Hype-Adjusted Probability Measure for NLP Stock Return Forecasting

This manuscript introduces the Hype-Adjusted Probability Measure developed in the context of a new Natural Language Processing (NLP) approach for stock return and volatility 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 tickers. This approach integrates machine learning techniques to analyze and improve the predictive value of news. Building on the research of Geman et al [6], this work improves forecast accuracy by addressing news bias, memory, and weight, and incorporating shifts in senti-ment direction. Finally, we propose the Hype-Adjusted Probability Measure, proving its existence and uniqueness, and discuss its theoretical applications in finance for NLP-based stock return forecasting, outlining future research pathways inspired by its concepts.