Stock price analysis using machine learning method(Non-sensory-parametric backup regression algorithm in linear and nonlinear mode)

Document Type : Research Paper


Department of Accounting, Babol Branch, Islamic Azad University, Babol, Iran



The most common starting point for investors when buying a stock is to look at the trend of price changes. In recent years, different models have been used to predict stock prices by researchers, and since artificial intelligence techniques, including neural networks, genetic algorithms and fuzzy logic, have achieved successful re-sults in solving complex problems; in this regard, more exploitation Are. In this research, the prediction of stock prices of companies accepted in the Tehran Stock Exchange using artificial intelligence algorithm (non-sensory-parametric support vector regression algorithm in linear and nonlinear mode) has been investigated. The results of the research show that the PINSVR algorithm in nonlinear mode has been able to predict the stock price over the years, rather than linear mode.


Main Subjects

[1] Alijani, M., Banimahd, B., Madanchi, M.. Study and Research on the Six-Year Process of Bitcoin Price and Return. Advances in Mathematical Finance and Applications, 2019, 4(1), 45-54.
Doi: 10.22034/amfa.2019.577434.1126
[2] Andrea ,B., Matteo, M. The impacts of oil price shocks on stock market volatility: Evidence from the G7 countries.Energy Policy, 2016, 98(c),  p160-169. Doi: 10.1016/j.enpol.2016.08.020
[3] Ashraf E. Elbakrya, J. Nwachukwub, H. Abdouc,d. Comparative evidence on the value relevance of IFRS-basedaccounting information in Germany and the UK,Journal of International Accounting. Auditing and Taxation 28, 2016,p 10–30. Doi:10.1016/j.intaccaudtax.2016.12.002
[4] BarzegariKhanagha, J., Jamali, Z. Predicting Stock Returns with Financial Ratios; An Exploration in Recent Researches. Journal of Accounting Research, 6(2), 71-92, 2016.
Doi: 10.22051/ijar.2016.2432.
[5] Bayat, A., Bagheri, Z. The Predication of Stock Price Using Firely Algorithm. Financial Knowledge of Securities Analysis, 2017,  10(35),P. 135-145.
[6] Alimohamadi, A., Abbasimehr, M., javaheri, A. Prediction of Stock Return Using Financial Ratios: A Decision Tree Approach. Financial Management Strategy, 2015, 3(4), 125-146. Doi: 10.22051/jfm.2016.2349.
[7] AhmadKhanBeygi, S., Abdolvand, N. Stock Price Prediction Modeling Using Artificial Neural Network Approach and Imperialist Competitive Algorithm Based On Chaos Theory. Financial Management Strategy, 2017,5(3), 27-73. Doi: 10.22051/jfm.2017.14635.1319.
[8] Bartov, E., Partha ,M. Does Income Statement Placement Matter to Investors? The Case of Gains/Losses from Early Debt Extinguishment Leonard N. Stern School of Business, New York University, New York, 2012.Doi: 10.2308/1558-7967-89.6.i.
[9] Chia,L.,Yung,L.A double-threshold GARCH model of stock market and currency shocks on stock returns، Article in Mathematics and Computers in Simulation,2008, 79(3) , p.458-474.
[10] Dibachi, H., Behzadi. MH, Izadikhah, M., Stochastic Modified MAJ Model for Measuring the Efficiency and Ranking of DMUs, Indian Journal of Science and Technology, 2015, 8 (8), P. 549–555, Doi: 10.17485/ijst/2015/v8iS8/71505
[11] Fakhari, H., RezaeiPitenoei Y. Explaining a Model for Measuring Corporate Information Environment. Quarterlyfinancialaccountingjournal,2017,9(33),P.121-147.
