Trends and Reversion in Financial Markets on Time Scales from Minutes to Decades

ArXiv ID: 2501.16772 “View on arXiv”

Authors: Unknown

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.

Keywords: Market Regimes, Trend Reversion, Lattice Gas Model, High-Frequency Data, Behavioral Finance, Multi-Asset

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced statistical and econometric modeling, including cubic polynomial regression and references to statistical physics concepts like lattice gas models, indicating high mathematical density. Empirically, it is exceptionally rigorous, leveraging decades of multi-asset, multi-frequency historical data (from tick to medieval) and providing detailed methodology for data aggregation and parameter estimation, making it highly backtest-ready.

  flowchart TD
    A["Research Goal: Analyze Market Trend Reversion<br>across Time Scales"] --> B["Methodology: Multi-Scale Empirical Analysis"]
    
    B --> C{"Data Inputs"}
    C --> C1["Minutes: 14 Yrs Futures Tick Data"]
    C --> C2["Days: 30 Yrs Daily Futures"]
    C --> C3["Months: 330 Yrs Monthly Prices"]
    C --> C4["Years: Medieval Data"]
    
    C --> D["Computational Process<br>Identify Trending vs Reversion Regimes"]
    
    D --> E{"Key Findings"}
    E --> E1["Trending Regime: 1hr - Years<br>Weak trends persist (herding)<br>Strong trends revert (intrinsic value)"]
    E --> E2["Reversion Regime: <br>Shorter & Longer scales<br>Weak trends revert<br>Significant trends persist"]
    E --> E3["Lattice Gas Model Fit<br>Critical point near market efficiency<br>Correlation time: few years"]