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http://dx.doi.org/10.13106/jafeb.2020.vol7.no10.009

Hybrid Model Approach to the Complexity of Stock Trading Decisions in Turkey  

CALISKAN CAVDAR, Seyma (Department of Economics. Faculty of Economics and Administrative Sciences, Dogus University)
AYDIN, Alev Dilek (Department of International Trade and Business, Faculty of Business, Halic University)
Publication Information
The Journal of Asian Finance, Economics and Business / v.7, no.10, 2020 , pp. 9-21 More about this Journal
Abstract
The aim of this paper is to predict the Borsa Istanbul (BIST) 30 index movements to determine the most accurate buy and sell decisions using the methods of Artificial Neural Networks (ANN) and Genetic Algorithm (GA). We combined these two methods to obtain a hybrid intelligence method, which we apply. In the financial markets, over 100 technical indicators can be used. However, several of them are preferred by analysts. In this study, we employed nine of these technical indicators. They are moving average convergence divergence (MACD), relative strength index (RSI), commodity channel index (CCI), momentum, directional movement index (DMI), stochastic oscillator, on-balance volume (OBV), average directional movement index (ADX), and simple moving averages (3-day moving average, 5-day moving average, 10-day moving average, 14-day moving average, 20-day moving average, 22-day moving average, 50-day moving average, 100-day moving average, 200-day moving average). In this regard, we combined these two techniques and obtained a hybrid intelligence method. By applying this hybrid model to each of these indicators, we forecast the movements of the Borsa Istanbul (BIST) 30 index. The experimental result indicates that our best proposed hybrid model has a successful forecast rate of 75%, which is higher than the single ANN or GA forecasting models.
Keywords
Artificial Neural Network; Genetic Algorithm; Technical ${\dot{I}}ndicators$; Hybrid Models; Borsa Istanbul (BIST);
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Times Cited By KSCI : 9  (Citation Analysis)
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1 Banik, S., Khan, A., & Anwer, M. (2014). Hybrid machine learning technique for forecasting Dhaka Stock Market timing decisions, Computational Intelligence and Neuroscience, 2014, 1-6. https://doi.org/10.1155/2014/318524
2 Caliskan, M. M. T., & Deniz, D. (2015). Yapay Sinir Aglariyla Hisse Senedi Fiyatlari ve Yonlerinin Tahmini. Forecasting the directions and prices of stocks by using artificial neural networks. Journal of the Faculty of the Economics and Adminstrative Sciences of Eskisehir Osmangazi University, 10(3), 177-194.
3 Camba, A. C. (2020). Capturing the Short-run and Long-run Causal Behavior of Philippine Stock Market Volatility under Vector Error Correction Environment. Journal of Asian Finance, Economics and Business, 7(8), 41-49. doi:10.13106/jafeb.2020. vol7.no8.041   DOI
4 Chang, P., Wang, D., & Zhou, C. (2012). A novel model by evolving partially connected neural network for stock price trend forecasting. Expert Systems with Applications, 39, 611-620.   DOI
5 Chatterjee, A. (2000). Artificial nural network and the financial markets: a survey. Managerial Finance, 26(12), 32-45.   DOI
6 l-hnaity, B., & Abbod, M. (2016). Predicting Financial Time Series Data Using Hybrid Model. In: Y. Bi et al. (Eds.), Intelligent Systems and Applications (pp. 19-41). Cham, Switzerland: Springer International Publishing.
7 Andre, C., & Tulio, R. (2007). Do artificial neural networks provide better forecasts? Evidence from Latin American stock indexes. Latin American Business Review, 8(3), 92-110.   DOI
8 Chen, A., Leung, M., & Daouk, H. (2003). Applications of neural networks to an emerging financial market: Forecasting and trading the Taiwan Stock Index. Computers and Operations Research, 30(6), 901-923.   DOI
9 Dash, R., & Dash, K. P. (2016). A hybrid stock trading framework integrating technical analysis with machine learning techniques. The Journal of Finance and Data Science, 4(1), 42-57.   DOI
10 Sheta, A., Ahmet, S., & Faris, H. (2015). A comparison between regression artificial neural networks and support vector machines for predicting stock market index, International Journal of Advanced Research in Artificial Intelligence, 4(7), 55-63.
11 Sugumaran, V. (2008). Intelligent Information Techologies: Concepts, Methodologies, Tools, and Applications. Hershey, PA: IGI Global.
12 Tektas, A., & Karatas, A. (2004). Artificial Neural Networks and Their Applications in the Field of Finance: Prediction of Stock Prices. Ataturk University Journal of Economics and Administrative Sciences, 3(3-4), 337-349.
13 Tsai, C., & Wang, S. P. (2009). Stock price forecasting by hybrid machine learning techniques. In: Proceedings of the International Multiconference of Engineers and Computer Scientists, IMECS 2009, Hong Kong.
14 Wei, L. (2013). A Hybrid Model Based on ANFIS and Adaptive Expectation Genetic Algorithm to Forecast TAIEX. Economic Modelling, 33, 893-899.   DOI
15 Zhang, P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175.   DOI
16 Hawley, D., Johnson, J., & Raina, D. (1990). Artificial neural systems: a new tool for financial decision-making. Financial Analysts Journal, 46(6), 63-72.   DOI
17 Faria, E. L., Albuqurque, M. P., Gonzales, J. L., Cavalcante, J. T. P., & Albuqurque, M. P. (2009). Predicting the Brazilian stock market through neural networks and adaptive exponential smoothing methods. Expert System with Applications, 36(10), 12506-12509.   DOI
18 Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Boston, MA: Addison Wesley Longman Publishing.
