This paper proposes tests of asset pricing models that allow for time variation in conditional covariances. The evidence indicates that the conditional covariances do change through time. Estimates of the expected excess return on the market divided by the variance of the market (reward-to-risk ratio) are presented for the Sharpe-Lintner CAPM, as well as a number of tests of the model specification. The patterns of the pricing errors through time suggest the model's inability to capture the dynamic behavior of assets returns.

One version of the consumption-based asset pricing model implies a linear relation between expected returns and expected consumption growth. This paper provides evidence that the expected real term structure contains information that can be used to forecast consumption growth. The evidence is strongest for the 1970s and 1980s. The real term structure contains more information than two alternative measures: lagged consumption growth and lagged stock returns. Further, the real term structure appears to have slightly more forecasting power than the leading commercial econemetric models.

We survey 392 CFOs about the cost of capital, capital budgeting, and capital structure. Large firms rely heavily on present value techniques and the capital asset pricing model, while small firms are relatively likely to use the payback criterion. A surprising number of firms use firm risk rather than project risk in evaluating new investments. Firms are concerned about financial flexibility and credit ratings when issuing debt, and earnings per share dilution and recent stock price appreciation when issuing equity. We find some support for the pecking-order and trade-off capital structure hypotheses but little evidence that executives are concerned about asset substitution, asymmetric information, transactions costs, free cash flows, or personal taxes.

We analyze the advice contained in a sample of 237 investment newsletter strategies over 1980-1992. Each newsletter strategy recommends a mix of equity and cash. We find no evidence that letters systematically increase equity weights before market rises or decrease weights before market declines. While there is no information in the newsletter strategies about future market returns, we document that disagreement among the newsletters is correlated with future realized and implied volatility.

We use a unique data set that contains information on more than 1,000 Chief Executive Officers (CEOs) and Chief Financial Officers (CFOs) around the world to investigate the degree to which executives delegate financial decisions and the circumstances that drive variation in delegation. Delegation does not appear to be monolithic; instead, our results show that it varies across corporate policies and also varies with the personal characteristics of the CEO. We find that CEOs delegate financial decisions for which they need the most input, when they are overloaded, and when they are distracted by recent acquisitions. CEOs delegate less when they are knowledgeable (long-tenured or with a finance background). Capital is allocated based on “gut feel” and the personal reputation of the manager running a given division. Finally, corporate politics and corporate socialism affect capital allocation in European and Asian firms.

We administer psychometric tests to senior executives to obtain evidence on their underlying psychological traits and attitudes. We find US CEOs differ significantly from non-US CEOs in terms of their underlying attitudes. In addition, we find that CEOs are significantly more optimistic and risk-tolerant than the lay population. We provide evidence that CEOs' behavioral traits such as optimism and managerial risk-aversion are related to corporate financial policies. Further, we provide new empirical evidence that CEO traits such as risk-aversion and time preference are related to their compensation.

We use slow and fast time-series momentum to characterize four stock market cycles—Bull, Correction, Bear, and Rebound. The steep market declines of Bears concentrate in high-risk states, yet predict negative expected returns, which is difficult to rationalize by most models of time-varying risk premia. Using a model to analyze slow and fast momentum strategies, we estimate both relatively high mean persistence and realization noise in U.S. stock market returns. Intermediate-speed momentum portfolios, formed by blending slow and fast momentum strategies, translate predictive information in market cycles into positive unconditional alpha, for which we propose a novel decomposition.

Identifying the factors that drive the cross-section of expected returns is challenging for at least three reasons. First, the choice of testing approach (time series versus cross-sectional) will deliver different sets of factors. Second, varying test portfolio sorts changes the importance of candidate factors. Finally, given the hundreds of factors that have been proposed, test multiplicity must be dealt with. We propose a new method that makes measured progress in addressing these key challenges. We apply our method in a panel regression setting and shed some light on the puzzling empirical result that the market factor drives the bulk of the variance of stock returns, but is often knocked out in cross-sectional tests. In our setup, the market factor is not eliminated. Further, we bypass arbitrary portfolio sorts and instead execute our tests on individual stocks with no loss in power. Finally, our bootstrap implementation, which allows us to impose the null hypothesis of no cross-sectional explanatory power, naturally controls for the multiple testing problem.

Our paper explores the link between cross-sectional fund return dispersion and performance evaluation. The foundation of our model is the simple intuition that in periods of high return dispersion, which is associated with high levels of idiosyncratic risk for zero-alpha funds, unskilled managers can more easily disguise themselves as skilled. Rational investors should be more skeptical and apply larger discounts to reported performance in high dispersion environments. Our empirical results are consistent with this prediction. Using fund flow data, we show that a one standard deviation increase in cross-sectional return dispersion is associated with an 11% to 17% decline in flow-performance sensitivity. The effect is stronger for recent data and among outperforming funds.

Understanding volatility in emerging capital markets is important for determining the cost of capital and for evaluating direct investment and asset allocation decisions. We provide an approach that allows the relative importance of world and local information to change through time in both the expected returns and conditional variance processes. Our time-series and cross-sectional models analyze the reasons that volatility is different across emerging markets, particularly with respect to the timing of capital market reforms. We find that capital market liberalizations often increase the correlation between local market returns and the world market but do not drive up local market volatility.