Predicting Stock Market Movement: Trend Deterministic Data

Yıl 2019, , 246 - 256, 30.04.2019

Hakan Pabuçcu

https://doi.org/10.29249/selcuksbmyd.487862

Cited By: 6

Öz

This study focuses on the estimation of negative and positive movements of BIST 100 stock index. The predictive performances of artificial neural network, support vector machine and naive Bayes algorithm are compared. The analyzes are carried out in two stages. In the first stage, nine technical indicators to be used as input in the estimation models are calculated by using the stock index, opening, closing, highest and lowest prices. In the second stage, the trend-setting dataset is used as input and the predictions are made by using three selected machine-learning algorithms. The BIST 100 data set includes the daily closing prices covering the range of 2009-2018. With the analysis, it is concluded that the support vector machines algorithm is the best classifier. In addition, comparisons with previous similar studies and the effects of both the data set used and the prediction models are discussed.

Anahtar Kelimeler

BIST 100, Artificial Neural Network, Support Vector Machine, Naive Bayes Classifier, Machine Learning

Borsa Endeksi Hareketlerinin Tahmini: Trend Belirleyici Veri

Öz

Bu çalışma BIST 100 borsa endeksinin negatif ve pozitif yönlü
hareketlerinin tahmin edilmesini konu edinmektedir. Yapay sinir ağı, destek
vektör makinesi ve naive Bayes algoritmasının tahmin performansları
karşılaştırılmaktadır. Analizler iki aşamalı olarak yapılmaktadır. Birinci
aşamada tahmin modellerinde girdi olarak kullanılacak dokuz adet teknik
gösterge, borsa endeksi açılış, kapanış, en yüksek ve en düşük fiyatlar,
kullanılarak hesaplanmakta ve sürekli olan bu teknik göstergeler
barındırdıkları trende göre kategorize edilerek yeni bir veri seti
oluşturulmaktadır. İkinci aşamada ise, trend belirleyici veri seti girdi olarak
kullanılmakta ve seçilen üç makine öğrenme algoritması kullanılarak tahminler
yapılmaktadır. BIST 100 veri seti 2009-2018 Aralığını kapsayan günlük kapanış
fiyatlarını içermektedir. Analizlerle, destek vektör makineleri algoritmasının
en iyi sınıflandırıcı olduğu sonucuna ulaşılmıştır. Ayrıca, daha önceki benzer
çalışmalarla karşılaştırmalar yapılarak gerek kullanılan veri seti gerekse
tahmin modellerinin etkileri tartışılmaktadır. 

Anahtar Kelimeler

BIST100, Yapay Sinir Ağları, Destek Vektör Makineleri, Naive Bayes, Makine Öğrenme

