  Here we present a novel approach to statistical analysis of financial time
series. The approach is based on $n$-grams frequency dictionaries derived from
the quantized market data. Such dictionaries are studied by evaluating their
information capacity using relative entropy. A specific quantization of
(originally continuous) financial data is considered: so called binary
quantization. Possible applications of the proposed technique include market
event study with the $n$-grams of higher information value. The finite length
of the input data presents certain computational and theoretical challenges
discussed in the paper. also, some other versions of a quantization are
discussed.
