A Genetic Programming Approach for EUR/USD Exchange Rate Forecasting and Trading
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Vasilakis:2013:CE,
-
author = "Georgios A. Vasilakis and
Konstantinos A. Theofilatos and Efstratios F. Georgopoulos and
Andreas Karathanasopoulos and Spiros D. Likothanassis",
-
title = "A Genetic Programming Approach for EUR/USD Exchange
Rate Forecasting and Trading",
-
journal = "Computational Economics",
-
year = "2013",
-
volume = "42",
-
number = "4",
-
pages = "415--431",
-
keywords = "genetic algorithms, genetic programming, Evolutionary
algorithms, Tournament selection, Exchange forecasting,
EUR/USD exchange rates, Financial trading strategies",
-
ISSN = "0927-7099",
-
publisher = "Springer",
-
DOI = "doi:10.1007/s10614-012-9345-8",
-
URL = "http://results.ref.ac.uk/Submissions/Output/1762292",
-
size = "17 pages",
-
abstract = "The purpose of this article is to present a novel
genetic programming trading technique in the task of
forecasting the next day returns when trading the
EUR/USD exchange rate based on the exchange rates of
historical data. Aiming at testing its effectiveness,
we benchmark the forecasting performance of our genetic
programming implementation with three traditional
strategies (naive strategy, MACD, and a buy & hold
strategy) plus a hybrid evolutionary artificial neural
network approach. The proposed genetic programming
technique was found to demonstrate the highest trading
performance in terms of annualised return and
information ratio when compared to all other strategies
which have been used. When more elaborate trading
techniques, such as leverage, were combined with the
examined models, the genetic programming approach still
presented the highest trading performance. To the best
of our knowledge, this is the first time that genetic
programming is applied in the problem of effectively
modelling and trading with the EUR/USD exchange rate.
Our application now offers practitioners with an
effective and extremely promising set of results when
forecasting in the foreign exchange market. The
developed genetic programming environment is
implemented using the C++ programming language and
includes a variation of the genetic programming
algorithm with tournament selection.",
-
uk_research_excellence_2014 = "D - Journal article",
- }
Genetic Programming entries for
Georgios A Vasilakis
Konstantinos A Theofilatos
Efstratios F Georgopoulos
Andreas S Karathanasopoulos
Spiridon D Likothanassis
Citations