This article is cited in 1 scientific paper (total in 1 paper)
Analysis of financial time series with binary $n$-grams frequency dictionaries
Michael G. Sadovskya, Igor Borovikovb
a Institute of Computational Modelling SB RAS, Akademgorodok, Krasnoyarsk, 660036 Russia
b Nekkar. Net Labs, Ltd., California, USA
The paper presents 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.
order, entropy, mutual entropy, indicator, trend.
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Received in revised form: 10.08.2013
Michael G. Sadovsky, Igor Borovikov, “Analysis of financial time series with binary $n$-grams frequency dictionaries”, J. Sib. Fed. Univ. Math. Phys., 7:1 (2014), 112–123
Citation in format AMSBIB
\by Michael~G.~Sadovsky, Igor~Borovikov
\paper Analysis of financial time series with binary $n$-grams frequency dictionaries
\jour J. Sib. Fed. Univ. Math. Phys.
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This publication is cited in the following articles:
I. Borovikov, M. Sadovsky, “N-gram events for analysis of financial time series”, Proceedings of Eccs 2014: European Conference on Complex Systems, Springer Proceedings in Complexity, eds. S. Battiston, F. DePellegrini, G. Caldarelli, E. Merelli, Springer, 2016, 155–167
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