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postgraduate thesis: Essays on behavioral finance and its application to the Hong Kong stock markets

TitleEssays on behavioral finance and its application to the Hong Kong stock markets
Authors
Issue Date2015
PublisherThe University of Hong Kong (Pokfulam, Hong Kong)
Citation
Fu, S. [付思]. (2015). Essays on behavioral finance and its application to the Hong Kong stock markets. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b5570789
AbstractThis dissertation consists three chapters of studies on behavioral finance and its application to the Hong Kong stock markets. In the first chapter, we incorporate prospect theory and the effect of heuristics into a benchmark agent-based artificial financial market filled with fundamentalists, loss-averse mean-variance investors, and trend followers. We then simulate the investment behaviors of different types of investors according to the proposed decision-making principles of them. We compare the simulation results of various specifications and find that this framework can reproduce the stylized facts of absence of autocorrelation, heavy tails, aggregational Gaussianity and volume/volatility correlations. In the second chapter, we investigate investors’ attention allocation to the profit warnings announcements of Hong Kong listed firms using market response measures, and analyze whether investors demonstrate asymmetric attention to positive and negative announcements, and whether their attention allocation is influenced by calendar effects. We find 4.2% and -2.4% abnormal returns on the first trading day following positive and negative profit warnings, respectively, indicating significant immediate market responses following the profit warnings announcements. However, the delayed market responses show that investors seem to overreact to the profit warnings announcements at the first day as prices reverse afterwards. DellaVigna and Pollet (2009) indicate that investors tend to be more inattentive to earnings announced on Friday due to the coming weekend. However, our evidence of Hong Kong stock market does not support this argument. We do not observe significant difference in market responses to announcements made on different days of the week, and we show that investor attention allocation does not differ in months and years, neither. In the third chapter coauthored with Dr. Xia, we test a strategy called Magic Formula on Hong Kong stock market. The idea behind magic formula investing is to rank the companies on return on capital and earnings yield, and then to buy the stocks with the best combined rank, i.e., good companies at bargain prices. We find that the top 10% of stocks with the best combined rank have an equal-weighted average portfolio return of 2.53% per month while the bottom 10% stocks portfolio has only 1.30% per month. We construct six portfolios as the intersections of two portfolios formed on the firm size and three portfolios formed on the combined rank computed from the magic formula. We show that, for both large and small stock groups, the portfolios of stocks with high rankings from the magic formula have larger returns than those with low rankings. For the large stocks, the portfolio with high rankings has 14.61% higher annualized return than that with low rankings. For the small stocks, the portfolio return of high-ranking stocks is 6.04% higher than that of the low-ranking stocks. The time-series regression shows that the risk factor constructed from the ranks calculated from the magic formula has explanatory power to the variation of stock returns in addition to the Fama-French three factors.
DegreeDoctor of Philosophy
SubjectInvestments - Psychological aspects
Stock exchanges - China - Hong Kong
Dept/ProgramEconomics and Finance
Persistent Identifierhttp://hdl.handle.net/10722/219982

 

DC FieldValueLanguage
dc.contributor.authorFu, Si-
dc.contributor.author付思-
dc.date.accessioned2015-10-08T23:12:16Z-
dc.date.available2015-10-08T23:12:16Z-
dc.date.issued2015-
dc.identifier.citationFu, S. [付思]. (2015). Essays on behavioral finance and its application to the Hong Kong stock markets. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b5570789-
dc.identifier.urihttp://hdl.handle.net/10722/219982-
dc.description.abstractThis dissertation consists three chapters of studies on behavioral finance and its application to the Hong Kong stock markets. In the first chapter, we incorporate prospect theory and the effect of heuristics into a benchmark agent-based artificial financial market filled with fundamentalists, loss-averse mean-variance investors, and trend followers. We then simulate the investment behaviors of different types of investors according to the proposed decision-making principles of them. We compare the simulation results of various specifications and find that this framework can reproduce the stylized facts of absence of autocorrelation, heavy tails, aggregational Gaussianity and volume/volatility correlations. In the second chapter, we investigate investors’ attention allocation to the profit warnings announcements of Hong Kong listed firms using market response measures, and analyze whether investors demonstrate asymmetric attention to positive and negative announcements, and whether their attention allocation is influenced by calendar effects. We find 4.2% and -2.4% abnormal returns on the first trading day following positive and negative profit warnings, respectively, indicating significant immediate market responses following the profit warnings announcements. However, the delayed market responses show that investors seem to overreact to the profit warnings announcements at the first day as prices reverse afterwards. DellaVigna and Pollet (2009) indicate that investors tend to be more inattentive to earnings announced on Friday due to the coming weekend. However, our evidence of Hong Kong stock market does not support this argument. We do not observe significant difference in market responses to announcements made on different days of the week, and we show that investor attention allocation does not differ in months and years, neither. In the third chapter coauthored with Dr. Xia, we test a strategy called Magic Formula on Hong Kong stock market. The idea behind magic formula investing is to rank the companies on return on capital and earnings yield, and then to buy the stocks with the best combined rank, i.e., good companies at bargain prices. We find that the top 10% of stocks with the best combined rank have an equal-weighted average portfolio return of 2.53% per month while the bottom 10% stocks portfolio has only 1.30% per month. We construct six portfolios as the intersections of two portfolios formed on the firm size and three portfolios formed on the combined rank computed from the magic formula. We show that, for both large and small stock groups, the portfolios of stocks with high rankings from the magic formula have larger returns than those with low rankings. For the large stocks, the portfolio with high rankings has 14.61% higher annualized return than that with low rankings. For the small stocks, the portfolio return of high-ranking stocks is 6.04% higher than that of the low-ranking stocks. The time-series regression shows that the risk factor constructed from the ranks calculated from the magic formula has explanatory power to the variation of stock returns in addition to the Fama-French three factors.-
dc.languageeng-
dc.publisherThe University of Hong Kong (Pokfulam, Hong Kong)-
dc.relation.ispartofHKU Theses Online (HKUTO)-
dc.rightsThe author retains all proprietary rights, (such as patent rights) and the right to use in future works.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subject.lcshInvestments - Psychological aspects-
dc.subject.lcshStock exchanges - China - Hong Kong-
dc.titleEssays on behavioral finance and its application to the Hong Kong stock markets-
dc.typePG_Thesis-
dc.identifier.hkulb5570789-
dc.description.thesisnameDoctor of Philosophy-
dc.description.thesislevelDoctoral-
dc.description.thesisdisciplineEconomics and Finance-
dc.description.naturepublished_or_final_version-

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