The Level, Slope, and Curve Factor Model for Stocks: Evidence, Theory, and Explanation

The reported number of firm characteristics that predict stock returns is growing at a rapid pace. This dissertation offers a reorganization of this exploding space. In the first chapter, I use regressions to aggregate the explanatory power of many anomalies into one proxy for expected returns. I find that sorting on this proxy creates large spreads in average returns and large alphas when compared to the leading factor models. The procedure allows me to evaluate the marginal economic significance of each anomaly. Asset growth, net stock issues and momentum are the strongest anomaly variables. Anomaly importance varies across size groups, but size provides relatively little explanatory power. I use principal components analysis to show that a strong multifactor structure underlies the spreads created from my one dimensional sort. In the second chapter, I develop a method to extract only the priced factors from stock returns. The first step estimates expected returns based on characteristics. The second uses the expected returns to form portfolios. The last step uses principal components to extract factors from the portfolio returns. The procedure isolates and emphasizes the comovement across assets that is related to expected returns as opposed to firm characteristics. It produces three factors--level, slope and curve--which perform as well or better than other leading models. Horse races show that other leading factors add little to the model. The factors have macroeconomic risk interpretations. The third chapter reevaluates the Consumption Capital Asset Pricing Model\u27s ability to price the cross-section of stocks. With a few adjustments that generate more informative tests by increasing test power, I find that the simple linearized CCAPM often matches key features of the cross-section: the consumption risk premium is positive and significant, the zero beta rate is near zero and insignificant, and the CCAPM captures much of the variation across average portfolio returns. A key stylized fact emerges that many interesting ``anomalies\u27\u27 share the characteristic that high expected return portfolios tend to have higher covariance with consumption