Trading styles and long-run variance of asset prices

TL;DR

This paper investigates how different trading styles, specifically trend-following and mean-reverting strategies, influence the long-term variance of asset prices and their predictability, using probabilistic models and empirical data.

Contribution

It introduces probabilistic models that explicitly incorporate trading styles to analyze their impact on long-run variance and asset predictability, providing new theoretical and empirical insights.

Findings

Trend-following increases long-run variance

Mean-reversion can reduce long-run variance

Predictability of asset prices improves with trading style models

Abstract

Trading styles can be classified into either trend-following or mean-reverting. If the net trading style is trend-following the traded asset is more likely to move in the same direction it moved previously (the opposite is true if the net style is mean-reverting). The result of this is to introduce positive (or negative) correlations into the time series. We here explore the effect of these correlations on the long-run variance of the series through probabilistic models designed to explicitly capture the direction of trading. Our theoretical insights suggests that relative to random walk models of asset prices the long-run variance is increased under trend-following strategies and can actually be reduced under mean-reversal conditions. We apply these models to some of the largest US stocks by market capitalisation as well as high-frequency EUR/USD data and show that in both these settings, the ability to predict the asset price is generally increased relative to a random walk.