Date of Award

2000

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Finance (Business Administration)

First Advisor

Ji-Chai Lin

Abstract

This dissertation conducts empirical examinations of a new normative (equilibrium) model, the Gain-Loss Pricing Model (GLPM) of Lin (1999a), in which loss aversion is intuitively incorporated into investors' portfolio decisions. In this equilibrium, the risk-return relation is based on the tradeoff between the expected market-related gain of an asset and its expected market-related loss. In addition to its economic intuition, the new model is shown to be more robust than the mean-variance-based Capital Asset Pricing Model (CAPM). This dissertation consists of two essays. The first essay examines the empirical power of the GLPM using NYSE/AMEX/NASDAQ stocks. This testing framework has a high testing standard that investigates whether there is a systematic component of asset returns left unexplained by the new model and places no restrictions on the sampling distribution of the statistic. The test results indicate that no more than 3% of sample stocks are mispriced according to the model during any given five-year test period in the 1948--1997 sample period. We also find that the mispricing in small size portfolios is not severe. The evidence implies that most sample firms are priced such that their risk-return relation is consistent with the GLPM. Based on these testing results, the second essay proposes a long-terrn performance evaluation framework. This framework is capable of mitigating the skewness problem of long-term abnormal return distributions and avoiding the aggregation problems in many long-term performance tests. The specification of the long-term performance is evaluated using samples of randomly selected NYSE/AMEX/NASDAQ stocks and simulated random event dates. Simulation results show that the long-term performance evaluation framework based on the GLPM is well-specified.

ISBN

9780599905771

Pages

75

DOI

10.31390/gradschool_disstheses.7251

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