基于貝葉斯網(wǎng)絡(luò)的上市公司財(cái)務(wù)危機(jī)預(yù)警研究
發(fā)布時(shí)間：2018-08-09 08:10
【摘要】：自2007年,美國(guó)次貸危機(jī)引發(fā)的金融危機(jī)席卷全球,使得各國(guó)金融市場(chǎng)劇烈震蕩,經(jīng)濟(jì)也深受其累。然而,2009年爆發(fā)的歐債危機(jī)再一次將全球經(jīng)濟(jì)拖入泥潭。這不僅使市場(chǎng)風(fēng)險(xiǎn)急劇上升,也使風(fēng)險(xiǎn)管理面臨更加嚴(yán)峻的挑戰(zhàn)。因此,如何強(qiáng)化風(fēng)險(xiǎn)管理意識(shí),提高風(fēng)險(xiǎn)預(yù)測(cè)精度,維護(hù)經(jīng)濟(jì)社會(huì)的和諧穩(wěn)定,既是政府經(jīng)濟(jì)管理部門(mén)面臨的主要任務(wù),也受到了學(xué)術(shù)界的廣泛關(guān)注。值得注意的是,隨著中國(guó)證券市場(chǎng)的繁榮發(fā)展,越來(lái)越多的公司通過(guò)上市獲得資金以擴(kuò)大發(fā)展,上市公司已然成為我國(guó)經(jīng)濟(jì)發(fā)展的核心力量,加上財(cái)務(wù)危機(jī)在企業(yè)集團(tuán)內(nèi)部具有傳染性,進(jìn)一步引起政府經(jīng)濟(jì)管理部門(mén)和投資者等利益相關(guān)者對(duì)中國(guó)上市公司財(cái)務(wù)危機(jī)的高度關(guān)注。上市公司一旦發(fā)生信用違約,不僅會(huì)給投資者帶來(lái)巨大損失,甚至還可能導(dǎo)致企業(yè)破產(chǎn)、社會(huì)動(dòng)蕩等嚴(yán)重后果。因此,構(gòu)建科學(xué)有效地財(cái)務(wù)危機(jī)預(yù)警方法,具有重要的現(xiàn)實(shí)意義�；诖�,本文以我國(guó)上市公司為研究對(duì)象,首先,運(yùn)用正態(tài)性檢驗(yàn)、參數(shù)與非參數(shù)檢驗(yàn)和多重共線性檢驗(yàn)提取出能顯著刻畫(huà)上市公司財(cái)務(wù)危機(jī)的特征指標(biāo);進(jìn)而,既引入NB模型克服BN在初始網(wǎng)絡(luò)結(jié)構(gòu)學(xué)習(xí)上過(guò)于依賴(lài)樣本數(shù)據(jù)導(dǎo)致網(wǎng)絡(luò)結(jié)構(gòu)學(xué)習(xí)復(fù)雜度增加的局限性,同時(shí)又引入基于約束的TPDA算法克服NB模型在結(jié)構(gòu)學(xué)習(xí)中過(guò)度依賴(lài)的條件獨(dú)立假設(shè)的局限性,構(gòu)造出一種改進(jìn)的貝葉斯網(wǎng)絡(luò)模型——TPDA-NB模型對(duì)上市公司財(cái)務(wù)危機(jī)進(jìn)行預(yù)警研究,并運(yùn)用性能評(píng)價(jià)指標(biāo)將TPDA-NB模型與NB模型、Logistic模型、神經(jīng)網(wǎng)絡(luò)模型進(jìn)行對(duì)比分析;最后,運(yùn)用配對(duì)樣本T檢驗(yàn)對(duì)各模型預(yù)測(cè)精度的差異性進(jìn)行顯著性檢驗(yàn),實(shí)證研究結(jié)果如下:(1)將Logistic模型與NB模型、TPDA-NB模型對(duì)比發(fā)現(xiàn),在預(yù)測(cè)精度與預(yù)測(cè)穩(wěn)定性上,不僅Logistic模型與NB模型之間存在顯著性差異,而且Logistic模型與TPDA-NB模型之間的差異更加顯著;(2)將神經(jīng)網(wǎng)絡(luò)模型與NB模型、TPDA-NB模型對(duì)比發(fā)現(xiàn),在預(yù)測(cè)精度與預(yù)測(cè)穩(wěn)定性方面,神經(jīng)網(wǎng)絡(luò)模型與TPDA-NB模型之間存在顯著差異,而與NB模型之間的差異性較弱;(3)更為重要的是,TPDA-NB模型能夠有效地提升NB模型對(duì)上市公司財(cái)務(wù)危機(jī)的預(yù)測(cè)精度和穩(wěn)定性。以上實(shí)證研究結(jié)果表明:運(yùn)用TPDA-NB模型能夠較為準(zhǔn)確的預(yù)測(cè)我國(guó)上市公司財(cái)務(wù)危機(jī),這在風(fēng)險(xiǎn)管理領(lǐng)域具有廣闊的應(yīng)用前景。對(duì)于投資者而言,能夠運(yùn)用TPDA-NB模型提前捕捉風(fēng)險(xiǎn)信號(hào),進(jìn)而做出合理的投資決策以規(guī)避風(fēng)險(xiǎn)帶來(lái)的損失;對(duì)于相關(guān)的政府經(jīng)濟(jì)管理者而言,能夠運(yùn)用TPDA-NB模型對(duì)可能發(fā)生風(fēng)險(xiǎn)問(wèn)題的領(lǐng)域進(jìn)行預(yù)測(cè),及時(shí)制定合理的監(jiān)管政策,從而穩(wěn)定市場(chǎng)秩序,促進(jìn)經(jīng)濟(jì)的持續(xù)健康發(fā)展。
[Abstract]:Since 2007, the financial crisis caused by the subprime mortgage crisis in the United States has swept the world. However, the European debt crisis in 2009 once again dragged the global economy into a quagmire. This not only causes the market risk to rise sharply, but also makes the risk management face more severe challenge. Therefore, how to strengthen the awareness of risk management, improve the accuracy of risk prediction, and maintain the economic and social harmony and stability is not only the main task of the government economic management department, but also has been widely concerned by the academic community. It is worth noting that with the prosperity and development of China's securities market, more and more companies have obtained funds through listing to expand their development, and listed companies have become the core force of our country's economic development. In addition, the financial crisis is contagious within the enterprise group, which causes the government economic management departments and investors and other stakeholders to pay close attention to the financial crisis of listed companies in China. Once a listed company defaults on credit, it will not only bring huge losses to investors, but also may lead to enterprise bankruptcy, social unrest and other serious consequences. Therefore, the construction of scientific and effective financial crisis warning method has important practical significance. Based on this, this paper takes the listed companies of our country as the research object. Firstly, using normal test, parameter and non-parameter test and multiple collinear test, we extract the characteristic indexes which can depict the financial crisis of listed companies. The NB model is introduced to overcome the limitation that the learning complexity of the network structure is increased due to the excessive reliance on the sample data in the learning of the initial network structure of BN. At the same time, the constraint based TPDA algorithm is introduced to overcome the limitations of the conditional independence hypothesis that NB model is over-dependent in structure learning, and an improved Bayesian network model, TPDA-NB model, is constructed to study the financial crisis of listed companies. TPDA-NB model, NB model, Logistic model and neural network model are compared and analyzed by performance evaluation index. Finally, the difference of prediction accuracy of each model is tested by paired sample T test. The empirical results are as follows: (1) comparing Logistic model with NB model TPDA-NB model, it is found that there are significant differences not only between Logistic model and NB model, but also between Logistic model and NB model in prediction accuracy and prediction stability. The difference between the Logistic model and the TPDA-NB model is more significant. (2) comparing the neural network model