Comparing the Accuracy of Multiple Discriminant Analyisis, Logistic Regression, and Neural Network to estimate pay and not to pay Dividend

Abstract

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.