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【摘要】：这篇论文旨在建立一个评级系统,通过采用神经网络模型来评估银行信用风险。我们通过对评估结果的略论来发现银行在信用方面存在的弱点的原因并采取相应方法来控制风险。本探讨采用的数据来自中国和越南的一些上市银行。我们将略论银行信用风险产生的原因并结合中国和越南的财政金融政策提出政策建议。
我们使用了一个新的探讨视角--神经网络--来探讨中国和越南上市银行的信用风险评估的问题。我们通过神经网络模型,结合信息技术的运用,得到每个国家的评估结果。从上市银行年度财务报告中搜集数据并在软件中计算得到模型结果后,我们采用神经网络模型来对上市银行进行信用风险评估,并给出可用于政府政策制定的管理措施。
这篇论文还介绍了中国和越南上市商业银行的信用风险状况和信用风险评估的情况。我们也探讨了中国和越南上市银行面临的挑战和原因。
在以前的探讨中,模型的输出结果是0或1,表明信用评估结果的“坏”或者“好”。现在我们用更多的层级来评估信用风险状况。本文中我们采用九级评级表来略论中国和越南的信用风险。这有利于我们得出更接近实际的评估结果。
在本探讨中,我们根据中国和越南上市银行的数据库对其信用风险进行了评估,并对两国的信用风险评估状况进行了比较。通过使用神经网络的措施和Clementinel2.0软件对中国和越南数据库进行略论,我们揭示了一些弱点。此外,我们采用的数据截止到2017年第二季度。我们发现南京银行股份有限企业是15家中国银行中最好的银行,而四家主要中国银行的信用等级的排序是中国银行,工商银行,农业银行和建设银行。在越南,VCB和EIB是最好的上市银行,等级是6,而HBB是最差的上市商业银行,等级为1。HBB已于2017年8月并入SHB。
我们也提出了如何让我们的评级更可靠以及合乎逻辑的建议,例如和国家审计部门合作以得到更准确的数据,增加人力资源投资以便有效控制和应用这套系统,支持用于统计、调查、略论和决策的设备投入等等。此外,我们可以控制系统的容忍度,并提高评估的等级。
通过神经网络模型,信息技术和机器学习技术对历史数据进行略论处理,我们可以快速得到符合逻辑性和可靠的评估结果。它将给我们对银行风险的预见能力,使我们可以及时作出有效的反应并处理危机
【关键词】：
摘要5-7 ABSTRACT7-11 LIST OF FIGURES11-13 List of Figures13-14 ABBREVIATE14-15 CHAPTER 1 INTRODUCTION15-30 1.1 BACKGROUND AND SIGNIFICANCE OF THE RESEARCH15-16 1.2 REVIEW OF RELATED LITERATURE (IN DOMESTIC AND OVERSEA)16-25 1.2.1 Some related literatures in China16 1.2.2 Some related literatures out of China16-25 1.3 THE STRUCTURE AND RESEARCH APPROACH OF THE PAPER25-27 1.4 POSSIBLE INNOVATION OFTHISRESEARCH27-28 1.5 THE FUTURE RESEARCH28-30 CHAPTER 2 OVERVIEW OF LISTED BANKS’ CREDIT RISK30-59 2.1 DEFINITION OF CREDIT RISK, BANK CREDIT RISK AND CREDIT RISK ASSESSMENT30-32 2.1.1 Credit risk30 2.1.2 Bank credit risk30-31 2.1.3 Credit risk assessment31-32 2.2 THE CONCEPT OF LISTED BANKS’ CREDIT RISK32-45 2.2.1 Some related concept32-40 2.2.1.1 Credit risk management and control32-34 2.2.1.2 Credit Rating system34-40 2.2.2 The contents of listed banks’ credit risk40-45 2.2.2.1 Some ratios for Financial risk evaluation of listed banks40-42 2.2.2.2 Credit risk Mitigation and transfer42-45 2.3 RELATED THEORY ABOUT LISTED BANKS’ CREDIT RISK EVALUATION45-52 2.3.1 Risk management theory45-47 2.3.2 Asymmetric Information theory47-50 2.3.2.1 Asymmetric Information47 2.3.2.2 Information asymmetry models47-48 2.3.2.3 Adverse selection48-49 2.3.2.4 Application of information asymmetry in research49-50 2.3.3 Financial fragility theory50-52 2.4 USING NEURALNETWORK METHOD FOR CREDITRISK