Prediction of Stock Index Movement Directions Using Machine Learning Methods: An Application on Developing Countries
Keywords:Machine Learning, Classification Algorithms, Stock Index Prediction, Developing Countries
The aim of this study is to compare the performances of the methods by predicting the movement directions of the stock market indices of developing countries with machine learning classification methods and to determine the best estimation method. For this purpose, the main stock market indices of E-7 countries, known as developing countries; Application was made on SSE (China), BOVESPA (Brazil), RTS (Russia), NIFTY 50 (India), IDX (Indonesia), IPC (Mexico) and BIST 100 (Turkey). k-nearest neighbors, decision trees, random forests, naive Bayes, support vector machines, artificial neural networks and logistic regression.methods, which are among the machine learning classification methods, were used to predict the movement directions of Stock Indices. In the analyses, the daily data for the period 01.01.2012-31.12.2021 and the technical indicators calculated based on the aforementioned data set were used as the data set. According to the results of the analysis, it has been determined that the best method for estimating the movement directions of the stock market indices of developing countries is logistic regression. Along with the logistic regression method, artificial neural networks and support vector machine methods were also determined to predict the direction of movement of all indices with an accuracy of over 70%.
It has also been determined that the best method, logistic regression, is not valid for all indices, in other words, it is not the method with the highest accuracy performance in all indices.
Adebiyi, A.A., Ayo, C.K., Adebiyi, M.O., and Otokiti, S.O. (2012). Stock Price Prediction using Neural Network with Hybridized Market Indicators.
Aktaş, S. (2019), Prediction of Dow Jones Industrial Average and Nasdaq 100 Index with Machine Learning Algorithms, Istanbul Technical University, Institute of Social Sciences, Master's Thesis, Istanbul.
Ali,M, Khan, D. M. Aamir M, Ali A. and Ahmad Z. (2021).Predicting the Direction Movement of Financial Time Series Using Artificial Neural Network and Support Vector Machine. Hindawi Complexity. ID 2906463, https://doi.org/10.1155/2021/2906463
Breiman, L., J. H. Friedman, R. A. Olshen, and C. J. Stone.(1984) Classification and regression trees. Wadsworth and Brooks/Cole, Monterey, California, USA.
Breiman, L. (2001). Random Forests, Machine Learning, 45(1), 1-32. https://link.springer.com/content/pdf/10.1023/A:1010933404324.pdf
Cao, L. and Tay, F. E. H.(2001). Financial Forecasting Using Support Vector Machines, Neural Computing and Applications, 2001)10:184–192.
Campesato, O. (2020), Artificial Intelligence, Machine Learning And Deep Learning, https://www.ebooks.com/en-us/book/209937164/artificial-intelligence-machine-learning-and-deep-learning/oswald-campesato/
Cilimkovic, M. (2010), Neural Networks and Back Propagation Algorithm, https://www.semanticscholar.org/paper/Neural-Networks-And-Back-Propagation-Algorithm-Cilimkovic/df2cd0a34aeebec6b13ccdc51f845e622b781254#citing-papers
Cortes, C., and Vapnik, V.N. (1995). Support-Vector Networks. Machine Learning, 20, 273-297.
Cover, T.M. ve Hart, P.E. (1967) “Nearest neighbor pattern classification”. IEEE Transactions on Information Theory, IT-13(1):21–27 (1967).
Dash, R. ve Dash, P. K. (2016). A Hybrid Stock Trading Framework Integrating Technical Analysis with Machine Learning Techniques. The Journal of Finance and Data Science, 2(1), 42-57.
Dimingo, R., Miwamba, J.W. M. and Bonga L.B.(2021) Predıctıon of Stock Market Dırectıon: Applıcatıon of Machıne Learnıng Models, Internatıonal Economıcs, 74(4), 499-536,
Enke, D. and Thawornwong, S.. (2005). The use of data mining and neural networks for forecasting stock market returns. Expert Systems with Applications. 29. 927-940. 10.1016/j.eswa.2005.06.024.
Fama, Eugene F. (1965), “Random Walks in Stock Market Prices”, Financial Analysts Journal, Vol. 21, No. 5, 55-59.
Fama, E. (1970), “Efficient capital markets: A Review of Theory and Empirical Work”, The Journal of Finance, 25(2), 383-417.
Han, J., Kamber, M., ve Pei, J. (2012). “Data Mining: Concepts and Techniques”. Third Edition, Elsevier Science ve Technology. USA.
Hilbe, J. M. (2015), Practical Guide to Logistic Regression, CRC press, 1st Edition, International Standard Book Number-13: 978-1-4987-0958-3. ISBN 9781498709576
Hosmer JR, David W, Lemeshow S, Sturdivant RX (2013), Applied Logistic Regression.3rd ed. New Jersey, USA, John Wiley ve Sons, 50.
