16 Feb
General

Robust Rolling Regime Detection (R2-RD): A Data-Driven Perspective of Financial Markets

The nonstationary and high-dimensional nature of financial markets poses significant challenges for navigation. Temporally stable regime classification offers a perspective to manage these challenges. We propose the Robust Rolling Regime Detection (R2-RD) framework that adaptively retrains with streaming data and employs temporal ensemble, label assignment, and threshold policies to address temporal instability resulting from nonstationarity, model mismatches, etc.  Since a learning-based model is only as powerful as the data it trains on, the more stable results of the R2-RD make it a better candidate for usage across AI-based applications.

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16 Feb
General

Robust Rolling K-Means (R2K-Means): an Updateable Nonlinear K-Means Clustering Methodology for Financial Time Series

K-Means is a popular clustering algorithm designed to group data points into k clusters. In the financial industry, grouping funds or assets can isolate behaviors and define investment universes using any number of  performance measures, holdings, or alternative features. Standard K-Means clustering at each time increment creates extremely unstable results due to the effects of random initialization and cluster mislabeling. Robust Rolling K-Means (R2K-Means) is the extension of K-Means to time series allowing investors to dynamically track and group funds in a stable and updateable framework.  Since a learning-based model is only as powerful as...

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