Understanding the sentiment behind financial news headlines plays a crucial role in how investors make decisions. Our study explores how the tone and emotion expressed in these headlines impact stock market values. We collected financial news headlines from sources like the Wall Street Journal, Washington Post, and Business-Standard, along with stock market data from Yahoo Finance and Kaggle, covering a specific period of time.
To analyze the sentiment in these headlines, we used a tool called VADER, which measures positive, negative, and neutral tones. We then looked at how this sentiment aligns with stock market movements. To handle the large amount of data, we used methods like Bag-of-Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) to represent the text, and we tested several machine learning and deep learning models.
In our experiments, deep learning models like CNN (80.86% accuracy) and LSTM (84% accuracy) outperformed traditional machine learning models such as Support Vector Machine (SVM) (50.3%), Random Forest (67.93%), and Naive Bayes (59.79%). Additionally, advanced techniques like BERT and RoBERTa achieved remarkable accuracy of 90% and 88%, respectively, showing their effectiveness in understanding the sentiment of financial news headlines. This demonstrates that using advanced sentiment analysis methods can provide valuable insights into stock market trends.
Here's a link to the research paper: https://www.mdpi.com/2227-7390/10/12/2001