本文關鍵詞： 財務危機 支持向量機 上市公司 加權平均　出處：《長沙理工大學》2013年碩士論文　論文類型：學位論文

【摘要】：上市公司是我國資本市場發(fā)展的基礎,其運行的好壞直接關系到資本市場的興衰,因此我們需要在上市公司發(fā)生財務危機之前,對其財務狀況做預警分析。本文以支持向量機為研究工具,旨在建立有效的財務預警模型。 在綜合了國內外研究現(xiàn)狀基礎上結合對財務危機概念的界定,利用支持向量機理論基礎建立數(shù)學模型進行財務危機的預測。 本文選取2008-2009年的20家發(fā)生財務危機的上市公司和與其配對的20家財務正常的公司為建模樣本,以2010年的30家上市公司(其中財務正常和發(fā)生財務危機的上市公司各15家)為驗模樣本,用來檢驗模型的好壞。一共選取了19個財務指標,通過一系列的指標篩選,利用上市公司發(fā)生財務危機的前三年的財務指標數(shù)據(jù)分別建立模型,來預測驗模樣本是否會發(fā)生財務危機,預測的精度分別為63.3%,90%,90%。由于只是運用單年的財務指標數(shù)據(jù)來建立模型,數(shù)據(jù)的信息量比較小,不同年份的財務指標數(shù)據(jù)產(chǎn)生的預測精度也不同,因此本文試圖將上市公司發(fā)生財務危機的前三年的財務指標數(shù)據(jù)做加權平均,權重為前三年財務指標單年的預測精度與三年總的預測精度之比,這樣既能保證更大范圍的運用數(shù)據(jù)信息,又能根據(jù)不同年份對預測精度的貢獻程度不同選擇不同的權重,使得最終的預測精度更為準確。經(jīng)過試驗,得到的預測精度為96.7%,這比使用單年財務指標數(shù)據(jù)進行預測的精度要高。本文以制造業(yè)的上市公司為樣本進行試驗得到了較好的結果,可以嘗試將此種方法推廣到其他行業(yè)的財務危機預警中。
[Abstract]:The listed company is the foundation of the capital market development in our country, and its operation is directly related to the rise and fall of the capital market. In this paper, the support vector machine is used as the research tool to establish an effective financial early warning model. On the basis of synthesizing the present research situation at home and abroad, combining with the definition of the concept of financial crisis, using the support vector machine theory to establish the mathematical model to predict the financial crisis. In this paper, 20 listed companies with financial crisis and 20 companies with normal financial situation in 2008-2009 were selected as modeling samples. In 2010, 30 listed companies (including 15 listed companies with normal financial affairs and 15 listed companies with financial crisis) were used to test the model. A total of 19 financial indicators were selected and selected through a series of indicators. Using the financial index data of the first three years of the financial crisis of a listed company to establish models separately to predict whether there would have been a financial crisis or not, the accuracy of the prediction is 63.33 / 90 / 900.Because the model is only established by using the single year's financial index data, The amount of information in the data is relatively small, and the forecasting accuracy of the financial index data in different years is different, so this paper tries to make the weighted average of the financial index data of the first three years of the financial crisis of listed companies. The weight is the ratio of the forecasting precision of the first three years to the total forecast precision of three years, which can not only guarantee the use of data information in a wider range, but also select different weights according to the contribution of different years to the forecast accuracy. The accuracy of the final prediction is more accurate. After the experiment, the prediction accuracy is 96. 7, which is higher than the prediction accuracy using the single year financial index data. This paper takes the listed companies of manufacturing industry as the sample to carry out the experiment and gets better results. Try to extend this approach to financial distress warnings in other industries.
【學位授予單位】：長沙理工大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：F275;F832.51;F224

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