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 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

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

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

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