lzhmw123's Stars
momostudy/stata-learning
Here are some resources for Stata learning.
DavionWu2018/Word_frequency
[数据+代码] 上市公司年报文本分词、关键词词频统计+数字化转型关键词表
rongzhiy/Word_frequency_of_finance
金融数据从年报爬取到词频统计
LiuChuang0059/Complex-Network
复杂网络研究资源整理和基础知识学习
nehajasani08/Loan-Default-Prediction-using-Machine-Learning-techniques
Loan default prediction is an important aspect in banking industry. In Finance and Banking sector the losses incurred by this Industry due to loan defaults or we can say customer not paying back their loan is increasing drastically. In this study we have built a loan default prediction model on the data collected for borrowers of multiple states in the Unites States of America. The research focuses on constructing a model that would predict whether the borrower would repay the loan or would end up being a defaulter. The research uses Random Forest classifier, Adaboost classifier and Artificial neural network model to compare the performance of these classifiers. It also works on understating and obtaining the important features that are to be monitored carefully before sanctioning any such credits. Keywords: Credit risk analysis, Loan Default, Machine Learning, Random Forest classifier, Adaboost Classifier, Artificial Neural network.
NicoHerrig95/Quant_Risk_Models
Parts of code from my MSc. dissertation project. Uses yahoo API to load past stock data for training and backtesting various traditional and experimental models for VaR calculation. Written in R & Python.
cevahir-koprulu/risk-aware-curriculum-generation
Risk-aware curriculum generation for heavy-tailed distributions
CopulaCoVaR/CoVaR-Jose-Vicente
El repositorio contiene los archivos asociados a Melo-velandia, L. F., Romero Chamorro, J. V., & Ramírez González, M. S. (2022). The Global Financial Cycle and Country Risk in Emerging Markets During Stress Episodes : A Copula-CoVaR Approach (Publicación pendiente; Borradores de Economía).
Topaceminem/DCC-GARCH
DCC GARCH modeling in Python