Date of Award
Doctor of Philosophy (PhD)
Business Administration - Finance
The body of literature on investor sentiment underlines its impact on future stock returns, with general consensus that investor sentiments and future returns are negatively correlated (Baker and Wurgler, 2006; Brown and Cliff, 2004). This extends to the notion that a bullish investor would expect returns to be above average, while a bearish investor anticipates below-average returns (Brown and Cliff, 2004).
The first essay proposes a model to examine the influence of unexpected volatility of investor sentiment on the equity risk premium. Assumptions underpinning the model include risk-averse investors, homogeneous expectations regarding asset returns and price changes, and sentiment-influenced expectations of asset returns. The model also presumes continuous-time stochastic (Weiner) processes for asset returns and sentiment. The developed model is rooted in several principles, including the Efficient Market Hypothesis, Martingale theory, and the impact of uncertain sentiment change on stock returns. Utilizing Thomson Reuters MarketPsych Indices for data analysis, the model tests sentiment metrics against the performance of the S&P 500. The results provide insights into the dynamics of investor sentiment and its impact on equity risk premium, laying the groundwork for further empirical investigation. In the first essay, we evaluate the link between industry tournament incentives and investment inefficiency. We find that firms with higher tournament incentives exhibit higher investment inefficiency. Additionally, cross-sectional tests suggest that these effects operate at least in part through both a financing channel and a monitoring channel. Taken together, our results suggest that industry tournament incentives place pressure on CEOs and affect the efficiency of firm investments.
In the second essay, we examines the phenomenon of sentiment transmission across stock markets, focusing on the influence of U.S. investors' sentiment on G7 countries. The study utilizes data from the Global Finance database, including stock indices for G7 countries and two measures of sentiment for the U.S. market: news sentiment and social media sentiment. News sentiment captures the impact of positive and negative news articles on market sentiment, while social media sentiment reflects the influence of social media posts on market sentiment. The analysis employs a vector autoregression (VAR) model and Multivariate GARCH model to understand the interdependence of these variables and how changes in U.S. investors' sentiment affect other markets. The study highlights the increasing prevalence and significant impact of sentiment transmission due to the global interconnectedness of markets, amplified by financial innovations like ETFs. The findings contribute to a better understanding of sentiment transmission and its implications for global financial markets, providing insights for policymakers and market participants.
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"Two Essays on Investor Sentiment"
(2023). Doctor of Philosophy (PhD), Dissertation, Finance, Old Dominion University, DOI: 10.25777/c93f-3n08