Computational techniques for fluid dynamics 2
Computational techniques for fluid dynamics 2
Computational science and engineering
ACM Computing Surveys (CSUR) - Special ACM 50th-anniversary issue: strategic directions in computing research
Ant colony optimization to price exotic options
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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People who trade in the world's financial markets must take chances. Willing as they may be to do this, the purpose of trading is profit, not the excitement of risk. Therefore, as much as possible market players want to predict what will happen, buy low and sell high, and hedge their bets to prevent losses. In the messy, nondeterministic, stochastic world of finance and human behavior this is not easy, but a large body of mathematical and computational technique has been brought to bear on the problems with considerable success. (Elsewhere in this issue a related story discusses the job market for this kind of "financial engineering.") Computational scientists may feel slightly adrift in the unfamiliar sea of jargon and concepts that are common conversation to financial traders: call and put options, futures, strike prices, hedging, arbitrage, and so forth. Beneath these specifics, however, the generality of more familiar mathematics (and even some physics) shines through brightly. We find partial differential equations, diffusion processes, stochastic time series, and the gamut of computational methods used to solve the mathematical problems--including, very promisingly, neural networks. References and a reading list serve as a guide to further information.