Hype-Adjusted Probability Measure for NLP Volatility Forecasting

This manuscript introduces the hype-adjusted probability measure developed in the context of a new Natural Language Processing (NLP) approach for market forecasting. A novel sentiment score equation is presented to capture component and memory effects and assign dynamic parameters, enhancing the impact of intraday news data on forecasting next-period volatility for selected U.S. semiconductor stocks. This approach integrates machine learning techniques to analyze and improve the predictive value of news. Building on the research of Geman's, this work improves forecast accuracy by assigning specific weights to each component of news sources and individual stocks in the portfolio, evaluating time-memory effects on market reactions, and incorporating shifts in sentiment direction. Finally, we propose the Hype-Adjusted Probability Measure, proving its existence and uniqueness, and discuss its theoretical applications in finance for NLP-based volatility forecasting, outlining future research pathways inspired by its concepts.