Tax Aggressiveness Prediction Method with Neural Network and Logistic Regression

Kautsar Riza Salman Kautsar Riza Salman

doi:10.17577/ijertv7is110007

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.

Highlights

This research attempts to predict the tax aggressiveness by entering the maqashid sharia index variable and the level of corporate social responsibility disclosure
The data used in this study were taken from the Indonesian Capital Market Directory (ICMD), as well as those listed on idx.co.id
The Hosmer and Lemeshow test is used as a goodness of fit test to determine whether the model can be used to interpret the relationship between the level of tax aggressiveness and the seven independent variables

Summary

Introduction

This research attempts to predict the tax aggressiveness by entering the maqashid sharia index variable and the level of corporate social responsibility disclosure. The prediction model that will be developed is called the Sharia-based Islamic Tax Aggressive Prediction Model and Social Disclosure. In this model, several control variables are included, such as company size, profitability, leverage, capital intensity, and inventory intensity. This study will find and compare which prediction models are more accurate to predict the level of tax aggressiveness. This study is intended to compare between the classical models represented by logistic regression with the new model represented by the neural network. Logistic regression analysis can be used to predict tax aggressiveness. Logistic regression in some literature is referred to as a classic model

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Conclusion