Please use this identifier to cite or link to this item:
|Title:||A stacked generalization system for automated FOREX portfolio trading||Authors:||Petropoulos, Anastasios
Chatzis, Sotirios P.
|Keywords:||Forex forecasting;Algorithmic trading;Portfolio management;Machine learning;Stacked generalization||Category:||Computer and Information Sciences||Field:||Natural Sciences||Issue Date:||30-Dec-2017||Source:||EXPERT SYSTEMS WITH APPLICATIONS, Volume: 90, Pages: 290-302||metadata.dc.doi:||http://dx.doi.org/10.1016/j.eswa.2017.08.011||Journal:||Expert Systems with Applications||Abstract:||Multiple FOREX time series forecasting is a hot research topic in the literature of portfolio trading. To this end, a large variety of machine learning algorithms have been examined. However, it is now widely understood that, in real-world trading settings, no single machine learning model can consistently outperform the alternatives. In this work, we examine the efficacy and the feasibility of developing a stacked generalization system, intelligently combining the predictions of diverse machine learning models. Our approach establishes a novel inferential framework that comprises the following levels of data processing: (i) We model the dependence patterns between major currency pairs via a diverse set of commonly used machine learning algorithms, namely support vector machines (SVMs), random forests (RFs), Bayesian autoregressive trees (BART), dense-layer neural networks (NNs), and naive Bayes (NB) classifiers. (ii) We generate implied signals of exchange rate fluctuation, based on the output of these models, as well as appropriate side information obtained by analyzing the correlations across currency pairs in our training datasets. (iii) We finally combine these implied signals into an aggregate predictive waveforth, by leveraging majority voting, genetic algorithm optimization, and regression weighting techniques. We thoroughly test our framework in real-world trading scenarios; we show that our system leads to significantly better trading performance than the considered benchmarks. Thus, it represents an attractive solution for financial firms and corporations that perform foreign exchange portfolio management and daily trading. Our system can be used as an integrated part in international commercial trade activities or in a quantitative investing framework for algorithmic trading and carry-trade speculation.||URI:||http://ktisis.cut.ac.cy/handle/10488/10493||ISSN:||0957-4174||Rights:||(C) 2017 Elsevier Ltd. All rights reserved.||Type:||Article|
|Appears in Collections:||Άρθρα/Articles|
Show full item record
checked on Nov 24, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.