Data Modeling
Mathematical and Computational Data Modeling
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Volume 3 Number 1 2022
RESEARCH ARTICLE (Open Access)
Does Financial News Sentiment Really Move Stock Prices? A Machine Learning Investigation Using Sentiment Analysis and LSTM Forecasting
Iffat Jahan
Data Modeling 3 (1) 1-8 https://doi.org/10.25163/data.3110810
Submitted: 25 October 2022 Revised: 10 December 2022 Accepted: 18 December 2022 Published: 20 December 2022
Abstract
Whether news sentiment genuinely moves markets, or merely appears to in hindsight, remains a surprisingly unsettled question — one this study revisits empirically rather than theoretically. Stock market behavior has long resisted prediction from historical price data alone, partly because unstructured signals such as financial news and social commentary inject volatility that purely numerical models tend to miss. Drawing on a large labeled corpus of financial headlines alongside seven years of company-level closing-price data, we built a two-stage system: first, a sentiment classifier (trained using Naive Bayes and Support Vector Machine algorithms) that scores news as positive, negative, or neutral; and second, a Long Short-Term Memory network that forecasts next-day price movement using both that sentiment score and historical closing price. The sentiment model reached roughly 75% accuracy — respectable, though not without room to grow — and, more tellingly, the forecasting model that incorporated sentiment outperformed the price-only baseline, suggesting sentiment is not just incidental noise but a meaningful predictive input. Correlational analysis reinforced this, showing closing price and news sentiment moving together on most observed trading days. These findings sit comfortably alongside, and to some extent extend, prior evidence from other exchanges and modeling approaches. What emerges is a cautious but fairly clear takeaway: text-derived sentiment adds real, if still imperfect, value to quantitative stock forecasting, and refining sentiment accuracy may be the most direct path toward sharper predictions.Keywords— Stock market prediction, financial news sentiment, Sentiment analysis, Long Short-Term Memory (LSTM), Support Vector Machine, Naive Bayes, Time series forecasting
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