2019 Hillcrest Behavioral Finance Award

A Picture is Worth a Thousand Words: Measuring Investor Sentiment by Combining Machine Learning and Photos from News

Khaled Obaid (California State University – East Bay)

Kuntara Pukthuanthong (University of Missouri-Columbia)

By applying machine learning to accurately and cost effectively classify photos based on sentiment, we introduce a daily market-level investor sentiment index (Photo Pessimism) from a large sample of news photos. Between 1926 and 2018, Photo Pessimism predicts market return reversal and increase trading volume. The return predictability pattern is concentrated among stocks with high limits to arbitrage and during high uncertainty and low investor distraction periods. Photo Pessimism complements sentiment in news test. These results are consistent with behavioral models, but inconsistent. with theories of media content as a proxy for new fundamental information.

This paper is located on SSRN.

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