This article was first published by the Global Association for Risk
Professionals on June 19, 2016
Six years since allowing algo trading, India
wrestles with questions familiar across financial markets
Since first being allowed in 2008, algorithmic trading has
grown to account for 40% to 50% of the turnover on the National Stock Exchange
(NSE) of India.
Widely regarded as a disruptive technology, and creator of
advantages for some that harness it, algo trading was defined by the Securities
Exchange Board of India (SEBI) as “any order that is generated using automated
execution logic.”
The late Gangadhar Darbha, who was executive director and head
of algorithmic trading strategies at Nomura Securities, said, “Algorithmic
trade per se is nothing but a reflection of what happens in our brains. Algo
trading is the use of electronic platforms for entering trading orders with an
algorithm which executes pre-programmed trading instructions whose variables
may include timing, price, or quantity of the order, or in many cases
initiating the order without human intervention.”
Institutional investors, insurance companies and mutual funds
use algorithmic techniques for portfolio rebalancing and risk control amid
large order flows on either side. Anonymity and mathematical logic to break
large orders into small pieces can counter adverse price moves and
manipulation.
Financial firms apply the cutting-edge technology to product
development and innovation in such areas as liquidity-seeking, cross-asset and
multiple-exchange trading.
While it is difficult to know the strategy or logic being
applied by looking at the trade data, it seems unlikely that the algorithms act
on fundamental information about companies or the economy. Algo trading takes
into account quantitative information regarding trends, reversion to mean,
arbitrage, etc.
It is argued that computer-assisted trading improves liquidity
in the markets by breaking down large trades into smaller trades, reduces
bid-ask spread and lowers risks of adverse selection and trade related price
discovery. Terrence Hendershott, Charles M. Jones and Albert J. Menkveld, in
“Does Algorithmic Trading Improve Liquidity?” (Journal of Finance, Vol. LXVI,
No. 1, February 2011), argue that algo traders are more likely to be providers
of liquidity. They also increase the speed and efficiency of trades and reduce
costs of trading.
Flash Crashes
There are also purported disadvantages.
There have been many instances of flash crashes on exchanges
around the world, in which automated trading has been implicated, whether due
to erroneous coding, a systems failure or cascading effects of certain trades.
Algo is also often blamed for causing large fluctuations or volatility in the
markets.
On October 5, 2012, the CNX Nifty Index of top 50 companies
traded on India’s NSE fell nearly 16% within seconds (before rebounding),
causing panic amid traders and institutional players, as stop losses were
triggered. Similarly, the Bombay Stock Exchange (BSE) had to annul all trades
on “muhurat day” in 2011 due to extraordinary volumes. (Muhurat day is a
special trading session on Indian bourses to mark to beginning of the new
financial year on the Hindu calendar. It coincides with the popular festival of
Diwali.)
Algo trading’s effect of improving liquidity has been
scrutinized. It is alleged that algo trades focus on a few large stocks,
resulting in short-term liquidity improvement only for those stocks. If this
also means that trading gets concentrated in fewer stocks, then this is not
good for an exchange. And if algo trading magnifies panic, it can have an
avalanche effect on prices.
Technology is reducing the cost of trading and attracting
large volumes. But that requires exchanges to improve the efficiency and
capacity of their data servers, matching engines and bandwidth, which in turn
increases infrastructure cost and has to be added back to the brokers
transaction cost.
Haves and Have Nots?
Does algorithmic trading "discriminate between rich and
influential brokers and common investors/retail investors and create
inequality” among constituencies on the BSE and NSE, as has been alleged by the
Intermediaries and Investor Welfare Association? Algo traders have a
technological advantage over the regular market participants, who are not able
to afford such high fixed costs.
The Technical Advisory Committee of the SEBI, it was reported
in April, noted that a certain broker benefited from loopholes in the systems
architecture of NSE, which were not prevented by the exchange. Larger questions
regarding the role of the regulator in coming out with policies on co-location
and algorithmic trading are being raised as well. The surveillance systems of
the exchanges and regulatory actions against manipulative activities have not
kept pace with the improvements in technology and the complexity of algorithms.
Interestingly, rampant accusations that flash orders favor
insiders led some lawmakers in the U.S. to urge in 2009 that the practice be
banned by the Securities and Exchange Commission. It remains legal. The Indian
counterpart, the SEBI, has set an agenda for itself to come out with a
discussion paper on high-frequency trading, or algo trading, in the coming
three months, which could lead to regulations of those activities.
Perhaps the right way to look at it is as Darbha once said in
an interview: “Algorithmic trading is not just a facility, but an aid. While
algorithmic trading gives you freedom to trade, it does not replace fundamental
research. It only enhances trading efficiency.”
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