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Sensing financial problems

Just as we can think of large-scale, detailed financial modeling as an exercise in simulation and linked data, we can perhaps also see the detection of hazards as an exercise in sensor fusion and pervasive computing, turning a multitude of sensor data into derived values for risk and/or situations requiring attention. There’s a wealth of research in these areas that might be applicable.

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What computer science can learn from finance

Science should be a two-way street. In the same way that we might view the financial system as a computational model that can be simulated, there are undoubtedly things that computing can learn from finance. In particular, a lot of financial instruments and techniques evolved because of uncertainty and risk: issues that are absolutely a part of computer science in general and of sensorised systems in particular. The financial crunch hasn’t invalidated theĀ  significance that these techniques may have for computer science and systems architecture.

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Computer science and the financial crisis

Many people expected a financial crisis; many also expected it to be caused by automated trading strategies driving share prices, As it turned out, that aspect of computing in finance didn’t behave as badly as expected, and the problems arose in the relatively computing-free mortgage sector. Is there any way computing could have helped, or could help avoid future crises? (more…)