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ORIGINAL RESEARCH article

Front. Artif. Intell.
Sec. AI in Finance
Volume 8 - 2025 | doi: 10.3389/frai.2025.1527180

A Hype-Adjusted Probability Measure for NLP Stock Return Forecasting

Provisionally accepted
Zheng Cao Zheng Cao *Helyette Geman Helyette Geman
  • Johns Hopkins University, Baltimore, United States

The final, formatted version of the article will be published soon.

    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.

    Keywords: Hype-adjusted Probability Measure, Natural Language Processing, sentiment analysis, Market Volatility Forecast, Semiconductor industry

    Received: 13 Nov 2024; Accepted: 29 Jan 2025.

    Copyright: © 2025 Cao and Geman. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Zheng Cao, Johns Hopkins University, Baltimore, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.