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

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  • Igor Borovikov
  • Michael Sadovsky

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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  • Igor Borovikov & Michael Sadovsky, 2013. "A relative information approach to financial time series analysis using binary $N$-grams dictionaries," Papers 1308.2732, arXiv.org.
  • Handle: RePEc:arx:papers:1308.2732
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    File URL: http://arxiv.org/pdf/1308.2732
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