本文關(guān)鍵詞： 商業(yè)銀行 危機預(yù)警 支持向量機 遺傳算法　出處：《大連理工大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

【摘要】：危機預(yù)警是商業(yè)銀行經(jīng)營領(lǐng)域的經(jīng)典且持續(xù)演進的重要命題。在2008年的金融危機背景下,如何識別銀行危機特征,揭示銀行危機發(fā)生機理,從而構(gòu)建準(zhǔn)確有效的預(yù)警模型等問題已日益引起理論界與實踐界的高度重視。 本文首先從銀行危機的界定、危機產(chǎn)生原因以及危機預(yù)警的指標(biāo)和方法這四個方面入手,系統(tǒng)地回顧了國內(nèi)外對銀行危機預(yù)警研究的相關(guān)文獻,進而構(gòu)建了本文的理論框架——中國商業(yè)銀行危機的誘因分析以及基于支持向量機的預(yù)警模型。并在此基礎(chǔ)上考慮了商業(yè)銀行危機產(chǎn)生的內(nèi)在根源、外來威脅,分析了中國商業(yè)銀行危機的誘發(fā)機理并確立了預(yù)警指標(biāo)體系,該體系對于內(nèi)部情況的指標(biāo)設(shè)計參照了美國著名的CAMEL銀行評級體系和中國銀行業(yè)監(jiān)督管理委員會頒布的《商業(yè)銀行監(jiān)管評級內(nèi)部指引》,而外部威脅方面則從宏觀經(jīng)濟環(huán)境、金融環(huán)境和國際收支環(huán)境三個角度設(shè)立相關(guān)指標(biāo)。在指標(biāo)體系的基礎(chǔ)上構(gòu)建了基于支持向量機方法的商業(yè)銀行危機預(yù)警模型。為提高模型的精確度,在實證中考慮采用多種方法優(yōu)化參數(shù),并且最后得到了較高的分類準(zhǔn)確率。對比實證結(jié)果,遺傳算法在優(yōu)化參數(shù)方面更具優(yōu)勢。采用遺傳算法優(yōu)化的支持向量機得到了高達96.6667%的分類準(zhǔn)確率,該結(jié)果也同樣驗證了指標(biāo)體系的有效性以及支持向量機方法的適用性。 本文的特色與創(chuàng)新之處一是構(gòu)建了支持向量機模型,驗證了其在預(yù)警中國商業(yè)銀行危機的有效性,并且檢驗遺傳算法在其參數(shù)改進方面的優(yōu)越性,解決了中國商業(yè)銀行業(yè)樣本有限、經(jīng)營異質(zhì)性強等特征下的危機預(yù)警難題；二是基于銀行危機的誘發(fā)機理分析,構(gòu)建了反映商業(yè)銀行危機產(chǎn)生的內(nèi)在根源、外來威脅兩方面特征的預(yù)警指標(biāo)體系；三是將民間融資規(guī)模、房地產(chǎn)和股市泡沫情況對銀行危機的影響納入研究中,彌補了現(xiàn)有研究只考慮社會信貸總額而忽視民間借貸與股市對銀行存款的替代作用對銀行影響的不足。
[Abstract]:Crisis early warning is an important proposition in the field of commercial bank management. Under the background of financial crisis in 2008, how to identify the characteristics of banking crisis and reveal the mechanism of bank crisis. Therefore, the construction of an accurate and effective early warning model has been paid more and more attention by the theorists and the practitioners. This paper begins with the definition of the banking crisis, the causes of the crisis, the indicators and methods of crisis warning, and systematically reviews the relevant literature on the banking crisis early warning research at home and abroad. Furthermore, the paper constructs the theoretical framework of this paper-the inducement analysis of the crisis of Chinese commercial banks and the early warning model based on support vector machine. On this basis, the internal causes and external threats of the crisis of commercial banks are considered. This paper analyzes the inducing mechanism of the crisis of Chinese commercial banks and establishes the early warning index system. The index design of this system for internal situation is based on the famous CAMEL bank rating system in the United States and the Internal guidance for Commercial Bank Supervision rating issued by the China Banking Regulatory Commission. External threats, on the other hand, come from the macroeconomic environment. In order to improve the accuracy of the model, the financial environment and the balance of payments environment establish the relevant indicators. On the basis of the index system, a commercial bank crisis warning model based on support vector machine (SVM) method is constructed in order to improve the accuracy of the model. In the empirical analysis, we consider using various methods to optimize the parameters, and finally get a higher classification accuracy. Compared with the empirical results. Genetic algorithm has more advantages in optimizing parameters. The support vector machine optimized by genetic algorithm has a classification accuracy of up to 96.6667%. The results also verify the effectiveness of the indicator system and the applicability of the support vector machine (SVM) method. The first feature and innovation of this paper is to build a support vector machine model to verify its effectiveness in early warning of the crisis of Chinese commercial banks and to test the superiority of genetic algorithm in improving its parameters. It solves the problem of crisis warning under the characteristics of limited sample and strong heterogeneity of management in China's commercial banking industry. Second, based on the analysis of the induced mechanism of the banking crisis, the paper constructs an early warning index system which reflects the internal root of the commercial bank crisis and the characteristics of the external threat. Third, the private financing scale, real estate and stock market bubble impact on the banking crisis into the study. It makes up for the deficiency of the existing research which only considers the total amount of social credit and neglects the substitution of private lending and stock market to the bank deposit.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：F832.33;F224

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