Motivation: Financial markets are often affected by sentiment conveyed in news headlines. Understanding the causal relationship between news sentiment and stock price movements can provide deeper insight into market dynamics.
Goal: Investigate the causal effects between news sentiments and stock price movements. This includes predicting stock movement trends based on news sentiment analysis and understanding how stock movement changes based on future news sentiment. This project aims to study these effects to improve stock movement predictions and optimize portfolio performance

Project Proposal Structure:
- Improve on the sentiment analysis tool used by exploring transformer models to get more accurate sentiment scores
- Explore Bi-directional Models and CNN
- Refine Baseline Model (used ARIMA in the last project)
- Refine simulation of trading strategy that is used to calculate average percentage of portfolio growth – did our models make profit?