Kaynakça

• Abu-Mostafa, Y. S., & Atiya, A. F. (1996). "Introduction to Financial Forecasting", Applied Intelligence, 6(3), s. 205–213. http://doi.org/10.1007/BF00126626
• Atsalakis, G. S., & Valavanis, K. P. (2009a). "Forecasting Stock Market Short-Term Trends Using a Neuro-Fuzzy Based Methodology", Expert Systems with Applications, 36(7), s. 10696–10707. http://doi.org/https://doi.org/10.1016/j.eswa.2009.02.043
• Atsalakis, G. S., & Valavanis, K. P. (2009b). "Surveying Stock Market Forecasting Techniques – Part II: Soft Computing Methods", Expert Systems with Applications, 36(3, Part 2), s. 5932–5941. http://doi.org/https://doi.org/10.1016/j.eswa.2008.07.006
• Cao, L., & Tay, F. E. H. (2001). "Application of support Vector Machines in Financial Time Series Forecasting", Omega, 29(4), s. 309–317. http://doi.org/10.1016/S0305-0483(01)00026-3
• 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), s. 901–923. http://doi.org/https://doi.org/10.1016/S0305-0548(02)00037-0
• Chen, Z., Matousek, R., & 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), s. 71–86. http://doi.org/10.1016/j.najef.2017.10.003
• 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), s., 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), s. 927–940. http://doi.org/https://doi.org/10.1016/j.eswa.2005.06.024
• Hassan, M. R., Nath, B., & Kirley, M. (2007). "A Fusion Model of HMM, ANN and GA for Stock Market Forecasting", Expert Systems with Applications, 33(1), s. 171–180. http://doi.org/https://doi.org/10.1016/j.eswa.2006.04.007
• Hsu, S. H., Hsieh, J. P. A., Chih, T. C., & Hsu, K. C. (2009). "A Two-Stage Architecture for Stock Price Forecasting by İntegrating Self-Organizing Map and Support Vector Regression", Expert Systems with Applications, 36(4), s. 7947–7951. http://doi.org/10.1016/j.eswa.2008.10.065
• Hua, S., & Sun, Z. (2001). "Support vector machine approach for protein subcellular localization prediction", Bioinformatics, 17(8), s. 721–728.
• Huang, W., Nakamori, Y., & Wang, S.-Y. (2005). "Forecasting Stock Market Movement Direction with Support Vector Machine", Computers & Operations Research, 32(10), s. 2513–2522. http://doi.org/https://doi.org/10.1016/j.cor.2004.03.016
• Kara, Y., Acar Boyacioglu, M., & 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), s. 5311–5319. http://doi.org/10.1016/j.eswa.2010.10.027
• Khemchandani, R., Jayadeva, & Chandra, S. (2009). "Knowledge Based Proximal Support Vector Machines", European Journal of Operational Research, 195(3), s. 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), s. 307–319. http://doi.org/10.1016/S0925-2312(03)00372-2
• Kimoto, T., Asakawa, K., Yoda, M., & Takeoka, M. (1990). "Stock Market Prediction System with Modular Neural Networks", In 1990 IJCNN International Joint Conference on Neural Networks (s. 1–6 vol.1). http://doi.org/10.1109/IJCNN.1990.137535
• Kumar, M., & 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. (s. 1–16). http://doi.org/10.2139/ssrn.876544
• Leung, M. T., Daouk, H., & Chen, A.-S. (2000). "Forecasting Stock Indices: A Comparison of Classification and Level Estimation Models", International Journal of Forecasting, 16(2), s. 173–190. http://doi.org/https://doi.org/10.1016/S0169-2070(99)00048-5
• Malkiel, B. G., & Fama, E. F. (1970)."Effıcient Capital Markets: A Review of Theory and Empirical Work", The Journal of Finance, 25(2), s. 383–417. http://doi.org/10.1111/j.1540-6261.1970.tb00518.x
• Olson, D., & Mossman, C. (2003). "Neural Network Forecasts of Canadian Stock Returns Using Accounting Ratios", International Journal of Forecasting, 19(3), s. 453–465. http://doi.org/https://doi.org/10.1016/S0169-2070(02)00058-4
• Patel, J., Shah, S., Thakkar, P., & 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), s. 259–268. http://doi.org/10.1016/j.eswa.2014.07.040
• Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015b). "Predicting Stock Market Index Using Fusion of Machine Learning Techniques. Expert Systems with Applications, 42(4), s. 2162–2172. http://doi.org/10.1016/j.eswa.2014.10.031
• Tan, T. Z., Quek, C., & Ng, G. S. (2007). "Biological Brain‐Inspired Genetic Complementary Learning for Stock Market and Bank Failure Prediction", Computational Intelligence, 23(2), s.236–261. http://doi.org/10.1111/j.1467-8640.2007.00303.x
• Tay, F. E. H., & Cao, L. J. (2001). "Improved Financial Time Series Forecasting by Combining Support Vector Machines with Self-Organizing Feature Map", Intelligent-Data-Analysis, 5, s. 339–354.
• Thawornwong, S., & Enke, D. (2004). "The Adaptive Selection of Financial And Economic Variables for Use with Artificial Neural Networks", Neurocomputing, 56, s. 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), s. 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.
• Xu, X., Zhou, C., & Wang, Z. (2009). "Credit scoring Algorithm Based on Link Analysis Ranking with Support Vector Machine", Expert Systems with Applications, 36(2, Part 2), s. 2625–2632. http://doi.org/https://doi.org/10.1016/j.eswa.2008.01.024
• Zhong, X., & Enke, D. (2017). "Forecasting Daily Stock Market Return Using Dimensionality Reduction", Expert Systems with Applications, 67, s. 126–139. http://doi.org/10.1016/j.eswa.2016.09.027