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Enhancing the context-aware FOREX market simulation using a parallel elastic network model

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Abstract

Foreign exchange (FOREX) market is a decentralized global marketplace in which different participants, such as international banks, companies or investors, can buy, sell, exchange and speculate on currencies. This market is considered to be the largest financial market in the world in terms of trading volume. Indeed, the just-in-time price prediction for a currency pair exchange rate (e.g., EUR/USD) provides valuable information for companies and investors as they can take different actions to improve their business. The trading volume in the FOREX market is huge, disperses, in continuous operations (24 h except weekends), and the context significantly affects the exchange rates. This paper introduces a context-aware algorithm to model the behavior of the FOREX Market, called parallel elastic network model (PENM). This algorithm is inspired by natural procedures like the behavior of macromolecules in dissolution. The main results of this work include the possibility to represent the market evolution of up to 21 currency pair, being all connected, thus emulating the real-world FOREX market behavior. Moreover, because the computational needs required are highly costly as the number of currency pairs increases, a hybrid parallelization using several shared memory and message passing algorithms studied on distributed cluster is evaluated to achieve a high-throughput algorithm that answers the real-time constraints of the FOREX market. The PENM is also compared with a vector autoregressive (VAR) model using both a classical statistical measure and a profitability measure. Specifically, the results indicate that PENM outperforms VAR models in terms of quality, achieving up to \(930\times\) speed-up factor compared to traditional R codes using in this field.

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References

  1. Bahrepour M, Akbarzadeh-T MR, Yaghoobi M, Naghibi-S MB (2011) An adaptive ordered fuzzy time series with application to FOREX. Expert Syst Appl 38(1):475–485

    Article  Google Scholar 

  2. Bank for International Settlements. https://www.bis.org/. Accessed 13 Feb 2013

  3. Bhattacharyya S, Pictet OV, Zumbach G (2002) Knowledge-intensive genetic discovery in foreign exchange markets. IEEE Trans Evolut Comput 6(2):169–181

    Article  Google Scholar 

  4. Bank of International Settlements (2016) Triennial central bank survey: foreign exchange turnover in April 2016, Basel

  5. Caporale GM, Gil-Alana L, Plastun A (2017) Searching for inefficiencies in exchange rate dynamics. Comput Econ 49(3):405–432

    Article  Google Scholar 

  6. De Grauwe P, Markiewicz A (2013) Learning to forecast the exchange rate: two competing approaches. J Int Money Finance 32:42–76

    Article  Google Scholar 

  7. Fama E (1970) Efficient capital markets: a review of theory and empirical work. J Finance 25(2):383–417

    Article  Google Scholar 

  8. Fama EF (1965) The behavior of stock-market prices. J Bus 38(1):34–105

    Article  Google Scholar 

  9. Fama EF (1970) Efficient capital markets: a review of theory and empirical work. J Finance 25(2):383–417

    Article  Google Scholar 

  10. Fuglebakk E, Reuter N, Hinsen K (2013) Evaluation of protein elastic network models based on an analysis of collective motions. J Chem Theory Comput 9(12):5618–5628

    Article  Google Scholar 

  11. Hanssens DM, Parsons LJ, Schultz RL (2003) Market response models: econometric and time series analysis, vol 12. Springer, New York

    Google Scholar 

  12. Kamruzzaman J, Sarker RA (2003) Forecasting of currency exchange rates using ANN: a case study. In: Proceedings of the 2003 International Conference on Neural Networks and Signal Processing, 2003, vol 1. IEEE, pp 793–797

  13. Kamruzzaman J, Sarker RA, Ahmad I (2003) SVM based models for predicting foreign currency exchange rates. In: Third IEEE International Conference on Data Mining, 2003. ICDM 2003, IEEE, pp. 557–560

  14. Karplus M, McCammon JA (2002) Molecular dynamics simulations of biomolecules. Nat Struct Mol Biol 9(9):646–652

    Article  Google Scholar 

  15. Kleen A (2015) Intel PMU profiling tools. https://github.com/andikleen/pmu-tools/tree/d70840ba. Accessed 15 Mar 2019

  16. Kuo RJ, Chen C, Hwang Y (2001) An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network. Fuzzy Sets Syst 118(1):21–45

