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arXiv · 1308.2732

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

Igor Borovikov·Michael Sadovsky
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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.

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BibTeXRIS

Igor Borovikov, Michael Sadovsky. 2013-08-13. A relative information approach to financial time series analysis using binary $N$-grams dictionaries. https://arxiv.org/abs/1308.2732

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