A hybrid approach of stepwise regression, logistic regression, support vector machine, and decision tree for forecasting fraudulent financial statements.

Suduan Chen,Zone-De Shen,Yeong-Jia James Goo

doi:10.1155/2014/968712

Abstract

As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%.

Highlights

The financial statement is the main basis of decision-making by investors, creditors, and other accounting information demanders and concurrently the concrete expression of management performance, financial condition, and possessing social responsibility of the listed and OTC companies, but the fraudulent financial statement (FFS) has the trend of becoming increasingly serious in recent years [1,2,3,4,5,6,7,8]
Because the fraudulent case is increasingly serious with each passing day, the United States Congress passed Sarbanes-Oxley Act in 2002 and mainly hope by which to improve the accuracy and reliability of the financial statement of a company and disclosure to make the auditors able to forecast the omen of the FFS before the FFS of an enterprise occurs
The FFS can be regarded as a typical classification problem [10]

Summary

Introduction

The financial statement is the main basis of decision-making by investors, creditors, and other accounting information demanders and concurrently the concrete expression of management performance, financial condition, and possessing social responsibility of the listed and OTC companies, but the fraudulent financial statement (FFS) has the trend of becoming increasingly serious in recent years [1,2,3,4,5,6,7,8]. This behavior makes the investing public subject to vast amount of loss and, more seriously, influences the capital market order. Many authors apply the logistic regression to make a fraudulent classification and acquire the result in the FFS issue in the past [3, 6, 7, 11,12,13]

Objectives

Methods

Findings

Conclusion