{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T15:14:28Z","timestamp":1789398868836,"version":"build-2803163510"},"reference-count":18,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T00:00:00Z","timestamp":1739923200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3389\/frai.2025.1527180","type":"journal-article","created":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T06:50:13Z","timestamp":1739947813000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["A hype-adjusted probability measure for NLP stock return forecasting"],"prefix":"10.3389","volume":"8","author":[{"given":"Zheng","family":"Cao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Helyette","family":"Geman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,2,19]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1645","DOI":"10.1111\/j.1540-6261.2006.00885.x","article-title":"Investor sentiment and stock returns","volume":"61","author":"Baker","year":"2006","journal-title":"J. Finance"},{"key":"B2","first-page":"1","article-title":"Linear discriminant analysis: a brief tutorial","volume":"18","author":"Balakrishnama","year":"1998","journal-title":"Instit. Signal Infor. Proc"},{"key":"B3","first-page":"1137","article-title":"A neural probabilistic language model","volume":"3","author":"Bengio","year":"2003","journal-title":"J. Mach. Learn. Res"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1091443","DOI":"10.3389\/frai.2023.1091443","article-title":"Masking important information to assess the robustness of a multimodal classifier for emotion recognition","volume":"6","author":"Cohen","year":"2023","journal-title":"Front. Artif. Intell"},{"key":"B5","doi-asserted-by":"publisher","first-page":"836809","DOI":"10.3389\/frai.2022.836809","article-title":"A sentiment analysis approach to the prediction of market volatility","volume":"5","author":"Deveikyte","year":"2022","journal-title":"Front. Artif. Intell"},{"key":"B6","doi-asserted-by":"publisher","first-page":"e0173151","DOI":"10.1371\/journal.pone.0173151","article-title":"Twitter sentiment around the earnings announcement events","volume":"12","author":"Gabrovsek","year":"2016","journal-title":"PLoS ONE"},{"key":"B7","author":"Geman","year":"1989","journal-title":"The importance of the forward measure for the pricing of interest rate derivatives"},{"key":"B8","first-page":"3397","article-title":"Does unusual news forecast market stress?","volume":"63","author":"Glasserman","year":"2017","journal-title":"Manage. Sci"},{"key":"B9","author":"Group","year":"2024","journal-title":"LSEG data and analytics"},{"key":"B10","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1016\/0022-0531(79)90043-7","article-title":"Martingales and arbitrage in multiperiod securities markets","volume":"20","author":"Harrison","year":"1979","journal-title":"J. Econ. Theory"},{"key":"B11","doi-asserted-by":"publisher","first-page":"2289","DOI":"10.1111\/j.1540-6261.2009.01501.x","article-title":"Driven to distraction: extraneous events and underreaction to earnings news","volume":"64","author":"Hirshleifer","year":"2009","journal-title":"J. Financ"},{"key":"B12","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1609\/icwsm.v8i1.14550","article-title":"\u201cVader: a parsimonious rule-based model for sentiment analysis of social media text,\u201d","author":"Hutto","year":"2014","journal-title":"Proceedings of the International AAAI Conference on Web and Social Media"},{"key":"B13","volume-title":"Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition","author":"Jurafsky","year":"2000"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-02145-9","author":"Liu","year":"2012","journal-title":"Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies"},{"key":"B15","first-page":"41","article-title":"Proof that properly anticipated prices fluctuate randomly","volume":"6","author":"Samuelson","year":"1965","journal-title":"Ind. Manage. Rev"},{"key":"B16","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1016\/j.jeconom.2020.07.053","article-title":"Measuring news sentiment","volume":"228","author":"Shapiro","year":"2020","journal-title":"J. Econometr"},{"key":"B17","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1111\/1540-5885.410043","article-title":"Marketing hype: a new perspective for new product research and introduction","volume":"4","author":"Wind","year":"1987","journal-title":"J. Product Innov. Manage"},{"key":"B18","doi-asserted-by":"publisher","first-page":"601105","DOI":"10.3389\/fams.2021.601105","article-title":"Sentiment-guided adversarial learning for stock price prediction","volume":"7","author":"Zhang","year":"2021","journal-title":"Front. Artif. Intell"}],"container-title":["Frontiers in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2025.1527180\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T06:50:18Z","timestamp":1739947818000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2025.1527180\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,19]]},"references-count":18,"alternative-id":["10.3389\/frai.2025.1527180"],"URL":"https:\/\/doi.org\/10.3389\/frai.2025.1527180","relation":{},"ISSN":["2624-8212"],"issn-type":[{"value":"2624-8212","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,19]]},"article-number":"1527180"}}