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Related Experiment Video

Updated: Jul 24, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Stock Index Spot-Futures Arbitrage Prediction Using Machine Learning Models.

Yankai Sheng¹, Ding Ma¹

¹School of Economics, Wuhan University of Technology, Wuhan 430070, China.

Entropy (Basel, Switzerland)

|July 8, 2023

Summary

This study forecasts stock index arbitrage opportunities using machine learning on CSI 300 data. Long Short-Term Memory (LSTM) networks showed superior performance in predicting arbitrage, outperforming other models.

Keywords:

Back Propagation Neural Network (BPNN)Extreme Gradient Boosting (XGBoost)Least Absolute Shrinkage and Selection Operator (LASSO)Long Short-Term Memory neural network (LSTM)spot–futures arbitrage

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Area of Science:

Quantitative Finance
Computational Finance
Financial Machine Learning

Background:

Machine learning is increasingly used in finance, but its application to stock index spot-futures arbitrage is limited.
Existing research often focuses on past arbitrage, lacking predictive capabilities for future opportunities.

Purpose of the Study:

To develop and evaluate machine learning models for forecasting arbitrage opportunities in the China Security Index (CSI) 300.
To identify anticipatory indicators for spot-futures arbitrage using historical high-frequency data.

Main Methods:

Econometric models to identify arbitrage possibility.
Exchange-Traded-Fund (ETF)-based portfolios for CSI 300 tracking.
Forecasting arbitrage indicators using LASSO, XGBoost, BPNN, and LSTM.
Performance evaluation based on error metrics (RMSE, MAPE, R²) and return metrics (yield, opportunities captured).

Main Results:

LSTM demonstrated superior performance over other models, achieving an RMSE of 0.00813, MAPE of 0.70%, R² of 92.09%, and an arbitrage return of 58.18%.
LASSO showed strong performance in specific market conditions (bull and bear markets over shorter periods).

Conclusions:

Machine learning, particularly LSTM, is effective for forecasting stock index spot-futures arbitrage opportunities.
Model performance can vary based on market conditions, suggesting adaptive strategies.