| Resumen |
In this paper we measure the relationship between messages in the social media and the stock market prices. First, we measure the correlation and association between the amount of stock related tweets and different financial indicators such as prices, returns and transaction volume. Then, we analyze the content of the messages and test whether the tweets generated during different trends of price change (up, down or steady) can be distinguished by automatic classifiers. Our corpus consist on messages related to nine IT companies and also their daily prices and volume during trading hours for over a period of three months. Two textual representations were used, bag of words and word embeddings. The tweets were automatically tagged using two thresholds to bin the changes in price. We have found a correlation between the amount of daily messages and the volume of financial transactions. We also found negative association (more specifically, what we define as local trend association) between tweet volume and financial indicators that were not found by using only the correlation analysis. Our main contribution is that the messages generated during a positive, negative and neutral trend can be distinguished by state of the art classifiers. |
