{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T15:47:19Z","timestamp":1777391239024,"version":"3.51.4"},"reference-count":27,"publisher":"SAGE Publications","issue":"3-4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AF"],"published-print":{"date-parts":[[2015,12,29]]},"abstract":"<jats:p>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 \u201cregularity erasing procedure\u201d (REP) implemented to use lossless compression algorithms on financial data. The REP is found to be necessary to detect hidden structures, as one should \u201cwash out\u201d 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.<\/jats:p>","DOI":"10.3233\/af-150052","type":"journal-article","created":{"date-parts":[[2016,1,5]],"date-time":"2016-01-05T10:57:48Z","timestamp":1451991468000},"page":"159-178","source":"Crossref","is-referenced-by-count":8,"title":["Estimating the algorithmic complexity of\u00a0stock markets"],"prefix":"10.1177","volume":"4","author":[{"given":"Olivier","family":"Brandouy","sequence":"first","affiliation":[{"name":"University of Bordeaux 4, Pessac, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jean-Paul","family":"Delahaye","sequence":"additional","affiliation":[{"name":"University of Lille 1, Villeneuve-d\u2019Ascq, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Ma","sequence":"additional","affiliation":[{"name":"University of Lille 1, Villeneuve-d\u2019Ascq, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/AF-150052_ref1","first-page":"107","article-title":"The komornik-loreti constant is transcendental","author":"Allouche","year":"2000","journal-title":"Amer Math Monthly"},{"key":"10.3233\/AF-150052_ref2","first-page":"72","article-title":"Data compression techniques for stock market prediction","author":"Azhar","year":"1994","journal-title":"Data Compression Conference, 1994. 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The construction of decimal normal in the scale of ten. J London Math Soc 8.","DOI":"10.1112\/jlms\/s1-8.4.254"},{"key":"10.3233\/AF-150052_ref8","unstructured":"ChenS.-H. , TanC.-W. ,1996. Measuring Randomness by Rissanen\u2019s Stochastic Complexity: Applications to the Financial Data, chaInformation, Statistics and Induction in Science, World Scientific, pp. 200\u2013211."},{"key":"10.3233\/AF-150052_ref9","unstructured":"ChenS.-H. , TanC.-W. ,1999. Estimating the complexity function of financial time series: An estimation basedon predictive stochastic complexity, Journal of Management and Economics, 3(3)."},{"key":"10.3233\/AF-150052_ref10","doi-asserted-by":"crossref","unstructured":"CilibrasiR. , VitanyiP. ,2005. Clustering by compression. 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