Predicting SET50 index trend using artificial neural network and support vector machine

Abstract

Finding ways to making better prediction of stock market trend has attracted a lot of attention from researchers because an accurate prediction can substantially reduce investment risk and increase profit gain for investors. This study investigated the use of two machine learning methods, Artificial Neural Network (ANN) and Support Vector Machine (SVM), for predicting the trend of Thailand’s emerging stock market, SET50 index. Raw SET50 index records from 2009 to 2013 were converted into 10 widely-accepted technical indicators that were then used as input for model construction and testing. Our test results showed that the accuracy of the ANN model outperforms that of the SVM model.