The Impact of Quantifying Financial News Features on Short-term Stock Return Volatility —— Backtesting analysis based on publicly available financial text information

Abstract

This study examines whether measurable features of public financial news can help explain short-term stock return volatility. Public announcements, financial news, institutional opinions, social media heat and market data are converted into five variables: sentiment, topic type, popularity, publication timing and market controls. Based on an initial backtesting sample of twenty anonymized events, the paper applies event-window calculation, group comparison and a simplified regression framework to observe T+1, T+3 and T+5 market reactions. The early results show that negative news, high-heat news and core-topic news related to performance, regulation, policy or capital flows are more closely associated with stronger T+3 volatility. However, abnormal returns do not show a stable direction across groups. These findings suggest that text quantification is more useful for identifying information shocks, risk attention and review priority than for producing independent trading signals. The study also provides a basic structure for future expansion to a larger sample with clearer stock codes, information sources and release times.