PREDICTING STOCK MARKET MOVEMENT BY USING MACHINELEARNING ALGORITHM

Year 2019, Issue: 23, 179 - 190, 09.04.2019

Hakan Pabuçcu

https://doi.org/10.18092/ulikidince.484138

Cited By: 6

Abstract

In addition to the uncertainty and chaotic movements of the financial
time series, the nonlinear dynamic structure makes the forecasts very
difficult. The fact that the stock market index are affected by the political
changes, the general outlook of the economy, the investors' expectations and
investment preferences, and the movements of other indexes, make the index
estimates quite difficult but attractive. It is known that the machine learning
algorithms are successful in estimating stock index movements and their future
values. In this study, the problem of forecasting the direction of BIST 100
index movements is discussed. Three different machine learning algorithms,
artificial neural networks, support vector machines and naïve Bayes classifier
were used and their performances were compared. Ten technical indicators were
used as inputs for the models. The data set consists of ten-year daily closing
price values covering the 2009-2018 period. Analysis results show that the
models can be used to capture stock market index movements, whereas artificial
neural network algorithm is a better classifier.

BORSA ENDEKSİ HAREKETLERİNİN MAKİNE ÖĞRENME ALGORİTMALARI İLE TAHMİNİ

Abstract

Finansal
zaman serilerinin barındırdığı belirsizlik, kaotik hareketler yanında doğrusal
olmayan dinamik yapı, tahminleri oldukça güçleştirmektedir. Borsa endekslerinin
politik değişimler, ekonominin genel görünümü, yatırımcıların beklenti ve
yatırım tercihleri ve diğer endekslerin hareketleri gibi birçok makroekonomik
faktörden etkilenmeleri, endeks tahminlerini oldukça zor ancak bir o kadar da
çekici kılmaktadır. Borsa endeksi hareketleri ve geleceğe dönük tahminler
üretmede makine öğrenme algoritmalarının başarılı oldukları bilinmektedir. Bu
çalışmada BIST 100 endeksi hareketlerinin yönünün tahmin edilmesi problemi ele
alınmıştır. Üç farklı makine öğrenme algoritması olan yapay sinir ağları,
destek vektör makineleri ve naive Bayes sınıflandırıcı algoritması kullanılmış
ve performansları karşılaştırılmıştır. Borsa endeksi tahminleri için kullanılan
on teknik gösterge modeller için girdi olarak kullanılmıştır. Veri seti
2009-2018 periyodunu kapsayan günlük kapanış değerlerini içermektedir. Analiz
sonuçları, her üç modelin de borsa endeks hareketlerini yakalamada
kullanılabilir olduğunu, yapay sinir ağı algoritmasının ise daha iyi bir
sınıflandırıcı olduğunu göstermiştir.

Keywords

BIST100, yapay sinir ağları, destek vektör makineleri, naive Bayes, makine öğrenme

