and Litzenberger 9 to a dynamic discrete time setting. We then test the model using data on futures prices for corn, wheat, and soybeans. The parameter estimates we obtain are similar to those obtained by other researchers using stock return data. The model itself is rejected and we offer some suggestions as to which assumption may be violated. We also give an interpretation to the Hansen-Singleton nonlinear instrumental variables estimation technique used in our empirical work.     

The Stock Market's Reaction to Unemployment News: Why Bad News Is Usually Good for Stocks

We find that on average, an announcement of rising unemployment is good news for stocks during economic expansions and bad news during economic contractions. Unemployment news bundles three types of primitive information relevant for valuing stocks: information about future interest rates, the equity risk premium, and corporate earnings and dividends. The nature of the information bundle, and hence the relative importance of the three effects, changes over time depending on the state of the economy. For stocks as a group, information about interest rates dominates during expansions and information about future corporate dividends dominates during contractions.     

Rational Pessimism, Rational Exuberance, and Asset Pricing Models

The paper estimates and examines the empirical plausibility of asset pricing models that attempt to explain features of financial markets such as the size of the equity premium and the volatility of the stock market. In one model, the long-run risks (LRR) model of Bansal and Yaron, low-frequency movements, and time-varying uncertainty in aggregate consumption growth are the key channels for understanding asset prices. In another, as typified by Campbell and Cochrane, habit formation, which generates time-varying risk aversion and consequently time variation in risk premia, is the key channel. These models are fitted to data using simulation estimators. Both models are found to fit the data equally well at conventional significance levels, and they can track quite closely a new measure of realized annual volatility. Further, scrutiny using a rich array of diagnostics suggests that the LRR model is preferred.     

A Note on “Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps”

This note corrects an error in the proof of Proposition 2 of “Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraint Helps” that appeared in the Journal of Finance, August 2003.     

Banking Panics,Information, and Rational Expectations Equilibrium

This paper shows that bank runs can be modeled as an equilibrium phenomenon. We demonstrate that some aspects of the intuitive “story” that bank runs start with fears of insolvency of banks can be rigorously modeled. If individuals observe long “lines” at the bank, they correctly infer that there is a possibility that the bank is about to fail and precipitate a bank run. However, bank runs occur even when no one has any adverse information. Extra market constraints such as suspension of convertibility can prevent bank runs and result in superior allocations.     

Sequential Tests of the Arbitrage Pricing Theory: A Comparison of Principal Components and Maximum Likelihood Factors

We examine the cross-sectional pricing equation of the APT using the elements of eigenvectors and the maximum likelihood factor loadings of the covariance matrix of returns as measures of risk. The results indicate that, for data assumed stationary over twenty years, the first vector is a surprisingly good measure of risk when compared with either a one- or a five-factor model or a five-vector model. We conclude that in some circumstances principal components analysis may be preferred to factor analysis.     

Cross-Sectional Dependence and the Use of Generalized Least Squares*

Abstract. This paper provides additional evidence on the relation between the actual and asymptotic standard errors under generalized least squares (GLS). Simulations are conducted in the event-study framework using both daily and weekly returns. The case of simultaneous calendar time and industry clustering is also considered. The major finding is that the asymptotic standard errors under GLS can significantly underestimate the true standard errors when the number of parameters estimated is not small compared to the number of observations available to estimate them. To minimize the possibility of incorrect inferences, use of GLS should in general be combined with standard errors obtained from other techniques such as bootstrap. Résumé. L'auteur apporte de nouvelles preuves de la relation entre les erreurs-types réelles et asymptotiques dans le cadre de l'utilisation des moindres carrés généralisés. Il procède à des simulations dans le contexte d'une étude d'événements, en utilisant les rendements à la fois quotidiens et hebdomadaires. Le cas de simultanéité de la date et du groupage par secteur d'activité sont également pris en considération. Principale conclusion de l'auteur: les erreurs-types asymptotiques dans le cadre de l'utilisation des moindres carrés généralisés peuvent entraîner une sous-estimation importante des erreurs-types véritables lorsque le nombre de paramètres estimés n'est pas restreint par rapport au nombre d'observations disponibles pour formuler ces estimations. Si l'on veut réduire au minimum le risque d'inférences inexactes, l'utilisation des moindres carrés généralisés devrait, de façon générale, être combinée aux erreurs-types obtenues à partir d'autres techniques comme celle de l'amorce.