AI summaryⓘ
The authors studied how lowering the precision of neural network calculations affects financial forecasting models for predicting stock volatility. They focused on a technique called post-training quantization (PTQ) which allows models to run faster and use less memory by using fewer bits for calculations. Their findings show that using 8-bit precision works well regardless of calibration, but at 4-bit precision, how the model's activation ranges are calibrated becomes crucial for maintaining prediction accuracy. They suggest that percentile-based calibration improves performance at 4 bits, but sometimes sticking to 8-bit activations or only quantizing weights at 4 bits is more reliable. This helps inform better deployment choices when balancing speed and prediction quality in financial models.
post-training quantizationactivation calibrationlow-precision inferencecross-sectional volatilityneural architectures8-bit quantization4-bit quantizationpercentile calibrationmean information coefficientfinancial forecasting
Authors
Junyi Ye, Ivy Gateri Wanjiku
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.