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Journals / Gazi University Journal of Science / 2021 / Cilt: 34 - Sayı: 1

Predicting Stock Prices Using Machine Learning Methods and Deep Learning Algorithms: The Sample of the Istanbul Stock Exchange

Uğur DEMİREL
Handan ÇAM
Ramazan ÜNLÜ
Pages
63–82
Publication Date
2021-03-01
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results of this study show that MLP and LSTM models become advantageous in estimating the opening and closing stock prices comparing to SVM model.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Stock market prediction in financial and commodity markets is a major challenge for speculators, investors, and companies but also profitable with an accurate prediction.
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Key Findings & Data:Thus, obtaining accurate prediction results becomes extremely important especially while the stock market is essentially volatile, nonlinear, complicated, adaptive, nonparametric and unpredictable in nature.
💡
Conclusion & Impact:The results of this study show that MLP and LSTM models become advantageous in estimating the opening and closing stock prices comparing to SVM model.
🔬Design:Modeling & Simulation
📍Scope:2010 Verileri
⚙️Variables:Stock market prices · Multiple layer perceptron · Support vector machines

Abstract

Stock market prediction in financial and commodity markets is a major challenge for speculators, investors, and companies but also profitable with an accurate prediction. Thus, obtaining accurate prediction results becomes extremely important especially while the stock market is essentially volatile, nonlinear, complicated, adaptive, nonparametric and unpredictable in nature. This study aims to forecast the opening and closing stock prices of 42 firms listed in Istanbul Stock Exchange National 100 Index (ISE-100) using well-known machine learning methods, Multilayer Perceptrons (MLP) and Support Vector Machines (SVM) models and deep learning algorithm, Long Short Term Memory (LSTM) by comparing their forecasting performances. The analysis includes 9 years of data from 01.01.2010 to 01.01.2019. For each firm 2249 data for the opening and 2249 for the closing stock prices were established as daily data sets. Forecasting performance of these methods was evaluated by applying different criteria for each model: root mean squared error (RMSE), mean squared error (MSE) and R-squared (R2). The results of this study show that MLP and LSTM models become advantageous in estimating the opening and closing stock prices comparing to SVM model.

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Cite This Article

DEMİREL, U., ÇAM, H., ÜNLÜ, R. (2021). Predicting Stock Prices Using Machine Learning Methods and Deep Learning Algorithms: The Sample of the Istanbul Stock Exchange. Gazi University Journal of Science, Cilt: 34 - Sayı: 1, 63–82. https://ajindex.com/articles/252898

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References (14)

  1. Abu-Mostafa, Y. S., Atiya, A. F., “Introduction to financial forecasting”, Applied Intelligence, 6(3):205-213, (1996)
  2. Huang, W., Nakamori, Y., Wang S. Y., “Forecasting stock market movement direction with support vector machine”, Computers and Operations Research, 32(10):2513-2522, (2005)
  3. Kara, Y., Boyacioglu, M. A., Baykan, O. K., “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)
  4. Northover, K., Lo, A. W., “Computational finance”, Computer in Science & Engineering, 1(6):22, (2000)
  5. Tan, T. Z., Quek, C., Ng, G. S., “Biological brain-inspired genetic complementary learning for stock market and bank failure prediction”, Computer Intelligence, 23(2):236-261, (2007)
  6. Blank, S. C., “'Chaos' in futures markets? A nonlinear dynamical analysis”, The Journal of Futures Markets, 11(6):711, (1991)
  7. Zhang, G., Patuwo, B. E., Hu, M. Y., “Forecasting with artificial neural networks: The state of the art” International Journal of Forecasting, 14(1):35-62, (1998)
  8. Lapedes, A., Farber, R., “Nonlinear signal processing using neural networks: Prediction and system modelling”, 1. IEEE international conference on neural networks, San Diego, CA, USA, No. LA-UR-87-2662; CONF-8706130-4, (1987)
  9. Gorr, W. L., Nagin, D., Szczypula, J., “Comparative study of artificial neural network and statistical models for predicting student grade point averages”, International Journal of Forecasting, 10(1):17-34, (1994)
  10. Ruiz-Suarez, J., Mayora-Ibarra, O., Torres-Jimenez, J., Ruiz-Suarez, L., “Short-term ozone forecasting by artificial neural networks”, Advances in Engineering Software, 23(3):143-9, (1995)
  11. Kohzadi, N., Boyd, M. S., Kermanshahi, B., Kaastra, I., “A comparison of artificial neural network and time series models for forecasting commodity prices”, Neurocomputing, 10(2):169-81, (1996)
  12. Poh, H. L., Yao, J., Jašic, T., “Neural networks for the analysis and forecasting of advertising and promotion impact”, Intelligent Systems in Accounting, Finance and Management, 7(4):253-68, (1998)
  13. Darbellay, G. A., Slama, M., “Forecasting the short-term demand for electricity: Do neural networks stand a better chance?”, International Journal of Forecasting, 16(1):71-83, (2000)
  14. Aminian, F., Suarez, E. D., Aminian, M., Walz, D. T., “Forecasting economic data with neural netw