Analysis of Financial Time Series with Binary N-Grams Frequency Dictionaries
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URI (for links/citations):
https://elib.sfu-kras.ru/handle/2311/10143Author:
Sadovsky, Michael G.
Borovikov, Igor
Садовский, Михаил Г.
Боровиков, Игорь
Date:
2014-01Abstract:
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 Рассмотрена простейшая модель динамики временных рядов финансовых рынков для бинарной
квантизации. Обсуждены наблюдаемые результаты и другие способы квантизации