with the NB model TPDA-NB model, it is found that there are significant differences between the neural network model and the TPDA-NB model in terms of prediction accuracy and prediction stability. But the difference between NB model and NB model is weak. (3) more important is that TPDA-NB model can effectively improve the accuracy and stability of NB model for financial crisis prediction of listed companies. The above empirical results show that the TPDA-NB model can accurately predict the financial crisis of listed companies in China, which has a broad application prospect in the field of risk management. For investors, the TPDA-NB model can be used to capture the risk signal in advance, and then make reasonable investment decisions to avoid the risk of loss; for the relevant government economic managers, The TPDA-NB model can be used to predict the areas where risk problems may occur, to formulate reasonable supervision policies in time, to stabilize the market order and to promote the sustained and healthy development of the economy.
【學(xué)位授予單位】：成都理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：F275
本文編號(hào)：2173465
[Abstract]:Since 2007, the financial crisis caused by the subprime mortgage crisis in the United States has swept the world. However, the European debt crisis in 2009 once again dragged the global economy into a quagmire. This not only causes the market risk to rise sharply, but also makes the risk management face more severe challenge. Therefore, how to strengthen the awareness of risk management, improve the accuracy of risk prediction, and maintain the economic and social harmony and stability is not only the main task of the government economic management department, but also has been widely concerned by the academic community. It is worth noting that with the prosperity and development of China's securities market, more and more companies have obtained funds through listing to expand their development, and listed companies have become the core force of our country's economic development. In addition, the financial crisis is contagious within the enterprise group, which causes the government economic management departments and investors and other stakeholders to pay close attention to the financial crisis of listed companies in China. Once a listed company defaults on credit, it will not only bring huge losses to investors, but also may lead to enterprise bankruptcy, social unrest and other serious consequences. Therefore, the construction of scientific and effective financial crisis warning method has important practical significance. Based on this, this paper takes the listed companies of our country as the research object. Firstly, using normal test, parameter and non-parameter test and multiple collinear test, we extract the characteristic indexes which can depict the financial crisis of listed companies. The NB model is introduced to overcome the limitation that the learning complexity of the network structure is increased due to the excessive reliance on the sample data in the learning of the initial network structure of BN. At the same time, the constraint based TPDA algorithm is introduced to overcome the limitations of the conditional independence hypothesis that NB model is over-dependent in structure learning, and an improved Bayesian network model, TPDA-NB model, is constructed to study the financial crisis of listed companies. TPDA-NB model, NB model, Logistic model and neural network model are compared and analyzed by performance evaluation index. Finally, the difference of prediction accuracy of each model is tested by paired sample T test. The empirical results are as follows: (1) comparing Logistic model with NB model TPDA-NB model, it is found that there are significant differences not only between Logistic model and NB model, but also between Logistic model and NB model in prediction accuracy and prediction stability. The difference between the Logistic model and the TPDA-NB model is more significant. (2) comparing the neural network model with the NB model TPDA-NB model, it is found that there are significant differences between the neural network model and the TPDA-NB model in terms of prediction accuracy and prediction stability. But the difference between NB model and NB model is weak. (3) more important is that TPDA-NB model can effectively improve the accuracy and stability of NB model for financial crisis prediction of listed companies. The above empirical results show that the TPDA-NB model can accurately predict the financial crisis of listed companies in China, which has a broad application prospect in the field of risk management. For investors, the TPDA-NB model can be used to capture the risk signal in advance, and then make reasonable investment decisions to avoid the risk of loss; for the relevant government economic managers, The TPDA-NB model can be used to predict the areas where risk problems may occur, to formulate reasonable supervision policies in time, to stabilize the market order and to promote the sustained and healthy development of the economy.
【學(xué)位授予單位】：成都理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：F275
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