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Data obtained from monthly Gallup/UBS surveys from 1998 to 2007 and from a special supplement to the Michigan Surveys of Consumer Attitudes and Behavior, run in 22 monthly surveys between 2000 and 2005, are used to analyze stock market beliefs and portfolio choices of household investors. We show that the key variables found to be positive predictors of actual stock returns in the asset-pricing literature are also highly correlated with investor's subjective expected returns, but with the opposite sign. Moreover, our analysis of the microdata indicates that subjective expectations of both risk and returns on stocks are strongly influenced by perceptions of economic conditions. In particular, when investors believe macroeconomic conditions are more expansionary, they tend to expect both higher returns and lower volatility. This is difficult to reconcile with the canonical view that expected returns on stocks rise during recessions to compensate household investors for increased exposure or sensitivity to macroeconomic risks. Finally, the relevance of these investors' subjective expectations is supported by the finding of a significant link between their expectations and portfolio choices. In particular, we show that portfolio equity positions tend to be higher for those respondents that anticipate higher expected returns or lower uncertainty. This paper was accepted by Brad Barber, finance.

Tournament theory analyzes labor market outcomes where rewards are distributed on the basis of relative rank. An important factor in these outcomes is the likely return to additional effort. Using National Basketball Association game event data across two seasons, we estimate each team’s game player portfolio and find that teams who were in contention to win the draft lottery reduce their portfolio’s return differential during the 2017–2018 season but not for the 2018–2019 season. We attribute the change to the reduction in the probability of obtaining a higher pick for the 2019 draft.

This study revisits the widely used assumptions in long-term asset allocation: the normal distribution of long-horizon returns and the negligible impacts of estimation errors on the expected returns. This study uses the innovative simulation method of Fama and French (2018) for horizons of up to 30 years. The data in use are the U.S. value-weighted market returns of stocks, Treasury bonds, Treasury bills, commodities, and real estate investment trusts (REITs) for the 1970-2018 period. Distributions of continuously compounded returns from the 10-year horizon are normal across asset classes. Stock return distribution has the slowest rate of convergence to normality among groups of assets. Estimation errors of the expected monthly returns or annual returns are negligible relative to the standard deviation of the unexpected return. As the imprecisions persist over the investment horizons, the estimation errors of the monthly return have a strong effect on the variability of long-term asset returns. This study has significant implications for academics and investors based on the commonly accepted assumptions of long-term asset allocation.

Prior literature predicts a positive relation between firms' expected pre-tax rates of return and investor-level capital gains tax rates. We show that this relation is more nuanced than suggested by prior literature and that in three circumstances the relation can actually be negative. The first circumstance is when a firm's systematic risk is very high. The second circumstance is when the market risk premium is very high. The third circumstance is when the risk-free rate of return is very low. The circumstances arise because, in addition to reducing investors' expected after-tax cash proceeds, capital gains taxes reduce the risk that investors associate with the expected after-tax cash proceeds.

We argue that tests of reduced-form factor models and horse races between \"characteristics\" and \"covariances\" cannot discriminate between alternative models of investor beliefs. Since asset returns have substantial commonality, absence of near-arbitrage opportunities implies that the stochastic discount factor can be represented as a function of a few dominant sources of return variation. As long as some arbitrageurs are present, this conclusion applies even in an economy in which all cross-sectional variation in expected returns is caused by sentiment. Sentiment-investor demand results in substantial mispricing only if arbitrageurs are exposed to factor risk when taking the other side of these trades.

Hundreds of papers and factors attempt to explain the cross-section of expected returns. Given this extensive data mining, it does not make sense to use the usual criteria for establishing significance. Which hurdle should be used for current research? Our paper introduces a new multiple testing framework and provides historical cutoffs from the first empirical tests in 1967 to today. A new factor needs to clear a much higher hurdle, with a t-statistic greater than 3.0. We argue that most claimed research findings in financial economics are likely false.

A growing body of literature in accounting and finance relies on implied cost of equity (COE) measures. Such measures are sensitive to assumptions about terminal earnings growth rates. In this paper we develop a new COE measure that is more accurate than existing measures because it incorporates endogenously estimated long-term growth in earnings. Our method extends Easton et al. (J Account Res, 40, 657–676, 2002 ) method of simultaneously estimating sample average COE and growth. Our method delivers COE (growth) estimates that are significantly positively associated with future realized stock returns (future realized earnings growth). Moreover, the predictive ability of our COE measure subsumes that of other commonly used COE measures and is incremental to commonly used risk characteristics. Our implied growth measure fills the void in the earnings forecasting literature by robustly predicting earnings growth beyond the five-year horizon.

We examine the relation between implied cost of capital and expected returns under an assumption that expected returns are stochastic, a property supported by theory and empirical evidence. We demonstrate that implied cost of capital differs from expected return, on average, by a function encompassing volatilities of, as well as correlation between, expected returns and cash flows, growth in cash flows, and leverage. These results provide alternative explanations for findings from empirical studies employing implied cost of capital on the magnitude of the market risk premium; predictability of future returns; and the relations between cost of capital and a host of firm characteristics, such as growth, leverage, idiosyncratic risk and the firm’s information environment.

Managed portfolios that take less risk when volatility is high produce large alphas, increase Sharpe ratios, and produce large utility gains for mean-variance investors. We document this for the market, value, momentum, profitability, return on equity, investment, and betting-against-beta factors, as well as the currency carry trade. Volatility timing increases Sharpe ratios because changes in volatility are not offset by proportional changes in expected returns. Our strategy is contrary to conventional wisdom because it takes relatively less risk in recessions. This rules out typical risk-based explanations and is a challenge to structural models of time-varying expected returns.

We derive a formula for the expected return on a stock in terms of the risk-neutral variance of the market and the stock's excess risk-neutral variance relative to that of the average stock. These quantities can be computed from index and stock option prices; the formula has no free parameters. The theory performs well empirically both in and out of sample. Our results suggest that there is considerably more variation in expected returns, over time and across stocks, than has previously been acknowledged.