High-Frequency Direction Forecasting of the Futures Market Using a Machine-Learning-Based Method

Shangkun Deng, Yingke Zhu, Xiaoru Huang, Shuangyang Duan and Zhe Fu
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Shangkun Deng: College of Economics and Management, China Three Gorges University, Yichang 443002, China
Yingke Zhu: College of Economics and Management, China Three Gorges University, Yichang 443002, China
Xiaoru Huang: College of Economics and Management, China Three Gorges University, Yichang 443002, China
Shuangyang Duan: College of Economics and Management, China Three Gorges University, Yichang 443002, China
Zhe Fu: School of History, Beijing Normal University, Beijing 100875, China

Future Internet, 2022, vol. 14, issue 6, 1-21

Abstract: Futures price-movement-direction forecasting has always been a significant and challenging subject in the financial market. In this paper, we propose a combination approach that integrates the XGBoost (eXtreme Gradient Boosting), SMOTE (Synthetic Minority Oversampling Technique), and NSGA-II (Non-dominated Sorting Genetic Algorithm-II) methods. We applied the proposed approach on the direction prediction and simulation trading of rebar futures, which are traded on the Shanghai Futures Exchange. Firstly, the minority classes of the high-frequency rebar futures price change magnitudes are oversampled using the SMOTE algorithm to overcome the imbalance problem of the class data. Then, XGBoost is adopted to construct a multiclassification model for the price-movement-direction prediction. Next, the proposed approach employs NSGA-II to optimize the parameters of the pre-designed trading rule for trading simulation. Finally, the price-movement direction is predicted, and we conducted the high-frequency trading based on the optimized XGBoost model and the trading rule, with the classification and trading performances empirically evaluated by four metrics over four testing periods. Meanwhile, the LIME (Local Interpretable Model-agnostic Explanations) is applied as a model explanation approach to quantify the prediction contributions of features to the forecasting samples. From the experimental results, we found that the proposed approach performed best in terms of direction prediction accuracy, profitability, and return–risk ratio. The proposed approach could be beneficial for decision-making of the rebar traders and related companies engaged in rebar futures trading.

Keywords: rebar futures; multiclassification; multiobjective optimization; decision making; model explanation (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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