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Business News/ Opinion / What Sebi can learn from Asic
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What Sebi can learn from Asic

Sebi would do well to take measures towards evidence-based policy making.

Shyamal Banerjee/MintPremium
Shyamal Banerjee/Mint

The process followed by Australian Securities and Investments Commission (Asic) before it proposed changes and additions to its market integrity rules last week is a brilliant example of evidence-based policy making. Asic, Australia’s corporate, markets and financial services regulator, first studied trading data for a nine-month period before determining the changes required in its regulations related to automated trading. In a separate move about three months ago, it engaged a technology company to deploy tools similar to those used by high frequency traders in its new market surveillance system.

Both of these are steps in the right direction (read tinyurl.com/Sebi-study), and the Securities and Exchange Board of India (Sebi) must take a leaf or two out its Australian counterpart’s books.

Asic commissioned an analysis of all trading on Australia’s equity markets over a nine-month period between January and September 2012. The internal taskforce which did the study also carried out additional statistical analysis on data for the period from May to July. Through this process it was able to test doubts pertaining to high-frequency trading (HFT)—such as whether HFT brings any value to market quality—as well as criticisms such as the “noise" created from excessive trading messages and concerns that HFT firms are predatory or that they “game" the orders of fundamental investors, manipulate prices and may contribute to market instability.

The study revealed that “for the most part, high-frequency traders (and the broader algorithmic trading classification) improve market quality through increasing price efficiency and market liquidity, while dampening the effects of volatility." It also found that concerns about high order-trade ratios and low holding times of HFT firms were unfounded. It must be noted here, however, that Australian market participants have had to bear a message fee, which rises with the number of order messages they send to an exchange, since February 2012. This had led to a drop in the order-trade ratio. Now this is just one example why other regulators including Sebi must use the findings of the study carefully and commission their own analysis of market data to arrive at meaningful conclusions for their own markets.

Asic’s internal taskforce did, however, find some basis for criticism that HFT creates excessive ‘noise’ and exhibits predatory or gaming behaviours, although it also quickly added that other traders (i.e. ones not classified as HFT in its study) are also contributing to the problem. This is simply because even non- HFT firms use automated trading systems that slice and dice orders for better execution. One of Asic’s solutions to cut ‘noise’ is to impose a minimum rest time (i.e. the time before which an order can be modified or cancelled) of 500 milliseconds for small orders of $500 or less. It’s interesting to note that in the study, 76% of such orders came from non-HFT firms. The other proposal is to amend rules related to manipulative trading by including additional circumstances in considering whether a false or misleading market has been created, such as the frequency with which orders are placed.

In sum, Asic’s study suggest that fears related to HFT are exaggerated. Meanwhile, a recent paper by Adam D. Clark-Joseph of Harvard University, which uses confidential trading data in the eMini S&P 500 futures market, concludes that HFT firms engage in a form of market manipulation called ‘exploratory trading’. They do this by sending small orders first to test the market’s reaction to such orders and resulting trades. While these small trades may turn out to be loss-making, they engage in much larger profitable trades after gauging market conditions. Clark-Joseph had access to order message level data from Commodity Futures Trading Commission.

The two studies are typical of what HFT studies have thrown up in recent years – while some conclude that they improve market quality, a few point to disruptive behaviour as well as market manipulation. In this backdrop, it’s best for Sebi to commission its own study based on the trading data provided by the National Stock Exchange and Bombay Stock Exchange. The results of the study can then be used to come up with effective rules and regulations for automated trading in India.

Asic’s idea of engaging with First Derivatives Pty Ltd for its new market surveillance system is also novel. First Derivatives’ solution is based on technology already used extensively for market data capture, alerts and analytics and high frequency and algorithmic trading. As pointed in this column earlier, a regulator’s surveillance system must have the same sophistication as systems used by the market participants it oversees. Theo Hildyard, director and capital markets product manager at Progress Software Corp., says in a recent post on Tabb Forum (it’s an online forum for capital markets professionals, hosted by financial markets’ research and strategic advisory firm Tabb Group), that there is a need for high frequency market surveillance, preferably in as near real time as possible so humans can respond to manipulative algorithms in timeframes less than minutes or hours or days or never. He adds, “Who knows -- a technology may even exist to apply an automated kill switch to algorithms that are misbehaving, be it in a manipulative or simply erroneous manner."

Given that India has had its share of automated trading accidents in the past 18 months, Sebi would do well to take similar measures towards evidence-based policy making and high frequency market surveillance.

Your comments are welcome at mintmoney@livemint.com.

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Published: 25 Mar 2013, 07:45 PM IST
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