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An automated econometric decision support system: forecasts for foreign exchange trades

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  • Bernd Brandl
  • Christian Keber
  • Matthias Schuster

Abstract

Making decisions challenges foreign exchange (FX) market brokers due to the volatility of the foreign exchange market, as well as the unmanageable flood of possibly relevant information. Thus, decision making in this complex and dynamically changing environment is a difficult task requiring automated decision support systems. In this contribution, we describe an econometric decision support approach, which enables the extraction of essential information indispensable to set up accurate forecasting models. Our approach is based on a genetic algorithm (GA) and applies the resulting models to forecast daily EUR/USD-exchange rates. In doing so, the genetic algorithm optimizes single-equation regression forecast models. The approach discussed is new in literature and, moreover, allows flexibility in automated model selection within a reasonably short time. Copyright Springer-Verlag 2006

Suggested Citation

  • Bernd Brandl & Christian Keber & Matthias Schuster, 2006. "An automated econometric decision support system: forecasts for foreign exchange trades," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 14(4), pages 401-415, December.
    • Handle: RePEc:spr:cejnor:v:14:y:2006:i:4:p:401-415
    DOI: 10.1007/s10100-006-0013-8
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    References listed on IDEAS

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    Cited by:

    1. Jan Tyrychtr & Martin Pelikán & Hana Štiková & Ivan Vrana, 2018. "EM-OLAP Framework," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 60(6), pages 543-562, December.
    2. Leitner, Johannes & Leopold-Wildburger, Ulrike, 2011. "Experiments on forecasting behavior with several sources of information - A review of the literature," European Journal of Operational Research, Elsevier, vol. 213(3), pages 459-469, September.
    3. Milan Vaclavik & Josef Jablonsky, 2012. "Revisions of modern portfolio theory optimization model," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(3), pages 473-483, September.
    4. Ana Paula Santos Gularte & Danusio Gadelha Guimarães Filho & Gabriel Oliveira Torres & Thiago Carvalho Nunes Silva & Vitor Venceslau Curtis, 2024. "Machine Learning-Based Time Series Prediction at Brazilian Stocks Exchange," Computational Economics, Springer;Society for Computational Economics, vol. 64(4), pages 2477-2508, October.

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