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Predicting the French Stock Market Using Social Media Analysis

Predicting the French Stock Market Using Social Media Analysis

Vincent Martin, Emmanuel Bruno, Elisabeth Murisasco
Copyright: © 2015 |Volume: 7 |Issue: 2 |Pages: 15
ISSN: 1942-9010|EISSN: 1942-9029|EISBN13: 9781466677265|DOI: 10.4018/IJVCSN.2015040104
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MLA

Martin, Vincent, et al. "Predicting the French Stock Market Using Social Media Analysis." IJVCSN vol.7, no.2 2015: pp.70-84. http://doi.org/10.4018/IJVCSN.2015040104

APA

Martin, V., Bruno, E., & Murisasco, E. (2015). Predicting the French Stock Market Using Social Media Analysis. International Journal of Virtual Communities and Social Networking (IJVCSN), 7(2), 70-84. http://doi.org/10.4018/IJVCSN.2015040104

Chicago

Martin, Vincent, Emmanuel Bruno, and Elisabeth Murisasco. "Predicting the French Stock Market Using Social Media Analysis," International Journal of Virtual Communities and Social Networking (IJVCSN) 7, no.2: 70-84. http://doi.org/10.4018/IJVCSN.2015040104

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Abstract

In this article, the authors try to predict the next-day CAC40 index. They apply the idea of Johan Bollen et al. from (Bollen, Mao, & Zeng, 2011) on the French stock market and they conduct their experiment using French tweets. Two analyses are applied on tweets: sentiment analysis and subjectivity analysis. Results of these analyses are then used to train a simple neural network. The input features are the sentiment, the subjectivity and the CAC40 closing value at day-1 and day-0. The single output value is the predicted CAC40 closing value at day+1. The authors propose an architecture using the JEE framework resulting in a better scalability and an easier industrialization. The main experiments are conducted over 5 months of data. The authors train their neural network on the first of the data and they test predictions on the remaining quarter. Their best run gives a direction accuracy of 80% and a mean absolute percentage error (MAPE) of 2.97%. In another experiment, the authors retrain the neural network each day which decreases the MAPE to 1.14%.

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