AI Reads between the Lines to Discover Corporate Risk
It’s even effective in identifying risks related to AI itself.
AI Reads between the Lines to Discover Corporate RiskConnecting borrowers and lenders can involve a long chain of financial intermediaries. While these go-betweens can benefit the broader economy by smoothing the flow of credit, there are now probably too many links in the credit chain, argue Chicago Booth’s Zhiguo He and Columbia’s Jian Li (a graduate of Booth’s PhD Program). A shorter chain could reduce both borrowing costs and overall financial risk, they suggest.
Rather than quantify the optimal number of intermediaries, the researchers developed a holistic framework for modeling the entire credit chain. This approach, they suggest, could help regulators target policies that promote overall access to capital while avoiding dangerous credit bubbles, such as the one that triggered the 2008–09 financial crisis.
Suppose a small manufacturer takes out a six-month loan, but when that debt comes due, the economy has soured and the company is hurting for cash. Worse, it can’t find a new lender to pay off its old loan. Strapped, the company is forced to unload its factory buildings, inventories, and other assets at deep discounts, potentially leaving its creditor with pennies on the dollar.
In recent years, a slew of “shadow banks” has sprung up to address this problem. Today, instead of borrowing directly from a lender, the same manufacturer might borrow from an investment fund for one year, while the fund itself borrows for just six months. If half a year later the fund can’t refinance its own debt, it, too, might have to sell assets, including its loan to the small manufacturer.
This six-month maturity mismatch creates a cushion in the credit chain, the researchers argue. That’s because there’s a chance that sales improve and the manufacturer pays off most or all of its debt—implying that the investment fund would take a smaller haircut on the one-year loan than the manufacturer would take by dumping its factories under duress after six months.
The amount of money flowing through financial intermediaries grew significantly in the 1990s, when structured finance and securitization became popular.
Smaller potential losses boost the fund’s theoretical ability to refinance its own debt, in turn emboldening its investors and ultimately maintaining the flow of credit throughout the system, the researchers find. “The credit chain provides the benefit of short-term financing yet reduces the additional liquidation risks associated with it,” Li says.
Over time, however, more shadow banks have piled in to gather, package, and syndicate loans to other intermediaries. As demand for—and profits from—such securitization grew in the 1990s, so too did the number of players in the credit chain. Today, each dollar originally invested flows through 2.3 layers before reaching the final borrower, up from 1.8 in 1960, the researchers find.
“Shadow banks serve an important function by spreading credit more efficiently,” Li says. “However, it is likely there are too many layers in the chain to maximize overall access to credit.”
He and Li designed their model to aid regulators in understanding how individual policies might ripple throughout the economy. For example, legislation that specifically curbs lending by commercial banks might induce riskier activity by other intermediaries elsewhere in the system.
“If we zoom out and look at the whole credit chain, the fundamental mismatch between the long-term nature of entrepreneurial projects and short-term needs of investors is still there,” He says. “That could lead to more instability outside of what regulators can see.”
Zhiguo He and Jian Li, “Intermediation via Credit Chains,” Working paper, January 2022.
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