Estimating the algorithmic complexity of stock markets

Olivier Brandouy, Jean-Paul Delahaye and Lin Ma ()
Additional contact information
Olivier Brandouy: University of Bordeaux 4, Postal: Pessac, France
Jean-Paul Delahaye: University of Lille 1, Villeneuve-d’Ascq, Postal: France
Lin Ma: University of Lille 1, Postal: 104, avenue peuple Belge. 59000 Lille, France.

Algorithmic Finance, 2015, vol. 4, issue 3-4, 159-178

Abstract: Randomness and regularities in finance are usually treated in probabilistic terms. In this paper, we develop a different approach in using a non-probabilistic framework based on the algorithmic information theory initially developed by Kolmogorov (1965). We develop a generic method to estimate the Kolmogorov complexity of numeric series. This approach is based on an iterative “regularity erasing procedure” (REP) implemented to use lossless compression algorithms on financial data. The REP is found to be necessary to detect hidden structures, as one should “wash out” well-established financial patterns (i.e. stylized facts) to prevent algorithmic tools from concentrating on these non-profitable patterns. The main contribution of this article is methodological: we show that some structural regularities, invisible with classical statistical tests, can be detected by this algorithmic method. Our final illustration on the daily Dow-Jones Index reveals a weak compression rate, once well- known regularities are removed from the raw data. This result could be associated to a high efficiency level of the New York Stock Exchange, although more effective algorithmic tools could improve this compression rate on detecting new structures in the future.

Keywords: Kolmogorov complexity; return; efficiency; compression (search for similar items in EconPapers)
JEL-codes: C00 (search for similar items in EconPapers)
Date: 2015
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ris:iosalg:0043

Access Statistics for this article

Algorithmic Finance is currently edited by Phil Maymin

More articles in Algorithmic Finance from IOS Press
Bibliographic data for series maintained by Saskia van Wijngaarden ().