The availability of high frequency data sets in finance has allowed the use of very data intensive techniques using large data sets in forecasting. An algorithm requiring fast k-NN type search has been implemented using AURA, a binary neural network based upon Correlation Matrix Memories. This work has also constructed probability distribution forecasts, the volume of data allowing this to be done in a nonparametric manner. In assistance to standard statistical error measures the implementation of simulations has allowed actual measures of profit to be calculated.
Bibliographical noteCopyright © 2004 Elsevier.
- financial forecasting
- neural networks
- associative memories
- probability distribution forecasting
- high frequency time series
- SHARPE RATIO