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Multi-strategy modified sparrow search algorithm for hyperparameter optimization in arbitrage prediction models

Author

Listed:
  • Shenjie Cheng
  • Panke Qin
  • Baoyun Lu
  • Jinxia Yu
  • Yongli Tang
  • Zeliang Zeng
  • Sensen Tu
  • Haoran Qi
  • Bo Ye
  • Zhongqi Cai

Abstract

Deep learning models struggle to effectively capture data features and make accurate predictions because of the strong non-linear characteristics of arbitrage data. Therefore, to fully exploit the model performance, researchers have focused on network structure and hyperparameter selection using various swarm intelligence algorithms for optimization. Sparrow Search Algorithm (SSA), a classic heuristic method that simulates the sparrows’ foraging and anti-predatory behavior, has demonstrated excellent performance in various optimization problems. Hence, in this study, the Multi-Strategy Modified Sparrow Search Algorithm (MSMSSA) is applied to the Long Short-Term Memory (LSTM) network to construct an arbitrage spread prediction model (MSMSSA-LSTM). In the modified algorithm, the good point set theory, the proportion-adaptive strategy, and the improved location update method are introduced to further enhance the spatial exploration capability of the sparrow. The proposed model was evaluated using the real spread data of rebar and hot coil futures in the Chinese futures market. The obtained results showed that the mean absolute percentage error, root mean square error, and mean absolute error of the proposed model had decreased by a maximum of 58.5%, 65.2%, and 67.6% compared to several classical models. The model has high accuracy in predicting arbitrage spreads, which can provide some reference for investors.

Suggested Citation

  • Shenjie Cheng & Panke Qin & Baoyun Lu & Jinxia Yu & Yongli Tang & Zeliang Zeng & Sensen Tu & Haoran Qi & Bo Ye & Zhongqi Cai, 2024. "Multi-strategy modified sparrow search algorithm for hyperparameter optimization in arbitrage prediction models," PLOS ONE, Public Library of Science, vol. 19(5), pages 1-24, May.
    • Handle: RePEc:plo:pone00:0303688
    DOI: 10.1371/journal.pone.0303688
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    References listed on IDEAS

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    1. Qingfeng “Wilson” Liu, 2005. "Price relations among hog, corn, and soybean meal futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 25(5), pages 491-514, May.
    2. Yiyang Zheng, 2022. "Neural Network and Order Flow, Technical Analysis: Predicting short-term direction of futures contract," Papers 2203.12457, arXiv.org.
    3. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
    4. Sen Wu & Shuaiqi Liu & Huimin Zong & Yiyuan Sun & Wei Wang, 2023. "Research on a Prediction Model and Influencing Factors of Cross-Regional Price Differences of Rebar Spot Based on Long Short-Term Memory Network," Sustainability, MDPI, vol. 15(6), pages 1-12, March.
    5. Tay, Francis E. H. & Cao, Lijuan, 2001. "Application of support vector machines in financial time series forecasting," Omega, Elsevier, vol. 29(4), pages 309-317, August.
    6. Luka Jovanovic & Dejan Jovanovic & Nebojsa Bacanin & Ana Jovancai Stakic & Milos Antonijevic & Hesham Magd & Ravi Thirumalaisamy & Miodrag Zivkovic, 2022. "Multi-Step Crude Oil Price Prediction Based on LSTM Approach Tuned by Salp Swarm Algorithm with Disputation Operator," Sustainability, MDPI, vol. 14(21), pages 1-29, November.
    7. Zhang, Chengyu & Ma, Liangdong & Luo, Zhiwen & Han, Xing & Zhao, Tianyi, 2024. "Forecasting building plug load electricity consumption employing occupant-building interaction input features and bidirectional LSTM with improved swarm intelligent algorithms," Energy, Elsevier, vol. 288(C).
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