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|Publication type:||Article in scientific journal|
|Type of review:||Peer review (publication)|
|Title:||Using financial news sentiment for stock price direction prediction|
|Publisher / Ed. Institution:||MDPI|
|Subjects:||Machine learning; Natural language processing; Sentiment analysis; Stock price prediction|
|Subject (DDC):||006: Special computer methods |
332: Financial economics
|Abstract:||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.|
|Fulltext version:||Published version|
|License (according to publishing contract):||CC BY 4.0: Attribution 4.0 International|
|Departement:||School of Management and Law|
|Organisational Unit:||Institute of Wealth & Asset Management (IWA)|
|Appears in collections:||Publikationen School of Management and Law|
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|2022_Fazlija-Harder_Using-financial-news-sentiment-stock-price-direction-prediction.pdf||2.8 MB||Adobe PDF|
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Fazlija, B., & Harder, P. (2022). Using financial news sentiment for stock price direction prediction. Mathematics, 10(13), 2156. https://doi.org/10.3390/math10132156
Fazlija, B. and Harder, P. (2022) ‘Using financial news sentiment for stock price direction prediction’, Mathematics, 10(13), p. 2156. Available at: https://doi.org/10.3390/math10132156.
B. Fazlija and P. Harder, “Using financial news sentiment for stock price direction prediction,” Mathematics, vol. 10, no. 13, p. 2156, 2022, doi: 10.3390/math10132156.
Fazlija, Bledar, and Pedro Harder. “Using Financial News Sentiment for Stock Price Direction Prediction.” Mathematics, vol. 10, no. 13, 2022, p. 2156, https://doi.org/10.3390/math10132156.
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