Online reviews can predict long-term returns of individual stocks
Junran Wu, Ke Xu, Jichang Zhao

TL;DR
This study demonstrates that online consumer reviews can be effectively used to predict the long-term stock returns of individual companies, offering a new tool for investors.
Contribution
It introduces a novel methodology framework that leverages extensive online review data to predict long-term stock returns at the individual stock level.
Findings
Achieved 13.94% accuracy improvement over existing models.
Selected 6,246 features from 13 categories based on 18 million reviews.
Confirmed the predictive power of online reviews for long-term stock performance.
Abstract
Online reviews are feedback voluntarily posted by consumers about their consumption experiences. This feedback indicates customer attitudes such as affection, awareness and faith towards a brand or a firm and demonstrates inherent connections with a company's future sales, cash flow and stock pricing. However, the predicting power of online reviews for long-term returns on stocks, especially at the individual level, has received little research attention, making a comprehensive exploration necessary to resolve existing debates. In this paper, which is based exclusively on online reviews, a methodology framework for predicting long-term returns of individual stocks with competent performance is established. Specifically, 6,246 features of 13 categories inferred from more than 18 million product reviews are selected to build the prediction models. With the best classifier selected from…
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Taxonomy
TopicsStock Market Forecasting Methods · Financial Markets and Investment Strategies · Digital Marketing and Social Media
