[1] Murphy, J., Technical Analysis of the Financial Markets, New York Institute of Finance, 1999, P. 24-31.
[2]
Huber, Peter J.,
Robust Estimation of a Location Parameter,
Annals of Mathematical Statistics, 1964, 53 (1), P.73–101.
Doi:10.1214/aoms/1177703732
[3] Warren S. and Walter P., A logical calculus of the ideas immanent in nervous activity, 1943, The bulletin of mathematical biophysics, 5, P.115–13.
[4] Vapnik, V., Golowich, S. E. and A. J. Smola, A. J., Support vector method for function approximation, regression estimation and signal processing, 1997, New York, Cambridge, MA: MIT Press.
[5] Hsu,
S., Hsieh, J., Chih,
T., and Hsu,
K., A two-stage architecture for stock price forecasting by integrating self-organizing map and support vector regression,
Expert Systems with Applications, 2009, 36(4), P.7947-7951. Doi:10.1016/j.eswa.2008.10.065
[6] Kimoto, T., Asakawa, K., Yoda, M., and Takeoka, M.,
Stock market prediction system with modular neural network, Proceedings of the International Joint Conference on Neural Networks, 1990, P.1–6.
Doi:10.1109/ijcnn.1990.137535
[8] Gandhmal, P., Dattatray and Kumar, K.,
Systematic analysis and review of stock market prediction techniques, Computer Science Review, 2019, 34, 100190.
Doi:10.1016/j.cosrev.2019.08.001
[9] Diler, A., Forecasting the direction of ISE National-100 index by neural networks backpropagation algorithm, ISE Review, 2003, 7, P.65-81.
[11] Nabipour, M., Nayyeri, P., Jabani, H., Mosavi, A., Salwana, E. and Shahab, S., Deep Learning for Stock Market Prediction, Entropy, 2020, 22, 840, Doi:10.3390/e22080840
[12] Huang, W., Nakamori, Y., and Shou-Yang, W.,
Forecasting stock market movement direction with support vector machine, Computers & Operations Research, 2005, 32, P.2513-2522.
Doi:10.1016/j.cor.2004.03.016
[13] Altay, E., and Satman, M. H., Stock Market Forecasting: Artificial Neural Networks and Linear Regression Comparison in an Emerging Market, Journal of Financial Management and Analysis, 2005, 18(2), P.18-33.
[14] Cao, Q., Leggio, K. B., and Schniederjans, M.J.,
A comparison between Fama and French’s model and artificial neural networks in predicting the Chinese stock market, Computers & Operations Research, 2005, 32, P.2499-2512.
Doi:10.1016/j.cor.2004.03.015
[15] Roh, T.H.,
Forecasting the volatility of stock price index, Expert Systems with Applications, 2007, 33, P.916–922.
Doi:10.1016/j.eswa.2006.08.001
[16] Hyup Roh, T.,
Forecasting the volatility of stock price index, Expert Systems with Applications, 2007 33(4), P.916–922.
Doi: 10.1016/j.eswa.2006.08.001
[17] Rashid, A., and Ahmad, S., Predicting stock returns volatility: An evaluation of linear vs. nonlinear methods, International Research Journal of Finance and Economics, 2008, 20, P.141–150.
[18] Manish, k., and Thenmozhi, M.,
Forecasting stock index movement: A comparison of support vector machines and random forest, Indian Institute of Capital Market Conference, Mumbai India, 2005.
Doi:10.2139/ssrn.876544
[19] Xu, X., Zhou, C., and Wang, Z.,
Credit scoring algorithm based on link analysis ranking with support vector machine, Expert Systems with Applications, 2009, 36 (2), P.2625-2632.
Doi:10.1016/j.eswa.2008.01.024
[20] Kara, Y., Boyacioglu, M.A., Baykan, O.K.,
Predicting direction of stock price index movement using artificial machines: the sample of the IStanbul STock Exchange, Expert Systems with Applications, 2011, 38(5), P.5311-5319.
Doi:10.1016/j.eswa.2010.10.027
[21] Kara, Y., AcarBoyacioglu, M., and 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, 2011, 38 (5), P.5311–5319.
Doi: 10.1016/j.eswa.2010.10.027
[22] Patel, J., Shah, S., Thakkar, P., and Kotecha, K.,
Predicting stock market index using fusion of machine learning techniques, Expert Systems with Applications, 2015, 42 (4), P.2162–2172.
Doi: 10.1016/ j.eswa.2014. 10.031
[23] Patel, J., Shah, S., Thakkar, P., and Kotecha, K.,
Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques, Expert Systems with Applications, 2015,42(1), P.259-268.
Doi:10.1016/j.eswa.2014.07.040
[24] Štěpánek, J., Šťovíček, J., Cimler, R., Application of Genetic Algorithms in Stock Market Simulation, Social and Behavioral Sciences, 2012, 47, P.93-97. Doi:10.1016/j.sbspro.2012.06.619
[25]
Montri, I.,
Veera B., and
Sarun I.,
Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend, Computational Intelligence and Neuroscience, 2016 ID, 6878524 Doi:
10.1155/2016/3045254
[26] Hsu, M.W., Lessmann, S., Sung, M.C., Ma, T. and Johnson, J.E.,
Bridging the divide in financial market forecasting: machine learners vs. Financial economists, Expert Systems with Applications, 2016, 61(1), P.215-234.