19 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, 171-180.   DOI
20 Holland, J. (1975). Adaptation in Natural and Artificial Systems. Ann Arbor, MI: University of Michigan Press.
21 Hsieh, C. T. (1993). Some potential applications of artificial neural systems in financial management. Journal of Systems Management, 44(4), 12-15.
22 Kashei, M., & Bijari, M. (2010). An artificial neural network (p, d, q) model for timeseries forecasting. Expert Systems with Applications, 37(1), 479-489.   DOI
23 Inthachot, M., Boonjing, V., & Intakosum, S. (2016). Artificial neural network and genetic algorithm hybrid intelligence for predicting Thai Stock Price Index. Computational Intelligence and Neuroscience, 2016, 1-8. https://doi.org/10.1155/2016/3045254
24 Kakinuma, Y. (2020). Return Premium of Financial Distress and Negative Book Value: Emerging Market Case. Journal of Asian Finance, Economics and Business, 7(8), 25-31. doi:10.13106/jafeb.2020.vol7.no8.025   DOI
25 Kara, Y., Boyacioglu, M. A., & Baykan, O. K. (2011). Prediction 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, 5311-5319.   DOI
26 Kutlu, B., & Badur, B. (2009). Yapay Sinir Aglari ile Borsa Endeksi Tahmini. Stock Market Index Prediction with Artificial Neural Networks. Journal of Yonetim, 20(63), 25-40.
27 Kiani, K., & Kastens, T. (2008). Testing forecast accuracy of foreign exchange rates: predictions from feed forward and various recurrent neural network architectures. Computational Economics, 32(4), 383-406.   DOI
28 Koza, J. R. (1994). Genetic Programming II: Automatic Discovery of Reusable Programs. Cambridge, MA: MIT Press.
29 Kumar, M. (2009). Nonlinear Prediction of the Standard and Poor’s 500 and the Hang Seng Index Under a Dynamic Increasing Sample. Asian Academy of Management Journal of Accounting and Finance, 5(2), 101-118.
30 Lee, J. W., & Brahmasrene, T. (2018). An Exploration of Dynamical Relationships between Macroeconomic Variables and Stock Prices in Korea. Journal of Asian Finance, Economics and Business, 5(3), 7-17. doi:10.13106/jafeb.2018.vol5.no3.7   DOI
31 Liao, Z., & Wang, J. (2010). Forecasting model of global stock index by stochastic time effective neural network, Expert Systems with Applications, 37(1), 834-841.   DOI
32 Maia, A., & de Carvalho, F. (2011). Holt’s exponential smoothing and neural network models for forecasting interval-valued time series. International Journal of Forecasting, 27, 740-759.   DOI
33 McCulloch, W., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity, The Bulletin of Mathematical Biophysics, 5(4), 115-133.   DOI
34 Mehrara, M. M., Ahrari, A., & Ghafari, A. (2010). Using technical analysis with neural network for prediction stock price index in Tehran Stock Exchange, Middle Eastern Finance and Economics, 6(6), 50-61.
35 Samanta, G. P., & Bordoloi, S. (2005). Predicting stock market: an application of artificial neural network technique through genetic algorithm. Finance India, 19(1), 173-188.
36 Nguyen, C. T., & Nguyen, M. H. (2019). Modeling Stock Price Volatility: Empirical Evidence from the Ho Chi Minh City Stock Exchange in Vietnam. Journal of Asian Finance, Economics and Business, 6(3), 19-26. https://doi.org/10.13106/jafeb.2019.vol6.no3.19   DOI
37 Niaki, S. T. A., & Hoseinzade, S. (2013). Forecasting S&P 500 index using artificial neural networks and design of experments, Journal of Industrial Engineering International, 9(1), 1-9.   DOI
38 O'Connor, N., & Madden, M. (2006). A neural network approach to predicting stock exchange movements using external factors, Knowledge-Based Systems, 19, 371-378.   DOI
39 Rajashree, D., & Pradipta, K. D. (2016). A hybrid stock trading framework integrating technical analysis with machine learning techniques. The Journal of Finance and Data Science, 4(1), 42-57.
40 Rumelhart, D., & McClelland, J. (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Cambridge, MA: MIT Press.
41 Sayed, S. A. (2014). Do Analyst Practices and Broker Resources Affect Target Price Accuracy? An Empirical Study on Sell Side Research in an Emerging Market. Journal of Asian Finance, Economics and Business, 1(3), 29-36. doi: 10.13106/jafeb.2014.vol1.no3.29.   DOI
42 Sevim, C., Oztekin, A., Bali, O., Gumus, S., & Guresen, E. (2014). Developing an early warning system to predict currency crises. European Journal of Operational Research, 237(1), 1095-1104.   DOI
43 Shen, L., & Loh, T. T. (2004). Applying rough sets to market timing decisions. Decision Support Systems, 37(4), 583-597.   DOI