Gene Expression Programming and Trading Strategies - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2013

Gene Expression Programming and Trading Strategies

Georgios Sermpinis
  • Fonction : Auteur
  • PersonId : 1000645
Anastasia Fountouli
  • Fonction : Auteur
  • PersonId : 1000706
Konstantinos Theofilatos
  • Fonction : Auteur
  • PersonId : 992359
Andreas Karathanasopoulos
  • Fonction : Auteur
  • PersonId : 1000707

Résumé

This paper applies a Gene Expression Programming (GEP) algorithm to the task of forecasting and trading the SPDR Down Jones Industrial Average (DIA), the SPDR S&P 500 (SPY) and the Powershares Qqq Trust Series 1 (QQQ) exchange traded funds (ETFs). The performance of the algorithm is benchmarked with a simple random walk model (RW), a Moving Average Convergence Divergence (MACD) model, a Genetic Programming (GP) algorithm, a Multi-Layer Perceptron (MLP), a Recurrent Neural Network (RNN) and a Gaussian Mixture Neural Network (GM). The forecasting performance of the models is evaluated in terms of statistical and trading efficiency. Three trading strategies are introduced to further improve the trading performance of the GEP algorithm. This paper finds that the GEP model outperforms all other models under consideration. The trading performance of GEP is further enhanced when the trading strategies are applied.
Fichier principal
Vignette du fichier
978-3-642-41142-7_50_Chapter.pdf (585.69 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01459683 , version 1 (07-02-2017)

Licence

Identifiants

Citer

Georgios Sermpinis, Anastasia Fountouli, Konstantinos Theofilatos, Andreas Karathanasopoulos. Gene Expression Programming and Trading Strategies. 9th Artificial Intelligence Applications and Innovations (AIAI), Sep 2013, Paphos, Greece. pp.497-505, ⟨10.1007/978-3-642-41142-7_50⟩. ⟨hal-01459683⟩
542 Consultations
490 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More