用LSTM预测资产收益分布，跨品类通用且精度远超传统方法。
Generalized Distribution Prediction for Asset Returns
- 分两阶段预测标准化收益分位数，先用资产特征，再融合市场数据。
- 在真实与合成数据上，性能比线性模型高98%，比全连接网络高50%以上。
- 仅用通用特征，适用于商品、加密货币等多类资产，适合量化研究者。
我们提出一种基于分位数的新型方法，利用长短期记忆（LSTM）网络预测资产收益分布。该模型分两个阶段：第一阶段使用资产特征求解归一化收益的分位数；第二阶段引入市场数据，调整预测以反映宏观经济环境。最终通过核密度估计将分位数转化为完整概率分布，实现更精确的收益分布预测。实验表明，该LSTM模型在真实与合成数据上均显著优于线性分位数回归基线（提升98%）和全连接神经网络（提升超50%）。模型仅依赖资产类别无关特征，具备强泛化能力，可广泛应用于商品、加密货币等多类资产。
原文摘要 · Abstract (English)
We present a novel approach for predicting the distribution of asset returns using a quantile-based method with Long Short-Term Memory (LSTM) networks. Our model is designed in two stages: the first focuses on predicting the quantiles of normalized asset returns using asset-specific features, while the second stage incorporates market data to adjust these predictions for broader economic conditions. This results in a generalized model that can be applied across various asset classes, including commodities, cryptocurrencies, as well as synthetic datasets. The predicted quantiles are then converted into full probability distributions through kernel density estimation, allowing for more precise return distribution predictions and inferencing. The LSTM model significantly outperforms a linear quantile regression baseline by 98% and a dense neural network model by over 50%, showcasing its ability to capture complex patterns in financial return distributions across both synthetic and real-world data. By using exclusively asset-class-neutral features, our model achieves robust, generalizable results.
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