Sentiment, emotions and stock market predictability in developed and emerging markets. A new GLO Discussion Paper

The Global Labor Organization (GLO) is an independent, non-partisan and non-governmental organization that functions as an international network and virtual platform to stimulate global research, debate and collaboration.

GLO Discussion Paper No. 502, 2020

GLO Fellows Talita Greyling and Stephanie Rossouw

Author Abstract: This paper investigates the predictability of stock market movements using text data extracted from the social media platform, Twitter. We analyze text data to determine the sentiment and the emotion embedded in the tweets and use them as explanatory variables to predict stock market movements. The study contributes to the literature by analyzing high-frequency data and comparing the results obtained from analyzing emerging and developed markets, respectively. To this end, the study uses three different Machine Learning Classification Algorithms, the Naïve Bayes, K-Nearest Neighbors and the Support Vector Machine algorithm. Furthermore, we use several evaluation metrics such as the Precision, Recall, Specificity and the F-1 score to test and compare the performance of these algorithms. Lastly, we use the K-Fold Cross-Validation technique to validate the results of our machine learning models and the Variable Importance Analysis to show which variables play an important role in the prediction of our models. The predictability of the market movements is estimated by first including sentiment only and then sentiment with emotions. Our results indicate that investor sentiment and emotions derived from stock market-related Tweets are significant predictors of stock market movements, not only in developed markets but also in emerging markets.

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