Title : ( Stock price forecasting for companies listed on Tehran stock exchange using multivariate adaptive regression splines model and semi-parametric splines technique )
Authors: Mohammad Mahdi Rounaghi , Mohammad Reza Abbaszadeh , Mohammad Arashi ,
Full TextAbstract
One of the most important topics of interesttoinvestorsisstockpricechanges.Investors whosegoalsarelongtermaresensitivetostockpriceanditschangesandreacttothem. Inthisregard,weusedmultivariateadaptiveregressionsplines(MARS)modeland semi- parametricsplinestechniqueforpredictingstockpriceinthisstudy.TheMARSmodelasa nonparametricmethodisanadaptivemethodforregressionanditfitsforproblemswith highdimensionsandseveralvariables.semi-parametricsplinestechniquewasusedinthis study.Smoothingsplinesisanonparametricregressionmethod.Inthisstudy,weused40 variables (30 accounting variables and 10 economic variables) for predicting stock price using the MARS model and using semi-parametric splines technique. After investigating the models,weselect4accountingvariables(book value per share ,predictedearningsper share,P/Eratioandrisk)asinfluencingvariablesonpredictingstockpriceusingtheMARS model. After fitting the semi-parametric splines technique, only 4 accounting variables (dividends, net EPS, EPS Forecast and P/E Ratio) were selected as variables effective in forecastingstockprices
Keywords
, MARS model, Predicting, Stock price, Regression, Semi-parametric splines techniques
@article{paperid:1049093,
author = {Mohammad Mahdi Rounaghi and Abbaszadeh, Mohammad Reza and Mohammad Arashi},
title = {Stock price forecasting for companies listed on Tehran stock exchange using multivariate adaptive regression splines model and semi-parametric splines technique},
journal = {Physica A: Statistical Mechanics and its Applications},
year = {2015},
volume = {438},
number = {38},
month = {July},
issn = {0378-4371},
pages = {625--633},
numpages = {8},
keywords = {MARS model; Predicting; Stock price; Regression; Semi-parametric splines techniques},
}
%0 Journal Article
%T Stock price forecasting for companies listed on Tehran stock exchange using multivariate adaptive regression splines model and semi-parametric splines technique
%A Mohammad Mahdi Rounaghi
%A Abbaszadeh, Mohammad Reza
%A Mohammad Arashi
%J Physica A: Statistical Mechanics and its Applications
%@ 0378-4371
%D 2015
