Stock Market Prediction in Istanbul Stock Exchangeby Combining Stock Price Information and Sentiment Analysis
Yıl: 2021 Cilt: 33 Sayı: 1 Sayfa Aralığı: 18 - 27 Metin Dili: İngilizce DOI: 10.7240/jeps.683952 İndeks Tarihi: 17-10-2021
Stock Market Prediction in Istanbul Stock Exchangeby Combining Stock Price Information and Sentiment Analysis
Öz: Predicting the stock market instrument price is a valuable but challenging machine learning task. Researchers use advanced techniques to improve the generalization ability of stock prediction models. However, considering that the stock market highly depends on the political and macroeconomic developments as well as the mood of the related investors, the models that use only stock prices fail to cover all factors affecting the stock market. Therefore, to improve the prediction accuracy of stock market prediction, in this study, we first apply sentiment analysis to the news related with the market and related stock, and then combine the sentiment labels of the news with stock prices and commonly used technical indicators. The obtained cumulative dataset is used to train a long short-term memory recurrent neural network, and the output of this regression model is used in the prediction of the closing price movement to decide whether the closing price next day will be higher. The experiments performed on 8-year data showed that while the F1 score of the model built without sentiment analysis was around 0.56, it has increased to 0.65 when stock prices are combined with sentiment labels. The results show that the model with sentiment labels fits better to the actual prices especially when there is a high volatility in the stock price.
Anahtar Kelime: Duygu Analizi ve Hisse Fiyat Bilgisinin Birleştirilmesiyle Hisse Senedi Piyasası Tahmini
Öz: Pay piyasasındaki bir enstrümanın fiyatını tahmin etmek, oldukça değerli ve aynı zamanda oldukça zor yapay öğrenmegörevlerinden biridir. Araştırmacılar hisse fiyat tahmin modellerinin genellenebilirlik kabiliyetlerinin arttırılması için ileriteknikler kullanmaktadır. Ancak, hisse senedi piyasasının politik ve makroekonomik haberler ve yatırımcıların duygu durumuile yakından ilişkili olduğu düşünüldüğünde, yalnızca hisse fiyat bilgisini kullanan modeller hisse senedi piyasasını etkileyentüm faktörleri kapsama konusunda yetersiz kalmaktadır. Bu nedenle, bu çalışmada, hisse senedi tahmin başarısının arttırılması için piyasa ve ilgili hisse haberlerine duygu analizi uygulanmakta ve daha sonra duygu etiketleri ile hisse fiyatları ve en sıkkullanılan teknik indikatörleri birleştirilmektedir. Elde edilen kümülatif veri kümesi bir kısa uzun-hafızalı özyinelemeli sinirağının eğitilmesinde kullanılmış ve bu regresyon modelinin çıktısı bir sonraki günün kapanış fiyatının daha yukarıda olupolmayacağını tahmin etmek için kullanılmıştır. Sekiz yıllık hisse senedi verisi üzerinde yapılan deneyler, duygu analizi olmadankurulan modelin F1 skoru 0,56 civarında iken, hisse fiyatı duygu etiketleri ile birlikte kullanıldığında bu değerin 0,65 civarınayükseldiğini göstermiştir. Elde edilen sonuçlar, özellikle piyasada yüksek volatilite olduğunda, duygu etiketini kullananmodelin gerçek hisse fiyatlarına daha yakın olduğunu göstermektedir.
Anahtar Kelime: Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
- [1] Henrique, B. M., Sobreiro, V. A., & Kimura, H. (2019). Literature review: Machine learning techniques applied to financial market prediction. Expert Systems with Applications, 124, 226-251.
- [2] Gandhmal, D. P., & Kumar, K. (2019). Systematic analysis and review of stock market prediction techniques. Computer Science Review, 34, 100190.
- [3] Akita, R., Yoshihara, A., Matsubara, T., & Uehara, K. (2016, June). Deep learning for stock prediction using numerical and textual information. In 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS) (pp. 1-6). IEEE.
