对比多种深度学习模型在金融时序预测中的表现，发现专为时间建模设计的模型更优。
Deep Learning for Financial Time Series: A Large-Scale Benchmark of Risk-Adjusted Performance
- 采用混合架构如VSN+LSTM，专注学习复杂时间特征
- VSN+LSTM获最高夏普比率，xLSTM抗交易摩擦能力最强
- 评估覆盖风险控制、鲁棒性与效率，适合量化交易研究者
我们构建了一个大规模基准，评估现代深度学习架构在金融时序预测与头寸规模调整任务中的表现，重点优化夏普比率。在2010至2025年涵盖商品、股指、债券和外汇的每日期货数据集上，测试了线性模型、循环网络、基于Transformer的架构、状态空间模型及近期序列表示方法。评估不仅包括平均收益，还涵盖统计显著性、下行风险与尾部风险度量、盈亏平衡交易成本分析、随机种子选择的鲁棒性以及计算效率。结果表明，专为学习丰富时间表征而设计的模型持续优于线性基线和通用深度学习模型，后者在标准时序基准中常领先。混合模型如VSN与LSTM结合（VSN+LSTM）取得最高总体夏普比率；而VSN+xLSTM与LSTM+PatchTST在下行风险调整方面表现更佳。xLSTM展现出最大的盈亏平衡交易成本缓冲，表明其对交易摩擦更具鲁棒性。
原文摘要 · Abstract (English)
We present a large scale benchmark of modern deep learning architectures for a financial time series prediction and position sizing task, with a primary focus on Sharpe ratio optimization. Evaluating linear models, recurrent networks, transformer based architectures, state space models, and recent sequence representation approaches, we assess out of sample performance on a daily futures dataset spanning commodities, equity indices, bonds, and FX spanning 2010 to 2025. Our evaluation goes beyond average returns and includes statistical significance, downside and tail risk measures, breakeven transaction cost analysis, robustness to random seed selection, and computational efficiency. We find that models explicitly designed to learn rich temporal representations consistently outperform linear benchmarks and generic deep learning models, which often lead the ranking in standard time series benchmarks. Hybrid models such as VSN with LSTM, a combination of Variable Selection Networks (VSN) and LSTMs, achieves the highest overall Sharpe ratio, while VSN with xLSTM and LSTM with PatchTST exhibit superior downside adjusted characteristics. xLSTM demonstrates the largest breakeven transaction cost buffer, indicating improved robustness to trading frictions.
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