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

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

Рассмотрена простейшая модель динамики временных рядов финансовых рынков для бинарной квантизации. Обсуждены наблюдаемые результаты и другие способы квантизации