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

Forthcoming, Journal of Financial Economics

54 Pages Posted: 10 May 2021 Last revised: 7 Jun 2021

See all articles by Khaled Obaid

Khaled Obaid

California State University-East Bay

Kuntara Pukthuanthong

University of Missouri, Columbia

Multiple version iconThere are 2 versions of this paper

Date Written: June 1, 2021

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 returns and find evidence that the two are substitutes.

Keywords: Sentiment, machine learning, photo, visual sentiment, predictability, convolution neural network

JEL Classification: C53; G10; G17

Suggested Citation

Obaid, Khaled and Pukthuanthong, Kuntara, A Picture is Worth a Thousand Words: Measuring Investor Sentiment by Combining Machine Learning and Photos from News (June 1, 2021). Forthcoming, Journal of Financial Economics, Available at SSRN: https://ssrn.com/abstract=3841844

Khaled Obaid

California State University-East Bay ( email )

435 Valley Business and Technology Center
College of Business and Economics
Hayward, CA 94621
United States

Kuntara Pukthuanthong (Contact Author)

University of Missouri, Columbia ( email )

Robert J. Trulaske, Sr. College of Business
403 Cornell Hall
Columbia, MO 65211
United States
6198076124 (Phone)

HOME PAGE: https://www.kuntara.net/

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