Investment strategy due to the minimization of portfolio noise level by observations of coarse-grained entropy

TL;DR

This paper introduces a noise-based investment strategy using coarse-grained entropy to estimate and minimize noise in stock data, leading to positive returns when applied to historical market data.

Contribution

It presents a novel method for portfolio optimization by minimizing noise levels estimated through coarse-grained entropy, applied to stock market data.

Findings

Noise level ranges from 40% to 80% of signal variance.

Threshold investment strategy yields positive historical returns.

Method effectively identifies optimal portfolios based on noise minimization.

Abstract

Using a recently developed method of noise level estimation that makes use of properties of the coarse grained-entropy we have analyzed the noise level for the Dow Jones index and a few stocks from the New York Stock Exchange. We have found that the noise level ranges from 40 to 80 percent of the signal variance. The condition of a minimal noise level has been applied to construct optimal portfolios from selected shares. We show that implementation of a corresponding threshold investment strategy leads to positive returns for historical data.