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

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