- [4] Chong, E., Han, C., & Park, F. C. (2017). Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies. Expert Systems with Applications, 83, 187-205.
- [5] Yoshihara, A., Fujikawa, K., Seki, K., & Uehara, K. (2014, December). Predicting stock market trends by recurrent deep neural networks. In Pacific rim international conference on artificial intelligence (pp. 759-769). Springer, Cham.
- [6] Nguyen, T. H., Shirai, K., & Velcin, J. (2015). Sentiment analysis on social media for stock movement prediction. Expert Systems with Applications, 42(24), 9603-9611.
- [7] Vargas, M. R., De Lima, B. S., & Evsukoff, A. G. (2017). Deep learning for stock market prediction from financial news articles. In 2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) (pp. 60-65). IEEE.
- [8] Pagolu, V. S., Reddy, K. N., Panda, G., & Majhi, B. (2016, October). Sentiment analysis of Twitter data for predicting stock market movements. In 2016 international conference on signal processing, communication, power and embedded system (SCOPES) (pp. 1345-1350). IEEE.
- [9] Liu, Y., Qin, Z., Li, P., & Wan, T. (2017). Stock volatility prediction using recurrent neural networks with sentiment analysis. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (pp. 192-201). Springer, Cham.
- [10] Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017). Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics, 5, 135-146.
- [11] Hövelmann, L., Allee, S., & Friedrich, C. M. (2017). Fasttext and gradient boosted trees at GermEval-2017 on relevance classification and document-level polarity. In Proceedings of the GermEval 2017 - Shared Task on Aspect-based Sentiment in Social Media Customer Feedback, (pp. 30-35).
- [12] Lin, J. W., Gao, Y. C., & Chang, R. G. (2019, February). Chinese Story Generation with FastText Transformer Network. In 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) (pp. 395-398). IEEE.
- [13] Santos, I., Nedjah, N., & de Macedo Mourelle, L. (2017). Sentiment analysis using convolutional neural network with fastText embeddings. In 2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI) (pp. 1-5). IEEE
- [14] Kuyumcu, B., Aksakalli, C., & Delil, S. (2019). An automated new approach in fast text classification (fastText) A case study for Turkish text classification without pre-processing. In Proceedings of the 2019 3rd International Conference on Natural Language Processing and Information Retrieval (pp. 1-4).
- [15] Uygur, U., & Taş, O. (2014). The impacts of investor sentiment on different economic sectors: evidence from Istanbul stock exchange. Borsa Istanbul Review, 14(4), 236-241.
- [16] Canbaş, S., & Kandır, S. Y. (2009). Investor sentiment and stock returns: Evidence from Turkey. Emerging Markets Finance and Trade, 45(4), 36-52.
- [17] Li, J., Bu, H., & Wu, J. (2017). Sentiment-aware stock market prediction: A deep learning method. In 2017 international conference on service systems and service management (pp. 1-6). IEEE.
- [18] Zhuge, Q., Xu, L., & Zhang, G. (2017). LSTM Neural Network with Emotional Analysis for prediction of stock price. Engineering letters, 25(2).
- [19] Day, M. Y., & Lee, C. C. (2016). Deep learning for financial sentiment analysis on finance news providers. In 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 1127-1134). IEEE.
- [20] Weng, B., Ahmed, M. A., & Megahed, F. M. (2017). Stock market one-day ahead movement prediction using disparate data sources. Expert Systems with Applications, 79, 153-163.
- [21] Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research.
- [22] Mikolov, T., Grave, E., Bojanowski, P., Puhrsch, C., & Joulin, A. (2017). Advances in pre-training distributed word representations. arXiv preprint arXiv:1712.09405.
- [23] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735- 1780.
- [24] Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE transactions on neural networks, 5(2), 157-166.
