Quantitative Analyst: a dream career for young mathematicians

With the development of derivative markets in the 1960?s in the United States, the use of sciences and particularly mathematics has seen a considerable explosion in finance and this has given way to a new type of analyst with an expertise in theory of probability, a computer science knowledge and a good financial culture. These are known as quantitative analysts or quants.

In France, it was during the 1990?s that this industry became strongly developed. Several university qualifications in this area where born, to the point where today, many young mathematicians descend upon the doors of the most prestigious Master degrees with the admitted ambition of practicing a quantitative analyst career and of earning lots of money by doing mathematics.

But how does one become a quant? What types of quants are there? What are their daily tasks? What must one know and what are the qualities required to follow this career? It is these questions that Professor Mark Joshi answered through a document published on his site, entitled On Becoming A Quant [1].

For Mark Joshi, a quant is a professional in finance who develops and implements mathematic models which evaluate derivative prices, predicts the movements in the workforce and who safeguards against the inherent risks of these movements. Generally, several categories of quants exist according to their activities:
 Front office Quant creates models used directly by the traders. A strong capacity to migrate towards trading posts.
 Model validating Quant, as the name suggests, implements the models independent from front office and checks that the valuation used in trading is correct. They generally work in Risk Management.
 Research Quant, he considers the approaches to innovative pricing and implements new models for the front office.
 Quant Developer, he essentially does the programming, the script writing and code debugging.
 Statistical Arbitrage Quant, he researches the inefficiencies present in the market with the help of automatic trading. Generally used in Hedge funds.
 Capital Quant deals with the modeling of the credit banks? exposure

The various active areas where work exits for these quants include Equity, Foreign Exchange, Credit, Bonds, Commodities and Hybrids. As for the employers, they are often distributed among commercial banks, investment banks, hedge funds, service companies and software publishers; here one finds numerous quants working on diverse problems.

Prerequisites in mathematics, computing and in finance

If you think that becoming a quant is not really an uphill battle when you are passionate and motivated, it could not be more true that there are a good number of pre-requisites. It is best to know the basis and fundamentals of analysis and of theory probability: certain students make every effort to diversify their knowledge without really fathoming the questions that they approach during their readings. It is not rare to be questioned about probability or combinatorics enigmas.

During interviews, recruiters need to feel that the aspiring quant masters what few notions may be brought up and that he is globally interested in the industry in which he envisages working. It is therefore important to read the financial news from time to time. A knowledge of basic theorems in stochastic calculus is also a prerequisite : Ito’s lemma, Girsanov?s Theorem, and all the Black-Scholes operations (probability approach, EDP, greek calculus, etc) and the methods of Monte Carlo also form part of the important skills needed to by candidates.

Besides this, most of the quants spend a good part of their time programming. The language most often used is C ++. It is therefore a good idea to prepare oneself for questions on syntax, algorithmic or code interpretation during interviews, according to the sought post. The VBA is also very useful even if it does not often arise during interviews, as it can be learnt fairly quickly.

Beyond the technical aspect, for Mark Joshi, it is also important to show qualities of dynamism and enthusiasm, because personality is also taken into account for this job, which, according to the tasks, can be stressful.

Besides an optimal mathematical knowledge of varying range and of a high level, being offered a job as a quant requires programming knowledge and financial culture, a good dose of motivation, voluntarism and self-abnegation. One should never forget that the subject of an internship or assignment which will be conferred to a junior post are excellent indicators of the contents of the interview.

DEA and DESS in applied mathematics are the norm

In France, Master’s degrees (e.g. DEA - diplôme d’études approfondies or DESS - diplôme d’études superieures spécialisées) in applied mathematics with orientation towards probability and finance have become the standard, or that one has an engineering background or a university education exclusively. It is not rare to come across quants who hold a scientific thesis (Mathematics or physical sciences) even if this reality is mainly apparent in the Anglo-Saxon countries.

Financial institutions have a preference for engineers who hold a Masters in applied mathematics obtained over and above their initial diploma, as they generally have the advantage of having more professional experience - sometimes in a bank - thanks to internships throughout their education. Moreover, they also have the advantage of having studied finance (in its most general sense) within the framework of their course and have completed some computer science projects in diverse computer science languages (C, C++, Java). This generally makes them more operational than their counterparts coming exclusively from a university background.

If students with only a university education were also considered to undergo quant training, the effort required to get a job would be far greater than that of engineers. More personal effort would be demanded in the first stages of training as one would need to learn about the workings and concepts of modern finance (in its more economic sense) as well as the programming languages. Often university education has the reputation of being too theoretical and very specialized.

In spite of the plethora of training which develops every year, only a minority of Master’s degrees [2] in the Paris region (Paris 6, Paris 7, Dauphine, Marne-la-vallée, Evry) yield mosts of the quants in France. It is thus essential that, apart from the pedagogic content, one relies on the reputation of the training and its network, as these are most important factors in securing junior quantitative analyst posts. Because, let us not forget: potential employers generally prefer the candidates with qualifications that closely resemble their own. That is, those candidates who have been trained in institutions or by teachers familiar to them. It is a form of risk management, thus ensuring a good quality return.