Survey on log-normally distributed market-technical trend data

TL;DR

This survey reviews the recent discovery that market-technical trend data follow a log-normal distribution, demonstrating its advantages over daily returns for evaluating trading systems and providing an example of an anti-cyclic trading approach.

Contribution

It introduces the log-normal distribution as a better fit for market-technical trend data and demonstrates its application in evaluating trading systems.

Findings

Log-normal distribution fits market-technical trend data better than daily returns.

Statistical evaluation of trend variables supports the log-normal assumption.

An example of an anti-cyclic trading system based on this approach.

Abstract

In this survey, a short introduction in the recent discovery of log-normally distributed market-technical trend data will be given. The results of the statistical evaluation of typical market-technical trend variables will be presented. It will be shown that the log-normal assumption fits better to empirical trend data than to daily returns of stock prices. This enables to mathematically evaluate trading systems depending on such variables. In this manner, a basic approach to an anti cyclic trading system will be given as an example.