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No / Several recent empirical tests of the Capital Asset Pricing Model have been based on the conditional relationship between betas and market returns. This paper shows that this method needs reconsideration. An adjusted version of this test is presented. It is then demonstrated that the adjusted technique has similar, or lower, power to the more easily implemented CAPM test of Fama and MacBeth (1973) if returns are normally distributed.

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In Chapter 1, I investigate whether returns of strategies based on asset pricing anomalies exhibit time series persistence which can be attributed to flow-induced trading by mutual funds. I find persistence for thirteen characteristics, which is statistically significant for five including size, corporate investment, and bankruptcy likelihood. The persistence is not explained by individual stock momentum and is not limited to certain calendar months. The return predictability can be used to construct new trading strategies, which on average earn 4.5% annually. A price pressure measure of mutual fund flow-driven trading explains a substantial part of the strategy performance persistence. In Chapter 2, we propose a new approach for estimating expected returns on individual stocks from firm characteristics. We treat expected returns as latent variables and develop a procedure that filters them out using the characteristics as signals and imposing restrictions implied by a one factor asset pricing model. The estimates of expected returns obtained by applying our method to thirteen asset pricing anomalies generate a wide cross-sectional dispersion of realized returns. Our results provide evidence of strong commonality in the anomalies. The use of portfolios based on the filtered expectations as test assets increases the power of asset pricing tests. In Chapter 3, we examine the sensitivity of fourteen asset pricing anomalies to extreme observations using robust regression methods. We find that although all anomalies except size are strong and robust for stocks with presumably low returns, most of them are sensitive to individual influential observations for stocks with presumably high returns. For some anomalies, extreme observations distort regression results for all stocks and even portfolio returns. When the impact of such observations is mitigated, eight anomalies become positively related to expected returns for stocks with low characteristics meaning that these anomalies have an inverted J-shaped form. Chapter 4 concludes by summarizing the main contributions of three chapters and their implications. / text

This dissertation contains two essays that study the implications of information arrival on asset prices. In the first essay, I study an important aspect of the firm-level information structure - the quantity of information - and its effect on the cross-section of stock returns. The main contribution of this essay is to propose a new proxy for information intensity (monthly information quantity) and establish a link between information intensity and stock returns. I find that higher information intensity reduces expected uncertainty and leads to a lower expected return, after controlling for a variety of traditional risk factors and asset pricing anomalies. An information-intensity-based long-short portfolio generates an abnormal return of 4.44% per year. My findings suggest that, as a key component of information structure, information quantity is of first order importance in determining stock returns, and more generally, that investor learning plays an important role in financial markets with incomplete information. The second essay, based on a joint work with John Maheu and Tom McCurdy, studies the asset-pricing implication of market-level information arrival, which can lead to large movements (jumps) in the market index. Deviating from the literature that studies the impact of jumps through option pricing and motivated by a nonlinear pricing kernel associated with general preferences, we focus on the pricing impact of jumps through the pricing of higher-order moments. We find that three components of a modeling device, including: a 2-component GARCH model for diffusive volatility, an autoregressive model for jump intensity, and a higher order moment specification of the equity premium, are particularly important for asset pricing with jumps. This modeling device enables us to be the first to uncover significant pricing of both diffusive risk and jump risk, using only a time series of equity return data. We find that the risk premium due to jumps is a significant part of the overall equity premium. Our results also suggest the existence of a significant skewness premium and offer a potential resolution to sometimes conflicting results on the intertemporal risk-return relationship. Furthermore, taking jumps into account improves the out-of-sample performance of a portfolio allocation application.

This study analyzes the impact of information quality on option returns. I find that firms with low-quality information have call option returns that are significantly lower than firms with high information quality. The findings hold in- and out-of-sample, over different time periods, and are robust to a battery of asset pricing tests. The results suggest that the risk caused by poor information quality has a powerful and non-diversifiable impact on the expected returns of option contracts. Further analysis shows that these results are important when examining the cross-sectional link between stock returns and information quality. Firms with low information quality and equity that is "option-like" have significantly lower future stock returns than firms with high information quality. If this option-like effect is not controlled for in empirical tests, the association between information quality and the cross-section of stock returns is often flat. This is especially true for accounting-based proxies of information quality.

This study analyzes the impact of information quality on option returns. I find that firms with low-quality information have call option returns that are significantly lower than firms with high information quality. The findings hold in- and out-of-sample, over different time periods, and are robust to a battery of asset pricing tests. The results suggest that the risk caused by poor information quality has a powerful and non-diversifiable impact on the expected returns of option contracts. Further analysis shows that these results are important when examining the cross-sectional link between stock returns and information quality. Firms with low information quality and equity that is "option-like" have significantly lower future stock returns than firms with high information quality. If this option-like effect is not controlled for in empirical tests, the association between information quality and the cross-section of stock returns is often flat. This is especially true for accounting-based proxies of information quality.

This dissertation contains two essays that study the implications of information arrival on asset prices. In the first essay, I study an important aspect of the firm-level information structure - the quantity of information - and its effect on the cross-section of stock returns. The main contribution of this essay is to propose a new proxy for information intensity (monthly information quantity) and establish a link between information intensity and stock returns. I find that higher information intensity reduces expected uncertainty and leads to a lower expected return, after controlling for a variety of traditional risk factors and asset pricing anomalies. An information-intensity-based long-short portfolio generates an abnormal return of 4.44% per year. My findings suggest that, as a key component of information structure, information quantity is of first order importance in determining stock returns, and more generally, that investor learning plays an important role in financial markets with incomplete information. The second essay, based on a joint work with John Maheu and Tom McCurdy, studies the asset-pricing implication of market-level information arrival, which can lead to large movements (jumps) in the market index. Deviating from the literature that studies the impact of jumps through option pricing and motivated by a nonlinear pricing kernel associated with general preferences, we focus on the pricing impact of jumps through the pricing of higher-order moments. We find that three components of a modeling device, including: a 2-component GARCH model for diffusive volatility, an autoregressive model for jump intensity, and a higher order moment specification of the equity premium, are particularly important for asset pricing with jumps. This modeling device enables us to be the first to uncover significant pricing of both diffusive risk and jump risk, using only a time series of equity return data. We find that the risk premium due to jumps is a significant part of the overall equity premium. Our results also suggest the existence of a significant skewness premium and offer a potential resolution to sometimes conflicting results on the intertemporal risk-return relationship. Furthermore, taking jumps into account improves the out-of-sample performance of a portfolio allocation application.