Wars and viruses: Are robots less prone to market panic?

Saikat Chatterjee

Widely blamed for volatile “flash crashes” in currencies and equities, high-frequency algorithms may also be why shock global events, including the current coronavirus, 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 U.S. killing of an Iranian general and Iran’s retaliatory missile attack are among potential catastrophes that triggered violent but surprisingly shortlived reactions just since the start of 2020.
In both cases, knee-jerk yen-buying and selling of equities faded within hours, allowing stocks to scale new record peaks.
Now even as China’s coronavirus 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 $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 $6.6 trillion-a-day FX market has more than doubled to 27% among fund managers, a survey by Greenwich Associates found.
There is some reason to believe algos cause volatility, especially when trading thins and the humans overseeing them vanish, for instance during public holidays. That’s what likely happened during the Wall Street flash crash of 2010 and dramatic but fleeting yen moves in Jan 2019.
But they also offer the advantage of being able to transact at lightening 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.”
In short, when left-field events hit, not only can algos scan and react swiftly to newsfeed, 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 algo use said a machine reading coronavirus cases would typically buy stocks if informed of “500 new cases, 10 deaths”. “If it’s ‘3000 new cases, 200 deaths’ they 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.
But the machines had ‘vol triggers’, he said, meaning they can stop trading when the market moves beyond specified limits.
Simple first- and second-generation programmes merely broke down large buy/sell orders into chunks to minimise market impact. Now algos can be hooked up to sophisticated language processing technology, to “read and analyse” 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 warned.
The impact in fast-moving markets can be outsized if the models rapidly push prices towards existing buy/sell order levels, trip them and trigger other orders.
That’s what happened when news broke of Iran’s Jan. 8 attack, according to a quant fund manager, who said algos had bought yen with the aim of triggering larger buy orders once a key option barrier was tripped.
A plausible comparison may be the 0.6% plunge 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, says in such instances algos are programmed to check if moves are in line with price trends.
If the swings are in response to the an incident, human traders can step in to smooth out the trade, Malrait added.
Next, the algos may gauge the seriousness of the incident based on patterns of investor behaviour and economic consequences that followed previous such episodes.
After the Iran missile attacks, the message from the specialised data-crunchers to the algos was: stand down.
One was Geoquant, a U.S. firm which 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 CEO Mark Rosenberg, who concluded the risk would subside, with minimal oil price impact.
Another firm, Predata, applied its machine-reading algorithm to last September’s attacks on Saudi oil facilities, which had raised fears of a regional war.
To predict what might happen next, the programme compares interested parties’ online attention to a subject, with the media coverage it receives, Predata CEO 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 algo strategies told Reuters their services were increasingly in demand, but declined to give figures.
But they acknowledged limitations.
Reactions to the coronavirus for instance could be hobbled because the only meaningful precedent is the 2003 SARS epidemic which also coincided with the U.S. 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.

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