用方向性损失函数提升金融预测模型表现，Transformer优于LSTM。
Alternative Loss Function in Evaluation of Transformer Models
- 引入均值绝对方向损失（MADL）优化金融时序预测
- Transformer在股票与加密资产上显著优于LSTM
- 适合量化交易与算法投资方向的研究者
在机器学习模型的测试设计中，尤其是在量化金融应用中，损失函数的选择至关重要。本文通过在股票和加密货币资产上的实证实验，采用更适用于生成预测模型的均值绝对方向损失（MADL）函数，用于训练、验证、估计及超参数调优。将Transformer与LSTM模型在MADL下的表现进行对比，结果显示，在几乎所有情况下，Transformer的性能均显著优于LSTM。
原文摘要 · Abstract (English)
The proper design and architecture of testing machine learning models, especially in their application to quantitative finance problems, is crucial. The most important aspect of this process is selecting an adequate loss function for training, validation, estimation purposes, and hyperparameter tuning. Therefore, in this research, through empirical experiments on equity and cryptocurrency assets, we apply the Mean Absolute Directional Loss (MADL) function, which is more adequate for optimizing forecast-generating models used in algorithmic investment strategies. The MADL function results are compared between Transformer and LSTM models, and we show that in almost every case, Transformer results are significantly better than those obtained with LSTM.
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