We consider the problem of breaking a multivariate (vector) time series into segments over
which the data is well explained as independent samples from a Gaussian distribution. We …

Regime-based asset allocation has been shown to add value over rebalancing to static
weights and, in particular, reduce potential drawdowns by reacting to changes in market …

We find economically and statistically significant gains when using machine learning for
portfolio allocation between the market index and risk-free asset. Optimal portfolio rules for …

Hidden Markov models are applied in many expert and intelligent systems to detect an
underlying sequence of persistent states. When the model is misspecified or misestimated …

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 …

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 develop a new method to find the number of volatility regimes in a nonstationary
financial time series by applying unsupervised learning to its volatility structure. We use …

The financial crisis of 2008 generated interest in more transparent, rules-based strategies for
portfolio construction, with smart beta strategies emerging as a trend among institutional …