Essays in Asset Pricing

<p>This dissertation consists of three essays in asset pricing with the common theme of return predictability.</p> <p><strong>Chapter 1:</strong> This chapter introduces the motivation, results, and structure of the dissertation.</p> <p><strong>Chapter 2:</strong> I examine the relation between the social ties between firms' headquarters locations and co-movements between their fundamentals and stock returns. The evidence indicates that firms in the same industry with socially connected locations exhibit co-movement in fundamentals and stock returns that exceed those without socially connected locations. However, the stock returns reflect the location information with a lag. To exploit this lagged relationship, we form portfolios that buy (sell) stocks when their socially-weighted industry peer returns in the previous month is high (low). The value-weighted version of this portfolio generates a monthly alpha of 84 basis points. Social peer firm returns also predict firms' future earnings surprises, analysts' forecast errors, and earnings announcement returns. Further evidence indicates that the mispricing is stronger for low-visibility firms and for firms located outside of industry clusters.</p> <p><strong>Chapter 3:</strong> I investigate the extent to which the return predictability presented in Chapter 2 can be explained by the existing cross-firm predictors in extant literature. I find that although economic connections that have been documented in previous studies (such as industry momentum, technology similarity, customer-supplier linkages, and labor flows across regions) can explain a significant portion of the alpha of a long-short strategy based on the social peer firm return, they still leave unexplained a substantial abnormal return of 43 basis points per month. The result remains robust in Fama and MacBeth (1973) regression analysis, in which I control for 18 other lead-lag return predictors and stock characteristics. Social peer firm return is also among the most important contributors to the predictive power of a PLS-based composite predictor that aggregates the effects of all 19 individual predictors.</p> <p><strong>Chapter 4:</strong> Recent microeconomic evidence suggests that risk aversion is largely determined by the changes in the state of the economy and mostly insensitive to the fluctuations in idiosyncratic wealth. I propose a consumption-based asset pricing model that is consistent with this evidence and capable of explaining various stylized facts about the U.S. stock market. In the model, agents have a power-utility type instantaneous utility function whose curvature explicitly depends on a stationary macroeconomic state variable. The model can produce a high equity risk premium with a low, stable and wealth-insensitive relative risk aversion if the utility curvature is mildly countercyclical (i.e., if the agents are mildly "moody") and consumption is sufficiently smaller than a predetermined benchmark (i.e., if the agents are sufficiently "dissatisfied") at the steady state. It also gives a low and stable risk-free rate, procyclical price-dividend ratio, countercylical risk premium and price of risk, return predictability, an upward sloping real yield curve and a downward sloping equity term structure.</p> <p><strong>Chapter 5:</strong> This chapter concludes the dissertation.</p>

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Business

Grantor

The Graduate School and University Center of The City University of New York

Year dc:date.available

2022

Author dc:creator

• Yonac, Muhammed

Advisor dc:contributor.advisor

• Lin Peng

Committee members dc:contributor.committeemember

• Dexin Zhou
• Sebastiano Manzan
• Yajun Wang
• Karl R. Lang

• Behavioral Economics
• Finance and Financial Management
• Macroeconomics
• return predictability
• social networks
• comovements
• machine learning
• equity premium puzzle
• term structure of equity

Repository record dc:identifier

https://academicworks.cuny.edu/gc_etds/4996

OAI identifier oai:identifier

oai:academicworks.cuny.edu:gc_etds-6081