{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T08:38:35Z","timestamp":1775896715533,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T00:00:00Z","timestamp":1755734400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["RGPIN-2020-04782"],"award-info":[{"award-number":["RGPIN-2020-04782"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>We propose a two-stage statistical learning framework to investigate how financial news headlines posted over weekends affect stock returns. In the first stage, Natural Language Processing (NLP) techniques are used to extract sentiment features from news headlines, including FinBERT sentiment scores and Impact Probabilities derived from Logistic Regression models (Binomial, Multinomial, and Bayesian). These Impact Probabilities estimate the likelihood that a given headline influences the stock\u2019s opening price on the following trading day. In the second stage, we predict over-weekend log returns using various sets of covariates: sentiment-based features, traditional financial indicators (e.g., trading volumes, past returns), and headline counts. We evaluate multiple statistical learning algorithms\u2014including Linear Regression, Polynomial Regression, Random Forests, and Support Vector Machines\u2014using cross-validation and two performance metrics. Our framework is demonstrated using financial news from MarketWatch and stock data for Apple Inc. (AAPL) from 2014 to 2023. The results show that incorporating sentiment features, particularly Impact Probabilities, improves predictive accuracy. This approach offers a robust way to quantify and model the influence of qualitative financial information on stock performance, especially in contexts where markets are closed but news continues to develop.<\/jats:p>","DOI":"10.3390\/computation13080201","type":"journal-article","created":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T14:46:04Z","timestamp":1755787564000},"page":"201","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Sentiment-Driven Statistical Modelling of Stock Returns over Weekends"],"prefix":"10.3390","volume":"13","author":[{"given":"Pablo","family":"Kowalski Kutz","sequence":"first","affiliation":[{"name":"Department of Mathematics, Wilfrid Laurier University, 75 University Ave W, Waterloo, ON N2L 3C5, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0935-9445","authenticated-orcid":false,"given":"Roman N.","family":"Makarov","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Wilfrid Laurier University, 75 University Ave W, Waterloo, ON N2L 3C5, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/S0304-405X(02)00148-4","article-title":"Market efficiency in real time","volume":"65","author":"Busse","year":"2002","journal-title":"J. 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