Technology Category
Machine Learning: Probabilistic Circuits and Graphical ModelsReasoning under Uncertainty: Relational Probabilistic ModelsIntelligent Robots: State Estimation
Modeling Market States with Clustering and State Machines
🤖 AI Summary
This paper addresses the challenge of modeling the dynamic evolution of financial market regimes—such as expansion, contraction, crisis, and recovery. Methodologically, it proposes an interpretable probabilistic state machine framework: typical market regimes are identified via clustering of multi-horizon momentum and risk features; a regime-transition matrix is constructed; and asset returns within each regime are modeled using a state-frequency-weighted Gaussian mixture model. The key contribution lies in embedding interpretability directly into both regime definitions and transition mechanisms, thereby significantly improving the fidelity of higher-order return statistics—particularly skewness and kurtosis. Empirical evaluations across multiple asset classes and time horizons demonstrate that the proposed model more accurately reproduces empirical return distributions than conventional benchmarks—including single-Gaussian and hidden Markov models—while exhibiting robust out-of-sample performance across diverse markets.
Technology Category
Application Category
📝 Abstract
This work introduces a new framework for modeling financial markets through an interpretable probabilistic state machine. By clustering historical returns based on momentum and risk features across multiple time horizons, we identify distinct market states that capture underlying regimes, such as expansion phase, contraction, crisis, or recovery. From a transition matrix representing the dynamics between these states, we construct a probabilistic state machine that models the temporal evolution of the market. This state machine enables the generation of a custom distribution of returns based on a mixture of Gaussian components weighted by state frequencies. We show that the proposed benchmark significantly outperforms the traditional approach in capturing key statistical properties of asset returns, including skewness and kurtosis, and our experiments across random assets and time periods confirm its robustness.
Problem
Research questions and friction points this paper is trying to address.
Modeling financial markets with interpretable probabilistic state machines
Identifying distinct market states through clustering momentum and risk
Generating custom return distributions capturing skewness and kurtosis properties
Innovation
Methods, ideas, or system contributions that make the work stand out.
Clustering historical returns using momentum and risk features
Constructing probabilistic state machine from transition matrix
Generating return distribution with Gaussian mixture weighted by states
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