model for FOREX market) algorithm. It is inspired by the behaviour of macromolecules in dissolution and enables accurately re-
produces the unstable nature of the FOREX market by allowing the simulation to go beyond the equilibrium. Moreover, ENMX is
capable of simulating up to 21 currency pairs at once, all of them connected, as really happens in the FOREX market arena. For the
diﬀerent probability distributions analysed, the Pseudo-Voigt best represented the variations in quotation prices. The experimental
results showed that the ENMX algorithm predicted values on the FOREX market more accurately than traditional econometric
approaches such as VAR and the driftless random walk.
The ENMX model is still in a relatively early stage of development, and we acknowledge that a relatively simple variant of the
algorithm was tested. But, with many other types of improvements still to be explored, this ﬁeld seems to oﬀer a promising and
potentially fruitful area of research. On the algorithm side, the Pseudo-Voigt function provided very good results so it might also be
interesting to evaluate other distribution function. On the hardware side, a computational bottleneck is to be expected whenever real-
time response is required. The use of Graphics Processing Units (GPUs) may oﬀer a good environment to enhance our simulations to
improve performance with unprecedented gains possible where parallelism is called to play a decisive role. Furthermore, the EMH
could be tested by assuming an asset/pricing model based on theories of exchange rate determination such as portfolio balance model
or uncovered interest parity model. Moreover, both another metrics as median absolute deviation (MAD) and econometric models as
Markov Switching and GARCH models may provide a more robust analysis on the forecasting accuracy of the ENMX model. Finally,
we have focused only on returns as most commercial rankings do. However, we see very interesting to implement risk measures to the
model in future works. Under this framework, the analysis of the risk-adjusted model proposed will be carried out with the purpose of
exploring strategies that lead to abnormal returns in a real world setting.
This work is supported by the Spanish MINECO under grants TIN2016-78799-P, TIN2016-80565-R and CTQ2017-87974-R (AEI/
FEDER, UE), and the Industrial Ph.D. program of the International Doctorate School at the Catholic University of San Antonio of
Murcia (UCAM). This research was partially supported by the supercomputing infrastructure of Poznan Supercomputing Center, by
the e-infrastructure program of the Research Council of Norway, and the supercomputer center of UiT - the Arctic University of
Norway. The authors also thankfully acknowledge the computer resources and the technical support provided by the Plataforma
Andaluza de Bioinformática of the University of Málaga. Powered@NLHPC: This research was partially supported by the super-
computing infrastructure of the NLHPC (ECM-02). Finally, we want to thank the anonymous reviewers for their valuable comments.
 A.K. Nassirtoussi, S. Aghabozorgi, T.Y. Wah, D.C.L. Ngo, Text mining of news-headlines for FOREX market prediction: a multi-layer dimension reduction
algorithm with semantics and sentiment, Expert Syst. Appl. 42 (2015) 306–324.
 E.F. Fama, Eﬃcient capital markets: a review of theory and empirical work, J. Finance 25 (1970) 383–417.
 E.F. Fama, The behavior of stock-market prices, J. Bus. 38 (1965) 34–105.
 P.A. Samuelson, et al., Proof that Properly Anticipated Prices Fluctuate Randomly, (1965).
 P. Makovskỳ, Modern approaches to eﬃcient market hypothesis of forex–the central european case, Procedia Econ. Finance 14 (2014) 397–406.
 K. Cuthbertson, D. Nitzsche, Quantitative Financial Economics: Stocks, Bonds and Foreign Exchange, John Wiley & Sons, 2005.
 A.G. Ţiţan, The eﬃcient market hypothesis: review of specialized literature and empirical research, Procedia Econ. Finance 32 (2015) 442–449.
 A. Degutis, L. Novickyte, The eﬃcient market hypothesis: a critical review of literature and methodology, Ekonomika 93 (2014) 7.
 A.J.S. García, Simulación del Mercado Forex: Aplicación de Modelado Macromolecular, (2015) Dissertation.
 J.F. García, Desarrollo de métodos de prediccin para el mercado Forex basados en la aplicación de tcnicas de Montecarlo, (2015) Dissertation.
 M. Karplus, J.A. McCammon, Molecular dynamics simulations of biomolecules, Nat. Struct. Mol. Biol. 9 (2002) 646–652.
 E. Fuglebakk, N. Reuter, K. Hinsen, Evaluation of protein elastic network models based on an analysis of collective motions, J. Chem. Theory Comput. 9 (2013)
 G.M. Caporale, L. Gil-Alana, A. Plastun, Searching for ineﬃciencies in exchange rate dynamics, Comput. Econ. 49 (2017) 405–432.
 R.A. Meese, K. Rogoﬀ, Empirical exchange rate models of the seventies: do they ﬁt out of sample? J. Int. Econ. 14 (1983) 3–24.
 W.T. Woo, The monetary approach to exchange rate determination under rational expectations: the dollar-deutschmark rate, J. Int. Econ. 18 (1985) 1–16.
 G.J. Schinasi, P.A.V.B. Swamy, The out-of-sample forecasting performance of exchange rate models when coeﬃcients are allowed to change, J. Int. Money
Finance 8 (1989) 375–390.
 N. Sarantis, C. Stewart, Structural, var and bvar models of exchange rate determination: a comparison of their forecasting performance, J Forecast 14 (1995)
 M. Manzur, Exchange rate economics is always and everywhere controversial, Appl. Econ. 50 (2018) 216–232.
 C.A. Sims, Macroeconomics and reality, Econometrica (1980) 1–48.
 T.R. Liu, M.E. Gerlow, S.H. Irwin, The performance of alternative var models in forecasting exchange rates, Int. J. Forecast. 10 (1994) 419–433.
 C. Redl, Noisy news and exchange rates: a svar approach, J. Int. Money Finance 58 (2015) 150–171.
 N.L. Joseph, Cointegration, error-correction models, and forecasting using realigned foreign exchange rates, J. Forecast. 14 (1995) 499–522.
 A. Trapletti, A. Geyer, F. Leisch, Forecasting exchange rates using cointegration models and intra-day data, J. Forecast. 21 (2002) 151–166.
 N. Meade, A comparison of the accuracy of short term foreign exchange forecasting methods, Int. J. Forecast. 18 (2002) 67–83.
 C. Engel, J.D. Hamilton, Long swings in the dollar: are they in the data and do markets know it? Am. Econ. Rev. (1990) 689–713.
 N.P. Bollen, S.F. Gray, R.E. Whaley, Regime switching in foreign exchange rates:: evidence from currency option prices, J. Econom. 94 (2000) 239–276.
 J. Yao, C.L. Tan, A case study on using neural networks to perform technical forecasting of forex, Neurocomputing 34 (2000) 79–98.
 J. Kamruzzaman, R.A. Sarker, Forecasting of currency exchange rates using ann: a case study, Neural Networks and Signal Processing, 2003. Proceedings of the
2003 International Conference on, 1 IEEE, 2003, pp. 793–797.
 J. Kamruzzaman, R.A. Sarker, I. Ahmad, Svm based models for predicting foreign currency exchange rates, Data Mining, 2003, ICDM 2003. Third IEEE
International Conference on, IEEE, 2003, pp. 557–560.
 R.J. Kuo, C. Chen, Y. Hwang, An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and
artiﬁcial neural network, Fuzzy Sets Syst. 118 (2001) 21–45.
 C. Neely, P. Weller, R. Dittmar, Is technical analysis in the foreign exchange market proﬁtable? a genetic programming approach, J. Financ. Quant. Anal. 32
A.V. Contreras et al. Simulation Modelling Practice and Theory 86 (2018) 1–10