Stock Market Direction Prediction Using Data Mining Classification

The key of success in stock trading is to buy and sell stocks at the right time for the right price. “Buy Low, Sell High” sounds easy, but it is so difficult to carry out since the direction of stock market in the near future is almost unpredictable. With the advances in data mining, it has now become possible to predict the future market direction based on historical data. In this study, different approaches are used to predict the future market direction of the Stock Exchange of Thailand (SET). Time series forecasting is conducted and a suitable span of time for the stock market data is examined. A novel approach to predict future market direction has been introduced based on chart patterns recognition by using data mining classification. Models are built through different methods including neural network, decision tree, naïve Bayes and k-nearest neighbors. Results were obtained, compared and discussed in details. Important chart patterns to support decision making in stock trading had been found out. In order to visualize the result, a visualization technique is also introduced.