News Sentiment and Stock Market Dynamics: A Machine Learning Investigation

The study relies on an extensive dataset (≈1.86 million news headlines) to investigate the heterogeneity and predictive power of explicit sentiment signals (TextBlob, VADER, and FinBERT) and implied sentiment (VIX) for stock market trends. We find that news content predominantly consists of objective or neutral information, with only a small portion carrying subjective or emotive weight. There is a structural market bias toward upswings (bullish market states). Market behavior appears anticipatory rather than reactive: forward-looking implied sentiment captures a substantial share (≈45–50%) of the variation in stock returns. By contrast, sentiment scores, even when disaggregated into firm- and non-firm-specific subscores, lack robust predictive power. However, weekend and holiday sentiment contains modest yet valuable market signals. Algorithm-wise, Gradient Boosting Machine (GBM) stands out in both classification (bullish vs. bearish) and regression tasks. Neither FinBERT news sentiment, historical returns, nor implied volatility offer a consistently exploitable edge over market efficiency. Thus, our findings lend empirical support to both the weak-form and semi-strong forms of the Efficient Market Hypothesis. In the realm of exploitable trading strategies, markets remain an enigma against systematic alpha.