As new generations of technology appear, legacy knowledge management solutions and
applications become increasingly out of date, necessitating a paradigm shift. Machine …

Regime-driven models are popular for addressing temporal patterns in both financial market
performance and underlying stylized factors, wherein a regime describes periods with …

This article introduces a novel hybrid regime identification-forecasting framework designed
to enhance multi-asset portfolio construction by integrating asset-specific regime forecasts …

Jump models switch infrequently between states to fit a sequence of data while taking the
ordering of the data into account We propose a new framework for joint feature selection …

This article investigates a regime-switching investment strategy aimed at mitigating
downside risk by reducing market exposure during anticipated unfavorable market regimes …

In many financial applications it is important to classify time series data without any latency
while maintaining persistence in the identified states. We propose a greedy online classifier …

We apply the statistical sparse jump model, a recently developed, interpretable and robust
regime-switching model, to infer key features that drive the return dynamics of the largest …

This article investigates the impact of regime switching on asset allocation decisions, with a
primary focus on comparing different regime identification models. In contrast to traditional …

We extend the generalized information criteria for high-dimensional penalized models to
sparse statistical jump models, a new class of statistically robust and computationally …

To address the challenges of big data analytics, several works have focused on big data
optimization using metaheuristics. The constraint satisfaction problem (CSP) is a …