Today, I present a new
As it often happens with scientific papers written in Eastern Europe or CIS countries, the translation quality in this one is also very low. Despite this fact, the article is not too hard to read as it contains very few formulas and it relies on a small number of math concepts.
The authors use a recurrent neural network composed of 2 input neurons and 1 output neuron with 100 hidden neurons inbetween. Two data sets are used for inputs — raw price rate of change and a moving average with a period set to 5. This input combination proved to be the most effective of several variants they have tried. For the sake of simplicity and prediction usability, moving average one bar ahead is used as the output value. The resulting NN is trained over 1,200 daily bars of EUR/USD, GBP/USD, USD/JPY and USD/CHF. It is then tested on the set of 103 daily bars. The results show a rather significant inclination of the designed neural network to predict both the sign and the size of future currency rate change.
Besides of that, the forecast of weekly data presumes, that trader, who uses this forecast will trade once a week, which is irrelevant from the practical point of view.
Why is it irrelevant? Unfortunately, there is no further explanations given by the authors. I would also like to point out a rather low number of bars used for testing (production set). Increasing it to something comparable to the training set size would be a justified step. The choice of the immediate future moving average value as the NN’s output value looks also suboptimal to me. Actual rate values 5, 10 or 20 bars ahead of the forecast point would be more interesting for practical considerations.
If you have any questions, comments or suggestions regarding this