Presenting an Explanatory Model of Stock Price Using Deep Learning Algorithm
محورهای موضوعی : Financial MathematicsMojtaba Bavaghar Zaeimi 1 , Gholamreza Zomorodian 2 , Mehrzad Minooee 3 , Amirreza Keyghobadi 4
1 - Department of Finance ,Central Tehran Branch , Islamic Azad University, Tehran,Iran
2 - Department of Business Management, Central Tehran Branch , Islamic Azad University, Tehran, Iran.
3 - Department of Industrial Management ,Islamic Azad University, Central Tehran Branch, Tehran, Iran
4 - Department of Accounting, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
کلید واژه: Stock Price , Learning Algorithm , Prediction ,
چکیده مقاله :
This study aimed to present an explanatory model of stock price using deep learning algorithm for companies listed in the Tehran Stock Exchange. In this study, a deep learning network was used to predict stock prices. The study was applied-developmental research in terms of purpose. To test the research questions, accounting data were prepared from 2011 to 2020 and input variables were calculated based on it for the model. The method of systematic elimination sampling has been used in this study. The results indicated that the precisions of prediction has a high precisions in the deep learning model. The proposed algorithm was reviewed according to its prediction accuracy and modeling cost. According to the data volume, it was found that the prediction accuracy in the deep learning model has a relative superiority and the diagram of performance characteristic and AUC criteria also showed this superiority in detection power.
This study aimed to present an explanatory model of stock price using deep learning algorithm for companies listed in the Tehran Stock Exchange. In this study, a deep learning network was used to predict stock prices. The study was applied-developmental research in terms of purpose. To test the research questions, accounting data were prepared from 2011 to 2020 and input variables were calculated based on it for the model. The method of systematic elimination sampling has been used in this study. The results indicated that the precisions of prediction has a high precisions in the deep learning model. The proposed algorithm was reviewed according to its prediction accuracy and modeling cost. According to the data volume, it was found that the prediction accuracy in the deep learning model has a relative superiority and the diagram of performance characteristic and AUC criteria also showed this superiority in detection power.
[1] Akhgar, M., Amini, P., Moradi, A., Reviewing the effect of environmental performance on the risk of stock price falls with emphasis on political relationships in the Tehran Stock Exchange, Journal of Finan-cial Accounting Research, 2021; 13(2), 48:101-120. Doi:10.22108/FAR.2021.128585.1754
[2] Khosravi Nejad, A., Shabani Sadrpisheh, M., Linear and non-linear evaluation in predicting the stock price index in Tehran Stock Exchange, journal of financial economics (financial economics and develop-ment), 2014; 8(27):51 - 64. (in persion)
[3] Zeinaddini, Sh., Karimi, M., Khanzadi, A., Evaluating the effect of oil price shocks on the Iran stock market' performance, Journal of Financial Economics (Financial Economics and Development), 2020; 14(50):145-169.
[4] Shakeri, A., Theories and Policies of Macroeconomics, Pars Navisa Publication, 2008.
[5] Abdolmaleki, A., Hamidian, M., Baghani, A., Investigation of Fractal Property Price and Stock Returns of Tehran Stock Exchange Companies Using Nonlinear Arifma Model, Financial Engineering and Securi-ties Management (Portfolio Management), 2020; 11(44): 207-226. (in persion)
[6] Arab Salehi, M., Kamali Dehkordi, A., Comparative Investigating of Stock Valuation Discount Models in Companies Listed in Tehran Stock Exchange, Quarterly Journal of Management Perspective, 2020; 11(33): 12-32. Doi:10.52547/jfmp.2021.215093.0
[7] Farazmand, S., Asadi, Gh., Abdeh Tabrizi, H., Hamidizadeh, M., Reviewing the mass behavior of stock prices in Tehran Stock Exchange, Quarterly Journal of Quantitative Economics, online publication from March 3. 2020. Doi:10.22055/JQE.2021.36054.2310
[8] Karimi Dastgerdi, A., Zamani Borujeni, F., A review on deep learning methods for financial market prediction, National Congress of Basic Research in Computer Engineering and Information Technology. 2019.
[9] Kiani Mavi, R., Sayadi Nik, K., Using different learning algorithms in the stock price prediction by using neural networks, Journal of Development Evolution Management, 2015; special issue:75-81.
[10] Mohammadi, M., Sadr, N., Presenting a method for predicting stock market prices with a deep learn-ing approach (Case study: Stock market), Master's Thesis in Computer Science, Faculty of Engineering of Ishraq, Bojnourd, 2018.
[11] Mehrara, A., Atf, Z., Askari, Z., Data Mining Techniques for predicting Stock Market, National Con-ference on Accounting, Financial Management and Investment, 2012.
[12] Momeni, A., Mohammadi, M., Reviewing the relationship between cost stickiness and stock price crash risk in companies listed in the Tehran Stock Exchange, Journal of New Research Approaches in Management and Accounting, 2020; 62:73 -90. (in persion)
[13] Mir Hashemi Dehnavi, S. M., Asymmetric effects of oil price shocks on the stock market: Case Study: Oil Exporting Countries, Quarterly Journal of Fiscal and Economic Policies, 2015; 77: 11-711.
