Turan G. Bali, Robert S. Parker Chair Professor of Business Administration, Georgetown University, McDonough School of Business
D. Craig Nichols, associate professor of accounting, Syracuse University, Whitman School
David Weinbaum, professor of finance, Syracuse University, Whitman School
Year of Publication: Journal of Financial and Quantitative Analysis (2023)
Summary of Findings: We use a classic accounting framework to answer important questions in finance and show that returns on the aggregate stock market can be predicted surprisingly well by using the cross-section of stock prices, earnings and book values.
Research Questions: Can aggregate market return expectations be inferred from the cross-section of stock prices and accounting quantities such as earnings and book values?
Does the resulting measure of discount rates have economically sensible properties?
Do discount rate shocks explain much of the variation in realized market returns?
Are discount rate shocks successful at explaining time-series variation in the returns on duration-sorted portfolios?
What we know: We know that the price of an asset is equal to the present value of its expected cash flows—price equals discounted expected cash flows. As economic events unfold, expected cash flows change, but discount rates change also. For example, in recessions, expectations of future cash flows deteriorate, and, at the same time, discount rates increase, as investors become more cautious, and more risk-averse. Both factors contribute to falling stock prices. While we have long had evidence that discount rates change over time, they are inherently unobservable and difficult to measure. In the paper, we introduce a new approach to estimating long-term discount rates using the cross-section of earnings and book values to explain current stock prices and extract expected market returns.
Novel Findings: Our discount rate measure performs well. It is countercyclical: it rises during recessions and is lower during expansions. It is strongly correlated with macroeconomic variables that reflect the business cycle, including the GDP growth rate, unemployment rate and the inflation rate. Shocks to it account for nearly half of all historical market-return variation; in contrast, shocks to other discount-rate measures account for no more than two percent. It dominates other measures in explaining time-series variation in returns on duration-sorted portfolios and delivers out-of-sample predictability that exceeds that afforded by other expected return measures and predictive variables. It also performs well in international equity markets.
Novel Methodology: The paper does not merely provide a new measure of aggregate expected returns, it also makes a broader methodological contribution. First, we go beyond the traditional in- and out-of-sample return prediction analysis and consider whether shocks to a candidate discount-rate measure explain a sufficiently large fraction of the variation in contemporaneous realized market returns. Second, we take our analysis to the cross-section, and consider the returns on duration-sorted portfolios. In doing so, we tie together the literature on stock market predictability and the growing literature on equity duration.
Implications for Practice: Our approach can be used by investors trying to gauge the return they can expect to earn (on average over the long term) by investing in the stock market at any point in time.
Full Citations: R. Bali, T., C. Nichols, and D. Weinbaum, Inferring Aggregate Market Expectations from the Cross-Section of Stock Prices, Journal of Financial and Quantitative Analysis, forthcoming.
Abstract: We introduce a new approach to estimating long-term aggregate discount rates using the cross-section of earnings and book values to explain current stock prices and extract expected market returns. The proposed discount rate measure is countercyclical. Shocks to it account for nearly half of all historical market return variation; in contrast, shocks to other discount rate measures account for no more than two percent. It dominates other measures in explaining time-series variation in returns on duration-sorted portfolios and delivers out-of-sample predictability that exceeds that afforded by other expected return measures and predictive variables. It also performs well in international equity markets.