基于BP-LVQ的組合神經網絡舞弊風險識別模型研究
發(fā)布時間：2018-11-20 16:07
【摘要】：近年來,國內外上市公司舞弊丑聞層出不窮,給投資者帶來巨大的投資風險和傷害的同時,沉重打擊了社會公眾對會計界和資本市場的信心。因此,如何有效識別企業(yè)舞弊行為成為會計理論界、實務界以及監(jiān)管部門關注的重中之重。實證研究表明,模型舞弊識別效果優(yōu)于舞弊案例分析,而有效的舞弊風險識別模型的構建離不開完善的舞弊識別指標和恰當的識別方法。目前,在舞弊識別指標方面的研究已經比較完善,但舞弊識別模型方面的研究較少。隨著人工智能技術的不斷發(fā)展和廣泛應用,人工神經網絡技術開始應用于舞弊識別領域。其中,以BP和LVQ神經網絡在舞弊識別領域的應用最為廣泛,舞弊識別率較高。本文在此背景下深入探究這兩種神經網絡技術,用同一舞弊樣本檢驗這兩種模型的舞弊識別率,并在此基礎上提出優(yōu)化的基于BP-LVQ的組合神經網絡舞弊風險識別模型。本文查閱整理國內外相關文獻后,在第二章文獻綜述部分簡單闡述了國際上最為流行的六種管理舞弊動機與成因理論以及國內流行的舞弊動機與成因觀點,梳理歸納了舞弊風險識別指標和舞弊風險識別方法的相關國內外文獻資料,明確舞弊風險識別模型的研究現(xiàn)狀、研究成果和現(xiàn)有的不足之處。在此基礎上提出本文選用BP、LVQ神經網絡技術作為舞弊風險識別模型的理由,并在第三章詳細介紹了人工神經網絡技術的特點與分類、BP神經網絡和LVQ神經網絡的結構及運作機制。第四章主要是樣本選取和舞弊風險識別指標篩選。本文選取2010年到2014年發(fā)生舞弊的506家上市公司作為舞弊樣本,按照Beasley原則一比一確定非舞弊的配對樣本公司506家,以此作為研究樣本。將根據文獻梳理出的識別效果較好的舞弊風險識別指標作為最初的指標體系,通過配對樣本T檢驗以及主成分分析消除共線性問題后,最終刪選出識別效果最好的10個指標。第五章主要對BP、LVQ神經網絡模型的舞弊風險識別效果進行檢驗,并對兩種識別模型的舞弊判別效果進行分析。第六章在分析BP、LVQ神經網絡模型各自優(yōu)缺點的基礎上提出構建基于BP-LVQ的組合神經網絡模型,介紹了組合模型的構建原理和思路,用同一研究樣本檢驗組合模型的舞弊識別效果,發(fā)現(xiàn)組合模型的舞弊判別率顯著高于單個神經網絡模型,用2015年舞弊樣本數據進行穩(wěn)定性檢驗后發(fā)現(xiàn),組合模型的舞弊識別效果更好而且判別率穩(wěn)定。最后,根據上文理論分析和實證研究總結全文,分析本次研究中的不足之處,并對未來舞弊風險識別模型的發(fā)展提出展望。
[Abstract]:In recent years, fraud scandals of listed companies at home and abroad have emerged one after another, which bring huge investment risks and injuries to investors, and at the same time, have dealt a heavy blow to the public's confidence in accounting and capital markets. Therefore, how to effectively identify corporate fraud has become the most important concern of accounting theory, practice and regulatory authorities. Empirical research shows that the effectiveness of fraud identification model is better than that of fraud case analysis, and the effective fraud risk identification model can not be constructed without perfect fraud identification index and appropriate identification method. At present, the research on fraud identification index has been perfect, but the research on fraud identification model is less. With the development and wide application of artificial intelligence technology, artificial neural network technology has been applied to the field of fraud recognition. Among them, BP and LVQ neural networks are the most widely used in the field of fraud identification, fraud recognition rate is high. In this context, this paper explores the two neural network technologies, and uses the same fraud sample to test the fraud recognition rate of the two models, and on this basis, an optimized combined neural network fraud risk identification model based on BP-LVQ is proposed. In the second chapter of literature review, the author briefly expounds the six most popular theories of management fraud motivation and cause, and the domestic popular theories of fraud motivation and cause of formation. Combing and summarizing the relevant domestic and foreign literature data of fraud risk identification index and fraud risk identification method, clarifying the research status of fraud risk identification model, research results and existing deficiencies. On this basis, the reason for choosing BP,LVQ neural network technology as the fraud risk identification model is put forward. In chapter 3, the characteristics and classification of artificial neural network technology are introduced in detail. The structure and operation mechanism of BP neural network and LVQ neural network. The fourth chapter is mainly about sample selection and fraud risk identification index selection. In this paper, 506 listed companies with fraud from 2010 to 2014 are selected as fraud samples, and 506 non-fraudulent matched sample companies are determined according to Beasley principle. In this paper, the fraud risk identification index, which has good identification effect according to the literature, is taken as the initial index system. After eliminating the collinearity problem by paired sample T test and principal component analysis, the best 10 indexes are selected. Chapter 5 mainly tests the effect of fraud risk identification of BP,LVQ neural network model, and analyzes the fraud discrimination effect of two kinds of identification models. In chapter 6, on the basis of analyzing the advantages and disadvantages of BP,LVQ neural network model, the author proposes to construct the combined neural network model based on BP-LVQ, and introduces the construction principle and train of thought of the combined neural network model. Using the same sample to test the fraud identification effect of the combined model, it is found that the fraud discrimination rate of the combined model is significantly higher than that of the single neural network model. The combined model has better effect of fraud identification and stable discriminant rate. Finally, according to the theoretical analysis and empirical research summarized the full text, analyzes the shortcomings of this study, and puts forward the future development of fraud risk identification model.
