Improving Stock Price Prediction with SVM by Simple Transformation: The Sample of Stock Exchange of Thailand (SET)

Piyatat Chatvorawit, Pairote Sattayatham, Bhusana Premanode

Abstract

A stock price at any particular time is represented by its closing price, i.e.,the last traded price in stock market prior to that particular time. By nature of a closing price movement, it is anon-stationary data, which is not suitable for prediction. Closing price difference, closing change, is a simple transformationto make a stationary data. However, with the price movement rules of SET, a range of closing change is not the same for adifference price interval. There are some rules to set a limitation and pattern for price movement. A tick size isthe smallest amount that stock price can change. For SET market, a tick size is varied for various price intervals, e.g., atick size is 0.01 Baht per step for price between 0.01 Baht and 2.00 Baht. We calculate a number of tick different inprice change, called tick change, which is also a stationary data and a range remains the same for difference price interval.From our experiment on 50 stocks listed in SET50, prediction by closing change based SVM gave the best MAPE result comparing toclosing price and tick change based SVM. Although, the overall performance of closing change is better than tick change;there are 10 out of 50 stocks that tick change did better than closing change, and only 5 out of 50 stocks that havemore than 10 precents difference in results. Both closing change and tick change achieved a lower prediction error comparingto using closing price model.