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Applications of Heuristics in Finance

In: Handbook on Information Technology in Finance

Author

Listed:
  • Manfred Gilli

    (University of Geneva)

  • Dietmar Maringer

    (University of Essex)

  • Peter Winker

    (University of Giessen)

Abstract

Having the optimal solution for a given problem is crucial in the competitive world of finance; finding this optimal solution, however, is often an utter challenge. Even if the problem is well defined and all necessary data are available, it is not always well behaved: rather simple constraints are often enough to prohibit closed form solutions or evade the reliable application of standard numerical solutions. A common way to avoid this difficulty is to restate the problem: restricting and cumbersome constraints are relaxed and simplifying assumptions are introduced until the revised problem is approachable with the available methods, and are afterwards superimposed onto the solution for the simplified problem. Unfortunately, subtleties as well as central properties of the initial problem can be lost in this process, and results assumed to be ideal can actually be far away from the true optimum.

Suggested Citation

  • Manfred Gilli & Dietmar Maringer & Peter Winker, 2008. "Applications of Heuristics in Finance," International Handbooks on Information Systems, in: Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), Handbook on Information Technology in Finance, chapter 26, pages 635-653, Springer.
    • Handle: RePEc:spr:ihichp:978-3-540-49487-4_26
    DOI: 10.1007/978-3-540-49487-4_26
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    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
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    Cited by:

    1. Jin Zhang & Dietmar Maringer, 2010. "Asset Pair-Copula Selection with Downside Risk Minimization," Working Papers 037, COMISEF.
    2. Manfred Gilli & Enrico Schumann, 2012. "Heuristic optimisation in financial modelling," Annals of Operations Research, Springer, vol. 193(1), pages 129-158, March.
    3. Sermpinis, Georgios & Stasinakis, Charalampos & Hassanniakalager, Arman, 2017. "Reverse adaptive krill herd locally weighted support vector regression for forecasting and trading exchange traded funds," European Journal of Operational Research, Elsevier, vol. 263(2), pages 540-558.
    4. Ardia, David & Boudt, Kris & Carl, Peter & Mullen, Katharine M. & Peterson, Brian, 2010. "Differential Evolution (DEoptim) for Non-Convex Portfolio Optimization," MPRA Paper 22135, University Library of Munich, Germany.
    5. Constantin Zopounidis & Michalis Doumpos & Dimitrios Niklis, 2018. "Financial decision support: an overview of developments and recent trends," EURO Journal on Decision Processes, Springer;EURO - The Association of European Operational Research Societies, vol. 6(1), pages 63-76, June.
    6. Billio, Monica & Caporin, Massimiliano & Costola, Michele, 2015. "Backward/forward optimal combination of performance measures for equity screening," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 63-83.
    7. Peter Winker & Marianna Lyra & Chris Sharpe, 2011. "Least median of squares estimation by optimization heuristics with an application to the CAPM and a multi-factor model," Computational Management Science, Springer, vol. 8(1), pages 103-123, April.
    8. Philip Z. MAYMIN, 2018. "The Conventional Past, Behavioral Present, and Algorithmic Future of Risk and Finance," Finante - provocarile viitorului (Finance - Challenges of the Future), University of Craiova, Faculty of Economics and Business Administration, vol. 1(20), pages 74-84, November.
    9. Dietmar Maringer & Olufemi Oyewumi, 2007. "Index tracking with constrained portfolios," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 15(1‐2), pages 57-71, January.
    10. Marianna Lyra, 2010. "Heuristic Strategies in Finance – An Overview," Working Papers 045, COMISEF.
    11. Filipa Fernandes & Charalampos Stasinakis & Zivile Zekaite, 2019. "Forecasting government bond spreads with heuristic models: evidence from the Eurozone periphery," Annals of Operations Research, Springer, vol. 282(1), pages 87-118, November.

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