Modelling and trading the London, New York and Frankfurt stock exchanges with a new gene expression programming trader tool

• Author: Andreas Karathanasopoulos
• DOI: http://doi.org/10.1002/isaf.1401
• Date: 01 January 2017
• Published date: 01 January 2017

RESEARCH ARTICLE

Modelling and trading the London, New York and Frankfurt

stock exchanges with a new gene expression programming

trader tool

Andreas Karathanasopoulos

Olayan School of Business, American

University of Beirut, Beirut, Lebanon

Correspondence

Andreas Karathanasopoulos, Olayan School of

Business, American University of Beirut,

Beirut, Lebanon.

Email: andreas.kara@hotmail.com

Summary

The scope of this manuscript is to present a new short‐term financial forecasting and trading tool:

the Gene Expression Programming (GEP) Trader Tool. It is based on the gene expression pro-

gramming algorithm. This algorithm is based on a genetic programming approach, and provides

supreme statistical and trading performance when used for modelling and trading financial time

series. The GEP Trader Tool is offered through a user‐friendly standalone Java interface. This

paper applies the GEP Trader Tool to the task of forecasting and trading the future contracts

of FTSE100, DAX30 and S&P500 daily closing prices from 2000 to 2015. It is the first time that

gene expression programming has been used in such massive datasets. The model's performance

is benchmarked against linear and nonlinear models such as random walk model, a moving‐

average convergence divergence model, an autoregressive moving average model, a genetic pro-

gramming algorithm, a multilayer perceptron neural network, a recurrent neural network a higher

order neural network. To gauge the accuracy of all models, both statistical and trading perfor-

mances are measured. Experimental results indicate that the proposed approach outperforms

all the others in the in‐sample and out‐of‐sample periods by producing superior empirical results.

Furthermore, the trading performances are improved further when trading strategies are imposed

on each of the models.

KEYWORDS

gene expression,genetic algorithm, trading, transaction

1|INTRODUCTION

Many technical trading algorithms have been introduced in the last

decade, with artificial neural networks being the dominant machine‐

learning technique for financial forecasting applications (Zhang,

2012). However, these techniques present certain drawbacks, such

as overfitting issues, data snooping bias, and a curse of dimensionality,

as have been mentioned by Karathanasopoulos, Sermpinis, Laws, and

Dunis (2014). Despite the vast number of different financial forecast-

ing algorithms which are continuously being published, only a few of

them have been combined to construct efficient trading strategies,

and even fewer have provided user‐friendly graphical user interfaces.

Related to the new tool presented in this research paper it is important

to show some description characteristics of the main partof the model.

Algorithms based on gene expression programming (GEP) are domain‐

independent problem‐solving techniques that run in various

environments. GEP can be categorized in the forecasting bracket

known in the finance world as ‘evolutionary algorithms’. These envi-

ronments have been structured in a manner which approximates prob-

lems in order to produce forecasts with a high degree of accuracy. The

basis for this type of problem‐solving technique derives from the

Darwinian principle of reproduction and survival of the fittest. Addi-

tionally, they can be considered similar to the biological genetic opera-

tions of crossover recombination and mutation. These algorithms have

been successfully applied to many real‐world problems. Some recent

research (Yaghouby, Ayatollahi, Bahramali, Yaghouby, & Alavi, 2010;

Alavi & Gandomi, 2011; Divsalar, Firouzabadi, Sadeghi, Behrooz, &

Alavi, 2011; Divsalar, Roodsaz, Vahdatinia, Norouzzadeh, & Behrooz,

2012; Gandomi & Alavi, 2011; Gandomi, Alavi, Mirzahosseini, &

Moghadas, 2011; Gandomi, Yang, Talatahari, & Alavi, 2013; Yang,

Gandomi, Talatahari, & Alavi, 2012; Zargari, Zabihi, Alavi, & Gandomi,

2012) proves that GEP outperforms all the genetic algorithms due to

Received: 7 February 2016 Revised: 5 August 2016 Accepted: 6 October 2016

DOI 10.1002/isaf.1401

Intell Sys Acc Fin Mgmt. 2017;24:3–11. Copyright © 2016 John Wiley & Sons, Ltd.wileyonlinelibrary.com/journal/isaf 3