Dynamics of predictability and variable influences identified in financial data using sliding window machine learning

Stephan Winkler; Gabriel Kronberger; Michael Kommenda; Stefan Fink; Michael Affenzeller

doi:10.1007/978-3-319-27340-2_41

Dynamics of predictability and variable influences identified in financial data using sliding window machine learning

Stephan Winkler, Gabriel Kronberger, Michael Kommenda, Stefan Fink, Michael Affenzeller

Research Center Hagenberg

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

2 Citations (Scopus)

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.

Original language	English
Title of host publication	Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers
Editors	Franz Pichler, Roberto Moreno-Díaz, Alexis Quesada-Arencibia
Publisher	Springer
Pages	326-333
Number of pages	8
ISBN (Print)	9783319273396
DOIs	https://doi.org/10.1007/978-3-319-27340-2_41
Publication status	Published - 2015
Event	15th International Conference on Computer Aided Systems Theory, Eurocast 2015 - Las Palmas, Gran Canaria, Spain Duration: 8 Feb 2015 → 13 Feb 2015 http://eurocast2015.fulp.ulpgc.es/

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9520
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Computer Aided Systems Theory, Eurocast 2015
Country/Territory	Spain
City	Las Palmas, Gran Canaria
Period	08.02.2015 → 13.02.2015
Internet address	http://eurocast2015.fulp.ulpgc.es/

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10.1007/978-3-319-27340-2_41

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Winkler, S., 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 F. Pichler, R. Moreno-Díaz, & A. Quesada-Arencibia (Eds.), Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers (pp. 326-333). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9520). Springer. https://doi.org/10.1007/978-3-319-27340-2_41

Winkler, Stephan ; Kronberger, Gabriel ; Kommenda, Michael et al. / Dynamics of predictability and variable influences identified in financial data using sliding window machine learning. Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers. editor / Franz Pichler ; Roberto Moreno-Díaz ; Alexis Quesada-Arencibia. Springer, 2015. pp. 326-333 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Dynamics of predictability and variable influences identified in financial data using sliding window machine learning",

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.",

author = "Stephan Winkler and Gabriel Kronberger and Michael Kommenda and Stefan Fink and Michael Affenzeller",

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Winkler, S , 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 F Pichler, R Moreno-Díaz & A Quesada-Arencibia (eds), Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9520, Springer, pp. 326-333, 15th International Conference on Computer Aided Systems Theory, Eurocast 2015, Las Palmas, Gran Canaria, Spain, 08.02.2015. https://doi.org/10.1007/978-3-319-27340-2_41

Dynamics of predictability and variable influences identified in financial data using sliding window machine learning. / Winkler, Stephan ; Kronberger, Gabriel; Kommenda, Michael et al.
Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers. ed. / Franz Pichler; Roberto Moreno-Díaz; Alexis Quesada-Arencibia. Springer, 2015. p. 326-333 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9520).

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

TY - GEN

T1 - Dynamics of predictability and variable influences identified in financial data using sliding window machine learning

AU - Winkler, Stephan

AU - Kronberger, Gabriel

AU - Kommenda, Michael

AU - Fink, Stefan

AU - Affenzeller, Michael

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

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SN - 9783319273396

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 326

EP - 333

BT - Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers

A2 - Pichler, Franz

A2 - Moreno-Díaz, Roberto

A2 - Quesada-Arencibia, Alexis

PB - Springer

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Y2 - 8 February 2015 through 13 February 2015

ER -

Winkler S , Kronberger G, Kommenda M, Fink S, Affenzeller M. Dynamics of predictability and variable influences identified in financial data using sliding window machine learning. In Pichler F, Moreno-Díaz R, Quesada-Arencibia A, editors, Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers. Springer. 2015. p. 326-333. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-27340-2_41

Dynamics of predictability and variable influences identified in financial data using sliding window machine learning

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