AUTHOR=Cao Zheng , Geman Helyette TITLE=A hype-adjusted probability measure for NLP stock return forecasting JOURNAL=Frontiers in Artificial Intelligence VOLUME=Volume 8 - 2025 YEAR=2025 URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1527180 DOI=10.3389/frai.2025.1527180 ISSN=2624-8212 ABSTRACT=This article introduces a Hype-Adjusted Probability Measure in the context of a new Natural Language Processing (NLP) approach for stock return and volatility forecasting. A novel sentiment score equation is proposed to represent the impact of intraday news on forecasting next-period stock return and volatility for selected U.S. semiconductor tickers, a very vibrant industry sector. This work improves the forecast accuracy by addressing news bias, memory, and weight, and incorporating shifts in sentiment direction. More importantly, it extends the use of the remarkable tool of change of Probability Measure developed in the finance of Asset Pricing to NLP forecasting by constructing a Hype-Adjusted Probability Measure, obtained from a redistribution of the weights in the probability space, meant to correct for excessive or insufficient news.