Well, who knew?
Algorithmic FX trading bots — which use computer formulae to trade currencies at high frequency — apparently don’t like news releases, or at least, not initially, according to a new working paper from the Federal Reserve. In fact, when ‘big’ economic news releases — such as US nonfarm payrolls — first appear, the trading bots back away:
Next, we study the relative provision of market liquidity by computers and humans at the times of the most influential U.S. macroeconomic data release, the nonfarm payroll report. We find that, as a share of total market-making activity, computers tend to pull back slightly at the precise time of the release [the first minute] but then increase their presence in the following hour. This result suggests that computers do provide liquidity during periods of market stress.
“Do provide liquidity” - just not for the first minute.
What’s the explanation for the initial pullback? Here’s what authors Alain Chaboud, Benjamin Chiquoine, Erik Hjalmarsson and Clara Vega say:
We note that, over our sample period, the U.S. nonfarm payroll data releases were clearly the most anticipated and most influential U.S. macroeconomic data releases. They often generated a large initial sharp movement in exchange rates, followed by an extended period of volatility. The behavior of computer traders observed in the first minute could reflect the fact that many algorithms are not designed to react to the sharp, almost discrete, moves in exchange rates that often come at the precise moment of the data release. Some algorithmic traders may then prefer to pull back from the market a few seconds before 8:30 a.m. ET on days of nonfarm payroll announcements, resuming trading once the risk of a sharp initial price movement has passed. But the data show that algorithmic traders, as a whole, do not shrink back from providing liquidity during the extended period of volatility that follows the data releases.
Remember that one of the criticisms of algorithmic trading is that it possibly reduces liquidity in times of stress — when it’s needed most.
The results from the above study, therefore, might be considered a bit of a mixed bag on that point. In the first minute after the non-farm payrolls, trading bots retreat from the market, leaving human traders to pick up a greater share of the trading volume. But in the following hour the bots regroup and increase their share.
That’s especially interesting result given there are some algorithms that, according to the paper, “now automatically read and interpret economic data releases, generating trading orders before economists have begun to read the first line”. So it’s not that some of the bots can’t react quickly enough to the releases, it’s just that some of them prefer not to. Clever bots.
Which leads to another question the paper addresses: are the machines now smarter than, or as smart as, man?
And the answer is . . . only in the euro-yen exchange market:
Finally, we estimate return-order flow dynamics using a structural VAR framework in the tradition of Hasbrouck (1991a). The VAR estimation provides two important insights. First, we find that human order flow accounts for much of the long-run variance in exchange rate returns in the euro-dollar and dollar-yen exchange rate markets, i.e., humans appear to be the “informed” traders in these markets. In contrast, in the euro-yen exchange rate market, computers and humans appear to be equally “informed.” In this cross-rate, we believe that computers have a clear advantage over humans in detecting and reacting more quickly to triangular arbitrage opportunities, where the euro-yen price is briefly out of line with prices in the euro-dollar and dollar-yen markets. Second, we find that, on average, computers or humans that trade on a price posted by a computer do not impact prices quite as much as they do when they trade on a price posted by a human. One possible interpretation of this result is that computers tend to place limit orders more strategically than humans do. This empirical evidence supports the literature that proposes to depart from the prevalent assumption that liquidity providers in limit order books are passive.
Much more — including whether algorithmic FX trading strategies tend to be more correlated than human trading strategies (yes) and whether they increase market volatility (no) — in the full paper.
Related links:
Mrs Robo-Jones starts to make an FX impact - FT Alphaville
Algorithmic trading in FX: How far can it go? - Advanced Trading
