Gene expression programming and trading strategies

Sermpinis, G. , Fountouli, A., Theofilatos, K. and Karathanasopoulos, A. (2013) Gene expression programming and trading strategies. Artificial Intelligence Applications and Innovations, 412, pp. 497-505. (doi: 10.1007/978-3-642-41142-7_50)

Full text not currently available from Enlighten.


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.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Sermpinis, Professor Georgios
Authors: Sermpinis, G., Fountouli, A., Theofilatos, K., and Karathanasopoulos, A.
College/School:College of Social Sciences > Adam Smith Business School > Economics
Journal Name:Artificial Intelligence Applications and Innovations

University Staff: Request a correction | Enlighten Editors: Update this record