IEEE Computer Society Press
Faculty of Computing, Health and Science
School of Computer and Information Science
Prediction of stock prices is an issue of interest to financial markets. Many prediction techniques have been reported in stock forecasting. Neural networks are viewed as one of the more suitable techniques. In this study, an experiment on the forecasting of the Stock Exchange of Thailand (SET) was conducted by using feedforward backpropagation neural networks. In the experiment, many combinations of parameters were investigated to identify the right set of parameters for the neural network models in the forecasting of SET. Several global and local factors influencing the Thai stock market were used in developing the models, including the Dow Jones index, Nikkei index, Hang Seng index, gold prices, Minimum Loan Rate (MLR), and the exchange rates of the Thai Baht and the US dollar. Two years’ historical data were used to train and test the models. Three suitable neural network models identified by this research are a three layer, a four layer and a five layer neural network. The Mean Absolute Percentage Error (MAPE) of the predictions of each models were 1.26594, 1.14719 and 1.14578 respectively.