Predicting Stock Market Index Using Hybrid Intelligence Model

Abstract

Predicting financial market changes is an important issue in time series analysis, receiving an increasing attention due to financial crisis. Autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting but ARIMA model cannot capture nonlinear patterns easily. Generalized autoregressive conditional heteroscedasticity (GARCH) model applied understanding of volatility depending to the estimation of previous forecast error and current volatility, improving ARI-MA model. Support vector machine (SVM) and artificial neural network (ANN) have been successfully applied in solving nonlinear regression estimation problems. This study proposes hybrid methodology that exploits unique strength of GARCH + SVM model, and GARCH + ANN model in forecasting stock index. Real data sets of stock prices FTSE Bursa Malaysia KLCI were used to examine the forecasting accuracy of the proposed model. The results shows that the proposed hybrid model achieves best forecasting compared to other model.

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

“…They also compare the results with the LSTM and RF models, where the proposed model outperforms other methods. [39] WT-SAEs-LSTM Financial Time Series Stock Price Prediction Shekhar and Varshney [66] GA-SVM Financial Time Series Stock Price Prediction Ahmadi et al [67] ICA-SVM Financial Time Series Stock Price Prediction Ebadati and Mortazavi [68] GA-ANN Financial Time Series Stock Price Prediction Johari et al [69] GARCH-SVM Financial Time Series Stock Price Prediction…”

“…Their study suggests that this hybrid machine learning model has an improved sum square error (SSE) (i.e., performance accuracy) by 99.99% and improved time (i.e., speed accuracy) by 90.66%. Johari et al [69] compared the accuracy and performance of GARCH-SVM and GARCH-ANN models in the financial time series data for stock price forecasting. They found that GARCH-SVM outperformed GARCH-ANN, SVM, ANN, and GARCH based on MSE and RMSE accuracy metrics.…”