發(fā)布時(shí)間：2018-01-30 06:01

  本文關(guān)鍵詞： 財(cái)務(wù)異常 樸素貝葉斯 分類識別　出處：《吉林大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：近年來,伴隨著信息技術(shù)的廣泛應(yīng)用,企業(yè)財(cái)務(wù)工作方式也在不斷的進(jìn)行改變。財(cái)務(wù)工作由最初的手工記賬,轉(zhuǎn)變?yōu)槿缃裥畔⑾到y(tǒng)的應(yīng)用;由簡單的財(cái)務(wù)核算不斷的向著更深層次的管理型財(cái)務(wù)發(fā)展,并最終轉(zhuǎn)變成戰(zhàn)略型財(cái)務(wù)。企業(yè)的記賬方式和財(cái)務(wù)思維正在逐漸的改變,企業(yè)的信息披露也越來越透明化。但無論如何改變,企業(yè)的財(cái)務(wù)狀況,一直以來都受到監(jiān)管部門、政府機(jī)構(gòu)、外部投資者、企業(yè)內(nèi)部經(jīng)營者的關(guān)注。企業(yè)財(cái)務(wù)狀況的微小異常對于企業(yè)會計(jì)假設(shè)的持續(xù)經(jīng)營、貨幣計(jì)量會帶來重大的影響,同時(shí)可能給與企業(yè)相關(guān)聯(lián)的各方帶來巨大的損失,因此研究企業(yè)的財(cái)務(wù)異常十分重要。引起企業(yè)財(cái)務(wù)異常的原因包含很多種,有因?yàn)槠髽I(yè)經(jīng)營出現(xiàn)問題的,有企業(yè)出于特定目的進(jìn)行財(cái)務(wù)造假的,但不管是什么原因,企業(yè)的財(cái)務(wù)異常最終會體現(xiàn)在財(cái)務(wù)指標(biāo)的波動(dòng)上。本文選取我國2003-2015年間受到處罰的滬深A(yù)股上市企業(yè)的財(cái)務(wù)指標(biāo)作為研究樣本,利用SPSS中的顯著性檢驗(yàn),對財(cái)務(wù)異常樣本與非異常樣本進(jìn)行方差和均值的檢驗(yàn),確定財(cái)務(wù)異常與非異常樣本存在明顯差異的指標(biāo)共有32個(gè)。通過因子分析對所列出的特征屬性進(jìn)行提取,提取出對論文研究具有主要影響的特征因子,形成11個(gè)新的財(cái)務(wù)指標(biāo)因子進(jìn)行接下來的研究。通過SPSS將連續(xù)型數(shù)據(jù)離散化,利用基于掃描個(gè)案的等百分位將連續(xù)型的指標(biāo)數(shù)據(jù)轉(zhuǎn)換為離散型指標(biāo)。通過樸素貝葉斯分類的方法,利用訓(xùn)練樣本確定分類器的特征屬性的概率值,然后將測試樣本應(yīng)用于形成的分類器,來驗(yàn)證分類器的分類精度,最后本文建立的模型的整體準(zhǔn)確率為77.92%,誤判率為22.08%,誤拒率為12.34%,誤識率為9.7%。本文利用樸素貝葉斯的方法研究上市公司的財(cái)務(wù)異常,通過研究發(fā)現(xiàn)企業(yè)的財(cái)務(wù)異常往往受到行業(yè)的影響,制造業(yè)、房地產(chǎn)業(yè)等行業(yè)的財(cái)務(wù)更容易發(fā)生異常,近幾年發(fā)展比較迅速的信息技術(shù)業(yè)、軟件和信息技術(shù)業(yè)企業(yè)的財(cái)務(wù)異�，F(xiàn)象也較為明顯。通過分析在所選取的指標(biāo)中償債能力、產(chǎn)權(quán)結(jié)構(gòu)指標(biāo)對企業(yè)的財(cái)務(wù)異常具有較強(qiáng)的指示作用,多數(shù)企業(yè)的財(cái)務(wù)狀況都存在連續(xù)多年的異常。
[Abstract]:In recent years, with the extensive application of information technology, the financial work mode of enterprises is constantly changing. The financial work has changed from manual bookkeeping to the application of information system. From simple financial accounting to a deeper level of management financial development, and finally into strategic finance. The accounting method and financial thinking of enterprises are gradually changing. Corporate disclosure is also becoming more and more transparent. However, the financial situation of enterprises has always been subject to regulatory authorities, government agencies, external investors. The concern of the enterprise's internal managers and the slight abnormal financial situation of the enterprise will have a significant impact on the continuous operation of the accounting assumptions of the enterprise and the monetary measurement. At the same time, it may bring huge losses to the parties associated with the enterprise, so it is very important to study the financial anomalies of enterprises. The causes of the financial anomalies of enterprises include many kinds of reasons, some of which are caused by the problems in the management of enterprises. There are enterprises for specific purposes of financial fraud, but no matter what the reason. The financial anomalies of enterprises will eventually be reflected in the fluctuations of financial indicators. This paper selects the financial indicators of Shanghai and Shenzhen A-share listed companies which were punished from 2003 to 2015 as the research samples. Using the significance test in SPSS, the variance and mean value of the financial abnormal sample and the non-abnormal sample are tested. There are 32 indexes to determine the significant difference between the financial anomaly and the non-abnormal sample. The feature attributes are extracted by factor analysis to extract the characteristic factors which have the main influence on the research of this paper. Form 11 new financial index factors to carry on the following research. Discretization of continuous data through SPSS. The continuous index data is transformed into discrete index by equal percentile based on scanning case, and the probability value of feature attribute of classifier is determined by training sample through naive Bayesian classification method. Then the test samples are applied to the formed classifier to verify the classification accuracy of the classifier. Finally, the overall accuracy of the model is 77.92, and the error rate is 22.08%. The false rejection rate is 12.34 and the false recognition rate is 9.7.The paper uses naive Bayes method to study the financial anomalies of listed companies, and finds that the financial anomalies of enterprises are often affected by the industry. Manufacturing, real estate and other industries are more prone to financial anomalies, in recent years, the relatively rapid development of information technology industry. The financial anomalies of software and information technology enterprises are also obvious. Through the analysis of the solvency of selected indicators, the property rights structure indicators have a strong indication of the financial anomalies of enterprises. The financial situation of most enterprises has been abnormal for many years.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F275;F832.51

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