Blogs -New Age Of Stock Market Volatility Driven By Machines

New Age Of Stock Market Volatility Driven By Machines


April 15, 2020

author

Beth Kindig

Lead Tech Analyst

This article was originally published on Forbes on Apr 10, 2020,04:05pm EDT

The Dow broke many records in March of 2020. We saw the fastest bear market in history, the largest one-day gain in history, and the Dow had its worst first quarter since 1987.

Many more records were broken on the intraday level. For instance, fourteen trading days between February 25th and March 20thmade the top twenty list for largest intraday swings. According to Wikipedia, nine of the top ten positions occurred in a span of three weeks between March 2nd and March 20th.

Throughout the generations, there have been world wars, depressions, recessions and financial market implosions, but this is the first market to be whipsawed by machines. What’s driving the intensity is algorithms which puts wealth preservation at stake as retirement funds compete against quant traders.

Flash rallies and flash crashes are occurring as I write this, with yet another record-breaking day of the Dow gaining 1,627 points on April 6th. Following the fastest bear market and worst first-quarter since ‘87, we have now closed out our best week on the Dow in 45 years on April 9th.

The full effects of this much machine trading is yet to be seen, especially as forward-looking markets must reconcile with double-digit unemployment and other economic uncertainties. While machines can change their allocation in the blink of an eye, many average Americans must grapple with the effects these swings may have on their livelihood.

Wikipedia

Source: Wikipedia, List of largest daily changes in Dow Jones

March 2020: Fastest Bear Market in History

Last month holds the record for how quickly the market plummeted into a bear market at only 16 days starting on February 19th. The contrast is even more severe when calculating how quickly the March 2020 crash hit 30%'

KNOX RIDLEY

Source: Knox Ridley

Last month was not for the faint of heart. The bear market of March of 2020 took 19 days to drop 30% while all other black swan events took 55 days or longer. Meanwhile, the economic backdrop includes a health care crisis, high unemployment, canceled school years, and state mandates to “shelter in place.” Machines driving record gains last week are clearly not in a quarantine.

The Fed recently warned that the country could face an unemployment rate of 32%, or 47 million. This would exceed the Great Depression at 24.9%. As we saw in 2008, massive unemployment forces the middle class to withdraw their 401Ks to pay bills. This could cause major unintended consequences from the Federal Reserve policy that pushed retirement accounts into equity markets in order to keep up with inflation.

Despite evidence of negative consequences, rampant algorithmic trading in the financial markets has become accepted as the new norm. However, this will be the first time that algorithmic trading could compound an economic recession as 401Ks have been squandered by the sheer speed of stock market machines. This, of course, depends which way the wind blows next week and how machines react to news headlines with natural language processing (NLP).

Machines Behaving Badly: Faster than “Blink of an Eye”

High-frequency trading costs regular investors up to $5 billion per year, according to a recent study released in January of 2020. The practice of “latency arbitrage” involves arbitraging prices extracted by lower latencies. Better prices are then quickly bought by machines that can move quickly.

The FCA found the machines racing against one another is faster than “the blink of an eye” at 79 millionths of a second. The FCA study tracked 2.2 billion transactions over 43 trading days and found 20% of trading volume was from latency arbitrages. The FCA concluded that latency eliminating latency arbitrage would reduce the cost of trading by 17%. Six firms won the arbitrages 82% of the time.

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In 2011, electronic inter-dealer broker ICAP had introduced a fifth decimal point to its EBS foreign exchange platform so that its currency pairs such as euro/dollar read as $1.24980, instead of the standard $1.2498. The fifth decimal attracted high-frequency computers, which disrupted the flow of liquidity on the EBS platform. This upset the banks with slower technology as they could not execute large transactions when super-fast computers sliced them into smaller trades. Notably, Deutsche Bank and Barclays had already offered tenth pricing to their customers.

In 2014, The Securities and Exchange Commission sanctioned a New York City based high frequency trading firm Athena Capital Research for placing a high number of aggressive, rapid-fire trades. The trades occurred in the final two seconds of nearly every trading day during the six-month period with an intent to manipulate the closing prices of thousands of NASDAQ-listed stocks. The Company had to pay a $1 million penalty to settle the SEC’s charges.

Tower Research Capital paid $67 million to settle spoofing charges. On thousands of occasions, between 2012 and 2013, the company manipulated the futures contracts on the Chicago Mercantile Exchange and the Chicago Board of Trade.

Evidence of Recent Algorithmic Damage

Over the last 10 years, commodity trading assets (CTAs) have collectively risen by about 36% to roughly $360 Billion. Because they are hedge funds, they are likely leveraged anywhere between 2-5 times this amount, putting the total amount of assets anywhere between $1-2 trillion dollars. These software driven funds tend to be crowded in the same trade together, which can make for violent swings.

Nearly a decade ago, there was a flash crash that occurred on May 6, 2010. This “flash crash” caused the Dow Jones to drop 998.5 points (about 9%) within minutes, only to recover a large part of the crash later in the day. According to the Commodity Futures Trading Commission (CFTC), high frequency trading “did not cause the Flash Crash, but contributed to it by demanding immediacy ahead of other market participants.”

Flash crashes and flash rallies of 1000 points are now the new normal with sixteen occurring since March 1st. Four of these historical daily gains were above 9%. Trading curbs, known has circuit breakers, were hit four times last month.

Wikipedia

Source: Wikipedia, List of Largest Daily Changes in the Dow Jones

Can’t Beat Them, Join ‘Em

During the Q4 2018 sell-off, Guy De Blonay, a fund manager at Jupiter Asset Management stated 80% of the stock market is controlled by machines. In 2017, JP Morgan stated that “fundamental discretionary traders” accounted for only 10 percent of stock trading volume.

Billionaire Steven A Cohen’s hedge fund had to focus more on quant trading in 2017 when it lost money in most of its traditional trading strategies in that year, while its quant investors made money. For example, Steven Cohen’s $12-billion hedge fund, Point 72 Asset Management, is moving about half of its portfolio managers to a “man plus machine” approach.

According to Wells Fargo, robots will replace 200,000 banking jobs over the next 10 years. Citigroup has formed a lab to cross-train traders and developers for machine learning and artificial intelligence. The programming language, Python, is especially in high demand at leading banks, such as JP Morgan and Goldman Sachs.

Conclusion:

Daniel Pinto, JP Morgan’s Co-President, stated that investors may have to get used to big, sudden moves in the stock market due to fewer institutions pushing equities to attractive valuations while hedge funds reach unprecedented levels of employing computerized momentum-based strategies. The result will be “faster and deeper” corrections.

The problem with “faster and deeper” corrections is there is essentially no time for the average investor to adjust. Perhaps we will get a coronavirus vaccine or antiviral tomorrow, and business will go on as usual. Or, the opposite could happen, and things will get worse. One thing is certain: Until there is regulation, the machines will profit either way.

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