An Algorithmic Trading system Based on Machine Learning in Tehran Stock Exchange

الموضوعات :

Hamidreza 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

تاريخ الإرسال : 28 السبت , جمادى الثانية, 1441 تاريخ التأكيد : 06 السبت , ربيع الثاني, 1442 تاريخ الإصدار : 21 الخميس , ذو القعدة, 1442

الکلمات المفتاحية: Neural network, Machine Learning, Stock Trading System, fuzzy logic, Genetic Algorithm,

ملخص المقالة :

Successful trades in financial markets have to be conducted close to the key recurrent points. Researchers have recently developed diverse systems to help the identification of these points. Technical analysis is one of the most valid and all-purpose kinds of these systems. With its numerous rules, the technical analysis endeavors to create well-timed and correct signals so that these points are identified. However, one of the drawbacks of this system is its overdependence on human analysis and knowledge in selecting and applying these rules. Employing the three tools of genetic algorithm, fuzzy logic, and neural network, this study attempts to develop an intelligent trading system based on the recognized rules of the technical analysis. Indeed, the genetic algorithm will assist with the optimization of technical rules owing to computing complexities. The fuzzy inference will also help the recognition of the total current condition in the market. It is because a set of rules will be selected based on the market kind (trending or non-trending). Finally, the signal developed by every rule will be translated into a single result (buy, sell, or hold). The obtained results reveal that there is a statistically meaningful difference between a stock's buy and hold and the trading system proposed by this research. In other words, our proposed system displays an extremely higher profitability potential.

المصادر:

   [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