Given the the endless mind-whirling acronyms, derivatives and structures of the financial markets, we’re rarely served with a visualization that so elegantly illustrates the arrival of Wall Street’s latest innovation. This is what High Frequency Trading — the official monicker of Wall Street’s robot army — looks like, when specially programmed computers make massive bets at lightning speed. Created by Nanex, the GIF charts the rise of HFT trading volumes across all US stock exchanges between 2007 and 2012. The initial murmur, the brewing storm, the final detonation: Not just unsettling, it’s terrifying.
The majority of all trades made everyday are now executed by robots looking to exploit micro-movements in stock price in a perpetual game of musical chairs. The finance industry insists that this is a net-positive operation. The established argument is that, by increasing liquidity and reducing price spreads, everyone benefits from price stability and lower transaction costs.
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This may be true, but there are side effects, as one HFT insider explained to Zero Hedge:
HFT affects all investors to an extent, because stocks are now priced differently than in the past. The market used to consist mostly of investors analyzing cash flows and balance sheets, trying to calculate a company’s fair value. HFTs, on the other hand, react to movements in stock prices alone. That is not necessarily a bad thing, but since HFTs are responsible for two-thirds of the trading volume, we have the strange situation where they can set the price based on what they perceive others’ perceptions to be.
We also don’t know is what the long term consequences are of all this hyper-volume as depicted by the Nanex GIF and the kind of systemic risks created from the market’s ongoing evolution from human traders to rapidfire AI. Sometimes things go wrong, a software glitch, an algorithm gone rogue and the music stops, like last week when Knight Capital lost $10 million a minute when it’s trading platform went haywire or during the infamous Flash Crash when the Dow dropped 1000 points in mere minutes.
These incidents have so far been contained with minimal collateral damage. But that may not always be the case. More likely, these are ominous signs of what’s to come, potential warnings we are failing to heed, as we did in recent past with the destructive concoction of leverage and complex off-balance sheet derivatives that led to the crash of 2008.
In the 90s, the finance industry was heavily marketing its mathematical risk models devised by rocket scientist quants. These new formulas were foolproof, they insisted, reducing risk while making people richer for it. One hedge fund, Long Term Capital Management, was the obvious star, pushing these models to the limit and claimed board members like Myron Scholes and Robert C. Merton, who shared the 1997 Nobel Memorial Prize in Economic Sciences. These guys were certified geniuses. In just three years, the fund quadrupled investor returns. Then in 1998, the machine broke.
When it crashed, LTCM had $4.72 billion with a further $124.5 billion borrowed but thanks to off balance derivatives, the fund had a notional value of approximately $1.25 trillion. If LTCM were to go down, it would send catastrophic losses rippling through the global system. The fund was officially too big to fail. Forced into action, the Fed swooped in with a consortium of banks producing a bailout worth $3.62 billion. In its aftermath, the tune shifted. Merrill Lynch observed that mathematical risk models “may provide a greater sense of security than warranted; therefore, reliance on these models should be limited.”
Instead of changing the music, then Fed Chairman Alan Greenspan dismissed the blowup as a one-off. Ten years later, encumbered by the same kind of massive leverage and a web of convoluted derivatives, the markets, held hostage by too-big-to-fail banks, finally imploded leaving in its wake an economic disaster the world has yet to fully shake off.
It’s possible that these robot-traders, the ones that have turned the markets into their battleground for pennies could be totally innocuous. Yet if something were to go wrong — some bug or mutated AI gone awry, big enough to create a feedback loop that cascades through the system — it would happen in the blink of an eye. By then, for the humans at least, it would already be too late.
Follow Alec on Twitter: @sfnuop
