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Three Essays on Machine Learning in Empirical Finance

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cam.depositDate2022-06-28
cam.restrictionthesis_access_open
cam.supervisorRau, Raghavendra
dc.contributor.authorWang, Jinhua
dc.date.accessioned2022-06-29T12:55:29Z
dc.date.available2022-06-29T12:55:29Z
dc.date.submitted2022-03-08
dc.date.updated2022-06-28T15:23:39Z
dc.description.abstractThe dissertation consists of three essays that contribute to the literature on machine learning in empirical finance. In the first paper, I create proxies for managers’ cultural fit using one of the latest machine learning technologies – the sentence embedding model - by analysing 11.5 million speeches in earnings calls. A better cultural fit is significantly and positively correlated with managerial tenure. I demonstrate that the effect of cultural fit on managerial tenure is causal using random survival forests. Firms that hire culturally disruptive managers have lower future market values and performance. The stock market reacts positively to signals that indicate low cultural dispersion within the firm. In the second paper, we document a gender-based attention effect in the sensitivity of mutual fund flows to fund performance using individual-level fund data from a fintech platform in China. Investors increase (decrease) flows to funds following positive and strong (negative and weak) prior-month performance. However, although there is no significant difference in the performance of male and female managers, the sensitivity effect significantly weakens if the fund manager is female. The effect persists after controlling for the tone of news articles on fund managers, measured using a state-of-the-art machine learning model. Simply put, investors react less to the performance of female fund managers. In the third paper, we document a significant, up to 10-fold increase in the synchronicity of intra-day, ultra-high frequency stock returns over the last decade. This surge in the intra-day synchronicity across stocks coincided with the advent of electronic, automated trading in U.S. markets. Using changes to the S&P500 index, we establish evidence of a causal relationship using a new machine learning tool - causal random forests. When firms are included in this major index, they enter the radar of high frequency arbitrageurs and market-making bots. These automated trading bots, who monitor prices in major securities closely and continuously, increase their quoting activities significantly and cause individual stocks’ returns to synchronize at the microstructure level.
dc.identifier.doi10.17863/CAM.85829
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/338416
dc.language.isoeng
dc.publisher.institutionUniversity of Cambridge
dc.rightsAll Rights Reserved
dc.rights.urihttps://www.rioxx.net/licenses/all-rights-reserved/
dc.subjectCorporate Culture
dc.subjectManager Turnover
dc.subjectNatural-Language-Processing
dc.subjectMachine Learning
dc.subjectBERT
dc.subjectTransformer
dc.subjectSurvival Analysis
dc.subjectCausal Machine Learning
dc.subjectMutual funds
dc.subjectflow-performance relationship
dc.subjectattention bias
dc.subjectgender bias
dc.subjectfintech
dc.subjectinclusive finance
dc.subjectbehavioral finance
dc.subjectpsychology
dc.subjectNatural Language Processing
dc.subjectmarket microstructure
dc.titleThree Essays on Machine Learning in Empirical Finance
dc.typeThesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnameDoctor of Philosophy (PhD)
pubs.licence-display-nameApollo Repository Deposit Licence Agreement
pubs.licence-identifierapollo-deposit-licence-2-1
rioxxterms.licenseref.urihttps://www.rioxx.net/licenses/all-rights-reserved/
rioxxterms.typeThesis

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