ASSESSMENT52-59 CHAPTER 3 CREDIT RISK OF CHINESE AND VIETNAMESE LISTED BANKS: COUNTRY SPECIFIC STUDY AND COMPARATIVE ANALYSIS59-80 3.1 CHINESE AND VIETNAMESE LISTED BANKS59-66 3.1.1 Chinese listed banks59-60 3.1.2 Vietnamese listed banks60-66 3.2 A COMPARATIVE STUDY BETWEEN THE STATUS QUO OF CHINA AND VIETNAM66-73 3.3 SOME CAUSES OF LISTED BANKS’ CREDIT RISK ASSESSMENT73-80 3.3.1 The insufficient cognition of credit risk73-75 3.3.2 Imperfect credit risk assessment75-77 3.3.3 Credit risk preventing method is poor77-78 3.3.4 The problems of China's commercial banks' credit risk management78-80 CHAPTER 4 CHALLENGES IN THE ASSESSMENT OF LISTED COMMERCIAL BANKS’ CREDIT RISK IN CHINA AND VIETNAM80-92 4.1 ANALYZE THE REASONS OF CREDIT RISK IN CHINESE AND VIETNAMESE LISTED BANKS80-87 4.1.1 Common status quo80-82 4.1.2 Chinese Status quo82-84 4.1.3 Vietnamese status quo84-87 4.2 THE NECESSARIES OF CREDIT RISK ASSESSMENT87 4.3 SOME SPECIFIC CHALLENGES FACED ASSESSMENT OF CREDIT RISK IN VIETNAM’S BANKS87-90 4.4 COMPARE CURRENT CREDIT RISK EVALUATIONS IN VIETNAM AND CHINA90-92 CHAPTER 5 EMPIRICAL STUDY ON THE ASSESSMENT OF LISTED BANKS’ CREDIT RISK BASED ON BP NEURAL NETWORK92-134 5.1 CONSTRUCTION OF CREDIT RISK ASSESSMENT92-94 5.2 CREDIT RISK EVALUATION INDEX SYSTEM94-100 5.3 APPLICATION OFMODELEVALUATION100-110 5.3.1 BP neural network assessment100-106 5.3.2 Entropy method for evaluation106-108 5.3.3 Establishing the nine levels assessment108-110 5.4 ANALYSIS OF BP NEURAL NETWORK ASSESSMENT110-134 5.4.1 Some Credit risk evaluations based on China banking database110-128 5.4.2 Some Credit risk evaluations based on Vietnam banking database128-134 CHAPTER 6 POLICY RECOMMENDATIONS ON ENHANCING THE ASSESSMENT OF LISTED BANKS’ CREDIT RISK134-141 6.1 IMPROVE TECHNOLOGICAL MEANS OF CREDIT RISK PREVENTION134-135 6.2 PERFECT THE ASSESSMENT SYSTEM OF CREDIT RISK135-137 6.3 EXPLORATION OF CREDIT RISK INTEGRATED MANAGEMENT137-139 6.4 THE SUGGESTION TO ELIMINATE LISTED BANKS’ CREDIT RISK IN VIETNAM CASES139-141 CONCLUSION141-144 APPENDIX 1: SOME FINANCIAL RATIOS144-146 APPENDIX 2: SEVEN INDICATORS146-152 APPENDIX 3: NPLS OF COMMERCIAL BANKS (2017)152-153 APPENDIX 4: DISTRIBUTION OF NPLS OF COMMERCIAL BANKS BY INDUSTRY (2017)139153-154 APPENDIX 5: DISTRIBUTION OF NPLS OF COMMERCIAL BANKS BY REGION (2017).140154-155 RESEARCH REFERENCE155-166 ，越语论文，越语论文网站 |
