發(fā)布時間：2018-05-29 04:08

  本文選題：上市公司 + 財務(wù)舞弊　； 參考：《西北大學(xué)》2012年碩士論文

【摘要】：財務(wù)舞弊泛世界化,嚴重阻礙了證券市場公開、公平及公正的發(fā)展,成為各國資本市場健康有序成長的屏障。在此背景下,如何有效的識別財務(wù)舞弊以凈化資本市場的誠信環(huán)境成為一個亟待解決的問題。因此,本文的研究重點即建立一套行之有效的財務(wù)舞弊識別模型。 本文首先闡釋國內(nèi)外財務(wù)舞弊識別模型的相關(guān)文獻,為設(shè)計識別模型的識別指標提供基礎(chǔ)及依據(jù)；第二,明確財務(wù)舞弊的概念并對財務(wù)舞弊進行經(jīng)濟學(xué)理論分析；第三,分析財務(wù)舞弊的動機、手段及征兆,并就本文應(yīng)選取何種技術(shù)手段構(gòu)建識別模型做出說明；第四,以1998-2009年期間的120組舞弊樣本和配對的非舞弊樣本為研究對象,選取相關(guān)的識別指標,建立四種財務(wù)舞弊識別模型,包括多元邏輯回歸識別模型、BP神經(jīng)網(wǎng)絡(luò)識別模型、概率神經(jīng)網(wǎng)絡(luò)識別模型和Elman神經(jīng)網(wǎng)絡(luò)識別模型；第五,以“期望錯誤分類成本法”為依據(jù)對比四種識別模型,分析得出Elman神經(jīng)網(wǎng)絡(luò)技術(shù)在財務(wù)舞弊識別問題上識別準確率最高；最后,基于上述研究結(jié)論,總結(jié)全文并提出研究的不足之處。 本文的創(chuàng)新點在于為財務(wù)舞弊識別引進了一種新的技術(shù)手段——Elman神經(jīng)網(wǎng)絡(luò),分析發(fā)現(xiàn)因其具有歷史回溯性等特點,因此可在龐大的財務(wù)數(shù)據(jù)信息中準確地把握財務(wù)舞弊識別規(guī)律,對提高監(jiān)管部門判別財務(wù)舞弊的準確性和效率方面都起到了積極作用。
[Abstract]:The universal financial fraud seriously hinders the open, fair and just development of the securities market, and becomes a barrier to the healthy and orderly growth of the capital markets of various countries. In this context, how to effectively identify financial fraud in order to purify the integrity of the capital market environment has become a problem to be solved. Therefore, the focus of this paper is to establish a set of effective financial fraud identification model. This article first explains the domestic and foreign financial fraud identification model related literature, provides the foundation and the basis for the design identification model identification index; second, clarifies the financial fraud concept and carries on the economic theory analysis to the financial fraud; third, This paper analyzes the motivation, means and symptoms of financial fraud, and explains what technical means should be selected in this paper to construct the identification model. Fourthly, 120 groups of fraud samples and matched non-fraud samples from 1998 to 2009 are taken as the research objects. Four kinds of financial fraud identification models are established, including multiple logic regression identification model and BP neural network identification model, probabilistic neural network identification model and Elman neural network identification model. On the basis of "expected error classification cost method", four recognition models are compared, and it is concluded that Elman neural network technology has the highest accuracy in identifying financial fraud. Finally, based on the above research conclusions, Summarize the full text and put forward the deficiencies of the research. The innovation of this paper lies in the introduction of a new technique for the identification of financial fraud-Elman neural network. Therefore, it can accurately grasp the identification law of financial fraud in the huge financial data information, which plays a positive role in improving the accuracy and efficiency of judging financial fraud.
【學(xué)位授予單位】：西北大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：F275;F832.51;F224

【引證文獻】

相關(guān)期刊論文 前1條

1 董程程;隋永帥;趙園;;關(guān)于上市公司財務(wù)舞弊識別的文獻綜述[J];中國證券期貨;2013年08期

，

本文編號：1949427