Details
| Original language | English |
|---|---|
| Pages (from-to) | 5094-5126 |
| Number of pages | 33 |
| Journal | Review of Financial Studies |
| Volume | 35 |
| Issue number | 11 |
| Early online date | 19 May 2022 |
| Publication status | Published - Nov 2022 |
Abstract
We apply a machine-learning algorithm, calibrated using general human vision, to predict the visual salience of prices of stock price charts. We hypothesize that the visual salience of adjacent prices increases the decision weights on returns computed from those prices. We analyze the inferred impact of these weights in two experimental studies that use either historical price charts or simpler artificial sequences. We find that decision weights derived from visual salience are associated with experimental investments. The predictability is not subsumed by statistical features and goes beyond established models.
ASJC Scopus subject areas
- Business, Management and Accounting(all)
- Accounting
- Economics, Econometrics and Finance(all)
- Finance
- Economics, Econometrics and Finance(all)
- Economics and Econometrics
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In: Review of Financial Studies, Vol. 35, No. 11, 11.2022, p. 5094-5126.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Decision Weights for Experimental Asset Prices Based on Visual Salience
AU - Bose, Devdeepta
AU - Cordes, Henning
AU - Nolte, Sven
AU - Schneider, Judith Christiane
AU - Camerer, Colin Farrell
N1 - Funding Information: We thank Nicholas Barberis, Lawrence Jin, Alex Imas, Elise Payzan-LeNestour, Cindy Xiong, and Thomas Langer and seminar and conference participants at Warwick Business School, Radboud University, University of Münster, Caltech, the 2019 Southwestern Economic and Behavioral Economics Conference, the 2019 Experimental Science Association, the 2019 Mid-Atlantic Meeting on Behavioral and Experimental Economics, the 2019 Boulder Summer Conference on Consumer Financial Decision Making, and the 2020 NBER Behavioral FinanceWorking Group SummerMeeting for helpful comments and suggestions. Financial support was provided by the Tianqiao and Chrissy Chen Center for Social and Decision Neuroscience Leadership Chair, Behavioral and Neuroeconomics Discovery Fund, the Alfred P. Sloan Foundation, and the Ronald and Maxine Linde Institute for Economics and Management Sciences.
PY - 2022/11
Y1 - 2022/11
N2 - We apply a machine-learning algorithm, calibrated using general human vision, to predict the visual salience of prices of stock price charts. We hypothesize that the visual salience of adjacent prices increases the decision weights on returns computed from those prices. We analyze the inferred impact of these weights in two experimental studies that use either historical price charts or simpler artificial sequences. We find that decision weights derived from visual salience are associated with experimental investments. The predictability is not subsumed by statistical features and goes beyond established models.
AB - We apply a machine-learning algorithm, calibrated using general human vision, to predict the visual salience of prices of stock price charts. We hypothesize that the visual salience of adjacent prices increases the decision weights on returns computed from those prices. We analyze the inferred impact of these weights in two experimental studies that use either historical price charts or simpler artificial sequences. We find that decision weights derived from visual salience are associated with experimental investments. The predictability is not subsumed by statistical features and goes beyond established models.
UR - http://www.scopus.com/inward/record.url?scp=85142390837&partnerID=8YFLogxK
U2 - 10.1093/rfs/hhac027
DO - 10.1093/rfs/hhac027
M3 - Article
AN - SCOPUS:85142390837
VL - 35
SP - 5094
EP - 5126
JO - Review of Financial Studies
JF - Review of Financial Studies
SN - 0893-9454
IS - 11
ER -