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# Using FinBert to predict / test for stock price movements based on news sentiment scores Link to Dataset : https://www.kaggle.com/datasets/miguelaenlle/massive-stock-news-analysis-db-for-nlpbacktests The idea is to use the dataset of stock news: headline, links, date, etc. to predict (or test) the prices movements of those stocks (increase or decrease or remain stable) based on sentiment score of the news using FinBert. > FinBert is an opensource pre trained Natural Language Processing (NLP) model, that has been specifically trained on Financial data, and outperforms almost all other NLP techniques for financial sentiment analysis. **Currently:** Sentiment scored on news headlines *not news content*
--- **RESULTS:** Result for df ( null values removed ) precision recall f1-score support decrease 0.09 0.23 0.13 31 increase 0.06 0.19 0.09 26 stable 0.86 0.60 0.71 355 accuracy 0.55 412 macro avg 0.34 0.34 0.31 412 weighted avg 0.75 0.55 0.63 412 ##### Using Headline Sentiment alone *is quite good(?)* to predict for stable days i.e. stock price remaining between +/- 0.20% (precision, recall and f1) *Note: supports are very high for 'stable' but then we only modeled/predict based on our threshold of sentiment scores* | Metric | Desc. | | --- | --- | | Accuracy | true prositive + true negative / total | | precision | true positives / actual results | | recall | true positve / predicted resuts | | f1-score | harmonic mean of precision and recall | --- Good Stability predictions: - Usefull for predicting higher / lower volatility during the trading days (closing prices) - Money can be made by using Option Strategies such as: 1. Iron Condor for less volatility (within -0.20% +0.20% price change range) 2. Straddle for high volatiolity (lower or higher than +/- 0.20% price change) --- Result for filtered df ( only for stocks that made over -/+0.20% ):
Removing 'stable' days to just look at 'increase' and 'decrease' performance: precision recall f1-score support decrease 0.58 0.23 0.33 31 increase 0.56 0.19 0.29 26 stable 0.00 0.00 0.00 0 accuracy 0.21 57 macro avg 0.38 0.14 0.20 57 weighted avg 0.57 0.21 0.31 57 ##### Not good enough. --- #### **Next Steps:** - [ ] Scrape each stock news *content* with the links in dataset - [ ] Use the news body content for sentiments - [ ] Compile results of predictions

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