Open AccessThe use of artificial neural network and multiple linear regressions for stock market forecasting
Author(s) -
Abdu Masanawa Sagir,
Saratha Sathasivan
Publication year - 2017
Publication title -
matematika
Language(s) - English
Resource type - Journals
eISSN - 0127-9602
pISSN - 0127-8274
DOI - 10.11113/matematika.v33.n1.956
Subject(s) - artificial neural network , stock exchange , econometrics , normalization (sociology) , computer science , stock market , linear regression , machine learning , stock market index , artificial intelligence , linear model , stock (firearms) , economics , engineering , finance , geography , sociology , mechanical engineering , context (archaeology) , archaeology , anthropology
In the recent economic crises, one of the precise uniqueness that all stock markets have in common is the uncertainty. An attempt was made to forecast future index of the Malaysia Stock Exchange Market using artificial neural network (ANN) model and a traditional forecasting tool – Multiple Linear Regressions (MLR). This paper starts with a brief introduction of stock exchange of Malaysia, an overview of artificial neural network and machine learning models used for prediction. System design and data normalization using MINITAB software were described. Training algorithm, MLR Model and network parameter models were presented. Best training graphs showing the training, validation, test and all regression values were analyzed.