- [25] Hochreiter, S., Bengio, Y., Frasconi, P., and Schmidhuber, J. (2011a). Gradient flow in recurrent nets: the difficulty of learning long-term dependencies, In Kremer, S. C. and Kolen, J. F., editors, A Field Guide to Dynamical Recurrent Neural Networks. IEEE Press.
- [26] Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747
| APA | Gümüş A, Sakar C (2021). Stock Market Prediction in Istanbul Stock Exchangeby Combining Stock Price Information and Sentiment Analysis. , 18 - 27. 10.7240/jeps.683952 |
| Chicago | Gümüş Adnan,Sakar C. Okan Stock Market Prediction in Istanbul Stock Exchangeby Combining Stock Price Information and Sentiment Analysis. (2021): 18 - 27. 10.7240/jeps.683952 |
| MLA | Gümüş Adnan,Sakar C. Okan Stock Market Prediction in Istanbul Stock Exchangeby Combining Stock Price Information and Sentiment Analysis. , 2021, ss.18 - 27. 10.7240/jeps.683952 |
| AMA | Gümüş A,Sakar C Stock Market Prediction in Istanbul Stock Exchangeby Combining Stock Price Information and Sentiment Analysis. . 2021; 18 - 27. 10.7240/jeps.683952 |
| Vancouver | Gümüş A,Sakar C Stock Market Prediction in Istanbul Stock Exchangeby Combining Stock Price Information and Sentiment Analysis. . 2021; 18 - 27. 10.7240/jeps.683952 |
| IEEE | Gümüş A,Sakar C "Stock Market Prediction in Istanbul Stock Exchangeby Combining Stock Price Information and Sentiment Analysis." , ss.18 - 27, 2021. 10.7240/jeps.683952 |
| ISNAD | Gümüş, Adnan - Sakar, C. Okan. "Stock Market Prediction in Istanbul Stock Exchangeby Combining Stock Price Information and Sentiment Analysis". (2021), 18-27. https://doi.org/10.7240/jeps.683952 |
| APA | Gümüş A, Sakar C (2021). Stock Market Prediction in Istanbul Stock Exchangeby Combining Stock Price Information and Sentiment Analysis. International journal of advances in engineering and pure sciences (Online), 33(1), 18 - 27. 10.7240/jeps.683952 |
| Chicago | Gümüş Adnan,Sakar C. Okan Stock Market Prediction in Istanbul Stock Exchangeby Combining Stock Price Information and Sentiment Analysis. International journal of advances in engineering and pure sciences (Online) 33, no.1 (2021): 18 - 27. 10.7240/jeps.683952 |
| MLA | Gümüş Adnan,Sakar C. Okan Stock Market Prediction in Istanbul Stock Exchangeby Combining Stock Price Information and Sentiment Analysis. International journal of advances in engineering and pure sciences (Online), vol.33, no.1, 2021, ss.18 - 27. 10.7240/jeps.683952 |
| AMA | Gümüş A,Sakar C Stock Market Prediction in Istanbul Stock Exchangeby Combining Stock Price Information and Sentiment Analysis. International journal of advances in engineering and pure sciences (Online). 2021; 33(1): 18 - 27. 10.7240/jeps.683952 |
| Vancouver | Gümüş A,Sakar C Stock Market Prediction in Istanbul Stock Exchangeby Combining Stock Price Information and Sentiment Analysis. International journal of advances in engineering and pure sciences (Online). 2021; 33(1): 18 - 27. 10.7240/jeps.683952 |
| IEEE | Gümüş A,Sakar C "Stock Market Prediction in Istanbul Stock Exchangeby Combining Stock Price Information and Sentiment Analysis." International journal of advances in engineering and pure sciences (Online), 33, ss.18 - 27, 2021. 10.7240/jeps.683952 |
| ISNAD | Gümüş, Adnan - Sakar, C. Okan. "Stock Market Prediction in Istanbul Stock Exchangeby Combining Stock Price Information and Sentiment Analysis". International journal of advances in engineering and pure sciences (Online) 33/1 (2021), 18-27. https://doi.org/10.7240/jeps.683952 |
