金融时间序列LSTM网络中的横截面异质性
Cross-Sectional Heterogeneity in LSTM Networks for Financial Time Series
AI总结：
针对金融时间序列预测中LSTM难处理横截面异质性的问题，本文提出含可学习行业嵌入的扩展LSTM模型，结合宏观金融协变量，在标普500成分股多空策略中表现优于基准模型，还通过可视化和贡献指标提升了可解释性。
AI中文摘要：
预测金融资产收益仍是实证金融学中最具挑战性的问题之一，其驱动因素是低信噪比和半强式市场效率。尽管深度学习模型，尤其是LSTM网络，在捕捉时间依赖关系方面展现出潜力，但标准架构往往难以考虑资产收益的横截面异质性。本文提出了一种对基础LSTM模型的新型架构扩展，旨在同时提高预测精度和模型可解释性。该框架整合了宏观金融协变量以捕捉更广泛的经济信号，并引入可学习的行业嵌入来涵盖行业层面的异质性。交易策略基于对每只标普500成分股的每日方向预测构建多空投资组合，目标是预期表现优于或劣于标普500横截面中位数收益的股票。模型性能与三个竞争性基准进行了评估：基础LSTM、随机森林模型和传统的市场买入并持有策略。实证结果表明，带有行业嵌入的LSTM在关键风险和收益指标上优于所有基准。通过利用行业嵌入，该模型明确纳入了横截面异质性，使其能够适应市场内不同行业的动态变化。为解决深度学习的黑箱特性，本文使用潜在空间可视化来分析模型如何区分不同行业，为LSTM中行业的内部表示提供了见解。通过检查LSTM的权重，可使用一种新型贡献指标量化行业信息的影响。预测信号由短期反转因子和行业动量因子驱动。
英文摘要：
Predicting financial asset returns remains one of the most difficult challenges in empirical finance, driven by the low signal-to-noise ratio and the semi-strong form of market efficiency. While deep learning models, especially LSTM networks, have shown promise in capturing temporal dependencies, standard architectures often struggle to account for the cross-sectional heterogeneity of asset returns. This paper proposes a novel architectural extension to the basic LSTM model designed to improve both predictive accuracy and model interpretability. The framework integrates macro-financial covariates to capture broader economic signals and learnable sector embeddings to encompass heterogeneity by sector. The trading strategy involves constructing a long-short portfolio based on daily directional forecasts for each S&P 500 constituent, targeting stocks expected to under- or outperform the cross-sectional median return of the S&P 500. Model Performance is evaluated against three competitive benchmarks: a basic LSTM, a Random Forest model and a traditional market buy-and-hold strategy. The empirical results demonstrate that the LSTM with sector embeddings outperforms all benchmarks across key risk and return metrics. By utilizing sector embeddings, the model explicitly incorporates cross-sectional heterogeneity, allowing it to adapt to varying industry dynamics within the market. To address the black-box nature of deep learning, I use latent space visualizations to analyse how the model differentiates between sectors, providing insights into the internal representation of the sectors in the LSTM. The impact of the sector information can be quantified using a novel contribution metric by inspecting the weights of the LSTM. The predictive signal is driven by a short-term reversal factor and an industry momentum factor.
