Information Flow and Market Risk: Transformer-Based Evidence on Sentiment and Volatility Dynamics

Abstract

This study examines whether transformer-based financial news sentiment improves equity volatility forecasts beyond conventional market-based predictors. Using data for eleven US stocks, we compare volatility persistence models with specifications incorporating the VIX, AAII investor sentiment, and news sentiment extracted using ModernFinBERT. The forecasting target is annualized volatility constructed from future averages of trailing 252-session Yang-Zhang estimates, evaluated at horizons of 1, 5, and 22 trading days. Models are assessed through walk-forward testing with expanding and rolling estimation windows, fixed initial hyperparameter selection, and statistical comparisons accounting for multiple testing and serial dependence. Results show that a naive persistence benchmark achieves the lowest average QLIKE loss at all horizons. Although HAR-VIX produces the largest number of individual forecasting wins under expanding windows, it does not achieve the lowest mean loss. Adding text-based sentiment improves HAR forecasts in only two of 33 expanding-window comparisons, and neither improvement remains statistically significant under longer HAC bandwidths. A blinded audit of 2,700 news texts further characterizes the agreement and confidence patterns of ModernFinBERT classifications. The findings do not establish a general incremental forecasting advantage from transformer-based news sentiment. They highlight the importance of strong benchmarks, temporal alignment, robust inference, and careful evaluation of text-derived predictors.