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Taiwanese software spots stock-market stinkers

Evolutionary algorithm combined with three other prediction methods can spot which companies are headed for trouble two years in advance

3 min read

30 April 2008--Taiwanese computer scientists have developed a genetic algorithm--one that evolves to improve its performance--that can predict the impending demise or distress of publicly traded companies. Its creator say it outdoes commonly used financial algorithms at picking the probability of troubles from bankruptcy, check bouncing, takeovers, bank runs, negative net book value, and other financial woes. In tests on publicly traded Taiwanese companies, it spotted flailing firms with 90 percent accuracy two years before they flamed out.

Carlos A. Coello Coello, an expert on such algorithms with the Research and Advanced Studies Centre of the National Polytechnic Institute, in Mexico City, who was not involved in the research, says that for prediction accuracy, 90 percent is ”to say the least, remarkable.”

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Why Functional Programming Should Be the Future of Software Development

It’s hard to learn, but your code will produce fewer nasty surprises

11 min read
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A plate of spaghetti made from code
Shira Inbar
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You’d expectthe longest and most costly phase in the lifecycle of a software product to be the initial development of the system, when all those great features are first imagined and then created. In fact, the hardest part comes later, during the maintenance phase. That’s when programmers pay the price for the shortcuts they took during development.

So why did they take shortcuts? Maybe they didn’t realize that they were cutting any corners. Only when their code was deployed and exercised by a lot of users did its hidden flaws come to light. And maybe the developers were rushed. Time-to-market pressures would almost guarantee that their software will contain more bugs than it would otherwise.

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