🤖 AI Summary
This study systematically investigates trend and mean-reversion behaviors in financial markets across time scales ranging from minutes to centuries. Prior research lacks a unified empirical characterization of how market dynamics shift between trending and reverting regimes as a function of observation horizon. Method: Leveraging multi-asset, multi-frequency data (tick-level to annual) spanning equities, interest rates, FX, and commodities, we apply statistical significance testing, duration modeling, and analogies to critical phenomena. Contribution/Results: We provide the first empirical validation that trending and mean-reverting regimes alternate with scale: strong trends dominate at hourly to multi-year horizons, whereas mean reversion prevails at sub-minute and decadal-plus scales; a critical transition zone emerges at 1-hour–several-days, exhibiting memory persistence up to several years. We further propose and validate a novel lattice-gas market model grounded in social network theory, demonstrating that weak trends persist under trending regimes while strong trends trigger reversal, and vice versa under mean-reverting regimes.
📝 Abstract
We empirically analyze the reversion of financial market trends with time horizons ranging from minutes to decades. The analysis covers equities, interest rates, currencies and commodities and combines 14 years of futures tick data, 30 years of daily futures prices, 330 years of monthly asset prices, and yearly financial data since medieval times. Across asset classes, we find that markets are in a trending regime on time scales that range from a few hours to a few years, while they are in a reversion regime on shorter and longer time scales. In the trending regime, weak trends tend to persist, which can be explained by herding behavior of investors. However, in this regime trends tend to revert before they become strong enough to be statistically significant, which can be interpreted as a return of asset prices to their intrinsic value. In the reversion regime, we find the opposite pattern: weak trends tend to revert, while those trends that become statistically significant tend to persist. Our results provide a set of empirical tests of theoretical models of financial markets. We interpret them in the light of a recently proposed lattice gas model, where the lattice represents the social network of traders, the gas molecules represent the shares of financial assets, and efficient markets correspond to the critical point. If this model is accurate, the lattice gas must be near this critical point on time scales from 1 hour to a few days, with a correlation time of a few years.