In a previous lab titled “Building News Sentiment and Stock Price Performance Analysis NLP Application With Python,” I briefly touched upon the concept of algorithmic trading using automated market news sentiment analysis and its correlation with stock price performance. Market movements, especially in the short term, are often influenced by investors’’ sentiment. One of the main components of sentiment analysis trading strategies is the algorithmic computation of a sentiment score from raw text and then incorporating the sentiment score into the trading strategy. The more accurate the sentiment score, the better the likelihood of algorithmic trading predicting potential stock price movements.
In that previous lab, I used the vaderSentiment library. This time, I’ve decided to explore another NLP contender, the FinBERT NLP algorithm, and compare it against Vader's sentiment score accuracy with the intent of improving trading strategy returns.