Financial Futures Prediction Using Fuzzy Rough Set and Synthetic Minority Oversampling Technique

Abstract

In this research, a novel approach called SMOTE-FRS is proposed for movement prediction and trading simulation of the Chinese Stock Index 300 (CSI300) futures, which is the most crucial financial futures in the Chinese A-share market. First, the SMOTE- (Synthetic Minority Oversampling Technique-) based method is employed to address the sample unbalance problem by oversampling the minority class and undersampling the majority class of the futures price change. Then, the FRS- (fuzzy rough set-) based method, as an efficient tool for analyzing complex and nonlinear information with high noise and uncertainty of financial time series, is adopted for the price change multiclassification of the CSI300 futures. Next, based on the multiclassification results of the futures price movement, a trading strategy is developed to execute a one-year simulated trading for an out-of-sample test of the trained model. From the experimental results, it is found that the proposed method averagely yielded an accumulated return of 6.36%, a F1-measure of 65.94%, and a hit ratio of 62.39% in the four testing periods, indicating that the proposed method is more accurate and more profitable than the benchmarks. Therefore, the proposed method could be applied by the market participants as an alternative prediction and trading system to forecast and trade in the Chinese financial futures market.

An integrated approach for fuzzy rule generation in dataset classification using hybrid grid partitioning and rough set theory

“…Rough Set Theory, Rough set theory has been extensively used for feature selection and attribute reduction in dataset classification (Das et al, 2018) (H. Li et al, 2016) (Al-Radaideh & Al-Qudah, 2017 (Inbarani et al, 2015). Previous research has leveraged rough set-based algorithms to identify the most relevant attributes for classification, reducing the dimensionality of the dataset and improving the performance of fuzzy rule-based classifiers (Liu et al, 2021) (Deng et al, 2022).…”