market, depression, and recession are all terms that map to discrete market states. Mental models of how markets behave in … each state and transition between states are then applied to decision-making. Implicit to that approach is the assumption … that states are persistent and recurrent over time. This article seeks to formalize notions of discrete market states by …

-based methodology to defining market states and making asset allocation decisions with respect to both current and future state … membership. State membership is based on attributes taken from traditional finance and portfolio theory namely expected growth …, and covariance. The transitional dynamics of the derived states are modeled as a Markovian process. Asset weighting and …

The hidden Markov model (HMM) is typically used to predict the hidden regimes of observation data. Therefore, this … economic variables and predict its regimes for the next month. We then look back into historical data to find the time periods … for which the four variables had similar regimes with the forecasted regimes. Within those similar periods, we analyze all …

Hidden Markov model (HMM) is a powerful machine-learning method for data regime detection, especially time series data … to predict the regimes of six global economic indicators and find the time periods in the past during which these … indicators have a combination of regimes that is similar to those predicted. Then, we analyze the five stock factors of the All …

We propose a novel asset allocation model using a Markov process of states defined by clustered efficient frontier … coefficients. While most research in Markov models of the market characterize regimes using return and volatility, we instead … market states, then defines a Markov process on the sequence of states. To incorporate these states into portfolio …