As the dominoes fall (or wobble) in the wave of financial contagion sweeping the crypto industry, one of the starkest lessons is that no matter the potential of a new technology, you can’t beat the leverage cycle.
It’s a lesson investors in traditional finance have repeatedly had to learn and relearn over the past 25 years as digital tech and data science worked their way into Wall Street and periodically fueled moments of excessive debt-fueled speculation.
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One pattern seen in various blowups – from Long-Term Capital Management (LTCM) in 1998, to the bursting of the dot-com bubble in 2000, to the financial crisis of 2008 – is that enthusiasm for the promises of new technologies can create a distorted mindset that unreasonably diminishes the investment risks.
In each case, the lesson was not that technologies offered zero benefits – ultimately, the related innovations invariably added tangible benefits to the economy and markets – but that they fed a delusional “this time is different” mindset to stoke unsustainable, debt-driven speculation. As credit bubbles grew, profiteers emerged and encouraged a “magical solution” narrative to entice late-arrival investors – both retail and institutions – into their projects, ultimately saddling them with the bill when the crash ensued.
We need to recognize that, while we can devise clever ways to disintermediate gatekeepers and automate certain market functions, we remain vulnerable to the cycle of greed and fear. We should figure out how to mitigate those human elements while enabling the technology to safely and steadily advance.
The LTCM crisis and its descendents
Gen Z and Millennial investors who dominate the crypto community are too young to hold meaningful memories of events like the LTCM crisis.
But ask a Wall Street veteran, and they’ll take a big breath, utter an “Oh, boy,” and launch into a tale of great drama.
LTCM, which placed widespread highly leveraged bets on bond market arbitrage opportunities, was founded in 1994 by John Meriwether, the head of bond trading at legendary Wall Street firm Salomon Brothers. Its board of directors included Myron Scholes and Robert C. Merton, who in 1997 shared the Nobel prize in economics for having developed the Black-Scholes model for pricing financial options.
That scientific pedigree introduced a halo effect to LTCM. Investors became enamored with the sophisticated data analysis algorithm it employed to identify supposed price anomalies across assets that had a history of correlation. The fund would take opposing positions on either side of those identified situations, betting they would pay off when markets reverted to the mean.
Here’s just one famous LTCM strategy: The algorithm identified situations where the spread had widened between the yield on the most recently issued 30-year U.S. Treasury bond and that of an “off-the-run” bond, such as one issued a year earlier (essentially, a 29-year bond). Historically, investors pay a modest liquidity premium for “on-the-run” bonds (which means their yields are slightly lower than the off-the-runs) but, given that both investments represented the same “risk-free” exposure to the U.S. government, the idea was that the spread between the two shouldn’t deviate too far for too long. So, at times when the liquidity premium rose and the yield spread widened, the LTCM algorithm would place a short position (sell) against the on-the-run bond and a long position (buy) on the off-the-run. Historical mean reversion would do the rest.
For years it worked like a charm. LTCM returned 21%, 43% and 41% to its investors in its first three years, respectively, which encouraged even more investors, including the Street’s biggest institutions, to entrust their money with it. Supposedly validated, the fund doubled down, borrowing ever more money to widen the bets identified by its high-tech machine. Eventually LTCM had more than $140 billion in assets under management.
Then in 1998 the LTCM behemoth came crashing to earth, with widespread fallout across Wall Street. The cause: the Russian financial crisis, a byproduct of the preceding Asian financial crisis, when global panic led investors everywhere to sell everything and rush to all but the safest investments. This unique moment meant that, instead of narrowing, the yield spreads on which LTCM had bet went even wider.
All of sudden, tens of billions of leveraged trading strategies were way out of the money. As lenders came knocking, the fund was forced to unwind its positions, worsening market distortions and fueling systemic risk for all investors, even those without LTCM exposure.
In late September of that year, the Federal Reserve, worried that mass contagion would cripple financial markets and harm the wider economy, engineered a bailout in which 14 major institutions injected $3.6 billion of equity into LTCM, giving it a capital buffer to halt its forced selling. After markets calmed, the fund was later liquidated and unwound.
There were many lessons to take from this episode, but an important one was that even the most sophisticated technology and smartest data analysis can be destroyed in “Black Swan” moments when market-wide panic stalls the normal mean-reversion pattern. The strategies that LTCM employed are still used by many long-short hedge funds, but most now put limits on leverage and employ measures of systemic risk for early warning signs of when human fear or greed are getting out of whack.
But while LTCM offered a lesson on the relationship between innovation narratives and investor behavior, it turned out to be overly narrow. Around the time of the LTCM bailout, the dot-com boom took hold as stock market investors started betting that the “new economy” of the internet age would deliver much higher returns on capital. After peaking in late March 2000, gravity took over and the tech-heavy Nasdaq market collapsed. It would take 15 years for its composite index to return to those highs even though, by then, the rise of Amazon, Google, Facebook and others had fully validated the idea of the internet as a transformative technology.
It took less than a decade after the dot-com bubble’s bursting for another, even bigger blowup: the housing bubble. This one stemmed from the invention of credit default swaps (CDSs) and collateralized debt obligations (CDOs), which unlocked bucket loads of cheap capital. The inflows were based on the false assumption that this combination of derivatives and structured finance had sufficiently spread the risk of individual defaults so ratings agencies would assign triple-A ratings to these top-tier portfolios of bundled mortgages.
Even Gen Zers and Millennials know what happened next. Underappreciated, though, is that the broad approach to bundling mortgages and spreading risk applied during the bubble continues to underpin the market for housing finance and enables broad home ownership in the U.S.
History as prologue
Each of these catastrophes contains parallels to the current crypto moment.
Though it’s not nearly on the same scale as LTCM, the widespread crypto institutional exposure to Three Arrows Capital’s collapse echoes that 1998 moment. The crypto hedge fund’s troubles have imposed big losses on firms such as Voyager Digital, BlockFi and now, Genesis, the latter of which is owned by the Digital Currency Group, also the parent of CoinDesk.
The hype that fueled the 1990s dot-com frenzy was every bit as feverish as the high-minded rhetoric of “decentralization” and “Web3” that helped fuel the speculative mindset among the broader crypto investor community and led to false promises of high yields by outfits such as failed lender/borrower Celsius.
The “quant” whizzes who created the CDSs and CDOs behind the 2008 financial crisis have been reincarnated as the inventors of decentralized finance (DeFi). The latter similarly generated misguided faith among investors in the risk-management powers of algorithmic projects such as Terraform Labs’ now defunct ecosystem of the UST stablecoin and LUNA investment token.
These antecedents are a reminder of how much harm can be done to mainstream investors swept up in the mania. But they also involve technologies that, despite the excesses they encouraged, proved valuable in the long run.
For crypto, we need to figure out how to mitigate the former but ensure the latter. There truly is promise in this industry’s technology. But it can’t magically put an end to risk.
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