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.

Refer to our technical paper1 – Hirsa, Malhotra, Xu (2024) for the financial engineering and implementation details.

Section: R2-RD

The below graphic shows the significant improvements in stability of the R2-RD with respect to a standard GMM models. R2-RD produces much more stable and meaningful regime classifications throughout time iteration.

  1. Hirsa, Ali, Xu, Sikun and Malhotra, Satyan. “Robust Rolling Regime Detection (R2-RD): A Data-Driven Perspective of Financial Markets” Working paper. Shortly to be available at SSRN (2024) ↩︎
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