Tax Aggressiveness Prediction Method with Neural Network and Logistic Regression

Abstract

This study aims to examine the predictive power of tax aggressiveness using neural network and logistic regression methods. This research sample is a company whose shares are listed in the Indonesian Sharia Stock Index (ISSI) in the period 2011-2015. A total of 71 public companies in Indonesia were obtained. Data obtained from Indonesia Stock Exchange. The technique of determining the sample was used purposive sampling. The independent variables used are maqashid sharia index, disclosure index of corporate social responsibility, company size, profitability, leverage, inventory intensity, and capital intensity. The analysis technique used is multiple regression, logistic regression, and neural networks. In the initial test, multiple regression method was used. At this initial stage, other independent variables will be known that can predict the level of tax aggressiveness. In the second stage of the test comparing the prediction model of tax aggressiveness that gives a higher level of accuracy between logistic regression analysis and neural network. Based on the results of the analysis and discussion, it can be concluded that the Neural Network method provides a better level of prediction than logistic regression for training data and testing data.