Machine learning techniques (support vector machine and artificial neural network) for fundamental and technical analysis: a study on SET and PSEI - Chin, Jern Tat (2021)
Our paper investigates the performance of two machine learning models, namely Support Vector Machine (SVM) and Artificial Neural Network (ANN) in predicting the direction of movement of Stock Exchange of Thailand (SET) index and Philippines Stock Exchange Composite Index (PSEI). We investigate the predictive abilities of two methods of forecasting stock prices, namely fundamental and technical analysis. Therefore, for technical analysis, ten technical indicators of various input window length are selected a s input features for our model. Input window length are the number of periods in the past used to calculate technical indicators. For fundamental analysis, 12 fundamental variables are selected as input features.