Essays on empirical asset pricing

• Main Author: Wang, Liyao
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Predictability; Asset pricing; Time-series momentum; Disagreement; Political uncertainty; Econometrics; Political Economy
• Online Access: https://ink.library.smu.edu.sg/etd_coll/289; https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1284&context=etd_coll
• Institution: Singapore Management University

The dissertation consists of four chapters on empirical asset pricing. The first chapter reexamines the existence of time-series momentum. Time-series momentum (TSM) refers to the predictability of the past 12-month return on the next one month return. Using the same data set as Moskowitz, Ooi, and Pedersen (2012) (MOP, henceforth), we show that asset-by-asset time-series regressions reveal little evidence of TSM, both in- and out-of-sample. While the t -statistic in a pooled regression appears large, it is not statistically reliable as it is less than the critical values of parametric and nonparametric bootstraps. From an investment perspective, the performance of the TSM strategy is virtually the same as that of a similar strategy that is based on the historical sample mean and does not require predictability. Overall, the evidence on TSM is weak, particularly for the large cross-section of assets. The second chapter focuses on disagreement, which is regarded as the best horse for behavioral finance to obtain as many insights as classic asset pricing theories. Existing disagreement measures are known to predict cross-sectional stock returns but fail to predict market returns. We propose a disagreement index by aggregating information across individual measures using the partial least squares (PLS) method. This index significantly predicts market returns both in- and out-of-sample. Consistent with the theory in Atmaz and Basak (2018), the disagreement index asymmetrically predicts market returns with greater power in high sentiment periods, is positively associated with investor expectations of market returns, predicts market returns through a cash flow channel, and can explain the positive volume-volatility relationship. The third and fourth chapters investigate the impacts of political uncertainty. We focus on one type of political uncertainty, partisan conflict, which is caused by the dispute or disagreement among party members or policymakers. Chapter 3 finds that partisan conflict positively predicts stock market returns, controlling for economic predictors and proxies for uncertainty, disagreement, geopolitical risk, and political sentiment. A one-standard-deviation increase in partisan conflict is associated with a 0.54% increase in next month's market return. The forecasting power is symmetric across political cycles and operates via a discount rate channel. Increased partisan conflict is associated with increased fiscal policy and healthcare policy uncertainties, and leads investors to switch their investments from equities to bonds. Chapter 4 shows that intensified partisan conflict widens corporate credit spreads. A one standard deviation increase in partisan conflict is associated with a 0.91% increase in the next one-month corporate credit spreads after controlling for bond-issue information, firm characteristics, macroeconomic variables, uncertainty measures, and sentiment measures. The result holds when using the instrumental variables to resolve endogeneity concerns. I further find that partisan conflict has a greater impact on corporate credit spreads for firms with higher exposure to government policies, including government spending policy and tax policy, and for firms with higher dependence on external finance. Firms that are actively involved in political activities are also more sensitive to changes in political polarization.