Stable Stock Dependence Patterns and Their Conditional Price Dynamics

Summary

The paper studies how consecutive stock price increments depend on one another. It represents the first increment as a push and the next as a response, then examines the bivariate probability distribution of these changes to identify asymmetric dependence patterns. Applying a previously developed identification method to 2,000 stocks, it describes individual stock profiles and groups them as correlation, anticorrelation, or market-mill patterns.

The reported portraits remain notably stable over time. Across the identified groups, conditional price behavior is trend-following for small push magnitudes and contrarian for large ones. This suggests that the relation between successive price changes can vary with the size of the initial move, rather than following one uniform rule. The excerpt does not specify the observation period, stock universe, or trading costs, and it does not establish that these patterns produce profitable strategies after execution costs. Its findings describe empirical dependence, not a direct trading recommendation.

Key ideas

• The study examines the joint distribution of consecutive price increments, treating the first move as a push and the next as a response.
• It classifies stock-specific asymmetry patterns into correlation, anticorrelation, and market-mill groups.
• The reported individual patterns are stable over time.
• Conditional dynamics are trend-following for small pushes and contrarian for large pushes.
• The excerpt describes statistical behavior but does not provide evidence of net trading profitability.

Tags

# Market Mill Dependence Pattern in the Stock Market: Individual Portraits


# Market Mill Dependence Pattern in the Stock Market: Individual Portraits









This paper continues a series of studies of dependence patterns following from properties of the bivariate probability distribution P(x,y) of two consecutive price increments x (push) and y (response). The paper focuses on individual differences of the P(x,y) for 2000 stocks using a methodology of identification of asymmetric market mill patterns developed in [1,2]. We show that individual asymmetry patterns (portraits) are remarkably stable over time and can be classified into three major groups - correlation, anticorrelation and market mill. We analyze the conditional dynamics resulting from the properties of P(x,y) for all groups and demonstrate that it is trend-following at small push magnitudes and contrarian at large ones

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