Thinking Outside the Box , Thinking Outside the Orthodoxy
Credit is a mystery to many. It has been dwarfed by financial products with built-in winners and losers. Credit makes everyone a winner or everyone a loser, depending on motive and structure. That is why credit analysis requires more than mathematics: psychology, design and strategy are also important. A humanities background is good training for surmizing motive, structure and outcome. Studying an MBA helps us see why credit is an endangered skill set. But the best training is dealing with financial crisis firsthand across the financial services. With this blog, I hope to give greater visibility to the credit's modest but firm role in rebuilding economic resilience: #JustCredit.
Ann Elaine Rutledge
My mission is to shed light on credit meaning in financial news.
Blog for the 99.965%
Credit analyst walks into a bar and says: “The chance of bumping into a credit analyst anywhere in the U.S. is the same as the risk that triple-A rated Microsoft (MSFT) will default on its bond.”
Woman sipping a beer at the bar pipes up:
“More like the risk of single-A rated Honeywell (HON) defaulting. I know that of the 338MM Americans in 2022. 72,000 were credit analysts and 45,000 credit controllers. But by the Bureau of Labor Statistics, the number of working Americans was 150MM. So we’re talking about 1.0% (of 150MM), not 0.035% (of 338MM).”
Turns out, she’s a credit analyst too.
Credit is a club where insiders leave out information outsiders need to follow along. Credit discourse can be hard to fathom for 99 or 99.965% of Americans because it is f, defined as [an] unexpressed recognition of the position of others that leads to strategies for common activity….The notion reflects the fact that people often behave ‘as if’ they have the described knowledge or have made the consent or communication in question.
Most credit analysts learn their trade by listening while seasoned professionals talk over their heads. The newbies prefer not to draw unwanted attention by asking “dumb” questions lest they put their coveted membership at risk. Instead, they quietly mirror their elders until credit talk and credit thought become second-nature.
Diffident apprentices who stay the course for ten or twenty years will morph into stereotypical tight-lipped, plainly skeptical, independent-minded, beware the landmines hidden in my ‘dumb’ question veterans who believe there is nothing new under the sun. That somewhere, lurking in the pitch deck (the deck) or deal documents (the docs), they will find that sour note buried in the fine print. Or picked up in a nonverbal cue.
They believe there is always a sour note—a detail designed to favor one counterparty over the other. Dedicated credit analysts (and I am one) will plumb the depths of the human psyche, considering every motivational angle that comes to mind when reviewing the facts and circumstances of a credit proposition, to find and neutralize that sour note and make credit exchange fairer.
Call it a passion for shedding light on hidden credit intent.
This blog is for making credit intent clearer to any non-credit professional reader who wants to understand it, because—
One: I want to see the credit market profession modernized and restored to the respected position it held in the 1960s and early 1970s. The credit analyst pipeline has been shrinking for decades as credit work has given way to a cheap, synthetic replacement framework (that doesn’t work).
Given the high levels of conformity in the socialization of a credit analyst, the fastest, and maybe the only way to build a pipeline of new credit thinkers is to make credit intent less tacit, more explicit.
Two: Credit is used as a powerful principle of exclusion. Perhaps it always has been. But since creativity doesn’t favor socio-economic divisions, credit could be repurposed as a powerful principle of inclusion.
Since the 2008 Crisis, many have opined that debt is the cause of everything wrong with today’s global economy. In reality, debt capital based on sound credit fundamentals promotes private ownership and jobs. Debt can create upward social and financial mobility. It is good for economies. Nevertheless, putting debt capital on sound fundamentals requires vigilance, restraint, and above all, new minds.
Three: Credit stories are fascinating once the subtext is grasped. They are cat and mouse games from the viewpoint of the cheese. You join the credit club when you rise above the political subtext and jargon, and start looking at how credit today turns into money tomorrow, the day after, and the day after that, etc..
Credit intent is everywhere. It can be sussed out when a credit exposure is structured and when or after it goes to market. But credit intent lies beyond the transactions. It’s in governmental fiscal, monetary, tax policies, corporate governance and strategy. It’s embedded in the structure of our conversations about capital.
The essential skills for decoding credit intent are finding the arguments and understanding how they impact future money. My blog on flash-in-the-pan offers an example of how to find credit arguments in a story. The blog exemplifies how to comprehend the credit story with the help of credit rating jargon.
Credit ratings are important because U.S. licensed credit rating agences are credit gatekeepers. In the early 1980s, the Big Two, Moody’s Investors Service and (now) S&P Global, declared that their historical ratings performance showed ratings have the force of statistical probability. Today, all U.S. licensed credit rating agencies (NRSROs) must disclose these performance data in Exhibit 1 of their required annual FormNRSRO filings.
Which brings us full-circle back to the anecdote in the bar:
Both analysts drew valid analogies between the probability of bumping into a credit analyst and a corporation defaulting on its bond. Both are marginal probabilities. A marginal probability represents the incidence of the thing observed divided by the population universe. Between the two, the woman’s comparison was more apt. Babies, retirees and other unemployed Americans would not be working as credit analysts or credit controllers, so it makes no sense to include these null subsets in the denominator.
But here’s another question. If the odds are so low, how come the two analysts ended up in the same bar? Was it Fate? The opening scene of a new romcom?
Conditional probability is a better answer. Conditional probability starts with a denominator representing a well-defined population and counts how many of the total are affected, how many unaffected, such that the sum equals the denominator.
Start with an even more representative denominator. Five out of the 50 United States (California, New York, Texas, Illinois, Florida) have bigger financial services sectors and more credit analyst residents. Limiting the count to large cities in those states, or to people who travel to zip codes with bars where credit analysts hang out, would make the estimate even more precise.
But we didn’t ask how many credit analysts came into the bar that night. We only focused on the two in the anecdote.
Many Forbes readers have great skills to discern credit intent and participate in credit dialogue. E.g., still in 2022, the Bureau of Labor Statistics reported many Americans working in or learning about measuring and managing risk: 1,660,000 engineers, 496,000 electronic equipment mechanics, 326,400 mathematical sciences professionals and 730,400 graduates of STEM programs. Another 445,000 graduated from liberal arts programs, where the curriculum teaches critical thinking.
The more open we are to seeking alternative working explanations for established facts, the easier it becomes to read and discern credit intent without belonging to any club.