Applying Optimized Mathematical Algorithms to Forecast Stock Price Average Accredited Banks in Tehran Stock Exchange and Iran Fara Bourse

Advances in Mathematical Finance and Applications

مقاله 6، دوره 4، شماره 3، مهر 2019، صفحه 77-94   

نوع مقاله: Research Paper

شناسه دیجیتال (DOI): 10.22034/amfa.2019.580802.1150

نویسندگان

Negar Aghaeefar؛ Mohammad Ebrahim Mohammad Pourzarandi^* ؛ Mohammad Ali Afshar Kazemi؛ Mehrzad Minoie

Faculty of Management, Islamic Azad University, Central Branch, Tehran, Iran.

چکیده

The effective role of capital in every country flows through giving guidelines for capital and resources, generalizing companies and sharing development projects with public, and also adding accredited companies stock market requires appropriate decision making for shareholders and investors who are willing to buy shares based on price mechanism. Forecasting stock price has always been a challenging task, since it is affected by many economic and non-economic factors and variables; therefore, selecting the best and the most efficient forecasting model is tough and essential. Up to now applying weighted mean called weighted mean price has been used to forecast industry average price for companies in the stock market and investors were forecasting based on this method. First we have identified 10 accredited banks in TSE and 10 banks in Iran Fara Bourse. In this article, by applying one of the mathematical optimizing techniques, industry means got calculated based on optimized parameters and compared with the industry average; in this statement we strived to find another variable that could forecast with less deviation.  In the following study, by calculating frequency level of deviations, average for price forecasting in banking industry during five years is examined. Finally, the research suggests that, instead of using mean of industry average, it is better to use mean average of golden number, which will lead us to more accurate results.

کلیدواژه‌ها

Forecasting stock price؛ Industry average؛ Optimization algorithm؛ Fuzzy time series؛ Golden Ratio algorithm

مراجع

[1] Al-Hmouz, R., Pedrycs, W., Balamash, A., Description and prediction of time series: A general framework of granular computing, Expert Systems with Applications ,2015,42 ,P.4830-4839. Doi:10.1016/j.eswa.2015.01.060

[2] Castillo,O., Castro, J R., Melin P., Rodriguez-Diaz, A., Application of interval type-2 fuzzy neural networks in non-linear I dentification and time series prediction[J], Soft Computing, 2014,18(6), P.1213-1224.  Doi:10.1007/s00500-013-1139-y

[3] Cheng,C H, Chen, T L., Chiang CH, Weighted fuzzy time-series model for TAIEX forecasting[J],Lectuer note in computer science, 2006, 42(34), P.469-477. Doi:10.1007/11893295_52

[4] Chen, M., Chen, B., Hybrid fuzzy series model based on granular computing for stock price forecasting, Information Sciences, 2015, 294, P. 227-241. Doi:10.1016/j.ins.2014.09.038

[5] Chen,T.L., Cheng, C.H.,Teoh, h.J.,  Fuzzy time-series based on Fibonacci sequence for stock price forecasting, PHYSICA A, 2007, 380, P. 377-390. Doi:10.1016/j.physa.2007.02.084

[6] Ciucurel,C., Georgescu, L., Ioana Iconaru, E., ECG response to submaximal exercise from the perspective of golden ration harmonic rhythm, Biomedical Signal Processing and control, 2018, 40, P.156-162.  Doi: 10.1016/j .bspc.2017.09.018

[7] Engle,R.F., Autoregressive conditional heteroscedasticity with estimate of the variance of United Kingdom inflation , Econometric a, 1982, 50(4), P. 987-1008. Doi: 10.2307/1912773

[8] Gholamian, E., Davoodi, M., Prediciting the direction of stock market prices using Random Forest, Finanacial engineering and Portfolio Management, 2018, 9(35), P. 301-322, (in Persian).

[9] Huamg, K., Y. Hu, N-Th order Heuristic Fuzzy Time Series Model for TAIEX forecasting[J], International Journal of fuzzy systems, 2003, 5(4), P.247-253.

