Multi-Step Multidimensional Statistical Arbitrage Prediction Using PSO Deep-ConvLSTM: An Enhanced Approach for Forecasting Price Spreads

• Published in: Applied sciences Vol. 14; no. 9; p. 3798
• Main Authors: Tu, Sensen, Qin, Panke, Zhu, Mingfu, Zeng, Zeliang, Cheng, Shenjie, Ye, Bo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2024
• Subjects: Accuracy; Algorithms; Analysis; Arbitrage; Artificial intelligence; Commodity futures; Computational linguistics; convolutional long short-term memory model (CONVLSTM); Crude oil; Crude oil prices; Datasets; Decision making; Deep learning; Econometrics; Financial markets; Forecasting; Forecasts and trends; Futures; Geospatial data; Hedging (Finance); inter-commodity spread; Investments; Language processing; Machine learning; Mathematical optimization; multistep and multidimensional forecast; Natural language interfaces; Neural networks; particle swarm optimization (PSO); Securities markets; Stochastic models; Time series; Volatility
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app14093798

Due to its effectiveness as a risk-hedging trading strategy in financial markets, futures arbitrage is highly sought after by investors in turbulent market conditions. The essence of futures arbitrage lies in formulating strategies based on predictions of future futures price differentials. However, contemporary research predominantly focuses on projections of single indicators for the subsequent temporal juncture, and devising efficacious arbitrage strategies often necessitates the examination of multiple indicators across timeframes. To tackle the aforementioned challenge, our methodology leverages a PSO Deep-ConvLSTM network, which, through particle swarm optimization (PSO), refines hyperparameters, including layer architectures and learning rates, culminating in superior predictive performance. By analyzing temporal-spatial data within financial markets through ConvLSTM, the model captures intricate market patterns, performing better in forecasting than traditional models. Multistep forward simulation experiments and extensive ablation studies using future data from the Shanghai Futures Exchange in China validate the effectiveness of the integrated model. Compared with the gate recurrent unit (GRU), long short-term memory (LSTM), Transformer, and FEDformer, this model exhibits an average reduction of 39.8% in root mean squared error (RMSE), 42.5% in mean absolute error (MAE), 45.6% in mean absolute percentage error (MAPE), and an average increase of 1.96% in coefficient of determination (R2) values.