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Essays in Financial Market and Information Acquisition

Liu, J. (2021). Essays in Financial Market and Information Acquisition. (Unpublished Doctoral thesis, City, University of London)

Abstract

This thesis explores how information is incorporated into the valuation of assets, including classical securities - such as stocks and the innovative financial instruments - such as cryptocurrencies. On the one hand, I study the stock market by analyzing information acquisition theories and testing the fundamental Homo economicus tenets of neoclassical economics with novel textual data from online media and newswires. On the other hand, I document stylized facts for the nine most liquid cryptocurrencies and investigate whether the cryptocurrencies' pricing behaviors are explained by information in their own factor structure rather than information in the traditional financial market.

Chapter 1 studies mood, measured by Twitter messages, which causes investors' insufficient acquisition of information about assets and the implications of asset pricing. Using a Twitter-based mood measure, the study finds that mood swings are negatively predictive of investors' acquisition of earnings-related information when seeking to learn about companies' performance. Therefore, this study argues that this bias effect contributes to the explanation of classical (unconditional) pricing models' failures. Conducting tests on cross-sectional stock returns, the empirical results show that stocks that are more sensitive to mood earn a higher expected excess return than less moodsensitive stocks. Sorting stocks to construct the risk factor portfolio based on mood betas as sensitivity to mood risk, this study is the first to quantify the risk premium (0.56% per month) by holding stocks subject to mood risk. The results are consistent with the theoretical prediction that investors mistakenly use mood as information rather than acquiring sufficient fundamental information about assets, thereby inducing mispricing in asset valuation.

Chapter 2 studies investors who use biased information from news media, with as subsequent tendency to make irrational decisions about acquiring firm-specific information compared to rational expectations. A static model of information acquisition by introducing a new irrationality channel in the form of biased information transmission yields testable predictions that are verified by using a novel dataset of news stories. First, when sentiment in news articles, as a proxy for biased public information, is more optimistic, investors tend to acquire less earnings-relevant information before the earnings announcement and vice versa. Second, the return predictability from firm-specific news sentiment confirms that it contributes to variations in asset information risk due, in a biased belief equilibrium, to the proportion of informed investors deviating from rational expectations. Overall, these findings suggest that biased public information inherent in news sentiment serves to irrationalize investors' acquisition of firm-specific information through a biased perception of uncertainties in the risky asset payoff.

Lastly, Chapter 3 studies stylized facts on the return and volatility dynamics of the nine most liquid cryptocurrencies by using high-frequency tick data. Factor structures exist in both returns and volatility, but the explanatory power from the common factor is much stronger for volatility. The factor structures do not relate strongly to fundamental economic factors, and Bitcoin - which this study proposes is a "crypto market factor" - has only weak explanatory power. Dating the bubble in Bitcoin pricing allows the analysis to split the sample into pre-bubble, bubble and post-bubble periods. The importance of these different periods is clear, revealing shifting relationships between the nine cryptocurrencies and Bitcoin. Model-free realized cryptocurrency betas with Bitcoin increase during the bubble and the explained fraction of cryptocurrency variance remains at an elevated level after the bubble burst. In sum, the results show that information in the factor structure explains variations of returns and volatilities in the cryptocurrency market.

Publication Type: Thesis (Doctoral)
Subjects: H Social Sciences > HG Finance
Departments: Bayes Business School
Bayes Business School > Finance
Doctoral Theses
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