---
title: "Calibration Bets on the Past: Post-Training Quantization for Financial Time-Series Forecasting"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2608.12259v1"
permalink: "https://feed.astralor.com/items/itm_d3843502cf55b79be49c794f"
published: "2026-08-12T17:02:06.000Z"
score: 75
channel: "tech"
tags: ["arXiv","S&P 500","Post-Training Quantization","Activation calibration","Financial time-series forecasting"]
---

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

## AI commentary

> This paper elevates activation calibration from a technical detail to a first-class deployment decision in quantization. For engineers building financial models, 4-bit quantization is no longer a pure compression problem but one tied to market volatility characteristics. The notable finding that percentile calibration recovers most of the degradation suggests many existing deployments could improve significantly with a one-line change.

## At a glance

A study shows that in financial time-series forecasting, activation calibration for post-training quantization (PTQ) has little effect at 8 bits but becomes the primary determinant of predictive performance at 4 bits. Based on S&P 500 cross-sectional volatility forecasting, covering 7 neural architectures, 8 walk-forward test years (2018-2025), and 560 trained models, default absolute-maximum calibration removes 11-62% of the full-precision mean information coefficient under static 4-bit quantization of weights and activations, while percentile calibration recovers 53-94% of this degradation. The preferred activation range varies across market periods, making activation calibration a first-class deployment decision for reliable 4-bit PTQ in financial forecasting.

## Key points

- Activation calibration becomes the primary determinant of predictive performance at 4-bit quantization, while having little effect at 8 bits.
- Default abs-max calibration causes 11-62% information coefficient degradation, and percentile calibration recovers most of it.
- The optimal activation range varies with market volatility, and historical calibration windows may not cover future extreme dispersion.
- For severe degradation, 8-bit activations or weight-only quantization are more robust deployment choices.

## Full text

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.

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