  We study the complexity of the stock market by constructing
$\epsilon$-machines of Standard and Poor's 500 index from February 1983 to
April 2006 and by measuring the statistical complexities. It is found that both
the statistical complexity and the number of causal states of constructed
$\epsilon$-machines have decreased for last twenty years and that the average
memory length needed to predict the future optimally has become shorter. These
results support that the information is delivered to the economic agents and
applied to the market prices more rapidly in year 2006 than in year 1983.
