Clustering Financial Time Series: How Long Is Enough?

Marti, Gautier; Andler, Sébastien; Nielsen, Frank; Donnat, Philippe

Clustering Financial Time Series: How Long Is Enough?

Gautier Marti, Sébastien Andler, Frank Nielsen, Philippe Donnat

Download BibTeX Cite

Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 2583-2588.

Abstract

Researchers have used from 30 days to several years of daily returns as source data for clustering financial time series based on their correlations. This paper sets up a statistical framework to study the validity of such practices. We first show that clustering correlated random variables from their observed values is statistically consistent. Then, we also give a first empirical answer to the much debated question: How long should the time series be? If too short, the clusters found can be spurious; if too long, dynamics can be smoothed out.

Gautier Marti, Sébastien Andler, Frank Nielsen, and Philippe Donnat. 2016. Clustering Financial Time Series: How Long Is Enough?. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16. IJCAI, 2583–2588. https://ijcai.org/proceedings/2016/367

@inproceedings{ijcai2016p367,
  title     = {Clustering Financial Time Series: How Long Is Enough?},
  author    = {Marti, Gautier and Andler, Sébastien and Nielsen, Frank and Donnat, Philippe},
  booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
               Artificial Intelligence, {IJCAI-16}},
  publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
  editor    = {Subbarao Kambhampati},
  pages     = {2583--2588},
  year      = {2016},
  month     = {7},
  url       = {https://ijcai.org/proceedings/2016/367},
}

TY  - CPAPER
TI  - Clustering Financial Time Series: How Long Is Enough?
AU  - Marti, Gautier
AU  - Andler, Sébastien
AU  - Nielsen, Frank
AU  - Donnat, Philippe
T2  - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16
PB  - AAAI Press / International Joint Conferences on Artificial Intelligence
PY  - 2016
SP  - 2583
EP  - 2588
UR  - https://ijcai.org/proceedings/2016/367
ER  -