Using Financial News Sentiment for Stock Price Direction Prediction
Using sentiment information in the analysis of financial markets has attracted much attention. Natural language processing methods can be used to extract market sentiment information from texts such as news articles. The objective of this paper is to extract financial market sentiment information fr...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-06-01
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| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/10/13/2156 |
| _version_ | 1797408678906691584 |
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| author | Bledar Fazlija Pedro Harder |
| author_facet | Bledar Fazlija Pedro Harder |
| author_sort | Bledar Fazlija |
| collection | DOAJ |
| description | Using sentiment information in the analysis of financial markets has attracted much attention. Natural language processing methods can be used to extract market sentiment information from texts such as news articles. The objective of this paper is to extract financial market sentiment information from news articles and use the estimated sentiment scores to predict the price direction of the stock market index Standard & Poor’s 500. To achieve the best possible performance in sentiment classification, state-of-the-art bidirectional encoder representations from transformers (BERT) models are used. The pretrained transformer networks are fine-tuned on a labeled financial text dataset and applied to news articles from known providers of financial news content to predict their sentiment scores. The generated sentiment scores for the titles of the given news articles, for the (text) content of said news articles, and for the combined title-content consideration are posited against past time series information of the stock market index. To forecast the price direction of the stock market index, the predicted sentiment scores are used in a simple strategy and as features for a random forest classifier. The results show that sentiment scores based on news content are particularly useful for stock price direction prediction. |
| first_indexed | 2024-03-09T04:02:58Z |
| format | Article |
| id | doaj.art-d65bddeea79e4948bf406b132de955f2 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T04:02:58Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-d65bddeea79e4948bf406b132de955f22023-12-03T14:11:26ZengMDPI AGMathematics2227-73902022-06-011013215610.3390/math10132156Using Financial News Sentiment for Stock Price Direction PredictionBledar Fazlija0Pedro Harder1School of Management and Law, ZHAW Zurich University of Applied Sciences, 8400 Winterthur, SwitzerlandSchool of Management and Law, ZHAW Zurich University of Applied Sciences, 8400 Winterthur, SwitzerlandUsing sentiment information in the analysis of financial markets has attracted much attention. Natural language processing methods can be used to extract market sentiment information from texts such as news articles. The objective of this paper is to extract financial market sentiment information from news articles and use the estimated sentiment scores to predict the price direction of the stock market index Standard & Poor’s 500. To achieve the best possible performance in sentiment classification, state-of-the-art bidirectional encoder representations from transformers (BERT) models are used. The pretrained transformer networks are fine-tuned on a labeled financial text dataset and applied to news articles from known providers of financial news content to predict their sentiment scores. The generated sentiment scores for the titles of the given news articles, for the (text) content of said news articles, and for the combined title-content consideration are posited against past time series information of the stock market index. To forecast the price direction of the stock market index, the predicted sentiment scores are used in a simple strategy and as features for a random forest classifier. The results show that sentiment scores based on news content are particularly useful for stock price direction prediction.https://www.mdpi.com/2227-7390/10/13/2156sentiment analysisnatural language processingmachine learningstock prize prediction |
| spellingShingle | Bledar Fazlija Pedro Harder Using Financial News Sentiment for Stock Price Direction Prediction Mathematics sentiment analysis natural language processing machine learning stock prize prediction |
| title | Using Financial News Sentiment for Stock Price Direction Prediction |
| title_full | Using Financial News Sentiment for Stock Price Direction Prediction |
| title_fullStr | Using Financial News Sentiment for Stock Price Direction Prediction |
| title_full_unstemmed | Using Financial News Sentiment for Stock Price Direction Prediction |
| title_short | Using Financial News Sentiment for Stock Price Direction Prediction |
| title_sort | using financial news sentiment for stock price direction prediction |
| topic | sentiment analysis natural language processing machine learning stock prize prediction |
| url | https://www.mdpi.com/2227-7390/10/13/2156 |
| work_keys_str_mv | AT bledarfazlija usingfinancialnewssentimentforstockpricedirectionprediction AT pedroharder usingfinancialnewssentimentforstockpricedirectionprediction |