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Algorithmic trading automates the work of fund managers and traders through computer algorithms. It ensures instant trade execution at the most optimum price. This automation is expected to reduce transaction costs and human errors.

High-frequency trading (HFT) is a type of algorithmic trading in which a large number of transactions take place at a lightening fast speed. Each transaction leads to a tiny gain, often due to arbitrage opportunities.

Algorithmic Trading

However these benefits come at a price, which is the risk associated with these transactions. Network connectivity errors, system failure, technical glitch, bugs in the program, a time lag between trade orders and executions are some of the examples. Amplification of systematic risk is the biggest threat of algorithmic and high-frequency trading.

What is systematic risk?

Risks are of two types: Systematic risk (or market risk) and specific risk. Systematic risk is inherent to the entire market or a market segment. For instance, situations like depression, recession, war or some government policy, might impact the entire market, giving rise to systematic risk. Hedging can mitigated such risks.

The Flash Crash of May 2010 is a prime example of this kind of risk. There was a quick drop and recovery in prices of some securities. The Dow Jones plunged almost 1,000 points on an intraday basis. Prices were highly volatile leading to a spike in trade volume.

Knight Capital Case

On August 1, 2012, a midsize financial firm named Knight Capital lost $440 million in 45 minutes. Knight Capital had created a computer program to be linked with the new trading platform. The New York Stock Exchange had launched it.

However, due to some problem in the program, they started losing $10 million a minute. They were buying high and selling low at a high rate. It took them 45 minutes to find the glitch and fix it, by which time they had lost $440 million. This caused a ripple in the whole market, increasing volatility and more than doubling the trade volume as compared to previous week’s average.

Algorithmic Trading

Due to widespread algorithmic HFT activity in today’s market, all algorithmic programs attempt to outperform the competition. These algorithms react instantaneously to different market conditions and may greatly widen their bid-ask spreads during uncertainty. It may also stop trading temporarily, which diminishes liquidity and increases volatility.

Present-day financial markets are highly integrated. Any disturbance in one market or asset class ripples to the rest of the market through a series of chain reactions.

A financial meltdown increases investor uncertainty in the short-run and decreases consumer confidence in the long-run. It leaves them wondering about the causes of such a meltdown. Many of these traders greatly decrease their trades, putting a downward pressure on the market. Therefore, algorithmic HFT if gone wrong can lead to a huge amplification in systematic risk, spreading over to different markets.

Conclusion

The benefits of algorithmic HFT have outweighed its risks. Also, appropriate protective measures taken by regulators and exchanges can help mitigate this amplification of systematic risk. For instance, NASDAQ OMX Group introduced a “kill switch” for its member firms that would cut off trading once a pre-set risk exposure level is breached. Kill switch ensures safety to counter rogue algorithms and one should use it extensively.

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