Open Access

Back propagation mathematical model for stock price prediction


Cite

Abu-Mostafa, Y. S., & Atiya, A. F. Introduction to financial forecasting. Applied intelligence., 1996 6(3):205–213. Abu-MostafaY. S. AtiyaA. F. Introduction to financial forecasting Applied intelligence 1996 6 3 205 213 Search in Google Scholar

Han, R., & Cao, Q. Fuzzy chance constrained least squares twin support vector machine for uncertain classification. Journal of Intelligent & Fuzzy Systems., 2017 33(5):3041–3049. HanR. CaoQ. Fuzzy chance constrained least squares twin support vector machine for uncertain classification Journal of Intelligent & Fuzzy Systems 2017 33 5 3041 3049 Search in Google Scholar

Hsieh, L. F., Hsieh, S. C., & Tai, P. H. Enhanced stock price variation prediction via DOE and BPNN-based optimization. Expert Systems with Applications., 2011 38(11):14178–14184. HsiehL. F. HsiehS. C. TaiP. H. Enhanced stock price variation prediction via DOE and BPNN-based optimization Expert Systems with Applications 2011 38 11 14178 14184 Search in Google Scholar

S. Ismail and A. Shabri and R. Samsudin, A hybrid model of self-organizing maps (som) and least square support vector machine (lssvm) for time-series forecasting, Expert Systems with Applications., 2011 38(8): 10574–10578. IsmailS. ShabriA. SamsudinR. A hybrid model of self-organizing maps (som) and least square support vector machine (lssvm) for time-series forecasting Expert Systems with Applications 2011 38 8 10574 10578 Search in Google Scholar

Abrahart, R. J., & See, L. Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments. Hydrological processes., 2000 14(11–12):2157–2172. AbrahartR. J. SeeL. Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments Hydrological processes 2000 14 11–12 2157 2172 Search in Google Scholar

Li, G., & Shi, J. On comparing three artificial neural networks for wind speed forecasting. Applied Energy., 2010 87(7):2313–2320. LiG. ShiJ. On comparing three artificial neural networks for wind speed forecasting Applied Energy 2010 87 7 2313 2320 Search in Google Scholar

C.J. Lu and J.Y. Wu, An efficient cmac neural network for stock index forecasting, Expert Systems with Applications., 2011 38(12):15194–15201. LuC.J. WuJ.Y. An efficient cmac neural network for stock index forecasting Expert Systems with Applications 2011 38 12 15194 15201 Search in Google Scholar

J. Lu and Y. Bai, Applications of grnn based on particle swarm algorithm forecasting stock prices, Perception & Psychophysics., 2013 30(3):303–307. LuJ. BaiY. Applications of grnn based on particle swarm algorithm forecasting stock prices Perception & Psychophysics 2013 30 3 303 307 Search in Google Scholar

K.R. Mller and A.J. Smola and G. Rätsch and B. Schölkopf and J. Kohlmorgen and V. Vapnik, Predicting time series with support vector machines, Advances in Kernel Methods-Support Vector Learning., 1999 20(2): 999–1004. MllerK.R. SmolaA.J. RätschG. SchölkopfB. KohlmorgenJ. VapnikV. Predicting time series with support vector machines Advances in Kernel Methods-Support Vector Learning 1999 20 2 999 1004 Search in Google Scholar

E.E. Osuna, Support vector machines: training and applications, A. I. Memo no. 1602, C. B. C. L. Paper., 1997 144(9):1308–16. OsunaE.E. Support vector machines: training and applications A. I. Memo no. 1602, C. B. C. L. Paper 1997 144 9 1308 16 Search in Google Scholar

W. Pan, Performing stock price prediction use of hybrid model, Chinese Management Studies., 2010 4(1):77–86. PanW. Performing stock price prediction use of hybrid model Chinese Management Studies 2010 4 1 77 86 Search in Google Scholar

R. Shan and H. Dai and J. Zhao and W. Liu, Forecasting study of Shanghai's and Shenzhen's stock markets using a hybrid forecast method, Communications in Statistics – Simulation and Computation., 2015 44(4):1066–1077. ShanR. DaiH. ZhaoJ. LiuW. Forecasting study of Shanghai's and Shenzhen's stock markets using a hybrid forecast method Communications in Statistics – Simulation and Computation 2015 44 4 1066 1077 Search in Google Scholar

B. Shirmohammadi and M. Vafakhah and V. Moosavi and A. Moghaddamnia, Application of several data-driven techniques for predicting groundwater level, Water Resources Management., 2013 27(2):419–432. ShirmohammadiB. VafakhahM. MoosaviV. MoghaddamniaA. Application of several data-driven techniques for predicting groundwater level Water Resources Management 2013 27 2 419 432 Search in Google Scholar

D.F. Specht, A general regression neural network, IEEE Trans Neural Netw., 1991 2(6):568–576. SpechtD.F. A general regression neural network IEEE Trans Neural Netw 1991 2 6 568 576 Search in Google Scholar

F. Sun and C. Liang and L. Zhang and P. Lee and Z. Lin and J. Cao, Optimal partition algorithm of the rbf neural network and its application to nancial time series forecasting, Neural Computing & Applications., 2005 14(1): 36–44. SunF. LiangC. ZhangL. LeeP. LinZ. CaoJ. Optimal partition algorithm of the rbf neural network and its application to nancial time series forecasting Neural Computing & Applications 2005 14 1 36 44 Search in Google Scholar

T.Z. Tan and Q. Chai and G.S. Ng, Biological brain-inspired genetic complementary learning for stock market and bank failure prediction1, Computational Intelligence., 2007 23(2):236C261. TanT.Z. ChaiQ. NgG.S. Biological brain-inspired genetic complementary learning for stock market and bank failure prediction1 Computational Intelligence 2007 23 2 236C261 Search in Google Scholar

Wang, J. Z., Wang, J. J., Zhang, Z. G., & Guo, S. P. Forecasting stock indices with back propagation neural network. Expert Systems with Applications., 2011 38(11):14346–14355. WangJ. Z. WangJ. J. ZhangZ. G. GuoS. P. Forecasting stock indices with back propagation neural network Expert Systems with Applications 2011 38 11 14346 14355 Search in Google Scholar

L. Wang and Y. Zeng and T. Chen, Back propagation neural network with adaptive differential evolution algorithm for time series forecasting, Expert Systems with Applications., 2015 42(2):855–863. WangL. ZengY. ChenT. Back propagation neural network with adaptive differential evolution algorithm for time series forecasting Expert Systems with Applications 2015 42 2 855 863 Search in Google Scholar

eISSN:
2444-8656
Language:
English
Publication timeframe:
Volume Open
Journal Subjects:
Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics