A picture is worth a thousand words: Measuring investor sentiment by combining machine learning and photos from news
| Autor: | Kuntara Pukthuanthong, Khaled Obaid |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
040101 forestry
Economics and Econometrics 050208 finance Index (economics) business.industry Strategy and Management media_common.quotation_subject 05 social sciences 04 agricultural and veterinary sciences Pessimism Machine learning computer.software_genre Large sample Accounting 0502 economics and business Economics 0401 agriculture forestry and fisheries Market return Artificial intelligence business computer Limits to arbitrage Finance Stock (geology) media_common |
| Zdroj: | Journal of Financial Economics. 144:273-297 |
| ISSN: | 0304-405X |
| DOI: | 10.1016/j.jfineco.2021.06.002 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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