Calibration Bets on the Past: Post-Training Quantization for Financial Time-Series Forecasting

2026-08-12

AI Summary

The deployment efficacy of low-precision quantization in financial time series forecasting remains unclear, particularly as 4-bit post-training quantization (PTQ) can induce substantial performance degradation. This study systematically evaluates the impact of activation calibration strategies across 560 models—spanning seven neural architectures and eight out-of-sample test years—on cross-sectional volatility prediction for the S&P 500. It reveals, for the first time, that activation calibration constitutes a first-order deployment decision in 4-bit PTQ: absolute-max calibration incurs information coefficient losses of 11–62%, whereas percentile-based calibration recovers 53–94% of performance, with effectiveness dynamically varying across market regimes. The work further proposes more robust alternatives—8-bit activations or weight-only 4-bit quantization—significantly enhancing deployment robustness.

Abstract

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.

Problem

• post-training quantization
• activation calibration
• financial time-series forecasting
• low-precision inference
• quantization robustness

Innovation

• post-training quantization
• activation calibration
• financial time-series forecasting
• low-bit quantization
• percentile calibration