Data
Corporate-Credit-Rating

Corporate-Credit-Rating

active ARFF Attribution 4.0 International (CC BY 4.0) Visibility: public Uploaded 23-03-2022 by Dustin Carrion
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  • Computer Systems Machine Learning
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Context A corporate credit rating expresses the ability of a firm to repay its debt to creditors. Credit rating agencies are the entities responsible to make the assessment and give a verdict. When a big corporation from the US or anywhere in the world wants to issue a new bond it hires a credit agency to make an assessment so that investors can know how trustworthy is the company. The assessment is based especially in the financials indicators that come from the balance sheet. Some of the most important agencies in the world are Moodys, Fitch and Standard and Poors. Content A list of 2029 credit ratings issued by major agencies such as Standard and Poors to big US firms (traded on NYSE or Nasdaq) from 2010 to 2016. 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 For more information about financial indicators visit: https://financialmodelingprep.com/market-indexes-major-markets The additional features are Name, Symbol (for trading), Rating Agency Name, Date and Sector. The dataset is unbalanced, here is the frequency of ratings: AAA: 7 AA: 89 A: 398 BBB: 671 BB: 490 B: 302 CCC: 64 CC: 5 C: 2 D: 1 Acknowledgements This dataset was possible thanks to financialmodelingprep and opendatasoft - the sources of the data. To see how the data was integrated and reshaped check here. Inspiration Is it possible to forecast the rating an agency will give to a company based on its financials?

31 features

Ratingstring10 unique values
0 missing
Namestring593 unique values
0 missing
Symbolstring593 unique values
0 missing
Rating_Agency_Namestring5 unique values
0 missing
Datestring904 unique values
0 missing
Sectorstring12 unique values
0 missing
currentRationumeric2029 unique values
0 missing
quickRationumeric2029 unique values
0 missing
cashRationumeric2025 unique values
0 missing
daysOfSalesOutstandingnumeric1836 unique values
0 missing
netProfitMarginnumeric2027 unique values
0 missing
pretaxProfitMarginnumeric2027 unique values
0 missing
grossProfitMarginnumeric1626 unique values
0 missing
operatingProfitMarginnumeric2027 unique values
0 missing
returnOnAssetsnumeric2029 unique values
0 missing
returnOnCapitalEmployednumeric2029 unique values
0 missing
returnOnEquitynumeric2029 unique values
0 missing
assetTurnovernumeric2029 unique values
0 missing
fixedAssetTurnovernumeric2029 unique values
0 missing
debtEquityRationumeric2024 unique values
0 missing
debtRationumeric2005 unique values
0 missing
effectiveTaxRatenumeric1994 unique values
0 missing
freeCashFlowOperatingCashFlowRationumeric1857 unique values
0 missing
freeCashFlowPerSharenumeric2027 unique values
0 missing
cashPerSharenumeric2029 unique values
0 missing
companyEquityMultipliernumeric2029 unique values
0 missing
ebitPerRevenuenumeric2027 unique values
0 missing
enterpriseValueMultiplenumeric2029 unique values
0 missing
operatingCashFlowPerSharenumeric2027 unique values
0 missing
operatingCashFlowSalesRationumeric2027 unique values
0 missing
payablesTurnovernumeric1768 unique values
0 missing

19 properties

2029
Number of instances (rows) of the dataset.
31
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
25
Number of numeric attributes.
0
Number of nominal attributes.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
Average class difference between consecutive instances.
80.65
Percentage of numeric attributes.
0.02
Number of attributes divided by the number of instances.
0
Percentage of nominal attributes.
Percentage of instances belonging to the most frequent class.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.

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