Abstract
We propose a novel multi-period trading model that allows portfolio managers to perform optimal portfolio allocation while incorporating their interpretable investment views. This model’s significant advantage is its intuitive and reactive design that incorporates the latest asset return regimes to quantitatively solve managers’ question: how certain should one be that a given investment view is occurring? First, we describe a framework for multi-period portfolio allocation formulated as a convex optimization problem that trades off expected return, risk and transaction costs. Using a framework borrowed from model predictive control introduced by Boyd et al., we employ optimization to plan a sequence of trades using forecasts of future quantities, only the first set being executed. Multi-period trading lends itself to dynamic readjustment of the portfolio when gaining new information. Second, we use the Black-Litterman model to combine investment views specified in a simple linear combination based format with the market portfolio. A data-driven method to adjust the confidence in the manager’s views by comparing them to dynamically updated regime-switching forecasts is proposed. Our contribution is to incorporate both multi-period trading and interpretable investment views into one framework and offer a novel method of using regime-switching to determine each view’s confidence. This method replaces portfolio managers’ need to provide estimated confidence levels for their views, substituting them with a dynamic quantitative approach. The framework is reactive, tractable and tested on 15 years of daily historical data. In a numerical example, this method’s benefits are found to deliver higher excess returns for the same degree of risk in both the case when an investment view proves to be correct, but, more notably, also the case when a view proves to be incorrect. To facilitate ease of use and future research, we also developed an open-source software library that replicates our results.
Highlights
Since Markowitz formulated portfolio selection as an optimization problem trading off risk and return over sixty years ago, mean-variance optimization has occupied a central role in constructing portfolios in both academic literature, and industry (Markowitz 1952)
This paper developed a novel multi-period trading model that allows portfolio managers to perform optimal dynamic asset allocation while incorporating their investment views in the market portfolio
This framework’s significant advantage is its intuitive design that provides a new quantitative tool for portfolio managers. It incorporates the latest asset return regimes obtained from Hidden Markov Models (HMMs) to quantitatively solve the question: how certain should one be that a given investment view is being realized in the current market?
Summary
Since Markowitz formulated portfolio selection as an optimization problem trading off risk and return over sixty years ago, mean-variance optimization has occupied a central role in constructing portfolios in both academic literature, and industry (Markowitz 1952). Superimposing one static set of returns and risk completely ignores the time-varying properties of asset prices over a long period of time To address this drawback, we propose a reactive multi-period portfolio optimization framework that allows the direct incorporation of investor views and quantitatively generated degrees of confidence in each view on behalf of the investor. Layered on top of the baseline equilibrium returns, the model allows an investor to incorporate their own return views for the areas where they have expertise while leaving the remaining assets to be allocated according to equilibrium returns This approach addresses both inadequacies that exist in the standard mean-variance optimization. Within the field of finance, their application is referred to as regime-switching Incorporating their predictions within mean-variance optimization has been found to improve portfolio performance in multiple ways. The proposed model improves traditional single-period mean-variance portfolios by incorporating multi-period trading and interpretable investment views into one easy-to-use framework.
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