Picture an investment bank drawn in a ‘Where’s Waldo?’ style. You’ve got traders, finance, the legal team, and management; human resources, and an IT team hammering away at their keyboards. Where would you expect to find the quantitative analytics, research & trading team (quants)?
Ten years ago, quants would have been tucked away on trading desks, inputting data into Excel spreadsheets and working through it manually to uncover patterns and statistics. Their findings would help traders to confirm the right price and most promising investment options.
Skip forward a decade and the role of the quant has changed substantially. For the most part, they are now incorporated into risk management teams, instead of being on a trading desk. There are more of them, they are a more diverse group, and they use new tools to carry out different tasks. They are in considerable demand and much valued by financial organisations. What brought about this change?
Why are there more quants?
The last decade has seen a breathtaking speed of technological development. Analytic software combined with increased opportunities to gather data have led to Big Data: more information, collected more quickly. There are sophisticated tools to integrate, sort, and process this data into state-of-the-art models.
Electronic modelling now means that trading can be carried out by computers, based on an algorithm calculation of the most favourable moment to buy and sell. If algorithms are often the brawn behind hedge funds, investment banks, asset management services, and private equity firms, quants are the brains: they program the algorithms that make the system work.
Quants are also used more and more in the business of risk, helping to calculate probabilities and statistics using advanced modelling. This enables risk management teams to keep on the right side of an increasing volume of laws and procedures needed to manage risks appropriately.
There are now many more quants employed in this reformulated role, shifting from revenue generation to risk management. Banks require quants for a range of functions including the valuation aspects of derivatives and pricing.
A more diverse quant workforce
Another notable change in the last decade is the increasing diversity of the quant workforce. Although this STEM-related field used to be dominated by men from a similar background, the talent pool is now much broader and includes many women. Female quant talent is much in demand by banks seeking to improve the diversity of their teams.
As with other STEM areas, fewer women studying related subjects such as Math and Physics means this female talent is hard to find. When female quants are recruited to firms, employers have an additional incentive to keep them motivated and committed to the role, in an effort to retain this highly sought after talent.
The use of models and tools: a systematic or discretionary approach?
The development of algorithms, machine learning, and related tools has transformed the nature of quantitative analysis. Quants need to ensure that data is interpreted and presented in the best possible way, but there is very little inputting and processing done through human labour any more.
This has led to a divergence of opinion on the best way to approach investment decisions. As Leda Braga, a high-flying quant known as the ‘Queen of Quants’ has said, trading is now dominated by two approaches to decision-making: systematic and discretionary.
A discretionary approach to trading is based on the trader’s own thought processes and decision-making skills. Systematic trading uses technology to indicate the best investment strategies, using algorithms to process reams of data. Quants are essential to the systematic approach, which is gaining in popularity.
However, the discretionary approach is still very common, particularly because people tend to respond more vehemently to an error made by an algorithm than to an error made by a human. As Braga observes: “We scrutinise the algos with a lot less tolerance than we scrutinise human action.”
What does it take to be a good quant?
To be successful as a quant, strong analytical skills are a must. Most professionals have advanced computer programming abilities, typically using SQL for database management and perhaps an object-oriented language such as Python or R to clean, sort, and process data.
Quants usually have advanced degrees in a STEM subject, such as computer science, mathematics, or physics. A PhD in one of these subjects is common. There is also increasing popularity of financial engineering master’s degrees such as financial engineering or quantitative/mathematical finance.
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