Corporate Credit Rating Forecasting

This repository contains the results of a data analysis performed on a set of corporate credit ratings given by ratings agencies to a set of companies. The aim of the data analysis is to build a machine learning model from the rating data that can be used to predict the rating a company will receive.

The Dataset

The dataset was generated with the file generateCreditRatingDataset.py. It makes use of a api and a previous dataset. More in the acknowledgement session.

There are 30 features for every company of which 25 are financial indicators. They can be divided in:

• Liquidity Measurement Ratios: currentRatio, quickRatio, cashRatio, daysOfSalesOutstanding
• Profitability Indicator Ratios: grossProfitMargin, operatingProfitMargin, pretaxProfitMargin, netProfitMargin, effectiveTaxRate, returnOnAssets, returnOnEquity, returnOnCapitalEmployed
• Debt Ratios: debtRatio, debtEquityRatio
• Operating Performance Ratios: assetTurnover
• Cash Flow Indicator Ratios: operatingCashFlowPerShare, freeCashFlowPerShare, cashPerShare, operatingCashFlowSalesRatio, freeCashFlowOperatingCashFlowRatio

Results

We achieve an accuracy of 69.14% with an XGboost model.

Companies

We can see companies such as Walt Disney and Philip Morris are low risk. Foot locker and MGM are considered risky companies.

Acknowledgement

Sorces of Data: Thanks a lot for these services and their amazing datasets. Credit Rating: opendatasoft Financial Informatino: financialmodelingprep