PRICE PREDICTION OF FINANCIAL ASSETS USING SENTIMENT ANALYSIS OF FINANCIAL TWEETS
DOI:
https://doi.org/10.24867/18BE12StevicKeywords:
sentiment analysis, NLP, transfer learning, DistilBERT, price prediction, LSTM, fintechAbstract
This paper presents a system for predicting the prices of financial assets by analyzing the sentiment of financial tweets and news. The developed system aims to test the following hypothesis: there is a correlation between the sentiment of financial tweets and the price of financial assets.The developed system consists of two subsystems. The first subsystem is responsible for sentiment analysis, while the second subsystem is responsible for predicting the prices of financial assets. The case study was conducted on the example of tweets and stocks of Apple Corporation in the period from January 2010 to December 2020. Steps for further system improvement are discussed.
References
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[5] Smailović, J., Grčar, M., Lavrač, N., & Žnidaršič, M. (2013, July). Predictive sentiment analysis of tweets: A stock market application. In International workshop on human-computer interaction and knowledge discovery in complex, unstructured, big data (pp. 77-88). Springer, Berlin, Heidelberg.
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[8] Mishev, K., Gjorgjevikj, A., Vodenska, I., Chitkushev, L. T., & Trajanov, D. (2020). Evaluation of sentiment analysis in finance: from lexicons to transformers. IEEE Access, 8, 131662-131682.
[9] Dogra, V., Singh, A., Verma, S., Jhanjhi, N. Z., & Talib, M. N. (2021). Analyzing DistilBERT for Sentiment Classification of Banking Financial News. In Intelligent Computing and Innovation on Data Science (pp. 501-510). Springer, Singapore.
[10] Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1), 1-167.
[2] Ao, S. (2018, July). Sentiment analysis based on financial tweets and market information. In 2018 International Conference on Audio, Language and Image Processing (ICALIP) (pp. 321-326). IEEE.
[3] Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10‐Ks. The Journal of finance, 66(1), 35-65.
[4] Darwich, M., Mohd, S. A., Omar, N., & Osman, N. A. (2019). Corpus-Based Techniques for Sentiment Lexicon Generation: A Review. J. Digit. Inf. Manag., 17(5), 296.
[5] Smailović, J., Grčar, M., Lavrač, N., & Žnidaršič, M. (2013, July). Predictive sentiment analysis of tweets: A stock market application. In International workshop on human-computer interaction and knowledge discovery in complex, unstructured, big data (pp. 77-88). Springer, Berlin, Heidelberg.
[6] Kordonis, J., Symeonidis, S., & Arampatzis, A. (2016, November). Stock price forecasting via sentiment analysis on Twitter. In Proceedings of the 20th Pan-Hellenic Conference on Informatics (pp. 1-6).
[7] Chen, C. C., Huang, H. H., & Chen, H. H. (2018, April). Fine-grained analysis of financial Tweets. In Companion Proceedings of the The Web Conference 2018 (pp. 1943-1949).
[8] Mishev, K., Gjorgjevikj, A., Vodenska, I., Chitkushev, L. T., & Trajanov, D. (2020). Evaluation of sentiment analysis in finance: from lexicons to transformers. IEEE Access, 8, 131662-131682.
[9] Dogra, V., Singh, A., Verma, S., Jhanjhi, N. Z., & Talib, M. N. (2021). Analyzing DistilBERT for Sentiment Classification of Banking Financial News. In Intelligent Computing and Innovation on Data Science (pp. 501-510). Springer, Singapore.
[10] Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1), 1-167.
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Published
2022-07-07
Issue
Section
Electrotechnical and Computer Engineering
