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

Journal of Financial Economics (Forthcoming)

74 Pages Posted: 10 Dec 2018 Last revised: 13 Oct 2024

See all articles by Khaled Obaid

Khaled Obaid

Mississippi State University

Kuntara Pukthuanthong

University of Missouri, Columbia

Multiple version iconThere are 2 versions of this paper

Date Written: June 29, 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 predictability pattern is concentrated among stocks with high limits to arbitrage and during high uncertainty periods. Photo Pessimism enhances pessimism embedded in text, but subsumes it during days with traumas and influential photos. Photo Pessimism from business news has over six times stronger predictive power than Photo Pessimism from general news.

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 (June 29, 2020). Journal of Financial Economics (Forthcoming), Available at SSRN: https://ssrn.com/abstract=3297930 or http://dx.doi.org/10.2139/ssrn.3297930

Khaled Obaid

Mississippi State University ( email )

Mississippi State, MS 39762
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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