Open AccessComparing the Accuracy of Multiple Discriminant Analyisis, Logistic Regression, and Neural Network to estimate pay and not to pay Dividend
Author(s) -
Triasesiarta Nur
Publication year - 2019
Publication title -
inobis
Language(s) - English
Resource type - Journals
ISSN - 2614-0462
DOI - 10.31842/jurnal-inobis.v3i1.123
Subject(s) - linear discriminant analysis , dividend , logistic regression , econometrics , dividend policy , artificial neural network , discriminant , stock exchange , regression analysis , statistics , economics , actuarial science , business , computer science , artificial intelligence , mathematics , finance
This study compares the accuracy of prediction to estimate the companies dividend policy; in this case, the company will pay or not pay dividends. The models used in this research are Multiple Discriminant Analysis, Logistic Regression, and Neural Network. The samples are divided into two groups, namely companies that always pay and not pay dividends during the 2015-2018 research period, resulting in 256 samples not paying dividends and 128 samples paying dividends. The results showed that the average Neural Network accuracy performance exceeded the other two models. The best predictor of the company's Dividend Policy in this study is Price to Book Value, Stock Price, Firm Cycle, current ratio, ROA and Exchange Rate.
Keywords: Multiple Discriminant Analysis, Logistic Regression, Neural Network, Dividend Policy