Improving long short-term memory (LSTM) networks for arbitrage spread forecasting: integrating cuckoo and zebra algorithms in chaotic mapping space for enhanced accuracy

Abstract

Long short-term memory (LSTM) networks, widely used for financial time series forecasting, face challenges in arbitrage spread prediction, especially in hyperparameter tuning for large datasets. These issues affect model complexity and adaptability to market dynamics. Existing heuristic algorithms for LSTM often struggle to capture the complex dynamics of futures spread data, limiting prediction accuracy. We propose an integrated Cuckoo and Zebra Algorithms-optimised LSTM (ICS-LSTM) network for arbitrage spread prediction. This method replaces the Lévy flight in the Cuckoo algorithm with the Zebra algorithm search, improving convergence speed and solution optimization. Experimental results showed a mean absolute percentage error (MAPE) of 0.011, mean square error (MSE) of 3.326, mean absolute error (MAE) of 1.267, and coefficient of determination (R2) of 0.996. The proposed model improved performance by reducing MAPE by 8.3–50.0%, MSE by 10.2–77.8%, and MAE by 9.3–63.0% compared to existing methods. These improvements translate to more accurate spread predictions, enhancing arbitrage opportunities and trading strategy profitability.

Keywords

• LSTM networks
• Arbitrage spread forecasting
• Hyperparameter setting
• Cuckoo algorithm
• Zebra algorithm