Stock Price Prediction Using Statistical and Deep Learning Models

Öz

The stock market analysis examines and evaluates the stock market by considering the financial, political, and social indicators to make future predictions. Breakthrough results of advancements in big data and deep learning technologies attract the attention of researchers and traders to computer-assisted stock market analysis. There are several studies on stock market analysis using conventional machine learning and deep learning models. In this paper, we used Autoregressive Integrated Moving Average (ARIMA) as a base model and compared it with three different models of recurrent neural networks: Long Short-Term Memory (LSTM) networks, Gated Recurrent Unit (GRU), LSTM with an attention layer model. While making intraday forecasts on Borsa Istanbul data, the results of four different models have been compared. The statistical model, ARIMA, is used as a baseline model for comparison with neural networks, but it has higher mean absolute error than other neural network models. Even though the LSTM results are very close to the GRU model, GRU slightly outperforms the others. The attention neural network model does not give better results than other basic neural networks.

Anahtar Kelimeler

• ARIMA
• BIST
• GRU
• LSTM

İstatistiksel ve Derin Öğrenme Modellerini Kullanarak Hisse Senedi Fiyat Tahmini

Öz

Borsa analizi, geleceğe yönelik tahminler yapmak için finansal, politik ve sosyal göstergeleri göz önünde bulundurarak borsayı inceler ve değerlendirir. Büyük veri ve derin öğrenme teknolojilerindeki gelişmelerin çığır açan sonuçları, araştırmacıların ve endüstrinin dikkatini bilgisayar destekli borsa analizine çekmektedir. Geleneksel makine öğrenimi ve derin öğrenme modellerini kullanarak borsa analizi konusunda çeşitli çalışmalar bulunmaktadır. Bu çalışmada, temel model olarak Otoregresif Entegre Hareketli Ortalama (ARIMA) yöntemini tekrarlayan sinir ağlarının üç farklı modeliyle karşılaştırılmıştır; Uzun Kısa Süreli Bellek (Long Short Term Memory- LSTM) ağları, Geçitli Tekrarlayan Birim (Gated Recurrent Unit- GRU), dikkat katmanlı LSTM modeli. Bu çalışmada literatürdeki diğer çalışmalardan farklı olarak 28 tane finansal indikatör kullanılarak Borsa İstanbul verileri üzerinde gün içi tahminler yaparken dört farklı modelin sonuçları karşılaştırılmıştır. İstatistiksel ve doğrusal bir model olan ARIMA, zaman serileri tahmini için doğrusal olmayan RNN modelleri ile karşılaştırılmıştır ancak 3 sinir ağı modelinden de yüksek ortalama hata oranına sahip olduğu görülmüştür. LSTM sonuçları GRU modeline çok yakın olsa da GRU diğerlerinden biraz daha iyi performans göstermektedir. Dikkat mekanizmalı sinir ağı diğer temel sinir ağlarından daha iyi sonuç vermemektedir.

Anahtar Kelimeler

• Otoregresif Entegre Hareketli Ortalama
• BIST
• Geçitli Tekrarlayan Birim Modeli
• Uzun Kısa Süreli Bellek Modeli

Kaynakça

1. Gunduz, H., Yaslan, Y., And Cataltepe, Z., Intraday prediction of borsa istanbul using convolutional neural networks and feature correlations. Knowledge- Based Systems, 137:138–148, 2017.
2. Boronovkova, S. And Tsiamas, I.,. An ensemble of lstm neural networks for high-frequency stock market classification., Journal of Forecasting, 38(6):600–619, 2019.
3. Qiu, J., Wang, B., And Zhou, C. , Forecasting stock prices with long-short term memory neural network based on attention mechanism. PLOS ONE, 15(1):1–15, 2020.
4. Hasan, A., Kalipsiz, O., And Akyoku, S. , Predicting financial market in big data: Deep learning, International Conference on Computer Science and Engineering (UBMK), pages 510–515, 2017.
5. Kara, Y., Acar Boyacioglu, M., And Ömer Kaan Baykan, Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the istanbul stock exchange, Expert Systems with Applications, 38(5):5311–5319, 2011
6. Rezaei, H., Faaljou, H., And Mansourfar, G. , Stock price prediction using deep learning and frequency decomposition, Expert Systems with Applications, 169:114332, 2021.
7. Fischer, T. And Krauss, C., Deep learning with long short-term memory networks for financial market predictions, European Journal of Operational Research, 270(2):654–669, 2018.
8. Nguyen, T.-T. And Yoon, S.,. A novel approach to short-term stock price movement prediction using transfer learning, Applied Sciences, 9(22):4745, 2019.

