Presentation of intelligent Meta-heuristic Hybrid models (ANFIS -MGGP ) to predict stock returns with more accuracy and speed than other Meta-heuristic methods.

Subject Areas : Financial engineering

mahmood  kohansal kafshgari ¹ (Department of Accounting,Isfahan,(Khorasgan)Branch,Islamic Azad University,Isfahan,Iran)
Alireza  Zarei ² (Department of Accounting ,Falavarjan Branch ,Islamic Azad University,Isfahan, Iran)
reza  behmanesh ³ (Department of Industrial Engineering, Naghsh Jahan Institute of Higher Education, Isfahan, Iran)

Received: 2020-12-30 Accepted : 2021-01-26 Published : 2021-06-22

Keywords: "Stock returns", "Meta-heuristic", "Neural Network",

Abstract :

Discussions about forecasting Stock returns in developed countries has long been regarded as one of the most interesting scientific topics.However,due to many problems,the correct prediction of stock returns has remained a matter of strengthTtherefore,the researcher seeks to provide an accurate,practical and effective model for predicting stock returns for investors.The statistics sampel of research is consist of 138 active companies in Tehran Stock Exchange from 2008 to 2017 wich are selected by the systematic removal method . ANFIS,MGGP, regresion and neural network and different statistics tests are used for data analysis. For impelement of these techniques MATLAB and GenXproTools software are used respectively.The result of the study showed that in oreder to predict stock returns.the use of a meta –heuristic Hybrid models is more accurate and faster than other meta huristic models.Because ,first the most optimal input variables are selected through the ANFIS technique and then predicted using theMGG meta heuristic model.Therefore,due to the correct choice of input variables,predicting stock returns is both more accurate and faster.In addition ,the mathematical model is used to predict.