对过去的校准赌注：面向金融时间序列预测的训练后量化

中文摘要 AI 辅助

金融预测模型通常以全精度开发，但生产部署常需低精度推理以降低内存和计算成本。训练后量化（PTQ）可实现此类部署，无需重新训练。然而，可靠的激活量化需要校准：激活范围在部署前从历史数据中估计，之后在未来推理期间保持固定。这种部署选择对金融预测的重要性仍知之甚少。我们对S&P 500的横截面波动率预测中PTQ的激活校准开展系统性研究。评估涵盖7种代表性神经架构、8个滚动测试年份（2018-2025）以及560个训练好的模型。我们发现，激活校准在8位精度下影响很小，但在4位精度下成为预测性能的主要决定因素。在默认绝对最大值（abs-max）校准下，权重和激活的静态4位量化会使受影响架构的全精度平均信息系数降低11%-62%。将abs-max替换为百分位校准，可在受影响最严重的4种架构中恢复53%-94%的性能下降。偏好的激活范围也因市场时期而异。较窄的范围在典型市场条件下提高分辨率，但当测试期市场分散度超过校准历史时，会失去部分优势。这些发现表明，激活校准是金融预测中可靠4位PTQ的一级部署决策。当仍存在显著性能下降时，8位激活或仅权重的4位量化提供更稳健的部署选择。

英文摘要

Financial forecasting models are typically developed in full precision, yet production deployment often requires low-precision inference to reduce memory and computational cost. Post-training quantization (PTQ) enables such deployment without retraining. However, reliable activation quantization requires calibration: activation ranges are estimated from historical data before deployment and then remain fixed during future inference. The importance of this deployment choice for financial forecasting remains poorly understood. We present a systematic study of activation calibration for PTQ in cross-sectional volatility forecasting on the S&P 500. Our evaluation covers seven representative neural architectures, eight walk-forward test years (2018-2025), and 560 trained models. We find that activation calibration has little effect at 8 bits but becomes the primary determinant of predictive performance at 4 bits. Under default absolute-maximum (abs-max) calibration, static 4-bit quantization of both weights and activations removes 11-62% of the full-precision mean information coefficient in affected architectures. Replacing abs-max with percentile calibration recovers 53-94% of this degradation in the four most affected architectures. The preferred activation range also varies across market periods. Narrow ranges improve resolution under typical market conditions but lose part of their advantage when test-period market dispersion exceeds the calibration history. These findings show that activation calibration is a first-class deployment decision for reliable 4-bit PTQ in financial forecasting. When substantial degradation remains, 8-bit activations or weight-only 4-bit quantization provide more robust deployment choices.

发表机构

• School of Computing, Montclair State University（蒙特克莱尔州立大学计算机学院）

机构由 AI 辅助整理，请以论文原文为准。