Forecasting the Stock Exchange of Thailand Index Employing Deep Learning by Back-Propagation Neural Network Process

  • บัญญัติ เต็มกิจถาวร
  • สมพร ปั่นโภชา
  • บำรุง พ่วงเกิด
Keywords: Back- Propagation Neural Network, Forecasting the Stock Exchange of Thailand Index


with the back propagation is a method in deep learning which is used to estimate the non-linear relationship between input and output. The stock price indexes have a tendency of fluctuating over time, therefore, using a back- propagation neural network method is an interesting way to predict the SET index.
This study aims to forecast the SET index employing back-propagation neural network process and investigate the artificial neural network models that are effective in forecasting the SET Index. The input data by expending closing price data of SET, Dow-Jones, Nikkei, Straits-Times, Hang-Seng Index and daily trading volume of SET since 1 January 2009 to 31 December 2018 total amount 2,437 days. Then the data is divided into 2 groups, the first 2,192 days is for the training data set in order to find the appropriate model for predicting the SET Index, which will be selected from the top 5 models with the lowest mean squared error (MSE). And 245 days remaining, uses for testing the 5 selected models to find the forecasting accuracy with the absolute average percentage error (MAPE). For modeling, it uses a multi-layer neural network which consists of 1 Input layer, 2 Hidden layer s and 1 Output layer, and trained with back propagation.
As a result of this study, in the training part : the models with the lowest 5 MSE are model [8,9,11,1], [8,5,13,1], [8,13,9,1], [8,17,17,1] and [8,12,8,1]. In the testing part, it was found that the above models gives forecasting accuracy with the MAPE of 0.690149%, 0.895387%, 0.763672%, 0.788011%, 0.728610% and 0.895387% respectively. So the model [8,9,11,1] has the lowest MAPE of 0.690149%. Prediction of the SET Index during that period with this model has very close results. Therefore, this model is quite effective for forecasting the Stock SET Index


Most read articles by the same author(s)

1 2 3 > >>