An Application of Quantum Optimization with Fuzzy Inference System for Stock Index Futures Forecasting

Abstract

Futures contracts of stock index futures had been trading remarkably worldwide in 2020. To mitigate the market risks and make the best of investments, predicting the movement of stock index futures is considered significant to ensure the potential gain in futures market trading. However, the accurate and precise prediction is not easy to obtain in the financial data which normally have high fluctuation. While the conventional econometric methods for forecasting require many assumptions, the machine learning models generally involve complex and unintelligible rules as well as a complicated network structure. In addition, the machine learning model itself did not guarantee a global optimum solution. It easily falls to the local optimum answer that directly affects the model's predicted value accuracy. To handle these drawbacks, in this study, we propose using a novel hybrid Wavelet Transformation-Quantum-behaved Particle Swarm Optimization-Adaptive Neuro-Fuzzy Inference System (WT-QPSO-ANFIS) model to forecast stock index futures. Generally speaking, three approaches, Wavelet Transformation (WT), Quantum Particle Swarm Optimization (QPSO), and Adaptive Neuro-Fuzzy Inference System (ANFIS), are combined in our forecasting model. The result reveals that the hybrid WT-QPSO-ANFIS model provides higher efficiency and accuracy in predicting all 11 stock index futures considered in this study compared to the conventional Sugeno-type ANFIS model, ANN model and ARIMA model in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Standard Error of Mean (SEM).