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用多元化算法策略对冲股票指数策略

文章 arXiv papers · 作者: Jakub Michańków et al.
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总结

本文研究基于非股票资产构建的策略能否分散与标普 500 相关的算法投资策略。研究比较了由 LSTM 预测、ARIMA-GARCH 预测、动量规则和反向规则生成的信号。考察的资产包括能源商品、贵金属、加密货币和软商品。这使分散化问题从组合单个资产转向组合交易这些资产的策略。

研究使用 2004 至 2022 的数据，报告称基于 LSTM 的策略表现优于其他方法，且基于比特币的策略对标普 500 策略组合的分散效果最强。研究还考察了小时级 LSTM 信号，报告称其结果优于日频数据。这些发现是实证结果，具体取决于所考察的资产、时期和策略构建方式；摘要未说明交易成本、风险调整或样本外验证情况。

核心观点

  • 研究评估算法策略之间的分散化，而不只是单个资产之间的分散化。
  • 研究将 LSTM 和 ARIMA-GARCH 预测与动量及反向信号进行比较。
  • 测试涵盖多种商品和加密货币策略，以及标普 500 策略组合。
  • 作者报告称，基于比特币的策略对标普 500 组合的分散效果最强。
  • 研究报告称，小时级 LSTM 策略优于相应的日频策略。

标签

多资产机器学习投资组合构建风险管理回测
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# Hedging Properties of Algorithmic Investment Strategies using Long Short-Term Memory and Time Series models for Equity Indices









This paper proposes a novel approach to hedging portfolios of risky assets when financial markets are affected by financial turmoils. We introduce a completely novel approach to diversification activity not on the level of single assets but on the level of ensemble algorithmic investment strategies (AIS) built based on the prices of these assets. We employ four types of diverse theoretical models (LSTM - Long Short-Term Memory, ARIMA-GARCH - Autoregressive Integrated Moving Average - Generalized Autoregressive Conditional Heteroskedasticity, momentum, and contrarian) to generate price forecasts, which are then used to produce investment signals in single and complex AIS. In such a way, we are able to verify the diversification potential of different types of investment strategies consisting of various assets (energy commodities, precious metals, cryptocurrencies, or soft commodities) in hedging ensemble AIS built for equity indices (S&P 500 index). Empirical data used in this study cover the period between 2004 and 2022. Our main conclusion is that LSTM-based strategies outperform the other models and that the best diversifier for the AIS built for the S&P 500 index is the AIS built for Bitcoin. Finally, we test the LSTM model for a higher frequency of data (1 hour). We conclude that it outperforms the results obtained using daily data.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。