Authors and Corporations: | , , |
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In: | International Journal of Virtual Communities and Social Networking, 7, 2015, 2, p. 70-84 |
published: |
IGI Global
|
Media Type: | Article, E-Article |
Physical Description: | 70-84 |
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ISSN: |
1942-9010
1942-9029 |
DOI: | 10.4018/ijvcsn.2015040104 |
published in: | International Journal of Virtual Communities and Social Networking |
Language: | Ndonga |
Subjects: | |
Collection: | IGI Global (CrossRef) |
<p>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%.</p> |