Journal of Computational Finance

Risk.net

Ensemble models in forecasting financial markets

Andreas Karathanasopoulos, Mitra Sovan, Chia Chun Lo, Adam Zaremba and Mohammed Osman

  • The motivation of this research paper is the optimization of neural networks with three different evolutionary algorithms such as genetic algorithms, differential evolution algorithms and particle swarm optimizers.
  • We use this ensemble methodology in terms of forecasting two exchange traded funds for a period of 12 years.  
  • The forecasted accuracy has been evaluated by using statistical and empirical measures.
  • The results clearly prove that differential optimizer improves the forecasting performance comparing to the other ensemble models.

In this paper, we study an evolutionary framework for the optimization of various types of neural network structures and parameters. Three different evolutionary algorithms – the genetic algorithm (GA), differential evolution (DE) and the particle swarm optimizer (PSO) – are developed to optimize the structure and the parameters of three different types of neural network: multilayer perceptrons (MLPs), recurrent neural networks (RNNs) and radial basis function (RBF) neural networks. The motivation of this project is to present novel methodologies for the task of forecasting and trading financial indexes. More specifically, the trading and statistical performance of all models is investigated in a forecast simulation of the SPY and the QQQ exchange-traded funds (ETFs) time series over the period January 2006 to December 2015, using the last three years as out-of-sample testing. As it turns out, the RBF-PSO, RBF-DE and RBF-GA ensemble methodologies do remarkably well and outperform all of the other models.

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