Many researchers are interesting in applying the neural networks methods to financial data. In fact these data are very complex, and classical methods do not always give satisfactory results. They need strong hypotheses which can be false, they have a strongly non-linear structures, and so on. But neural models must also be cautiously used. The black box aspect can be very dangerous. In this very simple paper, we try to indicate some specificity of financial data, to prevent some bad use of neural models.