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

65 Pages Posted: 10 Dec 2018 Last revised: 16 Jan 2020

See all articles by Khaled Obaid

Khaled Obaid

California State University-East Bay

Kuntara Pukthuanthong

University of Missouri, Columbia

Date Written: January 15, 2020

Abstract

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 in 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 text. Moreover, Photo Pessimism has over five times stronger predictive power when we focus only on business news. These results are consistent with behavioral models, but inconsistent with theories of media content as a proxy for new fundamental information.

Keywords: Investor Sentiment, Behavioral Finance, Return Predictability, Machine Learning, Big Data

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 (January 15, 2020). Available at SSRN: https://ssrn.com/abstract=3297930 or http://dx.doi.org/10.2139/ssrn.3297930

Khaled Obaid

California State University-East Bay ( email )

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

HOME PAGE: http://sites.google.com/alumni.wfu.edu/khaledobaid/

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: http://https://kuntara.weebly.com

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