Stock exchange shares ranking and binary-ternary compressive coding

TL;DR

This paper introduces a novel stock ranking method based on the compression of historical price data, offering an alternative to volatility-based risk assessment, demonstrated on Dow Jones stocks.

Contribution

It presents a new ranking algorithm utilizing binary-ternary data compression to evaluate stock attractiveness, highlighting its advantages over traditional volatility measures.

Findings

Compression-based ranking differs from volatility-based methods

Binary-ternary compression effectively captures financial data patterns

The method shows strengths in assessing investment risk

Abstract

This paper proposes a method for ranking the investment attractiveness of exchange-traded stocks where investment risk is not related to the volatility indicator but instead is related to the indicator of compression of the time series of price changes. The article describes in detail the ranking algorithm, provides an example of ranking the shares of all companies included in the Dow Jones stock index. The paper additionally compares the results of ranking these stocks by volatility and compression and also shows the strengths of the second indicator, which is formed using the method of binary-ternary compression of historical financial data.