High-low Strategy of Portfolio Composition using Evolino RNN Ensembles

Jelena Stankeviciene, Nijole Maknickiene, Algirdas Maknickas

Abstract


The originally configured 176 Evolino recurrent neural networks (RNN) connected to one ensemble and trained in parallel is an artificial intelligence solution, which allows the successful application of this tool for forecasting financial markets. Predictions made by this tool twice a day with different historical data give two distributions of expected values, which reflect future dynamic exchange rates. Constructing the portfolio, according to shape, parameters of distribution and the current value of the exchange rate allows the optimization of trading in daily exchange-rate fluctuations. Comparison of a high-low portfolio with a close-to-close portfolio shows the efficiency of the new forecasting tool and new proposed trading strategy.

DOI: http://dx.doi.org/10.5755/j01.ee.28.2.15852


Keywords


finance markets, Evolino, high-low strategy, investment portfolio, prediction

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Print ISSN: 1392-2785
Online ISSN: 2029-5839