[10] Huang,C.-J., Yang,D.X., Chuang,Y.T., Application of Wrapper approach and composite classifier to the stock trend prediction ,Expert Systems with Applications, 2008, 34(4), P.2870-2878. Doi:10.1016/j/eswa.2007.05.035

[11] Huang,W., Nakamori,Y., Wang,S.Y., Forecasting stock market movement direction with support vector machine, computer and operations research, 2005, 32(10), P.2513-2522. Doi:10.1016/j.cor.2004.03.016

[12] Izadikhah, M., Improving the Banks Shareholder Long Term Values by using Data Envelopment Analysis Model, Advances in mathematical finance, 2018, 3(2),27-41. Doi:10.22034/AMFA.2018.540829

[13] Jilani,TA., Amajd, U., Jaafar, J., & Hassan, S., An Improved Heuristic-Based Fuzzy time series forecasting model using Genetic algorithm [C], Computer & Information Science(ICCIS), International conference on. IEEE, 2012, 1, P.242-247. Doi:10.1109/ICCISci.2012.6297247

[14] Lei, Lei,Wavelet , Neural Network prediction method of stock price trend based on rough set attribute reduction, Applied Soft Computing, 2017, S1568-4946(17)30568-9. Doi:10.1016/j.asoc.2017.09.029

[15] Malkiel, Burton, G., The efficient market hypothesis and its critics, The Journal of Economic Perspectives,2003, 17(1), P.383-417. Doi:10.1257/089533003321164958

[16] Miller, G.A., The magical number seven, plus or minus two: some limits on your capacity of processing information, Psychol. Rev.63, 1956, P. 81-97. Doi:10.1037/h0043158

[17] Mohseni, Hossein, Fundamentals of Banking by Financial Approach, Bourse publications,2014, (in Persian).

[18] Mohammad Pourzarandi, M.E. Non-Linear Optimization, 2013, (in Persian).

[19] Nan,G., Zhou, S., Kou, J., Heuristic Bivariate Forecasting Model of Multi-Attribute fuzzy time series based on fuzzy clustering[J], International Journal of information Technology & Decision Making(IJITDM), 2012, 11(1), P.167-195. Doi: 10.1016/j.eswa.2007.05.016

[20] N Gadi,T., A mathematical model for the genetic code(s) based on Fibonacci numbers and their q-analogues, NeuroQuantology , 2015, 13(3) , P. 259-272. Doi: arXiv:1510.01278

[21] Omotehinva,T.Q., Ramon, S.O., Fibonacci numbers and Golden Ratio in mathematics and science, IJCIT, 2013, 2(4), P.630-638. Doi: 10.1.1.403.8464

[22] Raee, R., Rostami, M., Hashempour, M., Prediction of Tehran Stock Exchange using Ant Colony Optimization, IQBQ, 2014, 18 (1), P. 83-100.

[23] 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. 61-91. Doi:10.18869/acadpub.jeme.6.22.62

[24] Radmehr, F., Shams Gharneh, N., A Forecasting Model based on High-Order fuzzy Time Series and Simulated Annealing Algorithm ,Case study:Tehran’s Bourse Price Index(TEPIX), International Journal of Industrial Engineering &Production Management, 2012, 23(2). Doi:10.7166/23-2-339

[25] Rezaei, N., Elmi., Behavioural Finance Models and Behavioural Biases in stock Price Forecasting, Advances in mathematical finance& applications, 2018, 3(4), P. 67-82.Doi:10.22034/AMFA.2019.576127.1118

[26] Sadaei,H J, Enayatifar, R., & Abdullah A .H. , Short-term load forecasting using a hybrid model with a refined exponentially weighted fuzzy time series and an improved harmony search[J], International Journal of Electrical Power& Energy Systems, 2014, 62 , P.118-129. Doi: 10.1016/j.ijepes.2014.04.026

[27] Sadeh, Ehsan, Ehtesham rasi, Reza, &Ali Sheidaei Narmigi , Fuzzy-neural model with hybrid genetic algorithms for stock price forecasting in auto industry in Tehran security exchange, Financial Engineering and Portfolio Management, 2017, 8(33), P.113-136,(in Persian).

[28] Safa, Mozhgan, Panahian, Hossein, Application of HS Metaheuristic Algorithm in Designing a Mathematical Model foe forecasting P/E in the panel Data approach, Advances in mathematical finance & application, 2018, 3(1), P. 17-31. Doi:10.22034/AMFA.2018.539132

[29] Shanon, A., G,J.V.Leleyendekkers, The Golden ratio family and the binet equation, NNTDM, 2015, 21(2), P.35-42.

[30] Song Q, Chissom, B.S., Fuzzy Time Series and its Models. Fuzzy Sets and Systems, 1993, 54, P.269-277. Doi:10.1016/0165-0114(93)90372-O

[31] Stakhov,A ., The golden section, secrets of the Egyptians civilization and harmony Mathematics, Chaos Soliton, 2006, 30,P.490-505.

[32] Sun, BaiQing, Guo, Haifeng, Karimi, H., Ge Yuanjing, Xiong Shan, Prediction of stock index futures prices based on fuzzy sets and multivariate fuzzy time series, Neurocomputing, 2014. Doi:10.1016/j.neucom.2014 .09.018