  Modern mainstream financial theory is underpinned by the efficient market
hypothesis, which posits the rapid incorporation of relevant information into
asset pricing. Limited prior studies in the operational research literature
have investigated tests designed for random number generators to check for
these informational efficiencies. Treating binary daily returns as a hardware
random number generator analogue, tests of overlapping permutations have
indicated that these time series feature idiosyncratic recurrent patterns.
Contrary to prior studies, we split our analysis into two streams at the annual
and company level, and investigate longer-term efficiency over a larger time
frame for Nasdaq-listed public companies to diminish the effects of trading
noise and allow the market to realistically digest new information. Our results
demonstrate that information efficiency varies across years and reflects
large-scale market impacts such as financial crises. We also show the proximity
to results of a well-tested pseudo-random number generator, discuss the
distinction between theoretical and practical market efficiency, and find that
the statistical qualification of stock-separated returns in support of the
efficient market hypothesis is dependent on the driving factor of small
inefficient subsets that skew market assessments.
