Enhancing Financial Market Analysis and Prediction with Emotion Corpora and News Co-Occurrence Network

This study employs an improved natural language processing algorithm to analyze over 500,000 financial news articles from sixteen major sources across 12 sectors, with the top 10 companies in each sector. The analysis identifies shifting economic activity based on emotional news sentiment and develops a news co-occurrence network to show relationships between companies even across sectors. This study created an improved corpus and algorithm to identify emotions in financial news. The improved method identified 18 additional emotions beyond what was previously analyzed. The researchers labeled financial terms from Investopedia to validate the categorization performance of the new method. Using the improved algorithm, we analyzed how emotions in financial news relate to market movement of pairs of companies. We found a moderate correlation (above 60%) between emotion sentiment and market movement. To validate this finding, we further checked the correlation coefficients between sentiment alone, and found that consumer discretionary, consumer staples, financials, industrials, and technology sectors showed similar trends. Our findings suggest that emotional sentiment analysis provide valuable insights for financial market analysis and prediction. The technical analysis framework developed in this study can be integrated into a larger investment strategy, enabling organizations to identify potential opportunities and develop informed strategies. The insights derived from the co-occurrence model may be leveraged by companies to strengthen their risk management functions, making it an asset within a comprehensive investment strategy.