Asset allocation using a Markov process of clustered efficient frontier coefficients states

TL;DR

This paper introduces a new asset allocation approach using a Markov process based on clustered efficient frontier coefficients, offering a novel way to characterize market states and improve portfolio performance.

Contribution

The paper's novelty lies in modeling market states with efficient frontier coefficients and integrating this into a Markov process for enhanced asset allocation.

Findings

The model significantly outperforms benchmark portfolios in empirical tests.

Clustering efficient frontier coefficients captures market regime changes effectively.

Using state-specific tangency portfolios improves portfolio optimization.

Abstract

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 propose characterizing these states using efficient frontiers, which provide more information on the interactions of underlying assets that comprise the market. Efficient frontiers can be decomposed to their functional form, a square-root second-order polynomial defined by three coefficients, to provide a dimensionality reduction of the return vector and covariance matrix. Each month, the proposed model hierarchically clusters the monthly coefficients data up to the current month, to characterize the market states, then defines a Markov process on the sequence of states. To incorporate these states into portfolio optimization, for each state, we calculate the tangency portfolio using only return data in that state. We then take the expectation of these weights for each state, weighted by the probability of transitioning from the current state to each state. To empirically validate our proposed model, we employ three sets of assets that span the market, and show that our proposed model significantly outperforms benchmark portfolios.