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Number Game

April 2016

Quantitative analysts, or quants, apply a blend of mathematics, finance and computer skills in securities trading. But are they always right?

To say mathematicians have fastened their grip on the financial world over the last decade in a manner unlike before would not be too far away from the truth. The rise of quantitative trading bears witness to the growing inclination of finance towards technology and data. Aided by the increased demand for speed, algorithm development, the core of quant trading, is driving limitless possibilities within the field of trading.

“Quant traders basically use quantitative analysis (Analysis on historical data to find relationships) to set up mathematical algorithms that tries to capture trends or mispricing between different securities in order to generate a profit,” Yacoub Husein Nuseibeh, CFA, CQF, member at CFA Society Emirates elaborates. One of the drivers of quant trading strategies in recent years has been the emergence of High Frequency Trading (HFT). Making use of complex algorithms to analyze the markets, it is able to spot emerging trends in a fraction of a second. Blamed for the 6th May 2010 flash-crash on account of exacerbating price declines, this strategy has bought to the fore arguments including demands for regulation and unfair advantage larger firms possess. Yacoub prefers to look at this in a more mature manner highlighting that financial markets are ultimately out there to make profit; using an advanced algorithm in trading, fundamental research or technical analysis – provide the markets with depth involving divergent term views as well as different insights. Thus, enabling greater efficiency in the system.

A trend worth highlighting here is the appeal quant trading strategies are seeing with the retail investor. With an aim to minimize the human bias in the investing process, giving into fears stemming from the 2008 crisis, there is a growing segment within this category which is venturing out to try their hand at DIY algo-trading platforms. As an example, a very basic algorithm involves – If volume in a particular stock hits 100,000 and the 50-day moving average of the stock price crosses above the 100-day moving average, buy 100 shares. The appeal of the system identifying patterns in a stock which get overseen by humans is driving this behavior.

PrintHowever, it is prudent to highlight here that markets today are reflecting shorter cycles than before. While the complex quant strategies may display success, over dependence on the computer could spell disaster and thus needs to be used judiciously. As Yacoub outlines, “‘No one can predict the future, so the best guess is usually what happened in the past. Some quants will be more successful than others. However I do not believe that they will consistently generate profits all the time, because these relationships can change and they often do. Let’s not forget that economics and finance is not science like physics because humans run governments, banks, companies, etc…. and humans do not necessarily behave in fixed laws patterns, hence Quants can lose money like any traders or investors.”

In conclusion, quantitative trading is here to stay and, going ahead, is bound to see a continued degree of focus in terms of development. With the focus of trading primarily continues to remain on how to optimally execute trades, quant offers a platform integrating a myriad of market details and using that information to effect trades in time frames, beyond the human capacity. At the same time, it is expected to continue to invite the vigil of market watchmen and observers who view the role of computers in the overall investing process as one that needs to be exercised with a human touch.

What is Quantitative Trading?
Put simply, a quantitative trading strategy makes use of computer software programs developed to track patterns or trends in an asset class. These trends may be a product of the price, the volume and/ or frequency at which it is traded. By involving the computer in the same, quant traders aim to take much of the human element out of investment decisions.

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