Prediction and Trading in Crude Oil Markets Using Multi-Class Classification and Multi-Objective Optimization

Abstract

Crude oil price direction forecasting presents an extremely challenging task that attracts considerable attention from academic scholars, individual investors and institutional investors. In this research, we proposed an integration method by adopting the Multi-Class Support Vector Machine (MCSVM) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for forecasting and trading simulation in two well-known crude oil markets. Firstly, the proposed approach applied the MCSVM to train a multi-class classification model, and it adopted the NSGA-II to optimize the threshold values of trading rules. Then, the trained MCSVM model was used to forecast the movement direction and magnitude levels. Next, the proposed method forecasted the direction of crude oil price movements one week later and executed trading simulation according to the direction and magnitude level predictions. Finally, after a testing period lasted for four years, the performances of the proposed approach were gauged in terms of direction prediction correctness and investment yields. Experimental results demonstrated that the proposed approach produced outstanding results not only on hit ratio and accumulated return but also return-risk ratio. It indicates that the proposed approach can provide beneficial suggestions for individual investors, institutional investors, as well as for government officers engaged in energy investment policies making.

Keywords

• Crude oil
• multi-class classification
• direction prediction
• multi-objective optimization
• simulation trading