In such crisis times as these, the recently released chapter “Finding relevant variables in a financial distress prediction problem using Genetic Programming and Self-Organizing Maps” from volume 2 of “Natural Computing in Computational Finance” can come in quite handy.
In it we use Genetic Programming to generate models of prediction of book loses in companies, and we tackle the problem of finding the relevant variables for the prediction using Genetic Programming and Kohonen’s Self Organising Maps. This approach singificantly reduces the number of variables used for the analysis, while minimising the prediction error.
In other words, we tell you what you have to look at if you want to avoid losing money in your company – but don’t hold us responsible if you don’t like the results!
The chapter can also be obtained from SpringerLink.
You should also check our financial prediction tool PRESAGI
