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Memory and Beliefs in Financial Markets: A Machine Learning Approach
Memory and Beliefs in Financial Markets: A Machine Learning Approach
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103351
- ISBN
- 9798280757066
- DDC
- 658
- 저자명
- Chen, Zhongtian.
- 서명/저자
- Memory and Beliefs in Financial Markets: A Machine Learning Approach
- 발행사항
- [Sl] : University of Pennsylvania, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 95 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
- 주기사항
- Advisor: Roussanov, Nikolai.
- 학위논문주기
- Thesis (Ph.D.)--University of Pennsylvania, 2025.
- 초록/해제
- 요약This dissertation, based on joint work, explores the role of memory in shaping belief formation of financial market participants. We estimate a structural machine learning model of memory-based belief formation applied to consensus earnings forecasts of sell-side stock analysts. The estimated model reveals significant recall distortions compared to a benchmark model trained to fit realized earnings revisions. Specifically, analysts over-recall distant historical episodes most of the time, when recent events are more useful for forming forecasts than those in the distant past, but under-recall them during crisis times, when history helps to interpret unusual events. We document two potential driving forces behind these distortions. First, analyst memory overweights the importance of past earnings and forecasts. Second, analysts are more likely to selectively forget past positive events. Our model of analyst recalls strongly predicts their earnings forecast revisions and errors, as well as stock returns, which suggests that distorted recalls might contribute to mispricing of assets in financial markets.
- 일반주제명
- Finance
- 일반주제명
- Psychology
- 키워드
- Memory
- 키워드
- Beliefs
- 키워드
- Machine learning
- 키워드
- Trading
- 기타저자
- University of Pennsylvania Finance
- 기본자료저록
- Dissertations Abstracts International. 86-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798280757066
■035 ▼a(MiAaPQ)AAI31995321
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a658
■1001 ▼aChen, Zhongtian.
■24510▼aMemory and Beliefs in Financial Markets: A Machine Learning Approach
■260 ▼a[Sl]▼bUniversity of Pennsylvania▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a95 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-12, Section: B.
■500 ▼aAdvisor: Roussanov, Nikolai.
■5021 ▼aThesis (Ph.D.)--University of Pennsylvania, 2025.
■520 ▼aThis dissertation, based on joint work, explores the role of memory in shaping belief formation of financial market participants. We estimate a structural machine learning model of memory-based belief formation applied to consensus earnings forecasts of sell-side stock analysts. The estimated model reveals significant recall distortions compared to a benchmark model trained to fit realized earnings revisions. Specifically, analysts over-recall distant historical episodes most of the time, when recent events are more useful for forming forecasts than those in the distant past, but under-recall them during crisis times, when history helps to interpret unusual events. We document two potential driving forces behind these distortions. First, analyst memory overweights the importance of past earnings and forecasts. Second, analysts are more likely to selectively forget past positive events. Our model of analyst recalls strongly predicts their earnings forecast revisions and errors, as well as stock returns, which suggests that distorted recalls might contribute to mispricing of assets in financial markets.
■590 ▼aSchool code: 0175.
■650 4▼aFinance
■650 4▼aPsychology
■653 ▼aMemory
■653 ▼aBeliefs
■653 ▼aFinancial markets
■653 ▼aMachine learning
■653 ▼aTrading
■690 ▼a0508
■690 ▼a0621
■690 ▼a0501
■71020▼aUniversity of Pennsylvania▼bFinance.
■7730 ▼tDissertations Abstracts International▼g86-12B.
■790 ▼a0175
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17357364▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
