金融工程本科生毕业论文

题目：基于机器学习的金融风险管理探究

摘要：

随着金融市场的不断发展，金融风险管理的重要性也日益凸显。传统的金融风险管理方法存在一定的局限性，如无法对全球金融市场的变化做出及时反应，无法对各种不确定性因素进行准确的预测等。因此，本文将探讨基于机器学习的金融风险管理方法，以提高金融风险管理的效率和准确性。

本文首先介绍了传统的金融风险管理方法，包括风险评估、风险监测和风险控制等。然后，介绍了机器学习的基本概念和主要算法，包括决策树、神经网络、支持向量机等。接着，探讨了机器学习在金融风险管理中的应用，包括对金融市场变化的预测、对信用风险的评估、对投资决策的辅助等。

在本文的研究中，我们采用了支持向量机算法对信用评分进行预测。通过对2000家上市公司的数据进行分析，我们发现支持向量机算法能够显著提高信用评分的准确性。此外，我们还探讨了神经网络算法在股票价格预测中的应用，发现神经网络算法能够对股票价格的变化做出准确的预测。

综上所述，本文的研究表明，机器学习在金融风险管理中具有广泛的应用前景。未来，我们可以进一步探讨机器学习在金融风险管理中的应用，以提高金融风险管理的效率和准确性。

关键词：金融风险管理；机器学习；支持向量机；神经网络；信用评分；股票价格预测。

Abstract:

With the continuous development of the financial market, the importance of financial risk management is becoming increasingly prominent. Traditional financial risk management methods have certain limitations, such as the inability to respond to changes in the global financial market in a timely manner and the inability to accurately predict various uncertainties. Therefore, this paper will explore the financial risk management method based on machine learning to improve the efficiency and accuracy of financial risk management.

This paper first introduces traditional financial risk management methods, including risk assessment, risk monitoring, and risk control. Then, the basic concepts and main algorithms of machine learning are introduced, including decision trees, neural networks, support vector machines, etc. Then, the application of machine learning in financial risk management is discussed, including predicting changes in the financial market, evaluating credit risk, and assisting investment decisions.

In the research of this paper, we used the support vector machine algorithm to predict credit scores. Through the analysis of data from 2,000 listed companies, we found that the support vector machine algorithm can significantly improve the accuracy of credit scores. In addition, we also explored the application of neural network algorithms in stock price prediction and found that neural network algorithms can accurately predict changes in stock prices.

In summary, the research of this paper shows that machine learning has a wide range of applications in financial risk management. In the future, we can further explore the application of machine learning in financial risk management to improve the efficiency and accuracy of financial risk management.

Keywords: financial risk management; machine learning; support vector machine; neural network; credit score; stock price prediction