Widely blamed for volatile “flash crashes” in currencies and equities, high-frequency algorithms might also be why shock global events, including a coronavirus outbreak, seem to have lost their power to spook markets for any length of time.
Whether stocks, bonds, currencies or commodities, asset prices seem less prone to any selloff for very long; the US killing of an Iranian general and Iran’s retaliatory missile attack are among potential catastrophes that triggered violent, but surprisingly short-lived reactions just since the start of this year.
In both cases, knee-jerk yen-buying and selling of equities faded within hours, allowing stocks to scale new record peaks.
Illustration: Mountain People
Now even as the 2019 novelcoronavirus (2019-nCoV) outbreak in China threatens to throttle economic growth, global stocks are not far off all-time highs.
Certainly, many factors are shaping the resilience, not least central bank money printing and rising global savings, which boosted the value of world stocks by US$25 trillion in the past decade.
Yet it is hard not to link the shift in reaction by financial markets to the rise of automated trading strategies. In the past six years, the share of algo-trading in the US$6.6 trillion-a-day FX market has more than doubled to 27 percent among fund managers, a survey by Greenwich Associates found.
There is some reason to believe algorithms cause volatility, especially when trading thins and the humans overseeing them vanish, for instance during public holidays. That is what likely happened during the Wall Street flash crash of 2010 and dramatic, but fleeting yen moves in January last year.
However, they also offer the advantage of being able to transact at lightning speed at any hour of the day or night, with razor-sharp accuracy and lower overall costs.
Being machines, they are also alien to the common human impulses of fear and greed that tend to take over.
Algorithmic trading is dispassionate, said Scott Wacker, global head of fixed income, currency and commodity e-sales at JPMorgan, one bank at the forefront of the algo revolution.
“As a result, the reaction function in currency markets to even major geopolitical news has considerably shortened, which enables stability to return more quickly,” Wacker said.
In short, when left-field events hit, not only can algorithms scan and react swiftly to news feed, many now can gauge the potential asset price impact. The most sophisticated can be “trained” to learn from the experience before the next shock.
One currency trader familiar with algorithm use said that a machine reading 2019-nCoV cases would typically buy stocks if informed of “500 new cases, 10 deaths.” If it is “3,000 new cases, 200 deaths,” it might sell.
“The point being that as soon as a headline is out, the machine-led market is trading on it,” the trader said, speaking on condition of anonymity.
However, the machines had “vol triggers,” he said, meaning they can stop trading when the market moves beyond specified limits.
First and second-generation programs merely broke down large buy/sell orders into chunks to minimize market impact.
Now algorithms can be hooked up to sophisticated language processing technology to “read and analyze” news feeds, then react accordingly, all in the space of seconds, said Antony Foster, head of G10 FX trading at Nomura.
However, this can “lead to overreaction in the first instance,” Foster said.
The effects in fast-moving markets can be outsized if the models rapidly push prices toward existing buy/sell order levels, trip them and trigger other orders.
That is what happened when news broke of Iran’s attack on Jan. 8, according to a quantitative fund manager, who said that algorithms had bought yen with the aim of triggering larger buy orders once a key option barrier was tripped.
A plausible comparison might be the 0.6 percent decline in the S&P 500 within the space of half an hour on Jan. 29. The move came after American Airlines and Lufthansa said they were suspending their China flights, but an hour later, the losses had been recouped.
Stephane Malrait, ING Bank’s head of market structure and innovation, said that in such instances algorithms are programmed to check if moves are in line with price trends.
If the swings are in response to an incident, human traders can step in to smooth out the trade, Malrait added.
Next, the algorithms might gauge the seriousness of the incident based on patterns of investor behavior and economic consequences that followed previous such episodes.
After the Iran missile attacks, the message from the specialized data-crunchers to the algorithms was: stand down.
One was Geoquant, a US firm that monitors geopolitical events and models the asset price impact.
“We modelled our Iranian geopolitical indicator back to seven years ... and put the current tensions against that backdrop,” said Geoquant chief executive officer Mark Rosenberg, who concluded that the risk would subside, with minimal oil price impact.
Another firm, Predata, applied its machine-reading algorithm to attacks on Saudi oil facilities in September last year, which had raised fears of a regional war.
To predict what might happen next, the program compares interested parties’ online attention to a subject, with the media coverage it receives, Predata chief executive officer Hazem Dawani said.
The Saudi event elicited little reaction from military officials and policymakers.
“The amount of news was far more than the amount of attention being devoted on the particular subject by investors, politicians, companies directly impacted by them,” Dawani said.
He concluded — rightly — that there would be no escalation.
Executives at half a dozen firms providing risk analysis for algorithm strategies told reporters that their services were increasingly in demand, but declined to give figures.
However, they acknowledged limitations.
Reactions to the 2019-nCoV for instance could be hobbled, because the only meaningful precedent is the 2003 SARS epidemic, which coincided with the US invasion of Iraq.
Second, the pathogen and its dangers are relatively unknown.
Rosenberg said Geoquant’s China Health Risk index was at record highs.
That has pushed up the firm’s Social Instability Risk index for China, pressuring Chinese markets.
However, he said this correlation breaks down beyond short periods.
“The longer-term relationship between Chinese Health Risk and equities, Chinese or global, is basically zero,” he said.
Additional reporting by Thyagaraju Adinarayan
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