For financial forecasting models deployed at 4-bit precision, activation calibration method (how you estimate value ranges from past data) is critical—percentile calibration substantially outperforms standard approaches, but 8-bit activations remain the safest choice when accuracy matters most.
This paper studies how to properly calibrate neural networks for low-precision inference in financial forecasting. When deploying models with 4-bit quantization, the choice of how to estimate activation ranges from historical data significantly impacts prediction accuracy—more so than at 8 bits.