Classify Unexpected News Impacts to Stock Price by Incorporating Time Series Analysis into Support Vector Machine

The paper discusses an approach of using traditional time series analysis, as domain knowledge, to help the data-preparation of support vector machine for classifying documents. Classifying unexpected news impacts to the stock prices is selected as a case study. As a result, we present a novel approach for providing approximate answers to classifying news events into simple three categories. The process of constructing training datasets is emphasized, and some time series analysis techniques are utilized to pre-process the dataset. A rule-base associated with the net-of-market return and piecewise linear fitting constructs the training data set. A classifier mainly built by support vector machine uses the training data set to extract the interrelationship between unexpected news events and the stock price movements.