Ensemble models in forecasting financial markets

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

Abstract

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.
Author Andreas Karathanasopoulos - Dubai Business School, University of Dubai
Andreas Karathanasopoulos,,
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, Mitra Sovan
Mitra Sovan,,
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, Chia Chun Lo
Chia Chun Lo,,
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, Adam Zaremba (WZ / KIiRK)
Adam Zaremba,,
- Department of Investment and Capital Markets
, Mohammed Osman - Dubai Business School, University of Dubai
Mohammed Osman,,
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Journal seriesJournal of Computational Finance, ISSN 1460-1559, e-ISSN 1755-2850, (N/A 70 pkt)
Issue year2019
Vol23
No3
Pages101-119
Publication size in sheets0.9
Keywords in Polishprognozowanie rynków finansowych, sieci neuronowe
Keywords in Englishforecasting, multilayer perception (MLP), recurrent neural network (RNN), radial basis function (RBF), optimizers
ASJC Classification2003 Finance; 1706 Computer Science Applications; 2604 Applied Mathematics
DOIDOI:10.21314/JCF.2019.374
URL https://www.risk.net/journal-of-computational-finance/6485736/ensemble-models-in-forecasting-financial-markets
Languageen angielski
Score (nominal)70
Score sourcejournalList
ScoreMinisterial score = 70.0, 08-04-2020, ArticleFromJournal
Publication indicators WoS Citations = 0; Scopus SNIP (Source Normalised Impact per Paper): 2018 = 0.539; WoS Impact Factor: 2017 = 0.758 (2) - 2017=0.831 (5)
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