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

A relative information approach to financial time series analysis using binary $N$-grams dictionaries