A hybrid approach of adaptive wavelet transform, long short-term memory and ARIMA-GARCH family models for the stock index prediction

Abstract

Modelling and forecasting the stock price constitute an important area of financial research for both academics and practitioners. This study seeks to determine whether improvements can be achieved by forecasting the stock index and volatility with the use of a hybrid model and incorporating the financial variables. We extend the literature of stock market forecasting by applying a hybrid model which combines adaptive wavelet transform (AWT), long short-term memory (LSTM) and ARIMAX-GARCH family models to predict stock index and combines AWT, LSTM and heterogeneous autoregressive model of realized volatility (HAR-RV) model to predict stock volatility for two major indexes in the U.S. stock market including the Dow Jones Industrial Average (DJIA) and Nasdaq Composite (IXIC). The results indicate an overall improvement in forecasting of stock index using the AWT-LSTM-ARMAX-FIEGARCH model with student’s t distribution as compared to the benchmark models. The robust test proves that this model has a higher prediction accuracy in prediction of different time horizons (1-, 10-, 15-, 20-, 30-, and 60-days ahead) for both stock indexes. Also, AWT-LSTM improves ability of HAR (3) X-RV model in prediction of the stocks realized volatility for mentioned time horizons.