Huang, Wei Nakamori, Yoshiteru, Wang, Shou-Yang (2005), Forecasting Stock Market Movement Direction with Support Vector Machine”, ComputersveOperations Research 32, 2513–2522
Jurafsky, D. and Martin, J.(2020), “Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition” https://web.stanford.edu/~jurafsky/slp3/ed3book.pdf
Kara, İ. and Ecer, F. (2018).Comparison of Classification Methods Performance in Predicting BIST Index Movement Direction, Academic Social Research Journal, 6(83), 514-524, DOI: 10.16992/ASOS.14460.
Kara, Y., Boyacioglu, M. A. and 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.
Karlsson S. and Nordberg, M. (2015) Stock Market Index Prediction Using Artificial Neural Networks Trained On Foreign Markets And How They Compare To A Domestıc Artıfıcıal Neural Network, KTH Royal Instıtute of Technology School of Computer Scıence and Communıcatıon, Degree Project, Fırst Level Computer Scıence Stockholm, Sweden.
Kimoto, T., Asakawa, K., Yoda, M., and Takeoka, M. (1990). Stock Market Prediction System with Modular Neural Networks. In Proceedings of the International Joint Conference on Neural Networks, San Diego, California, 1–6.
Kumar, M. and Thenmozhi, M., (2006). Forecasting Stock Index Movement: A Comparison of Support Vector Machines and Random Forest. Indian Institute of Capital Markets 9th Capital Markets Conference Paper, Available at SSRN: https://ssrn.com/abstract=876544 or http://dx.doi.org/10.2139/ssrn.876544
Li, H. and Huang, S. (2021). Research on the Prediction Method of Stock Price Based on RBF Neural Network Optimization Algorithm. E3S Web of Conferences. DOI:10.1051/e3sconf/202123503088.
Liao, Z., and Wang, J. (2010). Forecasting model of global stock index by stochastic time effective neural network. Expert Systems with Applications, 37(1), 834-841.
Mallikarjuna, M. and Rao, R.P. (2019), Evaluation of forecasting methods from selected stock market returns. Financ Innov 5(40), https://doi.org/10.1186/s40854-019-0157-x
Marsland, S., (2015), Machine Learning an Algorithmic Perspective, USA, Second Edution, A Chapman ve Hall Book CRC Press.
Mitchell, T. (1997), Machine Learning, McGraw Hill, https://www.cin.ufpe.br/~cavmj/Machine%20-%20Learning%20-%20Tom%20Mitchell.pdf
Nevasalmi. (2020).Forecasting Multinormal Stock Returns Using Machine Learning, The Journal of Finance and Data Science.6, 87-92
Nou, A., Lapitskaya. D., Eratalay, M. Hakan and Sharma, R. (2015) Predicting Stock Return and Volatility with Machine Learning and Econometric Models: A Comparative Case Study of the Baltic Stock Market.Electronic Journal.DOI:10.2139/ssrn.3974770
Ou, P., ve Wang, H. (2009). Prediction of stock market index movement by ten data mining techniques. Modern Applied Science, 3(12), 28-42.
Özer, A. Sarı, S.S. and Başakın, E.E. (2018) Stock Index Forecasting with Fuzzy Logic and Artificial Neural Networks: Example of Developed and Developing Countries", Journal of Hitit University Institute of Social Sciences.1(1).99-124.7
Qiu M. and Song Y (2016) Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Mode, PLoS ONE 11(5): e0155133. https://doi.org/10.1371/journal.pone.0155133
Quinlan J.R., 1993, “C4.5: Programs for Machine Learning”, Morgan Kaufmann, San Mateo, CA, s.302
Papuçcu, H. (2019). “Forecasting Stock Market Index Movements: Trend Determining Data", Journal of Social Sciences Vocational School. 22(1) (E-Issn: 2564-7458). 246-256
Patel, J., Shah, S., Thakkar, P. and Kotecha, K. (2015). Predicting Stock and Stock Price Index Movement Using Trend Deterministic Data Preparation and Machine Learning Techniques. Expert Systems with Applications, 42(1), 259-268.
Phua, P. K. H., Zhu, X. and Koh, C. H., 2003. Forecasting stock index increments using neural networks with trust region methods, IEEE, 260-265.
Spahija, Denis and Xhaferi, Seadin (2019), “Fundamental and Technıcal Analysis of The Stock Price”, International Scientific Journal Monte 1(1), 32, DOI:10.33807/monte.1.201904160,
Subasi, A. Amir,F. Bagedo, K. Shams,A. and Sarirete, A. (2021). Stock Market Prediction Using Machine Learning, Procedia Computer Science.194, 173-179. https://doi.org/10.1016/j.procs.2021.10.071
Teker S. and Özer B. (2012), "Structural Comparison of Capital Markets: Developed, Developing Countries and Turkey", Accounting and Finance Journal. https://dergipark.org.tr/tr/download/article-file/426984