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AN ALGORITHMIC INFORMATION THEORETIC APPROACH TO THE BEHAVIOUR OF FINANCIAL MARKETS
Author(s) -
Zenil Hector,
Delahaye JeanPaul
Publication year - 2011
Publication title -
journal of economic surveys
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.657
H-Index - 92
eISSN - 1467-6419
pISSN - 0950-0804
DOI - 10.1111/j.1467-6419.2010.00666.x
Subject(s) - financial market , randomness , economics , econometrics , law of one price , efficient market hypothesis , stock market , probability distribution , econophysics , market microstructure , mathematical economics , mathematics , price level , finance , mid price , statistics , paleontology , horse , order (exchange) , keynesian economics , biology
. Using frequency distributions of daily closing price time series of several financial market indices, we investigate whether the bias away from an equiprobable sequence distribution found in the data, predicted by algorithmic information theory, may account for some of the deviation of financial markets from log‐normal, and if so for how much of said deviation and over what sequence lengths. We do so by comparing the distributions of binary sequences from actual time series of financial markets and series built up from purely algorithmic means. Our discussion is a starting point for a further investigation of the market as a rule‐based system with an algorithmic component, despite its apparent randomness, and the use of the theory of algorithmic probability with new tools that can be applied to the study of the market price phenomenon. The main discussion is cast in terms of assumptions common to areas of economics in agreement with an algorithmic view of the market.