A Stock Index Futures Price Prediction Approach Based on the MULTI-GARCH-LSTM Mixed Model

• Published in: Mathematics (Basel) 2024-06, Vol.12 (11), p.1677
• Main Authors: Pan, Haojun, Tang, Yuxiang, Wang, Guoqiang
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial intelligence; Autoregression (Statistics); Autoregressive models; Datasets; Deep learning; Dow Jones averages; Econometrics; Forecasting; Forecasting techniques; Forecasts and trends; Futures market; GARCH family; LSTM; Machine learning; Neural networks; Parameter estimation; price prediction; Prices; Prices and rates; Securities markets; Stochastic models; Stock exchanges; Stock index futures; Time series; Volatility
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math12111677

As a type of financial derivative, the price fluctuation of futures is influenced by a multitude of factors, including macroeconomic conditions, policy changes, and market sentiment. The interaction of these factors makes the future trend become complex and difficult to predict. However, for investors, the ability to accurately predict the future trend of stock index futures price is directly related to the correctness of investment decisions and investment returns. Therefore, predicting the stock index futures market remains a leading and critical issue in the field of finance. To improve the accuracy of predicting stock index futures price, this paper introduces an innovative forecasting method by combining the strengths of Long Short-Term Memory (LSTM) networks and various Generalized Autoregressive Conditional Heteroskedasticity (GARCH)-family models namely, MULTI-GARCH-LSTM. This integrated approach is specifically designed to tackle the challenges posed by the nonstationary and nonlinear characteristics of stock index futures price series. This synergy not only enhances the model’s ability to capture a wide range of market behaviors but also significantly improves the precision of future price predictions, catering to the intricate nature of financial time series data. Initially, we extract insights into the volatility characteristics, such as the aggregation of volatility in futures closing prices, by formulating a model from the GARCH family. Subsequently, the LSTM model decodes the complex nonlinear relationships inherent in the futures price series and incorporates assimilated volatility characteristics to predict future prices. The efficacy of this model is validated by applying it to an authentic dataset of gold futures. The empirical findings demonstrate that the performance of our proposed MULTI-GARCH-LSTM hybrid model consistently surpasses that of the individual models, thereby confirming the model’s effectiveness and superior predictive capability.