Doi:10.1016/j.eswa.2016.05.033
[27]
Inthachot, M.,
Boonjing, V., and
Intakosum, S.,
Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend, Computational Intelligence and Neuroscience, 2016, ID 3045254.
Doi:10.1155/2016/3045254
[28] Ramezanian, R., Peymanfar, A., and Ebrahimi, S. B., An integrated framework of genetic network programming and multi-layer perceptron neural network for prediction of daily stock return: An application in Tehran stock exchange market, Applied Soft Computing Journal, 2019, 82, 105551. Doi: 10.1016/ j.asoc.
[29] Matyjaszek, M., Fernández, P. R., Krzemień, A., Wodarski, K. and Valverde G. F.,
Forecasting coking coal prices by means of ARIMA models and neural networks, considering the transgenic time series theory. Resources Policy, 2019, 61, P.283-292.
Doi:10.1016/j.resourpol.2019.02.017
[30] Matheus, J. S. S., Fahad, F. W. A., Bruno, M. H.,
Ana B. N., aDanilo, G. R., Vinicius A.S. and Herbert K.,
Can artificial intelligence enhance the Bitcoin bonanza, The Journal of Finance and Data Science, 2019, 5, P.83-98.
Doi:10.1016/j.jfds.2019.01.002
[31] Alberta, A. A., Lópezb, L. F. M., and Blasb, N. G.,
Multi linear Weighted Regression (MWE) with Neural Networks for trend prediction, Applied Soft Computing Journal, 2019, 82, 105555.
Doi:0.1016/j.asoc.2019.105555
[32] Botchkarev, A.,
Performance Metrics (Error Measures) in Machine Learning Regression, Forecasting and Prognostics: Properties and Typology, Interdisciplinary Journal of Information, Knowledge, and Management, 2019, 14, P.45-79. Doi:
10.28945/4184
[33] Shah, D., Isah, H., Zulkernine, F., Stock market analysis: A review and taxonomy of prediction techniques, Int. J. Financial Stud., 2019, 7(2), 26. Doi: 10.3390/ijfs7020026.
[34] Zhong, X., Enke, D.,
Predicting the daily return direction of the stock market using hybrid machine learning algorithms. Financial Innovation, 2019, 5 (1), 1-20. Doi:
10.1186/s40854-019-0138-0
[35] Wen, M., Li, P., Zhang, L., Chen, Y.,
Stock Market Trend Prediction Using High-Order Information of Time Series, IEEE Access 2019, 7, 28299–28308. Doi
: 10.1109/ACCESS.2019.2901842
[36] Cervelló-Royo, R., Guijarro, F., Forecasting stock market trend: A comparison of machine learning algorithms, Financ. Mark. Valuat. 2020, 6, 37–49. Doi: 10.46503/NLUF8557
[37] Papageorgiou, K.I., Poczeta, K., Papageorgiou, E., Gerogiannis, V.C., Stamoulis, G.,
Exploring an Ensemble of Methods that Combines Fuzzy Cognitive Maps and Neural Networks in Solving the Time Series Prediction: Problem of Gas Consumption in Greece. Algorithms, 2019, 12 (11), 235.
Doi: 10.3390/a12110235
[39] Ghasemzadeha, M., Mohammad-Karimi, N., Ansari-Samani, H.,
Machine learning algorithms for time series in financial markets, Advances in Mathematical Finance and Applications, 2020, 4(5), P. 479-490. Doi:10.22034/amfa.2020.674946
[41] Tavana, M., Izadikhah, M., Di Caprio, D., Farzipoor Saen, R.,
A new dynamic range directional measure for two-stage data envelopment analysis models with negative data, Computers & Industrial Engineering, 2018,
115, P. 427-448, Doi:
10.1016/j.cie.2017.11.024
[42] Randall, S., Sexton, Jatinder, N. D., and Gupta, b., Comparative evaluation of genetic algorithm and backpropagation for training neural networks, Information Sciences, 2000, 129, P.45-59. Doi:10.1016/s0020-0255(00)00068-2
[43] Izadikhah, M., Improving the Banks Shareholder Long Term Values by Using Data Envelopment Analysis Model, Advances in Mathematical Finance and Applications, 2018, 3(2), P. 27-41. Doi: 10.22034/amfa.2018.540829
[44] Dorsey, R.E., Mayer, W.J., Optimization Using Genetic Algorithms, Advances, in: J.D. Johnson, A.B. Whinston (Eds.), Artificial Intelligence in Economics, Finance, and Management, Vol. 1. JAI Press Inc., Greenwich, CT, P.1994.
[45] Dorsey, R.E., Mayer, W.J.,
Genetic algorithms for estimation problems with multiple optima, non-differentiability and other irregular features, Journal of Business and Economic Statistics, 1995, 13, P.53-66.
Doi:10.2307/1392521
[46] 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
[47] Sinayi, H., Mortazavi, S., Teymoori Asl, Y., Tehran Stock Exchange Index forecasting using artificial
neural networks, Journal of Accounting and Auditing Review, 2005, 41, 59-83.