【摘要】：中國(guó)大陸上市公司的財(cái)務(wù)報(bào)表的欺詐行為由來(lái)已久,對(duì)投資人、債權(quán)人以及整個(gè)國(guó)民經(jīng)濟(jì)環(huán)境的危害十分嚴(yán)重,但同時(shí)對(duì)于注冊(cè)會(huì)計(jì)師、審計(jì)師來(lái)說(shuō),對(duì)欺詐財(cái)務(wù)報(bào)表的鑒別卻一直是難題。本文首先根據(jù)公開信息選擇出財(cái)務(wù)報(bào)表欺詐的風(fēng)險(xiǎn)因子(red flags),建立起財(cái)務(wù)欺詐合理懷疑指標(biāo)體系。然后利用中國(guó)滬市上市公司的財(cái)務(wù)報(bào)表歷史數(shù)據(jù)訓(xùn)練出財(cái)務(wù)報(bào)表欺詐的預(yù)測(cè)模型,并對(duì)模型的預(yù)測(cè)效果做出評(píng)估。由于欺詐財(cái)務(wù)報(bào)表在總體中的比例很少,所以我們采用不等概率概率抽樣,即在欺詐類別樣本的抽樣概率大于在非欺詐類別樣本的抽樣概率,在這種情況下傳統(tǒng)的參數(shù)估計(jì)方法需要修正。本文列舉了logistic回歸的在不等概率抽樣條件下進(jìn)行參數(shù)估計(jì)的方法,證明了神經(jīng)網(wǎng)絡(luò)模型在不等概率抽樣條件下修正輸出的方法。另外,由于論文的目的是估計(jì)財(cái)務(wù)報(bào)表欺詐的可能性,本文還分析的神經(jīng)網(wǎng)絡(luò)輸出貝葉斯后驗(yàn)概率所需的條件。
[Abstract]:There is a long history of fraud in the financial statements of listed companies in mainland China, which is very harmful to investors, creditors and the national economy as a whole. But at the same time, for certified public accountants and auditors, The identification of fraudulent financial statements has been a problem. This paper firstly selects the risk factor of financial statement fraud according to the public information, (red flags), and establishes the reasonable suspect index system of financial fraud. Then the forecasting model of financial statement fraud is trained by using the historical data of financial statements of listed companies in Shanghai Stock Exchange of China and the forecasting effect of the model is evaluated. Because of the small proportion of fraudulent financial statements in the total, we use unequal probability sampling, that is, the sampling probability in the fraud category is greater than that in the non-fraudulent sample. In this case, the traditional parameter estimation method needs to be modified. In this paper, the method of parameter estimation based on logistic regression under unequal probability sampling condition is listed, and the method of modifying the output of neural network model under unequal probability sampling condition is proved. In addition, because the purpose of this paper is to estimate the possibility of financial statement fraud, the conditions necessary for the neural network to output Bayesian posteriori probability are analyzed.
【學(xué)位授予單位】：北方工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2006
【分類號(hào)】：F239.4;F224

【引證文獻(xiàn)】

相關(guān)期刊論文 前1條

1 段玉峰;孟菲;;我國(guó)上市公司會(huì)計(jì)舞弊及其治理[J];合作經(jīng)濟(jì)與科技;2011年21期

相關(guān)博士學(xué)位論文 前1條

1 岳殿民;中國(guó)上市公司會(huì)計(jì)舞弊模式特征及識(shí)別研究[D];天津財(cái)經(jīng)大學(xué);2008年

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本文編號(hào)：2407086