Statistical Methods for Estimating the non-random Content of Financial Markets

TL;DR

This paper demonstrates that advanced multivariate statistical analysis, inspired by nuclear physics methods, can detect non-random, trend-following components in high-frequency financial market data, which appear nearly random to traditional analysis.

Contribution

It introduces a novel application of multivariate statistical methods to identify subtle non-random patterns in financial time series, particularly in high-frequency Euro futures data.

Findings

Detected non-random trend-following content in high-frequency data

Showed the difference between real prices and random walk is statistically significant

Identified volatility-dependent patterns in market movements

Abstract

For the pedestrian observer, financial markets look completely random with erratic and uncontrollable behavior. To a large extend, this is correct. At first approximation the difference between real price changes and the random walk model is too small to be detected using traditional time series analysis. However, we show in the following that this difference between real financial time series and random walks, as small as it is, is detectable using modern statistical multivariate analysis, with several triggers encoded in trading systems. This kind of analysis are based on methods widely used in nuclear physics, with large samples of data and advanced statistical inference. Considering the movements of the Euro future contract at high frequency, we show that a part of the non-random content of this series can be inferred, namely the trend-following content depending on volatility ranges.