ENMX: An elastic network model to predict the FOREX market evolution

被引:9
|
作者
Contreras, Antonio, V [1 ]
Llanes, Antonio [2 ]
Perez-Bernabeu, Alberto [3 ]
Navarro, Sergio [4 ]
Perez-Sanchez, Horacio [2 ]
Lopez-Espin, Jose J. [3 ]
Cecilia, Jose M. [2 ]
机构
[1] Univ Catolica Murcia UCAM, Murcia 30107, Spain
[2] Univ Catolica Murcia UCAM, Bioinformat & High Performance Comp BIO HPC Res G, Murcia 30107, Spain
[3] Miguel Hernandez Univ, Ctr Operat Res, Elche Campus, Elche, Spain
[4] Artificial Intelligence Talentum SL, Campus Univ Espinardo,Edificio CEEIM, Murcia 30100, Spain
关键词
FOREX prediction; Market prediction; Elastic network model; Pseudo-Voigt profile; Bio-inspired methods; EXCHANGE-RATE DETERMINATION; FORECASTING PERFORMANCE; RATES; ALGORITHM; DYNAMICS; COINTEGRATION; HYPOTHESIS; PRICES; DOLLAR; SAMPLE;
D O I
10.1016/j.simpat.2018.04.008
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The foreign exchange (FOREX) market is a financial market in which participants, such as international banks, companies or private investors, can both invest in and speculate on exchange rates. This market is considered one of the largest financial markets 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. This paper introduces a new algorithm, inspired by the behaviour of macromolecules in dissolution, to model the evolution of the FOREX market, called the ENMX (elastic network model for FOREX market) algorithm. This algorithm allows the system to escape from a potential local minimum, so it can reproduce the unstable nature of the FOREX market, allowing the simulation to get away from equilibrium. ENMX introduces several novelties in the simulation of the FOREX market. First, ENMX enables the user to simulate the market evolution of up to 21 currency pairs, connected, and thus emulating behaviour of the real-world FOREX market. Second, the interaction between investors and each particular quotation, which may introduce slight deviations from the quotation prices, is represented by a random movement. We analyse different probability distributions like Gaussian and Pseudo-Voigt, the latter showing better behaviour distributions, to model the variations in quotation prices. Finally, the ENMX algorithm is also compared to traditional econometric approaches such as the VAR model and a driftless random walk, using a classical statistical and a profitability measure. The results show that the ENMX outperforms both models in terms of quality by a wide margin.
引用
收藏
页码:1 / 10
页数:10
相关论文
共 50 条
  • [1] The Forex Market as an Elastic Network Model
    Contreras, Antonio V.
    Navarro, Sergio
    Llanes, Antonio
    Munoz, Andres
    Perez-Sanchez, Horacio
    Cecilia, Jose M.
    [J]. 2017 13TH INTERNATIONAL CONFERENCE ON INTELLIGENT ENVIRONMENTS (IE 2017), 2017, : 155 - 159
  • [2] Enhancing the context-aware FOREX market simulation using a parallel elastic network model
    Antonio V. Contreras
    Antonio Llanes
    Francisco J. Herrera
    Sergio Navarro
    Jose J. López-Espín
    José M. Cecilia
    [J]. The Journal of Supercomputing, 2020, 76 : 2022 - 2038
  • [3] Enhancing the context-aware FOREX market simulation using a parallel elastic network model
    Contreras, Antonio V.
    Llanes, Antonio
    Herrera, Francisco J.
    Navarro, Sergio
    Lopez-Espin, Jose J.
    Cecilia, Jose M.
    [J]. JOURNAL OF SUPERCOMPUTING, 2020, 76 (03): : 2022 - 2038
  • [4] Network Analysis of Cross-Correlations on Forex Market during Crises. Globalisation on Forex Market
    Miskiewicz, Janusz
    [J]. ENTROPY, 2021, 23 (03)
  • [5] A Trade Gap Scalability Model for the Forex Market
    Oyemade, David Ademola
    Allenotor, David
    [J]. 2014 IEEE 11TH INTL CONF ON UBIQUITOUS INTELLIGENCE AND COMPUTING AND 2014 IEEE 11TH INTL CONF ON AUTONOMIC AND TRUSTED COMPUTING AND 2014 IEEE 14TH INTL CONF ON SCALABLE COMPUTING AND COMMUNICATIONS AND ITS ASSOCIATED WORKSHOPS, 2014, : 867 - 873
  • [6] Forex Market Prediction Using NARX Neural Network with Bagging
    Shahbazi, Nima
    Memarzadeh, Masoud
    Gryz, Jarek
    [J]. 2016 3RD INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND APPLICATIONS (ICIEA 2016), 2016, 68
  • [7] Interconnectedness in the FOREX market during the high inflation regime: A network analysis
    Ahmed, Shamima
    Akhtaruzzaman, Md
    Le, Van
    Nath, Tamal
    Rahman, Molla Ramizur
    [J]. RESEARCH IN INTERNATIONAL BUSINESS AND FINANCE, 2024, 71
  • [8] Elastic network model of learned maintained contacts to predict protein motion
    Putz, Ines
    Brock, Oliver
    [J]. PLOS ONE, 2017, 12 (08):
  • [9] A multi-model approach to the development of algorithmic trading systems for the Forex market
    Sevastjanov, Pavel
    Kaczmarek, Krzysztof
    Rutkowski, Leszek
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2024, 236
  • [10] A Model for E-commerce Market Network with Improved Evolution Mechanism
    Tian, Zhihong
    Zhang, Zhenji
    Guan, Xiaolan
    [J]. STUDIES IN INFORMATICS AND CONTROL, 2014, 23 (01): : 77 - 86