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International Conference on Computer Aided Systems Theory

EUROCAST 2015: Computer Aided Systems Theory – EUROCAST 2015 pp 326–333Cite as

Dynamics of Predictability and Variable Influences Identified in Financial Data Using Sliding Window Machine Learning

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 9520)

Abstract

In this paper we analyze the dynamics of the predictability and variable interactions in financial data of the years 2007–2014. Using a sliding window approach, we have generated mathematical prediction models for various financial parameters using other available parameters in this data set. For each variable we identify the relevance of other variables with respect to prediction modeling. By applying sliding window machine learning we observe that changes of the predictability of financial variables as well as of influence factors can be identified by comparing modeling results generated for different periods of the last 8 years. We see changes of relationships and the predictability of financial variables over the last years, which corresponds to the fact that relationships and dynamics in the financial sector have changed significantly over the last decade. Still, our results show that the predictability has not decreased for all financial variables, indeed in numerous cases the prediction quality has even improved.

Keywords

  • Sliding Window
  • HeuristicLab
  • Strict Offspring Selection
  • Classification Confidence
  • Majority Vote Wins

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Author information

Authors and Affiliations

  1. Hagenberg Campus Heuristic and Evolutionary Algorithms Laboratory, University of Applied Sciences Upper Austria, Softwarepark 11, 4232, Hagenberg, Austria

    Stephan M. Winkler, Gabriel Kronberger, Michael Kommenda & Michael Affenzeller

  2. Department of Economics, Johannes Kepler University Linz, Altenberger Straße 69, 4040, Linz, Austria

    Stefan Fink

  3. Institute for Formal Models and Verification, Johannes Kepler University Linz, Altenberger Straße 69, 4040, Linz, Austria

    Stephan M. Winkler, Michael Kommenda & Michael Affenzeller

Authors
  1. Stephan M. Winkler
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  2. Gabriel Kronberger
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  3. Michael Kommenda
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  4. Stefan Fink
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  5. Michael Affenzeller
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Corresponding author

Correspondence to Stephan M. Winkler .

Editor information

Editors and Affiliations

  1. de Gran Canaria, Universidad de las Palmas de Gran Canari, Las Palmas de Gran Canaria, Spain

    Roberto Moreno-Díaz

  2. Johannes Kepler University Linz, Linz, Austria

    Franz Pichler

  3. de Gran Canaria, Universidad de las Palmas de Gran Canari, Las Palmas de Gran Canaria, Spain

    Alexis Quesada-Arencibia

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Winkler, S.M., Kronberger, G., Kommenda, M., Fink, S., Affenzeller, M. (2015). Dynamics of Predictability and Variable Influences Identified in Financial Data Using Sliding Window Machine Learning. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2015. EUROCAST 2015. Lecture Notes in Computer Science(), vol 9520. Springer, Cham. https://doi.org/10.1007/978-3-319-27340-2_41

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  • DOI: https://doi.org/10.1007/978-3-319-27340-2_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27339-6

  • Online ISBN: 978-3-319-27340-2

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