Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Design of High-Speed LVDS Data Communication Link Using FPGA Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Financial Time Series Clustering

Authors : Kartikay Gupta, Niladri Chatterjee

Published in: Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 2

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Financial time series clustering finds application in forecasting, noise reduction and enhanced index tracking. The central theme in all the available clustering algorithms is the dissimilarity measure employed by the algorithm. The dissimilarity measures, applicable in financial domain, as used or suggested in past researches, are correlation based dissimilarity measure, temporal correlation based dissimilarity measure and dynamic time wrapping (DTW) based dissimilarity measure. One shortcoming of these dissimilarity measures is that they do not take into account the lead or lag existing between the returns of different stocks which changes with time. Mostly, such stocks with high value of correlation at some lead or lag belong to the same cluster (or sector). The present paper, proposes two new dissimilarity measures which show superior clustering results as compared to past measures when compared over 3 data sets comprising of 526 companies. abstract environment.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Ontology Merging: A Practical Perspective
next chapter Routing Protocols for Wireless Multimedia Sensor Networks: Challenges and Research Issues
Appendix
Available only for authorised users
Literature
1.
Dose, C.: Clustering of financial time series with application to index and enhanced index tracking portfolio. Phys. A 355, 145–151 (2005)MathSciNetCrossRef
2.
Mantegna, S.: An Introduction to Econophysics Correlations and Complexity in Finance. Cambridge University Press, Cambridge (1999)CrossRef
3.
Basalto, N., et al.: Hausdorff clustering of financial time series. Phys. A 379, 635–644 (2007)
4.
Saeed, T.: Effective clustering of time-series data using FCM. Int. J. Mach. Learn. Comput. 4(2), 170–176 (2014)CrossRef
5.
Guan, J.: Cluster financial time series for portfolio. In: Proceedings of the 2007 ICWAPR, Beijing, China, 2–4 November 2007
6.
Marti, A., Nielson, D.: Clustering financial time series: how long is enough? In: Proceedings of the Twenty-Fifth IJCAI (2016)
7.
Aghabozorgi, S., Shirkhorshidi, A.S., Wah, T.Y.: Time-series clustering - a decade review. Inform. Syst. 53, 16–38 (2015)CrossRef
8.
John, G.: k-shape: efficient and accurate clustering of time series. SIGMOD Rec. 45(1), 69–76 (2016)CrossRef
9.
Montero, V.: TSclust: an r package for time series clustering. J. Stat. Softw. 62(1), 1–43 (2014)CrossRef
10.
Murtagh, L.: Ward’s hierarchical agglomerative clustering method: which algorithms implement Ward’s criterion? J. Classif. 31, 274–295 (2014)MathSciNetCrossRefMATH
11.
Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: KDD, Workshop, pp. 359–370 (1994)
12.
Chouakria, A.D., Nagabhushan, P.N.: Adaptive dissimilarity index for measuring time series proximity. Adv. Data Anal. Classif. 1(1), 5–21 (2007)MathSciNetCrossRefMATH
13.
Chouakria-Douzal, A.: Compression technique preserving correlations of a multivariate temporal sequence. In: Advances in Intelligent Data Analysis, pp 566–577. Springer, Heidelberg (2003)
14.
Gavrilov, M., et al.: Mining the stock market: which measure is best ? In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD, pp. 487–496 (2000)
15.
Giorgino, T.: Computing and visualizing dynamic time warping alignments in R: the dtw package. J. Stat. Softw. 31(7), 1–24 (2009)CrossRef
16.
Metadata
Title
Financial Time Series Clustering
Authors
Kartikay Gupta
Niladri Chatterjee
Copyright Year
2018
Book
Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 2

Print ISBN: 978-3-319-63644-3

Electronic ISBN: 978-3-319-63645-0

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-63645-0_16

Premium Partner

    Image Credits