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.

翻译：本文提出了一种在新型自然语言处理（NLP）市场预测框架下开发的炒作调整概率测度。通过构建一种新颖的情感评分方程来捕捉成分效应与记忆效应，并赋予动态参数，从而增强日内新闻数据对选定美国半导体股票下一期波动率预测的影响。该方法整合机器学习技术以分析并提升新闻的预测价值。本研究在Geman等人工作的基础上，通过对新闻源各成分及投资组合中个股分配特定权重、评估市场反应的时间记忆效应，并纳入情感方向转变，从而提升预测精度。最后，我们提出炒作调整概率测度，证明其存在性与唯一性，并探讨其在基于NLP的波动率预测金融领域的理论应用，基于其核心概念展望了未来研究方向。