Our Scientific Publications
Regime Switching Model with Nonlinear Representation for Discovery and Forecasting Regimes in Financial Markets
Kunpeng Xu, Lifei Chen, Jean-Marc Patenaude and Shengrui Wang
Presented at the Society for Industrial and Applied Mathematics Conference (SIAM 2024)
This paper introduces a new type of regime-switching model that uses nonlinear kernel-based representations to automatically detect and forecast shifts in financial market behavior across multiple time series. Its key innovation lies in uncovering hidden, time-varying patterns without prior knowledge of regimes—making it highly relevant for financial applications like volatility forecasting, risk assessment, and market trend analysis.
Kernel Representation Learning with Dynamic Regime Discovery for Time Series Forecasting
Kunpeng Xu, Lifei Chen, Jean-Marc Patenaude, and Shengrui Wang
Presented at the Asia-Pacific Conference on Knowledge Discovery and Data Mining (PAKDD-2024)
This paper introduces a kernel-based self-representation learning method for discovering dynamic regimes in co-evolving time series, enabling more accurate and interpretable multi-step forecasting. In finance, this approach is particularly relevant as it captures nonlinear interactions and regime shifts—such as market volatility or economic cycles—without requiring prior knowledge, enhancing predictive modeling for complex financial systems.
Clustering-Based Cross-Sectional Regime Identification for Financial Market Forecasting
Rongbo Chen, Mingxuan Sun, Kunpeng Xu, Jean-Marc Patenaude, and Shengrui Wang
Presented at the International Conference on Database and Expert Systems Applications (DEXA 2022)
This paper presents a new method for forecasting financial markets by identifying different market “regimes” using clustering across multiple time series. It captures nonlinear relationships between assets and adapts to changing conditions, leading to more accurate predictions. This approach helps investors and analysts understand shifts in market behavior, improving portfolio management, risk assessment, and trading strategies.
Dynamic Cross-sectional Regime Identification for Financial Market Prediction
Rongbo Chen, Kunpeng Xun, Jean-Marc Patenaude, Shengrui Wang
Presented at the IEEE Computers, Software and Applications Conference (COMPSAC 2022)
This paper proposes a novel model for financial market prediction that dynamically identifies cross-sectional regimes in multi-time-series data, allowing for the discovery of new market regimes as they emerge, rather than relying on a fixed set of pre-identified regimes. This is highly relevant in finance because it enhances the ability to detect structural market changes—such as those during crises or bubbles—improving prediction accuracy and offering better insights into market behavior through time-varying transition probabilities.
A Variable-Order Regime Switching Model to Identify Significant Patterns in Financial Markets
Philippe Chatigny, Rongbo Chen, Jean-Marc Patenaude and Shengrui Wang
Presented at the IEEE International Conference on Data Mining (ICDM 2018)
This paper presents t a variable-order regime switching framework that identifies and predicts financial market regimes by extracting statistically significant behavioral patterns from time series data. Applied to volatility forecasting of 200 S&P 500 stocks, the model outperforms traditional regime-switching methods by offering interpretable insights into market dynamics and improving predictive accuracy.
