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With string model to time series forecasting

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  • Pinčák, Richard
  • Bartoš, Erik

Abstract

Overwhelming majority of econometric models applied on a long term basis in the financial forex market do not work sufficiently well. The reason is that transaction costs and arbitrage opportunity are not included, as this does not simulate the real financial markets. Analyses are not conducted on the non equidistant date but rather on the aggregate date, which is also not a real financial case. In this paper, we would like to show a new way how to analyze and, moreover, forecast financial market. We utilize the projections of the real exchange rate dynamics onto the string-like topology in the OANDA market. The latter approach allows us to build the stable prediction models in trading in the financial forex market. The real application of the multi-string structures is provided to demonstrate our ideas for the solution of the problem of the robust portfolio selection. The comparison with the trend following strategies was performed, the stability of the algorithm on the transaction costs for long trade periods was confirmed.

Suggested Citation

  • Pinčák, Richard & Bartoš, Erik, 2015. "With string model to time series forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 135-146.
    • Handle: RePEc:eee:phsmap:v:436:y:2015:i:c:p:135-146
    DOI: 10.1016/j.physa.2015.05.013
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    References listed on IDEAS

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    1. Gençay, Ramazan & Dacorogna, Michel & Muller, Ulrich A. & Pictet, Olivier & Olsen, Richard, 2001. "An Introduction to High-Frequency Finance," Elsevier Monographs, Elsevier, edition 1, number 9780122796715.
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    Cited by:

    1. Jahangoshai Rezaee, Mustafa & Jozmaleki, Mehrdad & Valipour, Mahsa, 2018. "Integrating dynamic fuzzy C-means, data envelopment analysis and artificial neural network to online prediction performance of companies in stock exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 489(C), pages 78-93.
    2. Kanjamapornkul, K. & Pinčák, Richard & Bartoš, Erik, 2016. "The study of Thai stock market across the 2008 financial crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 117-133.
    3. Rounaghi, Mohammad Mahdi & Nassir Zadeh, Farzaneh, 2016. "Investigation of market efficiency and Financial Stability between S&P 500 and London Stock Exchange: Monthly and yearly Forecasting of Time Series Stock Returns using ARMA model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 10-21.
    4. Bartoš, Erik & Pinčák, Richard, 2017. "Identification of market trends with string and D2-brane maps," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 57-70.
    5. Bundzel, Marek & Kasanický, Tomáš & Pinčák, Richard, 2016. "Using string invariants for prediction searching for optimal parameters," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 680-688.

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