Accounting rules compound the diffi culties, allowing both parties to a zero- sum bet to essentially assume each will win. For example,
if one fi rm bets interest rates will rise, and another bets they will fall, each can create its own models to assess the probability it will need to pay out.100 It is up to the fi rm to accurately record changes in those probabilities. As with LEVELS, sluggish updating of mod-els can seriously exaggerate the fi nancial health of a fi rm once things start to break against it.
What’s the big deal, you may ask— gamblers suffer from biases all the time. But the scale and scope of large fi rms’ bets is way beyond what we formally call “casinos.” One fi nancial writer observed,
“Information- age tools allowed Lehman Brothers to assemble and manage a portfolio that contained 930,000 derivative transactions at the time of its bankruptcy.”101 Lehman had $738 billion in deriva-tive contracts labeled as “off balance sheet arrangements” in its 2007 accounts.102 In many derivatives contracts (as in bets), each side is ex-pecting an opposing outcome, and each can base its accounting on some degree of wishful thinking.
That last point— that both parties could simultaneously claim a gain on what had to be zero- sum arrangements— is critical to under-standing the risks posed by black box fi nance. It amplifi es pie- in- the- sky modeling enabled by credit default swaps, leaving both the
“insured” and the “insurers” capable of assuming away risk whenever it was con ve nient to do so. “We’ll never need to pay,” insurers told themselves and their creditors; “We’ll always be paid,” said the in-sured to themselves and their creditors. Fannie and Freddie’s implicit guarantees encouraged similarly “double realities”— government never needed to bud get for a bailout, while holders of bonds backed by these government- sponsored entities (GSEs) assumed they’d al-ways get paid.103
Opportunistic modeling and accounting also explains why deal complexity is often pursued for its own sake, and not for a genuine economic or investment purpose. Technologist Jaron Lanier puts the matter starkly: “The wave of fi nancial calamities that took place in 2008 was cloud- based. No one in the pre- digital- cloud era had the mental capacity to lie to himself in the way we are routinely able to now. The limitations of organic human memory and calculation put a cap on the intricacies of self- delusion.”104 Webs of credit and debt become a smoke screen for institutions rendered vulnerable
(both individually and collectively) so that privileged parties within them can use leverage to multiply potential upside gains. The cor-poration absorbs losses (and, in the worst- case scenarios, bank-ruptcy or bailouts). Its leading managers and traders take a large share of the gains. Murky accounting lets a mountain of leverage and misallocated capital accumulate.
These dynamics persist. Consider, for instance, JP Morgan Chase’s
“London Whale” trades, which lost the bank billions of dollars. In 2012, the bank’s Chief Investment Offi ce (CIO) had about $350 bil-lion in excess deposits to manage, and devoted some to a very risky synthetic credit portfolio (SCP). The CIO asserted that it had “fi ve key metrics and limits to gauge and control the risks associated with its trading activities.” But when several of those metrics indicated unacceptable losses, managers decided to change the metrics.105 The Senate Report on the London Whale helpfully encapsulates just how suspect this practice was:
The head of the CIO’s London offi ce . . . once compared man-aging the Synthetic Credit Portfolio, with its massive, complex, moving parts, to fl ying an airplane. The OCC [Offi ce of the Comptroller of Currency] Examiner- in- Charge at JPMorgan Chase [said] that if the Synthetic Credit Portfolio were an air-plane, then the risk metrics were the fl ight instruments.
In the fi rst quarter of 2012, those fl ight instruments began fl ashing red and sounding alarms, but rather than change course, JPMorgan Chase personnel disregarded, discounted, or ques-tioned the accuracy of the instruments instead. The bank’s ac-tions not only exposed the many risk management defi ciencies at JPMorgan Chase, but also raise systemic concerns about how many other fi nancial institutions may be disregarding risk in-dicators and manipulating models to artifi cially lower risk re-sults and capital requirements.106
This excerpt elegantly turns Wall Street’s usual arguments for deregulation on their head. The airplane meta phor at fi rst suggests the complexity of the CIO’s work— and, by implication, warns pesky regulators away from trying to meddle in something too technical
for them to fully grasp. But who would trust a pi lot who ignored his own instruments? Regulators can’t simply assume technical competence— much less, good faith— at behemoths like JP Morgan Chase.
The Whale trades imposed enormous risk on JP Morgan Chase—
and had that massive bank failed, that would have triggered very dangerous knock- on effects. Assets are highly concentrated in about a dozen U.S. banks, and JP Morgan Chase has one of the largest bal-ance sheets, exceeding the total assets of 5,400 community banks.107 Financial regulators have recognized it as a “systemically important fi nancial institution” (SIFI), but there is not much hope it will act responsibly if it can simply move the goalposts whenever it gets into trouble.