Gold price plunge triggers flash crash memories

In the early hours of Monday, gold prices fell more than over 4% or $50 per ounce in China to touch $1,086, the lowest level since March 2010


While there is active debate on whether algo trading or high frequency trading is to be blamed for greater trading volatility, regulators are trying to keep a close watch on the spread of such trading. Photo: Reuters
While there is active debate on whether algo trading or high frequency trading is to be blamed for greater trading volatility, regulators are trying to keep a close watch on the spread of such trading. Photo: Reuters

Mumbai: Flash crash, or a sudden sharp fall in the price of a stock, commodity or an index, is fast becoming a recurring phenomena, as newer and faster trading technologies expand their footprint in trading across asset classes.

In the early hours of Monday, gold prices fell more than over 4% or $50 per ounce in China to touch $1,086, the lowest level since March 2010.

“... just before 9:30pm Eastern time, or right as China opened for trading, gold (as well as platinum, silver, and virtually all precious metals) flashed crashed when ‘someone’ sold $2.7 billion notional in gold, resulting in a 4.2% or about $50 to just over $1,086/oz, the lowest level since March 2010,” said a report on financial blogging website zerohedge.com, adding that this did not appear to be a “normal order”.

While the cause of the sudden fall is not known, such instances appear to have become more frequent. Some say this is partly because of the increase in the share of algorithmic trading (algo trading), even though not everyone agrees with this. Software codes or algorithms are frequently used to automate and enhance order-matching processes.

Exchanges globally have seen a number of notable flash crashes in recent years, for various reasons.

US treasury flash crash

On 15 October, a flash crash in the US treasury markets led to a sudden plunge in US treasury prices. Yields first fell sharply between 9.33am and 9.39am Eastern Standard time and then spiked back up.

A 13 July US staff report on the flash crash found that there was no single cause of the treasury flash crash. The report said that investors looking to unwind positions, along with computer-driven trading may have played a role in the flash crash.

Explaining the ‘Great Treasury Flash Crash’ of 15 October 2014, Bloomberg View columnist Matt Levine wrote that the “flash crash, in the cash Treasuries market, was mostly a story of high-frequency traders making money off of other high-frequency traders... Should we fear our new electronic overlords for their control of the market? Should we pity them for their folly? I don’t know”.

Comex gold flash crash

US-based Comex Inc., which is the world’s largest commodity exchange for gold, saw a similar crash in gold futures on 6 January 2014, when prices fell from about $1,245 to around $1,215 an ounce in just moments. In less than 60 seconds, more than 12,000 contracts were traded, which was equal to almost 10% of the daily volume. Investigations showed that the crash was on account of an error in the trading systems of a member that led to unusually high trades, resulting in “disruptive and rapid price movement”.

NSE flash crash

India, too, has seen some instances of such flash crashes.

In the equity segment, the National Stock Exchange of India Ltd (NSE) saw trading coming to a halt on 5 October 2012. This took the Nifty 50 index down by over 15% and led to a trading halt for 15 minutes. Following the crash, NSE said 59 erroneous orders worth Rs.650 crore placed by Emkay had triggered the halt and the member responsible had been disabled from trading. This instance, however, was linked to a punching error and did not have anything to do with technology-driven trading systems.

Is algo trading to blame?

While there is active debate on whether algo trading or high frequency trading is to be blamed for greater trading volatility, regulators are trying to keep a close watch on the spread of such trading.

A process to review the existing framework for algo trading (or high frequency trading) is underway in India and globally as well, but the view on the quantum of checks that one can or should impose is divided. Too many checks and the speed advantage is killed. Too few checks and market safety is compromised. A fine balancing act is what is required.

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