Oct 19 (Reuters) – A new generation of computer
trading models is emerging as banks come under pressure to prove
they are offering clients the best price on currency trades in
an ever more competitive market.
Whereas investors might once have picked up the phone to ask
brokers or traders for a quote, they are increasingly using
complex models designed by the banks, known as algorithms, or
algos, to transact large orders at the best possible price.
Pressure from regulators, who are keen to ensure a fair deal
for investors in the $4.5 trillion dollar-a-day market, is
adding impetus to the trend.
“We’re moving away from the old opaque world of FX trading,”
said Pete Eggleston, head of Quantitative Solutions at Morgan
“Regulatory change and responsibility to investors are
leading us into this world of best execution and transparency.
Algos are part of the solution in helping clients adjust to
With a slew of new regulations coming into force on both
sides of the Atlantic, the asset managers who deal with currency
trading desks must ensure “best execution” for their clients and
prove, rather than simply promise, they are keeping transaction
Spot currency trading will be exempt from reporting
requirements outlined in the Dodd-Frank Act, a regulatory
overhaul of financial markets passed by the U.S. government.
But James Dalton, director of FX algorithmic execution at
Citi, said some asset managers were already applying those rules
to their currency transactions as the trend towards transparency
was likely to spread.
“I am seeing for the first time when I talk to clients that
questions are starting to come from the underlying investor to
asset managers on how they measure transaction costs in FX,”
“A lot of the main banks already have algo execution
products or are about to launch. It’s a must-have in the
portfolio of execution tools.”
Lawsuits involving U.S. custodial banks BNY Mellon and State
Street have prompted investors to look more carefully at whether
they are getting good prices from their banks.
A report by consulting firm Greenwich Associates found a
growing number of the world’s largest institutional investors
are analysing – and cutting down on – the costs of trading
Algorithms can provide electronic records of exactly how and
when a trade is transacted, allowing investors to check the
price at which a bank bought or sold a currency on their behalf
against the price quoted in the market. Some banks offer a
time-stamp to the millisecond for each part of an order.
As this activity, known as algo execution, becomes more
popular, strategists are developing increasingly sophisticated
models that can detect and adapt to sudden market swings, in
much the same way as a human trader would.
The earliest models were inherited from equity markets, and
simply broke up large orders into smaller amounts that were
traded at fixed intervals, irrespective of market rallies or
The next step was to equip models with different strategies
for different market conditions. Passive strategies helped hide
the order flow, while aggressive strategies were used at times
of deep liquidity to move a large order quickly.
But an investor or trader still had to decide which strategy
was suitable for particular market conditions, and the algo
would plough on regardless of a turnaround in the market.
Now the more adaptive algos, like BNP Paribas’ Cortex iX,
can process feedback from trading activity and decide which
execution rules are best suited to market conditions, helping
capture the best price for clients.
“Think of second generation algos as blind and deaf … With
third generation algos we have given them the power of sight and
hearing,” said Asif Razaq, global head of FX algo execution at
Another feature of adaptive technology is that the execution
of the order appears to be random, helping avoid detection from
other algos in the marketplace which are on the lookout for
trading patterns that they can exploit.
PROTECTING THE HERD
So far it has been the larger players in the currency
market, such as Citi, Credit Suisse and BNP Paribas, which have
forged ahead with the costly task of building these more
sophisticated execution algos.
Some banks have even recruited people with doctorates in
physics and mathematics from the world of academia to help
construct models that will win clients and protect their
currency trading profits.
Investment banks tend to lump currency trading in with bond
and commodities trading. Overall, these units accounted for
more than half of revenues at the top 10 investment banks in the
first half of 2012, according to analytics group Coalition.
But the contribution that currency trading made to this
overall trading revenue stream dipped to 8 percent from 9
percent in the same period a year earlier, Coalition said.
Unlike the rapid-fire models used by high-frequency trading
firms (HFTs) to pounce on trading opportunities, execution algos
are designed to ease large orders into the market without
alerting other players to the flow, thus ensuring a good price.
By limiting price “slippage” – the difference between the
price at which a market player wants to execute an order and the
price at which they are able to do so – they are especially
useful for what Aite Group analyst Javier Paz defines as
“slowing-moving buy-side clients” at risk from predatory HFTs.
“The banks have to tweak their own execution algorithms that
they provide to slow-moving clients so they can have that
invisibility cloak. It’s like protecting the herd from the
velociraptors,” Paz said.