    Article  MathSciNet  Google Scholar 

  17. LeBaron B, Arthur WB, Palmer R (1999) Time series properties of an artificial stock market. J Econ Dyn Control 23(9):1487–1516

    Article  Google Scholar 

  18. Li Q, Chen Y, Wang J, Chen Y, Chen H (2017) Web media and stock markets: a survey and future directions from a big data perspective. IEEE Trans Knowl Data Eng 30:381–399

    Article  Google Scholar 

  19. Luetkepohl H (2009) Econometric analysis with vector autoregressive models. In: Belsley DA, Kontoghiorghes EJ (eds) Handbook of computational econometrics. Wiley, New York, pp 281–319

    Chapter  Google Scholar 

  20. Makovskỳ P (2014) Modern approaches to efficient market hypothesis of FOREX—the central European case. Proc Econ Finance 14:397–406

    Article  Google Scholar 

  21. Meade N (2002) A comparison of the accuracy of short term foreign exchange forecasting methods. Int J Forecast 18(1):67–83

    Article  Google Scholar 

  22. Meese RA, Rogoff K (1983) Empirical exchange rate models of the seventies: do they fit out of sample? J Int Econ 14(1–2):3–24

    Article  Google Scholar 

  23. Mockus J, Raudys A (2010) On the efficient-market hypothesis and stock exchange game model. Expert Syst Appl 37(8):5673–5681

    Article  Google Scholar 

  24. Nassirtoussi AK, Aghabozorgi S, Wah TY, Ngo DCL (2014) Text mining for market prediction: a systematic review. Expert Syst Appl 41(16):7653–7670

    Article  Google Scholar 

  25. Neely C, Weller P, Dittmar R (1997) Is technical analysis in the foreign exchange market profitable? A genetic programming approach. J Financial Quant Anal 32(4):405–426

    Article  Google Scholar 

  26. Pincak R (2013) The string prediction models as invariants of time series in the FOREX market. Phys A: Stat Mech Appl 392(24):6414–6426

    Article  Google Scholar 

  27. Samuelson PA (2016) Proof that properly anticipated prices fluctuate randomly. In: The World Scientific Handbook of Futures Markets, pp 25–38

  28. Sarantis N, Stewart C (1995) Structural, VAR and BVAR models of exchange rate determination: a comparison of their forecasting performance. J Forecast 14(3):201–215

    Article  Google Scholar 

  29. Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117

    Article  Google Scholar 

  30. Sims CA (1980) Macroeconomics and reality. Econ: J Econ Soc. 48:1–48

    Article  Google Scholar 

  31. Ţiţan AG (2015) The efficient market hypothesis: review of specialized literature and empirical research. Proc Econ Finance 32:442–449

    Article  Google Scholar 

  32. Yao J, Tan CL (2000) A case study on using neural networks to perform technical forecasting of FOREX. Neurocomputing 34(1):79–98

    Article  Google Scholar 

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Acknowledgements

This work was jointly supported by the Fundación Séneca (Agencia Regional de Ciencia y Tecnología, Región de Murcia) under Grant 20813/PI/18 and by the Spanish MEC and European Commission FEDER under Grants TIN2016-78799-P and TIN2016-80565-R (AEI/FEDER, UE).

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Authors and Affiliations

  1. Artificial Intelligence Talentum, S.L., Edificio CEEIM, Campus Universitario de Espinardo, 30100, Murcia, Spain

    Antonio V. Contreras, Francisco J. Herrera & Sergio Navarro

  2. Center of Operations Research, Miguel Hernández University, Elche Campus, Elche, Alicante, Spain

    Jose J. López-Espín

  3. Polytechnic School, Catholic University of San Antonio of Murcia (UCAM), 30107, Murcia, Spain

    Antonio Llanes & José M. Cecilia

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  1. Antonio V. Contreras
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  2. Antonio Llanes
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  3. Francisco J. Herrera
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  4. Sergio Navarro
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  5. Jose J. López-Espín
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  6. José M. Cecilia
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Correspondence to Antonio Llanes.

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Contreras, A.V., Llanes, A., Herrera, F.J. et al. Enhancing the context-aware FOREX market simulation using a parallel elastic network model. J Supercomput 76, 2022–2038 (2020). https://doi.org/10.1007/s11227-019-02838-1

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