References

• Abu-Mostafa, Y. S., ve Atiya, A. F. (1996). Introduction to financial forecasting. Applied Intelligence, 6(3), 205–213. http://doi.org/10.1007/BF00126626
• Atsalakis, G. S., ve Valavanis, K. P. (2009). Surveying stock market forecasting techniques – Part II: Soft computing methods. Expert Systems with Applications, 36(3, Part 2), 5932–5941. http://doi.org/https://doi.org/10.1016/j.eswa.2008.07.006
• Cao, L., ve Tay, F. E. H. (2001). Application of support vector machines in financial time series forecasting. Omega, 29(4), 309–317. http://doi.org/10.1016/S0305-0483(01)00026-3
• Chen, A.-S., Leung, M. T., ve Daouk, H. (2003). Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index. Computers & Operations Research, 30(6), 901–923. http://doi.org/https://doi.org/10.1016/S0305-0548(02)00037-0
• Chen, Z., Matousek, R., ve Wanke, P. (2018). Chinese bank efficiency during the global financial crisis: A combined approach using satisficing DEA and Support Vector Machines☆. North American Journal of Economics and Finance, 43(September 2017), 71–86. http://doi.org/10.1016/j.najef.2017.10.003
• Chun, S.-H., ve Kim, S. H. (2004). Data mining for financial prediction and trading: application to single and multiple markets. Expert Systems with Applications, 26(2), 131–139. http://doi.org/https://doi.org/10.1016/S0957-4174(03)00113-1
• Enke, D., ve Thawornwong, S. (2005). The use of data mining and neural networks for forecasting stock market returns. Expert Systems with Applications, 29(4), 927–940. http://doi.org/https://doi.org/10.1016/j.eswa.2005.06.024
• Hsu, S. H., Hsieh, J. P. A., Chih, T. C., ve Hsu, K. C. (2009). A two-stage architecture for stock price forecasting by integrating self-organizing map and support vector regression. Expert Systems with Applications, 36(4), 7947–7951. http://doi.org/10.1016/j.eswa.2008.10.065
• Hua, S., ve Sun, Z. (2001). Support vector machine approach for protein subcellular localization prediction. Bioinformatics, 17(8), 721–728.
• Huang, W., Nakamori, Y., ve Wang, S.-Y. (2005). Forecasting stock market movement direction with support vector machine. Computers & Operations Research, 32(10), 2513–2522. http://doi.org/https://doi.org/10.1016/j.cor.2004.03.016
• Kara, Y., Acar Boyacioglu, M., ve Baykan, Ö. K. (2011). 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. http://doi.org/10.1016/j.eswa.2010.10.027
• Khemchandani, R., Jayadeva, ve Chandra, S. (2009). Knowledge based proximal support vector machines. European Journal of Operational Research, 195(3), 914–923. http://doi.org/https://doi.org/10.1016/j.ejor.2007.11.023
• Kim, K. J. (2003). Financial time series forecasting using support vector machines. Neurocomputing, 55(1–2), 307–319. http://doi.org/10.1016/S0925-2312(03)00372-2
• Kimoto, T., Asakawa, K., Yoda, M., ve Takeoka, M. (1990). Stock market prediction system with modular neural networks. In 1990 IJCNN International Joint Conference on Neural Networks (pp. 1–6 vol.1). http://doi.org/10.1109/IJCNN.1990.137535
• Kumar, M., ve Thenmozhi, M. (2005). Forecasting Stock Index Movement: A Comparison of Support Vector Machines and Random Fores. In Forest, Indian Institute of Capital Markets 9th Capital Markets Conference Paper. (pp. 1–16). http://doi.org/10.2139/ssrn.876544
• Leung, M. T., Daouk, H., ve Chen, A.-S. (2000). Forecasting stock indices: a comparison of classification and level estimation models. International Journal of Forecasting, 16(2), 173–190. http://doi.org/https://doi.org/10.1016/S0169-2070(99)00048-5
• Malkiel, B. G., ve Fama, E. F. (1970). Effıcient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383–417. http://doi.org/10.1111/j.1540-6261.1970.tb00518.x
• Olson, D., ve Mossman, C. (2003). Neural network forecasts of Canadian stock returns using accounting ratios. International Journal of Forecasting, 19(3), 453–465. http://doi.org/https://doi.org/10.1016/S0169-2070(02)00058-4
• Patel, J., Shah, S., Thakkar, P., ve Kotecha, K. (2015a). Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques. Expert Systems with Applications, 42(1), 259–268. http://doi.org/10.1016/j.eswa.2014.07.040
• Patel, J., Shah, S., Thakkar, P., ve Kotecha, K. (2015b). Predicting stock market index using fusion of machine learning techniques. Expert Systems with Applications, 42(4), 2162–2172. http://doi.org/10.1016/j.eswa.2014.10.031
• Tan, T. Z., Quek, C., ve Ng, G. S. (2007). Biological Brain‐Inspired Genetic Complementary Learning for Stock Market and Bank Failure Prediction. Computational Intelligence, 23(2), 236–261. http://doi.org/10.1111/j.1467-8640.2007.00303.x
• Tay, F. E. H., ve Cao, L. J. (2001). Improved financial time series forecasting by combining support vector machines with self-organizing feature map. Intelligent-Data-Analysis, 5, 339–354.
• Thawornwong, S., ve Enke, D. (2004). The adaptive selection of financial and economic variables for use with artificial neural networks. Neurocomputing, 56, 205–232. http://doi.org/https://doi.org/10.1016/j.neucom.2003.05.001
• Vanstone, B., ve Finnie, G. (2009). An empirical methodology for developing stockmarket trading systems using artificial neural networks. Expert Systems with Applications, 36(3, Part 2), 6668–6680. http://doi.org/https://doi.org/10.1016/j.eswa.2008.08.019
• Vapnik, V. (1995). The nature of statistical learning theory. New York, NY: Springer.
• Atsalakis, G. S., & Valavanis, K. P. (2009). Surveying stock market forecasting techniques – Part II: Soft computing methods. Expert Systems with Applications, 36(3, Part 2), 5932–5941. http://doi.org/https://doi.org/10.1016/j.eswa.2008.07.006
• Chen, A.-S., Leung, M. T., & Daouk, H. (2003). Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index. Computers & Operations Research, 30(6), 901–923. http://doi.org/https://doi.org/10.1016/S0305-0548(02)00037-0
• Chun, S.-H., & Kim, S. H. (2004). Data mining for financial prediction and trading: application to single and multiple markets. Expert Systems with Applications, 26(2), 131–139. http://doi.org/https://doi.org/10.1016/S0957-4174(03)00113-1
• Enke, D., & Thawornwong, S. (2005). The use of data mining and neural networks for forecasting stock market returns. Expert Systems with Applications, 29(4), 927–940. http://doi.org/https://doi.org/10.1016/j.eswa.2005.06.024
• Thawornwong, S., & Enke, D. (2004). The adaptive selection of financial and economic variables for use with artificial neural networks. Neurocomputing, 56, 205–232. http://doi.org/https://doi.org/10.1016/j.neucom.2003.05.001
• Vanstone, B., & Finnie, G. (2009). An empirical methodology for developing stockmarket trading systems using artificial neural networks. Expert Systems with Applications, 36(3, Part 2), 6668–6680. http://doi.org/https://doi.org/10.1016/j.eswa.2008.08.019
• Zhong, X., ve Enke, D. (2017). Forecasting daily stock market return using dimensionality reduction. Expert Systems with Applications, 67, 126–139. http://doi.org/10.1016/j.eswa.2016.09.027
• Xu, X., Zhou, C., ve Wang, Z. (2009). Credit scoring algorithm based on link analysis ranking with support vector machine. Expert Systems with Applications, 36(2, Part 2), 2625–2632. http://doi.org/https://doi.org/10.1016/j.eswa.2008.01.024