Analyzing Moroccan Stock Market using Machine Learning and Sentiment Analysis

Abstract

Behavioral Finance studies demonstrated over the last decade that stock market can be driven by emotions for market participants. On the other hand, online sentiment tracking over social media network and news showed promising results in predicting financial markets. Hence, measuring investor sentiment has become a key research issue in financial predictions. In this paper, we present our methodology for collecting, analyzing and inferring sentiments from several information sources regarding Casablanca Stock Exchange Market. With this data we apply sentiment analysis and machine learning algorithms to infer the relationship between the general public view regarding a stock and its evolution within the stock market.

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