  We utilize a recently developed genetic algorithm, in conjunction with
discrete wavelets, for carrying out successful forecasts of the trend in
financial time series, that includes the NASDAQ composite index. Discrete
wavelets isolate the local, small scale variations in these non-stationary time
series, after which the genetic algorithm's predictions are found to be quite
accurate. The power law behavior in Fourier domain reveals an underlying
self-affine dynamical behavior, well captured by the algorithm, in the form of
an analytic equation. Remarkably, the same equation captures the trend of the
Bombay stock exchange composite index quite well.
