| 深度信用风险 (Deep Credit Risk) - 使用Python进行机器学习 Contributor(s): Scheule, Harald (Author), Rösch, Daniel (Author) |
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ISBN: 0645245208 ISBN-13: 9780645245202 Publisher: Deep Credit Risk OUR PRICE: $65.55 Product Type: Paperback Language: Chinese Published: July 2021 |
| Additional Information |
| BISAC Categories: - Business & Economics | Banks & Banking - Business & Economics | Finance - Financial Risk Management - Computers | Programming Languages - Python |
| Physical Information: 0.92" H x 7.52" W x 9.25" (1.71 lbs) 456 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: - 了解流动性，房屋净值和许多其他关键银行业特征变量的作用； - 选择并处理变量； - 预测违约、偿付、损失率和风险敞口； - 利用危机前特征预测经济衰退和危机后果； - 理解COVID-19对信用风险带来的影响； - 将创新的抽样技术应用于模型训练和验证； - 从Logit分类器到随机森林和神经网络的深入学习; - 进行无监督聚类、主成分和贝叶斯技术的应用； - 为CECL、IFRS 9和CCAR建立多周期模型； - 建立用于在险价值和期望损失的信贷组合相关模型； - 使用更多真实的信用风险数据并运行超过1500行的代码... - Understand the role of liquidity, equity and many other key banking features - Engineer and select features - Predict defaults, payoffs, loss rates and exposures - Predict downturn and crisis outcomes using pre-crisis features - Understand the implications of COVID-19 - Apply innovative sampling techniques for model training and validation - Deep-learn from Logit Classifiers to Random Forests and Neural Networks - Do unsupervised Clustering, Principal Components and Bayesian Techniques - Build multi-period models for CECL, IFRS 9 and CCAR - Build credit portfolio correlation models for VaR and Expected Shortfal - Run over 1,500 lines of pandas, statsmodels and scikit-learn Python code - Access real credit data and much more ... |