Analysis of Financial Time Series with Binary N-Grams Frequency Dictionaries

Full text (.pdf)
Issue
Математика и физика. Mathematics & Physics. 2014 7 (1)
Authors
Sadovsky, Michael G.; Borovikov, Igor
Contact information
Sadovsky, Michael G.:Institute of computational modelling SB RAS, Akademgorodok, Krasnoyarsk, 660036 Russia;; Borovikov, Igor:Nekkar.Net Labs, Ltd. California, USA;
Keywords
order; entropy; mutual entropy; indicator; trend
Abstract

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 con- tinuous) 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

Pages
112–123
Paper at repository of SibFU
http://elib.sfu-kras.ru/handle/2311/10143