  Using frequency distributions of daily closing price time series of several
financial market indexes, 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.
