An Algorithmic Trading system Based on Machine Learning in Tehran Stock Exchange
Subject Areas : Financial MathematicsHamidreza Haddadian 1 , Morteza Baky Haskuee 2 , Gholamreza Zomorodian 3
1 - Department of Financial Management, Management Faculty, Central Branch, Islamic Azad university, Tehran, iran
2 - Department of Economics, Imam Sadiq University, Tehran, Iran
3 - Department of Business Management, Central Tehran Branch, Islamic Azad University, Tehran,
Iran
Keywords:
Abstract :
[1] Goldberg, D., Approximation by superposition of a sigmoidal function, Journal of Mathematics of Control Signals & Systems, 1988, 2, P. 303-314. Doi: https://doi.org/10.1007/BF02551274
[2] Baba, N., Nomura T., Knowledge-Based Decision Support Systems for Dealing Nikkei-225 by Soft Computing Techniques. in Knowledge-Based Intelligent Information Engineering Systems and Allied Technologies KES 2001. 2001: IOS Press, Netherlands. Doi: https://doi.org/10.1007/11552413_2
[3] Refenes A.N., Zapranis A., Francis G, Stock ranking: Neural Networks vs multiple linear regression, 1994 IEEE. Doi: https://doi.org/10.1109/ICNN.1993.298765
[4] Tan, H., Prokhorov, D., Wunsch, D., Probabilistic and time-delay neural-network techniques for conservative short-term stock trend prediction, in Proc. World Congr. Neural Networks, Washington, D.C., July 1995. Doi: https://doi.org/10.1109/72.728395
[5] Kuo, R. J., Chen, C., Hwang, Y., An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network. Fuzzy Sets and Systems, 2001, 118, P. 21–45. Doi: https://doi.org/10.1016/S0165-0114(98)00399-6
[6] Juliana Y., A Comparison of Neural Networks with Time Series Models for Forecasting Returns on a Stock Market Index, Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2002, Cairns, Australia, June 17-20, 2002, Proceedings P.25-35. Doi: https://doi.org/10.1007/3-540-48035-8_4
[7] Nouri, A, et al. Comparing the performance Of Artificial Neural Networks(ANN) and Auto Regressive Moving Average(ARIMA) Model in Modeling and Forecasting Short-term Exchange Rate Trend in Iran, Investment Science Journal, 2014, 10, P.85-100. Doi: http://jik.srbiau.ac.ir/article_7607.html
[8] Dariush Forougi et al. Earnings Per Share Forecast: the Combination of Artificial Neural Networks and Particle Swarm Optimization Algorithm, Investment Science, 2014, 6, P.63-82. Doi: http://jik.srbiau.ac.ir/article_7480.html
[9] Souto, M., S&P 500 Index Direction Forecasting from 1976 to 2010: A Fuzzy System Approach, The International Journal of Digital Accounting Research, 2011, P.111-134. Doi: 10.4192/1577-8517-v11_6
[10] Baba, N., Kawachi, T., Nomura, T., Sakatani, Y., Utilization of NNs & Gas for improving the traditional technical analysis in the financial market, SICE annual Conference, 2004, 2(2), P. 1409-1412.
[11] Alejandro Rodríguez et al, Using neural networks to improve trading systems based on technical analysis by means of the RSI financial indicator, Expert Systems with Applications, 2011, 38(9):11489-11500. Doi: 10.1016/j.eswa.2011.03.023
[12] Lin, X., Yang, Z., Song, Y., Intelligent stock trading system based on improved technical analysis and Echo State Network, Expert Systems with Applications, 2011, 34, P. 620-627. Doi: https://doi.org/10.1016/j.eswa.2011.03.001
[13] Rahnamay Roodposhty et al, Optimization of portfolio Constituted from mutual funds of Tehran stock exchange using genetic algorithm, Investment Science, 2015, 12, P.217-232.Doi: http://jik.srbiau.ac.ir/article_7672.html
[14] Paak Maram, A., Bahri, J, Selecting and optimizing stock portfolio by the genetic algorithm: Using Markowitz mean semi-variance model, Journal of Financial Engineering and Security Exchange Management, 2017, 31. Doi: http://fej.iauctb.ac.ir/article_532529.html
[15] Elman JL, Finding Structure in Time, Cogn Sci , 1990, 14, P.179-211.
[16] Zhi-Hong et al. On delayed impulsive Hopfield neural networks,Neural Networks, 1999, 12, P.273-280. Doi: https://doi.org/10.1016/S0893-6080(98)00133-6
[17] Brogaard, A.J., Carrion, T., Moyaert, R., Riordan, A., Shkilko, K. SokolovHigh-frequency trading and extreme price movementsJ. Financ. Econ., 2018, 128 (2), P. 253-265, Doi: 10.1016/j.jfineco.2018.02.002
[18] Kevin M., Werner K., Strongly-typed genetic programming and fuzzy inference system: An embedded approach to model and generate trading rules, Journal of Applied soft computing, 2020, 90, P.6-25. Doi: https://doi.org/10.1016/j.asoc.2020.106169
[19] Izadikhah, M., Farzipoor Saen, R., Ranking sustainable suppliers by context-dependent data envelopment analysis. Ann Oper Res, 2020, 293, P.607–637, Doi: 10.1007/s10479-019-03370-4
[20] Manahov, V., The rise of the machines in commodities markets: new evidence obtained using strongly typed genetic programming, Annals of Operations Research, 2018, 260(1-2), P. 321–352. Doi: https://doi.org/10.1007/s10479-016-2286-1
[21] Ha, S., Moon, B.R., Finding attractive technical patterns in cryptocurrency markets, Memetic Computing, 2018, 10(3), P. 301– 306, Doi: https://doi.org/10.1007/s12293-018-0252-y
[22] Davoodi .A., Dadashi .I., Azinfar K., Stock price analysis using machine learning method(Non-sensory-parametric backup regression algorithm in lin-ear and nonlinear mode), Advances in mathematical finance & applications, 2020, 5(1), P.197-213. Doi: 10.22034/AMFA.2019.1869838.1232
[23] Tone, K., Toloo, M., Izadikhah, M., A modified slacks-based measure of efficiency in data envelopment analysis, European Journal of Operational Research, 2020, 287 (2), P. 560-571, Doi: 10.1016/j.ejor.2020.04.019.
[24] Davoodi, A., Dadashi, I., Stock price prediction using the Chaid rule-based algorithm and particle swarm optimization, ), Advances in mathematical finance & applications, 2020, 5(2), P.197-213. Doi: 10.22034/AMFA.2019.585043.1184
[25] Farshadfar, Z., Prokopczuk, M., Improving Stock Return Forecasting by Deep Learning Algorithm, Advances in mathematical finance & applications, 2020, 4(1), P.1-13. Doi: 10.22034/AMFA.2019.1869838.1232
