Quantizing Financial Forecasting Models

Summary & Methodology Analysis

The paper investigates the impact of post-training quantization, which reduces the numerical precision of a trained model to save memory and compute, and specifically activation calibration on cross-sectional volatility forecasting models applied to financial time series. The evaluation protocol uses a walk-forward approach spanning seven representative neural architectures, eight walk-forward test years from 2018 to 2025, and 560 trained models on an S&P 500 equity panel. The evaluated low-precision quantization treatments include dynamic INT8, static W8A8, weight-only W4, and static W4A4 under various activation calibration strategies. Every learned matrix multiplication is quantized together with the activation presented to it, while keeping biases, normalization layers, and elementwise nonlinearities in FP32. Activation ranges are estimated using historical validation datasets prior to deployment via absolute-maximum or percentile calibration statistics.

Under default absolute-maximum calibration, static 4-bit quantization of both weights and activations removes 11 to 62 percent of the full-precision mean information coefficient in affected architectures. However, replacing absolute-maximum with percentile calibration recovers 53 to 94 percent of the degradation in the four most affected architectures. The models and datasets used in the study include S&P 500, Yahoo Finance, Tiingo, DLinear, TSMixer, TimeMixer, Transformer, PatchTST, iTransformer, and SegRNN. The exact performance, cost, and memory figures outside of these percentages and counts are not specified by the paper.

There are several notable limitations to the study. The reported information coefficient levels are survivorship-tilted because the evaluation panel uses current S&P 500 constituents. Additionally, quantization is simulated in FP32, meaning it captures the predictive cost of range selection but does not account for backend-level efficiency or accumulation effects. Finally, the study evaluates only symmetric round-to-nearest quantization in a standard hardware-friendly layout. The paper does not specify hardware execution latencies or other deployment metrics.