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面向异常感知的中期股票预测模型 Mid-LSTM

文章 arXiv papers · 作者: Xinyi Li et al.
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总结

论文提出 Mid-LSTM，这是一种旨在预测中期股价并考虑市场异常的深度学习模型。其设计将市场趋势表示为隐藏状态。所述方法首先构建一个纳入隐藏状态和CAPM的中期ARMA模型，然后结合LSTM、隐马尔可夫模型和线性回归网络。其目标是限制预测误差的累积，并解释与股价相关的因素。

据报告，在标准普尔500股票上的实验显示，预测准确率提高了2–4%。作者还报告称，投资组合配置结果的年化收益率最高为120.16%，平均夏普比率为2.99。这些是论文摘要中的主张；摘录未说明评估期、基准方法、交易成本、验证设计或风险控制。因此，这些结果本身无法证明样本外表现或预期实盘收益。

核心观点

  • Mid-LSTM将市场趋势表示为隐藏状态，用于中期股票预测。
  • 该模型结合了LSTM、隐马尔可夫和线性回归组件。
  • 其模型构建还借鉴了ARMA和CAPM。
  • 据报告，标准普尔500实验显示预测准确率和投资组合指标有所改善，但摘录未提供评估细节。

标签

股票机器学习统计学投资组合构建回测
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# Risk Management via Anomaly Circumvent: Mnemonic Deep Learning for Midterm Stock Prediction


# Risk Management via Anomaly Circumvent: Mnemonic Deep Learning for Midterm Stock Prediction









Midterm stock price prediction is crucial for value investments in the stock market. However, most deep learning models are essentially short-term and applying them to midterm predictions encounters large cumulative errors because they cannot avoid anomalies. In this paper, we propose a novel deep neural network Mid-LSTM for midterm stock prediction, which incorporates the market trend as hidden states. First, based on the autoregressive moving average model (ARMA), a midterm ARMA is formulated by taking into consideration both hidden states and the capital asset pricing model. Then, a midterm LSTM-based deep neural network is designed, which consists of three components: LSTM, hidden Markov model and linear regression networks. The proposed Mid-LSTM can avoid anomalies to reduce large prediction errors, and has good explanatory effects on the factors affecting stock prices. Extensive experiments on S&P 500 stocks show that (i) the proposed Mid-LSTM achieves 2-4% improvement in prediction accuracy, and (ii) in portfolio allocation investment, we achieve up to 120.16% annual return and 2.99 average Sharpe ratio.

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。