Bitcoin price movement prediction by news sentiment using machine learning approach - Pham, Huy Tam; Chu, Quang Thuy (Journal of Finance - Marketing Research, 2024)
This study investigates the potential of news sentiment, derived from Google News, in predicting Bitcoin price movements. It explores the correlation between sentiment in news headlines and Bitcoin's market behavior. Employing a data set of news headlines related to Bitcoin from Google News, this research applies sentiment analysis and various machine learning algorithms, including Decision Tree, Random Forest, Logistic Regression, Support Vector Machine, Naïve Bayes, and KNN. The approach involves extracting sentiment scores and correlating these with historical Bitcoin price data. The analysis revealed that Decision Tree and Random Forest algorithms provide a balanced prediction for Bitcoin price movements. Logistic Regression and Support Vector Machine showed high AUC scores but with unbalanced class predictions. Naïve Bayes and KNN were less effective. Overall, sentiment analysis of news headlines can predict short-term Bitcoin price movements to a reasonable degree. This research offers a novel tool for investors and market analysts, enhancing understanding of the impact of news sentiment on cryptocurrency prices. It provides a predictive model to assist in investment decision-making and market analysis. This study contributes to the literature on financial forecasting by integrating sentiment analysis with machine learning for cryptocurrency price prediction. It's one of the few studies to analyze the impact of news sentiment on Bitcoin price, offering a new perspective in the realm of financial technology and digital currencies.