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

Igor Borovikov and Michael Sadovsky

Papers from arXiv.org

Abstract: 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.

Date: 2013-08
New Economics Papers: this item is included in nep-ecm
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