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A Case Study - Classification of Stock Exchange News by Support Vector Machines

In this paper, we present a case study concerning the classification of text messages with the use of Support Vector Machines. We collected about 700.000 news and stated the hypothesis saying that when markets are going down then negative messages have a majority and when markets are going up then positive messages have a majority. This hypothesis is based on the assumption of news-driven behavior of investors. To check the hypothesis given above we needed to classify the market news. We describe the application of Support Vector Machines for this purpose including our experiments that showed interesting results. We found that the news classification has some interesting correlation with long-term market trends.