Essays in Empirical Asset Pricing
- Author(s): Sabbatucci, Riccardo
- Advisor(s): Timmermann, Allan
- et al.
The focus of my dissertation is the study of stock market predictability. More precisely, I use econometric tools to understand, explain, and predict aggregate and cross-sectional patterns in stock prices. Predictability of aggregate stock market returns and dividend growth is a widely studied topic, of great interest to both academics and practitioners. It is related to theories of market efficiency and information diffusion, both rational and behavioral. It also allows us to determine which types of information generate the movements in stock prices that we observe. Understanding why stock prices move and what factors drive their variation is critical from theoretical and policy-making perspectives. Chapter 1 of my dissertation revisits one of the main findings of the predictability literature, namely that all variation in aggregate stock prices is explained by changes in aggregate risk through discount rates and none by news about firms' expected cash flows. I propose a more comprehensive measure of dividends that includes M&A cash flows and show that dividend growth is predictable and that cash flow news explains around 60% of the observed variation in prices, while the remaining 40% is accounted for by discount rate news. Chapter 2 shows that information about fundamentals of the aggregate economy derived from closely held firms help predict stock returns of public firms. A common feature of most stock market predictors is that they are constructed using financial data of public firms. I construct a new economy-wide dividend-price ratio that takes into account dividends and market capitalization of both listed (public) and non-listed (private) U.S. companies and show that it strongly predicts stock returns both in-sample and out-of-sample. I also find that changes in dividends of private firms lead those of public firms and that the economy-wide dividend-price ratio subsumes the standard dividend-price ratio in predictive regressions. Chapter 3, co-authored with Christopher A. Parsons and Sheridan Titman, explores geographic momentum: a positive lead-lag stock return relation between neighboring firms operating in different sectors. It shows that a portfolio of firms headquartered in the same area, but operating in different sectors, strongly forecasts individual stock returns up to one year ahead. The economic significance of a city-momentum trading strategy is of similar magnitude to that observed with industry momentum. However, while industry momentum is strongest among thinly traded, small firms, and/or those with scant analyst following, geographic momentum is unrelated to these proxies for information processing. We propose an explanation linking this to the structure of the investment analyst business, which is organized by sector, rather than by geographic region.