建構盈餘管理預測模型

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Please use this identifier to cite or link to this item: https://irlib.pccu.edu.tw/handle/987654321/44301

Title:	建構盈餘管理預測模型 Construction of Earnings Management Prediction Models
Authors:	王瀚陞 (WANG, HAN-SHENG)
Contributors:	會計學系
Keywords:	資料探勘盈餘管理支援向量機類神經網路決策樹 data mining earnings management support vector machine artificial neural network decision tree
Date:	2018
Issue Date:	2019-05-29 12:54:28 (UTC+8)
Abstract:	企業本身的財務報表由於公司內部人基於不同的因素而藉由操縱裁決性應計項目來達到盈餘管理的行為，資訊不對稱的情況下使財務報表無法忠實表述而導致外部投資人處於一個較不公平的立場。故本研究的目的在建立一個盈餘管理預測模型，探討不同的指標對於盈餘管理程度上的影響，資料來自台灣經濟新報(Taiwan Economic Journal)，資料期間為2009~2016年生技產業。在先前的文獻裡大多是以傳統的回歸模型做預測，近年來由於許多學者應用資料探勘的方法來做研究，故本篇使用較新的資料探勘的方式來做預測模型。本研究第一階段使用支援向量機、類神經網路來篩選具影響力之變數，第二階段再使用決策樹CART、CHAID、C5.0來建立可預測企業盈餘操縱程度之模型。本研究實證結果顯示類神經搭配決策樹 (ANN-C5.0)為最佳預測模型，準確率最高為88.03%。 The purpose of this study is to establish an earnings management prediction model for enterprises. The data is from the Taiwan Economic Journal (TEJ), from 2009 to 2016 of the biotechnology industry. Most of the previous literatures use traditional regression models on earnings management. In recent years, many researchers try to use data mining methods to improve the accuracy of earnings management prediction. Therefore, this study uses data mining to construct prediction models. In the first stage of this study, support vector machine (SVM), and artificial neural network (ANN) are used to select important variables. In the second stage, decision trees-CART, CHAID and CHAID are used to establish models. The empirical results of this study show that the ANN-C5.0 model has the highest prediction accuracy of 88.03%.
Appears in Collections:	[Department of Accounting & Graduate Institute of Accounting] Thesis

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