股票指数现货期货套利预测使用机器学习模型
1School of Economics, Wuhan University of Technology, Wuhan 430070, China.
Entropy (Basel, Switzerland)
|July 8, 2023
概括
本研究预测了使用CSI 300数据的机器学习来预测股票指数套利机会. 长短期内存 (LSTM) 网络在预测套利方面表现出卓越的表现,优于其他模型.
科学领域:
- 量化金融 量化金融
- 计算金融是指计算金融.
- 金融机器学习 金融机器学习
背景情况:
- 机器学习越来越多地用于金融领域,但它对股票指数即时期货套利的应用是有限的.
- 现有的研究往往侧重于过去的套利,缺乏对未来机会的预测能力.
研究的目的:
- 开发和评估机器学习模型,用于预测中国安全指数 (CSI) 300中的套利机会.
- 通过使用历史高频数据,确定现货期货套利的预期指标.
主要方法:
- 计量经济学模型,以确定套利的可能性.
- 基于交易所交易基金 (ETF) 的投资组合用于CSI 300跟踪.
- 使用LASSO,XGBoost,BPNN和LSTM预测套利指标.
- 基于错误指标 (RMSE,MAPE,R2) 和回报指标 (收益率,抓住的机会) 的绩效评估.
主要成果:
- 与其他模型相比,LSTM表现优越,RMSE为0.00813,MAPE为0.70%,R2为92.09%,套利回报率为58.18%.
- 拉索在特定的市场条件下表现强 (较短时间的牛市和熊市).
结论:
- 机器学习,特别是LSTM,对于预测股票指数即时期货套利机会是有效的.
- 模型性能可以根据市场状况而有所不同,这表明适应性策略.
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