Investigation of exchange market prediction model based on high-low daily data
Santrauka
The model of Evolino recurrent neural networks (RNN) based on ensemble for prediction of
daily extremes of financial market is investigated. The prediction distributions of each high and lows of
daily values of exchange rates were obtained. Obtained distributions show an accuracy of predictions, reflects
true features of direct time interval unpredictability of chaotic process. Changing of time series data
from close to extremes allows to create new strategy of investment built on distributions basic parameters:
standard deviation, skewness, kurtosis. Extension of close distribution to the pair of high-low distribution
is opening extra capabilities of optimal portfolio creation and risk management for investors.