[12] Islami, M., Abadi, H., Saeedi, P., and Khalilzadeh, S. Investigating the Effect of Internal Factors on Stock Price Change in Investment Companies Accepted in Tehran Stock Exchange, Accounting Research, 2017, 6(4), P. 103-138, (in Persian).Doi: 10.22051/IJAR.2017.2080.
[13] Izadikhah, M., Using goal programming method to solve DEA problems with value judgments, Yugoslav Journal of Operations Research, 2016, 24 (2), P. 267 – 282, Doi: 10.2298/YJOR121221015I
[14] Khaleghi, P., Aghaei, M., Rezaei, F. Salience Theory and Pricing Stock of Corporates in Tehran Stock Exchange. Advances in Mathematical Finance and Applications, 2018, 3(4), 1-16.
Doi: 10.22034/amfa.2019.577140.1120
[15] KiyaniMavi, R., SayadiNik, K. Using different learning algorithms in the stock price prediction by using neural networks. Journal of Development & Evolution Mnagement, . (2015) ,P.75-81.
 [16] Lakshmi, P.,Maheswaran, S. A New Statistic to Capture the Level Dependence in Stock Price Volatility.The Quarterly Review of Economics and Finance, 2017, P. 355-362. Doi: 10.1016/j.qref.2016.12.001
[17]Monadjemi, S., Abzari, M., RayatiShavazi, A..Modeling of Stock Price Forecasting in Stock Exchange Market, using Fuzzy Neural Networks and Genetic Algorithms , 2009,6(22),p 1-26.
Doi: 10.22055/jqe.2009.10697.
[18]Meshki, M., ElahiRodposhti, S. Acounting Conservatism and the Value of Cash Holdings. Empirical Research in Accounting, 2014, 4(1), p23-43. Doi: 10.22051/jera.2015.1882
[19]Metghalchi,M., Linda ,A. History of share prices and market efficiency of the Madrid general stock index, International Review of Financial Analysis Elsevier, 2015Doi: 10.1016/j.irfa.2015.05.016
[20]Naghdi, S., Arab MazarYazdi, M. Forecasting EPS o with Hybrid Genetic algorithm, particle swarm optimization and Neural networks, 2017  , 8(3),P. 7-34.Doi: 10.22103/jak.2017.7086.2051
[21]Nguyen Ngoc Thanh, V. Determinants of Stock Price Synchronicityevidence from HoChiMinh city stock exchange.Doctoraldissertation,Vietnam,InternationalUniversityHCMC,2013. Doi: 10.22161/ijaers.4.4.14
[22]Pakraei, A. Predict the trend of stock prices using XCS based on genetic algorithms and reinforcement learning. Financial Knowledge of Securities Analysis, 2017, 10(34),P. 39-54.
[23]Parsa, B., Sarraf, F. Financial Statement Comparability and the Expected Crash Risk of Stock Prices. Advances in Mathematical Finance and Applications,2018, 3(3), 77-93.Doi: 10.22034/amfa.2018.544951
[24]Ramazani, M., Ameli, A. Forecasting of Stock Price Using Fuzzy Neural Network Based on GA and Compaision with Fuzzy Neural Network. Jemr, 2016, 6 (22) P. 61-91Doi: 10.18869/acadpub.jemr.6.22.61
[25]Setayesh, M., Kazemnejad, M. Effective Factors on Disclosure Quality of the Firms Listed in Tehran Stock Exchange, Journal of Accounting Advances, 2012 ,4(1), 49-79.
Doi: 10.22099/jaa.2012.514.
[26]Yu, Lean,W., Lai, K. Neural network-based mean–variance–skewness model for portfolio selection. Computers & Operations Research, 2008,35(1), P 34-46Doi:10.1016/j.cor.2006.02.012
[27]Yang, S., and Fu, L. Critical chain and evidence reasoning applied to multi-project resource schedule in automobile R&D process, International Journal of Project Management, 2014, 32(1), P. 166-177.
[28]Zamani, A., AnvaryRostamy, A., BadavarNahandi, Y.,andSaeedi, A. The Effect of Agency Costs on the Speed of Stock Price Adjustment: Evidence of Tehran Stock Exchange. Financial Management Strategy, 2018,5(4), 25-44.Doi: 10.22051/jfm.2018.15774.1403.