Multiclass Sentiment Prediction for Stock Trading
This addresses the problem of improving stock trading strategies for investors in low-cap biotech companies, but it is incremental as it applies existing sentiment analysis methods to a new dataset.
The study tackled predicting multiclass sentiment from news articles for 400 low-cap biotech companies and found that trading based on this sentiment could achieve market-beating returns.
Python was used to download and format NewsAPI article data relating to 400 publicly traded, low cap. Biotech companies. Crowd-sourcing was used to label a subset of this data to then train and evaluate a variety of models to classify the public sentiment of each company. The best performing models were then used to show that trading entirely off public sentiment could provide market beating returns.