Computational finance

Generally, individuals who fill positions in computational finance are known as “quants”, referring to the quantitative skills necessary to perform the job. Specifically, knowledge of the C++ programming language, as well as of the mathematical subfields of stochastic calculus, multivariate calculus, linear algebra, differential equations, probability theory and statistical inference are often entry level requisites for such a position. C++ has become the dominant language for two main reasons: the computationally intensive nature of many algorithms, and the focus on libraries rather than applications. Computational finance was traditionally populated by Ph.Ds in finance, physics and mathematics who moved into the field from more pure, academic backgrounds (either directly from graduate school, or after teaching or research). However, as the actual use of computers has become essential to rapidly carrying out computational finance decisions, a background in computer programming has become useful, and hence many computer programmers enter the field either from Ph.D. programs or from other fields of software engineering. Specially with the advent of more complex computational machines, a knowledge of computer software and hardware has become a necessity. In recent years, advanced computational methods, such as neural network and evolutionary computation have opened new doors in computational finance. Practitioners of computational finance have come from the fields of signal processing and computational fluid dynamics and artificial intelligence. Masters level degree holders are also increasingly making their presence felt as more terminal programs become available at the leading schools; see Master of Computational Finance. Today, all full service institutional finance firms employ computational finance professionals in their banking and finance operations (as opposed to being ancillary information technology specialists), while there are many other boutique firms ranging from 20 or fewer employees to several thousand that specialize in quantitative trading alone. JPMorgan Chase & Co. was one of the first firms to create a large derivatives business and employ computational finance (including through the formation of RiskMetrics), while Renaissance Technologies, founded in 1982, is probably the oldest and most notable quant fund (along with D.E. Shaw & Co.).