Sentiment-Driven Statistical Modelling of Stock Returns over Weekends
I tiakina i:
| I whakaputaina i: | Computation vol. 13, no. 8 (2025), p. 201-237 |
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| Kaituhi matua: | |
| Ētahi atu kaituhi: | |
| I whakaputaina: |
MDPI AG
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| Ngā marau: | |
| Urunga tuihono: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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MARC
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| 022 | |a 2079-3197 | ||
| 024 | 7 | |a 10.3390/computation13080201 |2 doi | |
| 035 | |a 3244001119 | ||
| 045 | 2 | |b d20250801 |b d20250831 | |
| 084 | |a 231446 |2 nlm | ||
| 100 | 1 | |a Kowalski, Kutz Pablo | |
| 245 | 1 | |a Sentiment-Driven Statistical Modelling of Stock Returns over Weekends | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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’s 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—including Linear Regression, Polynomial Regression, Random Forests, and Support Vector Machines—using 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. | |
| 610 | 4 | |a Apple Inc | |
| 653 | |a Language | ||
| 653 | |a Dictionaries | ||
| 653 | |a Hypothesis testing | ||
| 653 | |a Regression analysis | ||
| 653 | |a Regression models | ||
| 653 | |a Polynomials | ||
| 653 | |a News | ||
| 653 | |a Prices | ||
| 653 | |a Machine learning | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Influence | ||
| 653 | |a Internet stocks | ||
| 653 | |a Rates of return | ||
| 653 | |a Investor behavior | ||
| 653 | |a Performance measurement | ||
| 653 | |a Sentiment analysis | ||
| 653 | |a Support vector machines | ||
| 653 | |a Hypotheses | ||
| 653 | |a Product reviews | ||
| 653 | |a Volatility | ||
| 653 | |a Pessimism | ||
| 653 | |a Natural language processing | ||
| 653 | |a Algorithms | ||
| 653 | |a Statistical models | ||
| 653 | |a Large language models | ||
| 700 | 1 | |a Makarov, Roman N | |
| 773 | 0 | |t Computation |g vol. 13, no. 8 (2025), p. 201-237 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3244001119/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3244001119/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3244001119/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |