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Concept
Computational finance
Summary
Computational finance is a branch of applied computer science that deals with problems of practical interest in finance. Some slightly different definitions are the study of data and algorithms currently used in finance and the mathematics of computer programs that realize financial models or systems.
Computational finance emphasizes practical numerical methods rather than mathematical proofs and focuses on techniques that apply directly to economic analyses. It is an interdisciplinary field between mathematical finance and numerical methods. Two major areas are efficient and accurate computation of fair values of financial securities and the modeling of stochastic time series.
The birth of computational finance as a discipline can be traced to Harry Markowitz in the early 1950s. Markowitz conceived of the portfolio selection problem as an exercise in mean-variance optimization. This required more computer power than was available at the time, so he worked on useful algorithms for approximate solutions. Mathematical finance began with the same insight, but diverged by making simplifying assumptions to express relations in simple closed forms that did not require sophisticated computer science to evaluate.
In the 1960s, hedge fund managers such as Ed Thorp and Michael Goodkin (working with Harry Markowitz, Paul Samuelson and Robert C. Merton) pioneered the use of computers in arbitrage trading. In academics, sophisticated computer processing was needed by researchers such as Eugene Fama in order to analyze large amounts of financial data in support of the efficient-market hypothesis.
During the 1970s, the main focus of computational finance shifted to options pricing and analyzing mortgage securitizations. In the late 1970s and early 1980s, a group of young quantitative practitioners who became known as "rocket scientists" arrived on Wall Street and brought along personal computers. This led to an explosion of both the amount and variety of computational finance applications.
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