發(fā)布時間：2018-05-19 17:15

  本文選題：上市公司 + 財務(wù)欺詐�。� 參考：《西南財經(jīng)大學(xué)》2013年碩士論文

【摘要】：上市公司通過財務(wù)信息將企業(yè)經(jīng)營狀況和經(jīng)營成果等傳遞給企業(yè)利益相關(guān)者,使他們能夠了解企業(yè)的過去、現(xiàn)在和未來。目前,上市公司財務(wù)欺詐問題嚴(yán)重干擾我國證券市場的健康發(fā)展,成為證券監(jiān)管部門、廣大投資者等利益相關(guān)者關(guān)注的焦點(diǎn)問題,建立一套精確的財務(wù)欺詐識別模型具有重要的研究意義。 財務(wù)欺詐的識別方法有很多。本文詳細(xì)闡述了如下的方法：單變量分析、基于案例的推理、Logstic、線性概率模型、神經(jīng)網(wǎng)絡(luò)、多元判別分析、支持向量機(jī)模型、決策樹、貝葉斯分類模型、主成分回歸模型。國內(nèi)外對財務(wù)欺詐的識別大都把所有行業(yè)的欺詐公司放在一起研究,而針對按行業(yè)分類的研究卻很少。 本文首先提出研究背景及意義,總結(jié)國內(nèi)外的研究現(xiàn)狀,闡明了研究內(nèi)容和研究方法,然后研究財務(wù)欺詐的內(nèi)涵及界定、財務(wù)欺詐的識別方法以及財務(wù)欺詐的識別變量。在此基礎(chǔ)上,選擇了148家財務(wù)欺詐企業(yè)和與之配對的148家非財務(wù)欺詐企業(yè)作為研究對象,先用神經(jīng)網(wǎng)絡(luò)模型和邏輯回歸模型建模,再選出表現(xiàn)最好的模型,最后用這個最好的模型對樣本進(jìn)行分行業(yè)建模,分析本研究對財務(wù)欺詐識別的效果,并指出研究存在的局限及后續(xù)研究方向。 研究結(jié)果表明,分行業(yè)的建模能夠明顯提高財務(wù)欺詐識別模型的識別精度,它可以有效的幫助政府監(jiān)管部門、投資者和審計部門正確的識別上市公司財務(wù)欺詐行為。
[Abstract]:Through the financial information, the listed company passes on the enterprise management status and the management result to the enterprise stakeholders, so that they can understand the past, present and future of the enterprise. At present, the financial fraud of listed companies seriously interferes with the healthy development of China's securities market, and has become the focus of attention of stakeholders, such as securities regulatory authorities, investors and other stakeholders. It is of great significance to establish a set of accurate identification model of financial fraud. There are many ways to identify financial fraud. This paper describes the following methods in detail: univariate analysis, case-based reasoning logstictics, linear probability model, neural network, multivariate discriminant analysis, support vector machine model, decision tree, Bayesian classification model, principal component regression model. The identification of financial fraud at home and abroad mostly studies the fraud companies in all industries, but there are few researches on the classification of financial fraud by industry. This paper first puts forward the research background and significance, summarizes the current research situation at home and abroad, clarifies the research content and research methods, and then studies the connotation and definition of financial fraud, the identification method of financial fraud and the identification variables of financial fraud. On this basis, 148 financial fraud enterprises and 148 matched non-financial fraud enterprises were selected as the research objects. Neural network model and logical regression model were used to model the model, and then the best performance model was selected. Finally, the best model is used to analyze the effect of this study on the identification of financial fraud, and the limitations of the research and the future research direction are pointed out. The results show that the industry modeling can obviously improve the identification accuracy of the financial fraud identification model, it can effectively help government regulators, investors and audit departments to correctly identify the financial fraud behavior of listed companies.
【學(xué)位授予單位】：西南財經(jīng)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：F275;F276.6;F832.51

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