Why Does 4-Bit Quantization Break Financial Forecasts Unless You Change How You Calibrate?

The Calibration Problem Nobody Measured

Financial forecasting models are trained in full precision. Production deployment demands low-precision inference to cut memory and compute. Post-training quantization (PTQ) enables this without retraining. Reliable activation quantization requires calibration. Activation ranges are estimated from historical data before deployment and then stay fixed during future inference. The importance of this deployment choice for financial forecasting remained poorly understood until now.

Junyi Ye and Ivy Gateri Wanjiku submitted their study on 12 August 2026. They present a systematic evaluation of activation calibration for PTQ in cross-sectional volatility forecasting on the S&P 500. The evaluation covers seven representative neural architectures. It spans eight walk-forward test years from 2018 to 2025. It includes 560 trained models.

What Happens at 4 Bits

Activation calibration has little effect at 8 bits. At 4 bits it becomes the primary determinant of predictive performance. 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. The degradation is not uniform. Some architectures lose more than half their signal. Others hold most of it.

Whether the remaining architectures are immune or simply less damaged goes unreported. The paper states the range across affected ones.

Percentile Calibration Changes the Outcome

Replacing abs-max with percentile calibration recovers 53-94% of this degradation in the four most affected architectures. The recovery is substantial. It does not reach full precision. The gap that remains varies by architecture and market period.

Percentile calibration uses a quantile of the observed activation distribution instead of the observed maximum. This shrinks the range. It increases resolution for typical values. It clips outliers. The trade-off depends on how frequently outliers actually appear during inference.

Market Regimes Shift the Sweet Spot

The preferred activation range also varies across market periods. Narrow ranges improve resolution under typical market conditions. They lose part of their advantage when test-period market dispersion exceeds the calibration history. The calibration data comes from the past. The test data comes from the future. When the future is wilder than the past, a narrow range clips too much. A wider range preserves signal at the cost of resolution.

The authors do not quantify how frequently this regime shift occurs in the eight test years. They describe the mechanism and the directional effect.

When to Stop Quantizing Activations

When substantial degradation remains, 8-bit activations provide a more robust deployment choice. Weight-only 4-bit quantization keeps activations in higher precision. It avoids the calibration problem entirely for activations. The paper frames this as a practical fallback. It does not benchmark mixed-precision schemes beyond this statement.

Cross-sectional volatility forecasting on the S&P 500 defines the setting. Seven architectures. Eight walk-forward years. 560 models. The findings are specific to this setting. The authors do not claim generality to other assets, horizons, or model families.