[14] Namazi, M., Kiamehr, M. M., Predicting the daily stock returns of companies listed in the Tehran Stock Exchange using artificial neural networks, Financial Research Journal, 2007; 115-134.
[15] Cha, K, H., Hadjiiski, L. M., Samala, R. K., et al., Blad-der Cancer Segmenttion in CT for Treatment Response Assessment: Application of Deep‐Learning Convolu-tion Neural Nework Pilot Study, Tomog-raphy A Journal for Imaging Research, 2016; 2(4):421-429. Doi: 10.18383/j.tom.2016.00184
[16] Chan, M.C., Wong, C.C., Lam, C.C., Financial time series forecasting by Neural Network using Conju-gate Gradient Learning Algorithm and Multiple Linear Regression, Weight initialization, Department of computing, 2000.
[17] Chih, M. H., a hybrid procedure for stock price prediction by integrating self-organizing map and genetic programming, Expert Systems with Applications, 2011; 38: 14026-14036. Doi;10.1016/j.eswa.2011.04.210
[18] Das, S. P., Padhy, S., A novel hybrid model using teachinglearningbased optimization and a support vector machine for commodity futures index forecasting, International Journal of Machine Learning & Cybernetics, 2018; 9(1): 97111. Doi:10.1007/s13042-015-0359-0
[19] Göçken M, Özçalıcı, Mehmet, Boru A, et al., Integrat-ing metaheuristics and Artificial Neural Networks for improved stock price prediction, Ex-pert Systems with Applications, 2016, 44(C), P.320‐331.
Doi: 10.1016/j.eswa.2015.09.029
[20] Jie W., Wang J., Forecasting stochastic neural network based on financial empirical mode decomposition, Neural Networks, 2017; 90 :8‐20. Doi: 10.1016/j.neunet.2017.03.004
[21] Kim, K. J., Han, I., Genetic algorithms approach to characteristic discrimination in artificial neural networks for the prediction of stock price index, Published by Elsevier science, Ltd, Expert systems with applications, 2000; 19: 125 132. Doi:10.1016/S0957-4174(00)00027-0
[22]Lahmiri S., A Technical Analysis Information Fusion Approach for Stock Price Analysis and Modeling, Fluctuation and Noise Letters, 2018; 17(1): 1850007. Doi:10.1142/S0219477518500074
[23] Mahmud, M., Kaiser, M. S., Hussain, A., et al., Applica-tions of Deep Learning and Reinforcement Learning to Biological Data, IEEE Transactions on Neural Networks and Learning Systems, 2017; 29(6):2063-2079. Doi: 10.1109/TNNLS.2018.2790388
[24] Meihua, X., Haiyan, L., Yuanjun, Z., Blockchain financial investment based on deep learning network algorithm, Journal of Computational and Applied Mathematics, 2020; 372(8):112723.
Doi: 10.1016/j.cam.2020.112723
[25] Oh, K. J., Kim, K. J., Analyzing stock market tick data using piecewise nonlinear model, Expert Sys-tem with Applications, 2002; 22(3): 249–255.
[26] Pang X, Zhou Y, Pan W, et al., An innovative neural network approach for stock market prediction, Journal of Supercomputing, 2018; 76: 2098–2118. Doi: 10.1007/s11227-017-2228-y
[27]Sato M, Horie K, Hara A, et al., Application of deep learning to the classification of images from colposcopy, Oncology Letters, 2018; 15(3):3518‐3523. Doi: 10.3892/ol.2018.7762
[28]Song, W., et al., A Double‐Layer Neural Network Framework for High‐Frequency Forecasting, Acm Transactions on Management Information Systems, 2017; 7(4): 1‐17. Doi:10.1145/3021380
[29] Ubbens, J., Cieslak, M., Prusinkiewicz, P., et al., The use of plant models in deep learning: an application to leaf counting in rosette plants, Plant Methods, 2018; 14(1):6. Doi: 10.1186/s13007-018-0273-z
[30] Zhang, G.P., Time Series Forecasting Using a Hybrid ARIMA and Neural Network Model Neuro com-puting, 2003; 50: 159-175. Doi:10.1016/S0925-2312(01)00702-0
[31] Lendasse, A., Non-Linear financial time series forecasting application to Bell 20 stock market Index, European Journal of Economic and social system, 2000; 14(1): 81-91
[32] Park, C.H., Irwin, S.H., What Do We Know About the Profitability of Technical Analysis? , Journal of Economic Surveys, 2007; 21: 786–826. Doi: 10.1111/j.1467-6419.2007.00519.x
[33] Monajemi, S. A., Abzari, M., Rayati, A., predicting the stock price in the stock market using fuzzy neural network and genetic algorithm and comparing it with artificial neural network, Quarterly Journal of Quantitative Economics (Former Economic Studies), 2009; 6(3):1-26. Doi: 10.22055/JQE.2009.10697