【學位授予單位】：杭州電子科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F275;F832.51
本文編號：2345350
[Abstract]:In recent years, fraud scandals of listed companies at home and abroad have emerged one after another, which bring huge investment risks and injuries to investors, and at the same time, have dealt a heavy blow to the public's confidence in accounting and capital markets. Therefore, how to effectively identify corporate fraud has become the most important concern of accounting theory, practice and regulatory authorities. Empirical research shows that the effectiveness of fraud identification model is better than that of fraud case analysis, and the effective fraud risk identification model can not be constructed without perfect fraud identification index and appropriate identification method. At present, the research on fraud identification index has been perfect, but the research on fraud identification model is less. With the development and wide application of artificial intelligence technology, artificial neural network technology has been applied to the field of fraud recognition. Among them, BP and LVQ neural networks are the most widely used in the field of fraud identification, fraud recognition rate is high. In this context, this paper explores the two neural network technologies, and uses the same fraud sample to test the fraud recognition rate of the two models, and on this basis, an optimized combined neural network fraud risk identification model based on BP-LVQ is proposed. In the second chapter of literature review, the author briefly expounds the six most popular theories of management fraud motivation and cause, and the domestic popular theories of fraud motivation and cause of formation. Combing and summarizing the relevant domestic and foreign literature data of fraud risk identification index and fraud risk identification method, clarifying the research status of fraud risk identification model, research results and existing deficiencies. On this basis, the reason for choosing BP,LVQ neural network technology as the fraud risk identification model is put forward. In chapter 3, the characteristics and classification of artificial neural network technology are introduced in detail. The structure and operation mechanism of BP neural network and LVQ neural network. The fourth chapter is mainly about sample selection and fraud risk identification index selection. In this paper, 506 listed companies with fraud from 2010 to 2014 are selected as fraud samples, and 506 non-fraudulent matched sample companies are determined according to Beasley principle. In this paper, the fraud risk identification index, which has good identification effect according to the literature, is taken as the initial index system. After eliminating the collinearity problem by paired sample T test and principal component analysis, the best 10 indexes are selected. Chapter 5 mainly tests the effect of fraud risk identification of BP,LVQ neural network model, and analyzes the fraud discrimination effect of two kinds of identification models. In chapter 6, on the basis of analyzing the advantages and disadvantages of BP,LVQ neural network model, the author proposes to construct the combined neural network model based on BP-LVQ, and introduces the construction principle and train of thought of the combined neural network model. Using the same sample to test the fraud identification effect of the combined model, it is found that the fraud discrimination rate of the combined model is significantly higher than that of the single neural network model. The combined model has better effect of fraud identification and stable discriminant rate. Finally, according to the theoretical analysis and empirical research summarized the full text, analyzes the shortcomings of this study, and puts forward the future development of fraud risk identification model.
【學位授予單位】：杭州電子科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F275;F832.51
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