9. Ariyo, A. A., Adewumi, A. O., And Ayo, C. K.,. Stock price prediction using the ARIMA model, 16th International Conference on Computer Modelling and Simulation. IEEE, 2014.
10. Ma, Q.,. Comparison of ARIMA, ANN and LSTM for stock price prediction, E3S Web of Conferences, 218:01026, 2020.
11. Adebiyi, A. A., Adewumi, A. O., And Ayo, C. K. ,. Comparison of ARIMA and artificial neural networks models for stock price prediction, Journal of Applied Mathematics, 2014:1–7, 2014.
12. Wang, J.-H. And Leu, J.-Y. , Stock Market Trend Prediction Using Arima-Based neural networks,In Proceedings of International Conference on Neural Networks (ICNN’96), volume 4, pages 2160–2165 vol.4,1996.
13. Kumar, M. And Thenmozhi, M. , Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models. 5(3):284,2014.
14. Rathnayaka, R. M. K. T., Seneviratna, D., Jianguo, W., And Arumawadu, H. I. ,. A hybrid statistical approach for stock market forecasting based on artificial neural network and ARIMA time series models. IEEE, 2015.
15. Yang, Y. ,Can the strengths of aic and bic be shared? a conflict between model indentification and regression estimation. Biometrika, 92(4):937–950,2005.
16. Banerjee, D., Forecasting of indian stock market using time-series ARIMA model. IEEE, 2014.
17. Almasarweh, M. And Wadi, S. A., ARIMA model in predicting banking stock market data,12(11):309.2018.
18. Cho, K., Merrienboer, B. V., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., And Bengio, Y., Learning phrase representations using rnn encoder–decoder for statistical machine translation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014.
19. Luong, T., Pham, H., And Manning, C. D., Effective approaches to attention- based neural machine translation, Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015.
20. Chen, S. And Ge, L. , Exploring the attention mechanism in lstm-based Hong Kong stock price movement prediction, Quantitative Finance, 19(9):1507–1515, 2019.
21. Albayrak E. , Stock Prıce Prediction Using Deep Learning Methods In High-Frequency Trading ,(MSc. Thesis, Çankaya University), 2021.
22. Hyndman, R. J., And Athanasopoulos G., Forecasting: principles and practice. Otexts, 2018.
23. Bollerslev, T., Generalized autoregressive conditional heteroskedasticity., Journal of econometrics ,31.3, 307-327, 1986.
24. Bontempi, G., Souhaib B. T., And Yann-Aël L. B., Machine learning strategies for time series forecasting., European business intelligence summer school, 62-77, Springer, Berlin, Heidelberg, 2012.
25. Alpaydin, E., Introduction to machine learning, MIT press, 2020.
26. Tay, F. E., and Lijuan C., Application of support vector machines in financial time series forecasting.,Omega 29.4, 309-317,2001.
27. Lo, A. W., Harry M., And Jiang W., Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation, The journal of finance 55.4: 1705-1765,2000.
28. Shintate, T., And Lukáš Pi., Trend prediction classification for high frequency bitcoin time series with deep learning, Journal of Risk and Financial Management 12.1: 17,2019.
29. Bao, W., Jun Y., And Yulei R., A deep learning framework for financial time series using stacked autoencoders and long-short term memory., PloS one 12.7: e0180944,2017.
30. Raşo H., And Demirci M. Predicting the turkish stock market bist 30 index using deep learning., International Journal of Engineering Research and Development 11.1: 253-265,2019.
31. Li Y., Zhu Z., Kong D., And Han H., & Zhao, Y., EA-LSTM: Evolutionary attention-based LSTM for time series prediction, Knowledge-Based Systems, 181, 104785, 2019.
32. Zhang, X., Liang X., Zhiyuli A., Zhang S., Xu R., And Wu, B., AT-LSTM: An attention-based LSTM model for financial time series prediction, IOP Conference Series: Materials Science and Engineering (Vol. 569, No. 5, p. 052037). IOP Publishing, 2019.
33. Bahdanau, D., Cho, K., And Bengio, Y., Neural machine translation by jointly learning to align and translate, arXiv preprint arXiv:1409.0473, 2014.
34. Wilder, J.W., New concepts in technical trading systems, Trend Research, 1978. Bilgisayar Bilimleri