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Machine Learning in Finance: Understanding Investor Behavior and Asset Prices
Machine Learning in Finance: Understanding Investor Behavior and Asset Prices
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103036
- ISBN
- 9798286444489
- DDC
- 658
- 저자명
- Zhou, Kangying.
- 서명/저자
- Machine Learning in Finance: Understanding Investor Behavior and Asset Prices
- 발행사항
- [Sl] : Yale University, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 304 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-12, Section: A.
- 주기사항
- Advisor: Barberis, Nicholas C.;Kelly, Bryan T.
- 학위논문주기
- Thesis (Ph.D.)--Yale University, 2025.
- 초록/해제
- 요약This dissertation consists of three essays on machine learning in finance, focusing on its applications to understanding investor behavior and asset prices.The first essay is titled "Professional Investors and Media Narratives." I investigate the impact of media narratives on the portfolio strategies of active equity mutual funds. Using 1.5 million Wall Street Journal articles from 1984 to 2023, I use ChatGPT to distill media narratives into 59 distinct topics, and quantify each topic's time-varying share of news attention and sentiment. I then define a fund as having exposure to a topic if it overweights stocks expected to perform well when the topic grows in importance, and hence attention. I find that the topics that fund managers choose to have high exposure to are high-sentiment topics, but not those with high attention. This strategy leads to mutual fund underperformance but attracts investor flows. Topic-oriented strategies account for a large fraction, specifically 37%, of mutual fund tilts, and are a key driver of the underperformance associated with active tilts.The second essay, joint with Bryan Kelly and Semyon Malamud, is titled "The Virtue of Complexity in Return Prediction." Much of the extant literature predicts market returns with "simple" models that use only a few parameters. Contrary to conventional wisdom, we theoretically prove that simple models severely understate return predictability compared to "complex" models in which the number of parameters exceeds the number of observations. We empirically document the virtue of complexity in US equity market return prediction. Our findings establish the rationale for modeling expected returns through machine learning.The third essay, joint with Bryan Kelly and Semyon Malamud, is titled "The Virtue of Complexity Everywhere." We investigate the performance of non-linear return prediction models in the high complexity regime, i.e., when the number of model parameters exceeds the number of observations. We document a "virtue of complexity": Return prediction R2 and optimal portfolio Sharpe ratio generally increase with model parameterization in all asset classes that we study (US equities, international equities, bonds, commodities, currencies, and interest rates). The virtue of complexity is present even in extremely data-scarce environments, e.g., for predictive models with less than twenty observations and tens of thousands of predictors. The empirical association between model complexity and out-of-sample model performance exhibits a striking consistency with theoretical predictions.
- 일반주제명
- Finance
- 일반주제명
- Information science
- 키워드
- Machine learning
- 키워드
- Asset prices
- 키워드
- Mutual funds
- 기타저자
- Yale University Management
- 기본자료저록
- Dissertations Abstracts International. 86-12A.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798286444489
■035 ▼a(MiAaPQ)AAI31846269
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a658
■1001 ▼aZhou, Kangying.
■24510▼aMachine Learning in Finance: Understanding Investor Behavior and Asset Prices
■260 ▼a[Sl]▼bYale University▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a304 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-12, Section: A.
■500 ▼aAdvisor: Barberis, Nicholas C.;Kelly, Bryan T.
■5021 ▼aThesis (Ph.D.)--Yale University, 2025.
■520 ▼aThis dissertation consists of three essays on machine learning in finance, focusing on its applications to understanding investor behavior and asset prices.The first essay is titled "Professional Investors and Media Narratives." I investigate the impact of media narratives on the portfolio strategies of active equity mutual funds. Using 1.5 million Wall Street Journal articles from 1984 to 2023, I use ChatGPT to distill media narratives into 59 distinct topics, and quantify each topic's time-varying share of news attention and sentiment. I then define a fund as having exposure to a topic if it overweights stocks expected to perform well when the topic grows in importance, and hence attention. I find that the topics that fund managers choose to have high exposure to are high-sentiment topics, but not those with high attention. This strategy leads to mutual fund underperformance but attracts investor flows. Topic-oriented strategies account for a large fraction, specifically 37%, of mutual fund tilts, and are a key driver of the underperformance associated with active tilts.The second essay, joint with Bryan Kelly and Semyon Malamud, is titled "The Virtue of Complexity in Return Prediction." Much of the extant literature predicts market returns with "simple" models that use only a few parameters. Contrary to conventional wisdom, we theoretically prove that simple models severely understate return predictability compared to "complex" models in which the number of parameters exceeds the number of observations. We empirically document the virtue of complexity in US equity market return prediction. Our findings establish the rationale for modeling expected returns through machine learning.The third essay, joint with Bryan Kelly and Semyon Malamud, is titled "The Virtue of Complexity Everywhere." We investigate the performance of non-linear return prediction models in the high complexity regime, i.e., when the number of model parameters exceeds the number of observations. We document a "virtue of complexity": Return prediction R2 and optimal portfolio Sharpe ratio generally increase with model parameterization in all asset classes that we study (US equities, international equities, bonds, commodities, currencies, and interest rates). The virtue of complexity is present even in extremely data-scarce environments, e.g., for predictive models with less than twenty observations and tens of thousands of predictors. The empirical association between model complexity and out-of-sample model performance exhibits a striking consistency with theoretical predictions.
■590 ▼aSchool code: 0265.
■650 4▼aFinance
■650 4▼aInformation science
■653 ▼aMachine learning
■653 ▼aInvestor behavior
■653 ▼aAsset prices
■653 ▼aMutual funds
■690 ▼a0508
■690 ▼a0511
■690 ▼a0723
■690 ▼a0338
■690 ▼a0501
■71020▼aYale University▼bManagement.
■7730 ▼tDissertations Abstracts International▼g86-12A.
■790 ▼a0265
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17356792▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
