Robust technical trading strategies using GP for algorithmic portfolio selection

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Berutich:2016:ESA,
  author =       "Jose Manuel Berutich and Francisco Lopez and 
                 Francisco Luna and David Quintana",
  title =        "Robust technical trading strategies using {GP} for
                 algorithmic portfolio selection",
  journal =      "Expert Systems with Applications",
  volume =       "46",
  pages =        "307--315",
  year =         "2016",
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/j.eswa.2015.10.040",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0957417415007447",
  abstract =     "This paper presents a Robust Genetic Programming
                 approach for discovering profitable trading rules which
                 are used to manage a portfolio of stocks from the
                 Spanish market. The investigated method is used to
                 determine potential buy and sell conditions for stocks,
                 aiming to yield robust solutions able to withstand
                 extreme market conditions, while producing high returns
                 at a minimal risk. One of the biggest challenges GP
                 evolved solutions face is over-fitting. GP trading
                 rules need to have similar performance when tested with
                 new data in order to be deployed in a real situation.
                 We explore a random sampling method (RSFGP) which
                 instead of calculating the fitness over the whole
                 dataset, calculates it on randomly selected segments.
                 This method shows improved robustness and out-of-sample
                 results compared to standard genetic programming (SGP)
                 and a volatility adjusted fitness (VAFGP). Trading
                 strategies (TS) are evolved using financial metrics
                 like the volatility, CAPM alpha and beta, and the
                 Sharpe ratio alongside other Technical Indicators (TI)
                 to find the best investment strategy. These strategies
                 are evaluated using 21 of the most liquid stocks of the
                 Spanish market. The achieved results clearly outperform
                 Buy and Hold, SGP and VAFGP. Additionally, the
                 solutions obtained with the training data during the
                 experiments clearly show during testing robustness to
                 step market declines as seen during the European
                 sovereign debt crisis experienced recently in Spain. In
                 this paper the solutions learned were able to operate
                 for prolonged periods, which demonstrated the validity
                 and robustness of the rules learned, which are able to
                 operate continuously and with minimal human
                 intervention. To sum up, the developed method is able
                 to evolve TSs suitable for all market conditions with
                 promising results, which suggests great potential in
                 the method generalization capabilities. The use of
                 financial metrics alongside popular TI enables the
                 system to increase the stock return while proving
                 resilient through time. The RSFGP system is able to
                 cope with different types of markets achieving a
                 portfolio return of 31.81percent for the testing period
                 2009-2013 in the Spanish market, having the IBEX35
                 index returned 2.67percent.",
  keywords =     "genetic algorithms, genetic programming, Algorithmic
                 trading, Portfolio management, Trading rule, Finance",
}

Genetic Programming entries for Jose Manuel Berutich Francisco Lopez Francisco Luna David Quintana

Citations