Looking into what makes a corporate rating stronger or weaker¶

The project topic I choose merges both of my majors and focuses on a topic that was a catalyst in the 2008 market crash. Corporate ratings are given to a debt security by an external firm to grade the quality and riskiness of the bond. In other words "a corporate credit rating is a numerical assessment of a company's creditworthiness, measuring the likelihood of it defaulting on its debt" (Reem Heakal). This had come under scrutiny due to the fact that it isn't an exact science and that some firms may skew these ratings in favor of their clients (banks). For example, "the agencies have been blamed for exaggerated ratings of risky mortgage-backed securities, giving investors false confidence that they were safe for investing" (Deena Zaidi). These credit rating agencies build such good relations with the banks and oftentimes graded debt securities more favorably so that the banks could have an easier time selling. Looking more in depth into these securities would shed some more light into the industry as a whole and serve to inform about what makes up a corporate rating.

https://www.investopedia.com/articles/03/102203.asp#:~:text=A%20corporate%20credit%20rating%20is%20a%20numerical%20assessment%20of%20a,investing%20in%20a%20corporate%20bond.

https://truthout.org/articles/the-indisputable-role-of-credit-ratings-agencies-in-the-2008-collapse-and-why-nothing-has-changed/

https://www.bmo.com/mybmoretirement/pdf/resource-library/11-325-157_BHBMI430_An_Explanation_of_Bond_Ratings_7-3.pdf

In [3]:

import pandas as pd

file = "corporate_rating.csv"

corporate_rating_df = pd.read_csv(file)

corporate_rating_df

Out[3]:

	Rating	Name	Symbol	Rating Agency Name	Date	Sector	currentRatio	quickRatio	cashRatio	daysOfSalesOutstanding	...	effectiveTaxRate	freeCashFlowOperatingCashFlowRatio	freeCashFlowPerShare	cashPerShare	companyEquityMultiplier	ebitPerRevenue	enterpriseValueMultiple	operatingCashFlowPerShare	operatingCashFlowSalesRatio	payablesTurnover
0	A	Whirlpool Corporation	WHR	Egan-Jones Ratings Company	11/27/2015	Consumer Durables	0.945894	0.426395	0.099690	44.203245	...	0.202716	0.437551	6.810673	9.809403	4.008012	0.049351	7.057088	15.565438	0.058638	3.906655
1	BBB	Whirlpool Corporation	WHR	Egan-Jones Ratings Company	2/13/2014	Consumer Durables	1.033559	0.498234	0.203120	38.991156	...	0.074155	0.541997	8.625473	17.402270	3.156783	0.048857	6.460618	15.914250	0.067239	4.002846
2	BBB	Whirlpool Corporation	WHR	Fitch Ratings	3/6/2015	Consumer Durables	0.963703	0.451505	0.122099	50.841385	...	0.214529	0.513185	9.693487	13.103448	4.094575	0.044334	10.491970	18.888889	0.074426	3.483510
3	BBB	Whirlpool Corporation	WHR	Fitch Ratings	6/15/2012	Consumer Durables	1.019851	0.510402	0.176116	41.161738	...	1.816667	-0.147170	-1.015625	14.440104	3.630950	-0.012858	4.080741	6.901042	0.028394	4.581150
4	BBB	Whirlpool Corporation	WHR	Standard & Poor's Ratings Services	10/24/2016	Consumer Durables	0.957844	0.495432	0.141608	47.761126	...	0.166966	0.451372	7.135348	14.257556	4.012780	0.053770	8.293505	15.808147	0.058065	3.857790
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2024	BBB	NVR, Inc.	NVR	Moody's Investors Service	9/5/2012	Capital Goods	11.757767	7.115059	7.057420	1.079762	...	0.343500	0.953231	50.293155	230.194971	1.759461	0.086376	15.867701	52.760726	0.083018	15.758348
2025	BB	Kaiser Aluminum Corporation	KALU	Standard & Poor's Ratings Services	4/28/2016	Capital Goods	2.962788	1.294743	0.428234	30.602414	...	0.363636	0.602645	5.563630	5.958956	1.614282	-0.267117	-4.729967	9.232021	0.114089	14.542373
2026	B	Cresud S.A.C.I.F. y A.	CRESY	Fitch Ratings	11/30/2012	Finance	0.883875	0.842553	0.233830	147.599371	...	35.017544	0.926665	1.459801	1.056480	4.034952	0.265092	9.358311	1.575328	0.283634	2.300168
2027	B	Cresud S.A.C.I.F. y A.	CRESY	Fitch Ratings	6/15/2012	Finance	0.911713	0.748356	0.310640	131.644566	...	0.331525	0.692804	0.744377	1.470201	3.825856	0.130692	22.440102	1.074441	0.217783	1.997608
2028	CCC	Cresud S.A.C.I.F. y A.	CRESY	Fitch Ratings	8/1/2014	Finance	1.085007	1.026375	0.203490	151.660513	...	0.266987	1.101462	2.487817	6.109814	3.939161	0.302997	9.604061	2.258650	0.252606	1.865682

2029 rows × 31 columns

Data Breakdown¶

The dataset is filled with a variety of corporate ratings, names, agencies, and financial metrics. The more important to understand column would be the first. Corporate ratings go from AAA to D and each debt security has a variety of financial metrics to go with it. Each metric relates to the make up of the company and explains a different facet of the company. For example current ratio refers to current assets divided by current liabilities and represents the companies' ability to pay short term obligations. Effective tax rate just represents the firm's corporate tax (the lower the better!).

How the data will be used¶

I plan to organize the data by ratings and use a variety of machine learning models to check which ones are the most consistant as well as juxtapose all results.