A picture is worth a thousand words: Measuring investor sentiment by combining machine learning and photos from news

Khaled Obaid and Kuntara Pukthuanthong

Journal of Financial Economics, 2022, vol. 144, issue 1, 273-297

Abstract: By applying machine learning to the accurate and cost-effective classification of photos based on sentiment, we introduce a daily market-level investor sentiment index (Photo Pessimism) obtained from a large sample of news photos. Consistent with behavioral models, Photo Pessimism predicts market return reversals and trading volume. The relation is strongest among stocks with high limits to arbitrage and during periods of elevated fear. We examine whether Photo Pessimism and pessimism embedded in news text act as complements or substitutes for each other in predicting stock returns and find evidence that the two are substitutes.

Keywords: Investor sentiment; Behavioral finance; Return predictability; Machine learning; Deep learning; Big data (search for similar items in EconPapers)
JEL-codes: C53 G10 G17 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (33)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jfinec:v:144:y:2022:i:1:p:273-297

DOI: 10.1016/j.jfineco.2021.06.002

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