Abstract
Mean-variance optimization often leads to unreasonable asset allocations. This problem has forced scholars and practitioners alike to introduce portfolio constraints. The scope of our study is to verify which type of constraint is more suitable for achieving efficient performance. We have applied the main techniques developed by the financial community, including classical weight, flexible, norm-based, variance-based, tracking error volatility, and beta constraints. We employed panel data on the monthly returns of the sector indices forming the MSCI All Country World Index from January 1995 to December 2020. The assessment of each strategy was based on out-of-sample performance, measured using a rolling window method with annual rebalancing. We observed that the best strategies are those subject to constraints derived from the equal-weighted model. If the goal is the best compromise between absolute return, efficiency, total risk, economic sustainability, diversification, and ease of implementation, the best solution is a portfolio subject to no short selling and bound either to the equal weighting or to TEV limits. Overall, we found that constrained optimization models represent an efficient alternative to classic investment strategies that provide substantial advantages to investors.
Highlights
Mean-variance (MV) optimization often leads to unreasonable asset allocations, which are often highly unstable and show an excessive concentration, hindering diversification (Black and Litterman 1992; Michaud 1989)
The assessment of each strategy was based on out-of-sample performance, measured using a rolling window method with annual rebalancing
We observed that the best strategies are those subject to constraints derived from the equal-weighted model
Summary
Mean-variance (MV) optimization often leads to unreasonable asset allocations, which are often highly unstable and show an excessive concentration, hindering diversification (Black and Litterman 1992; Michaud 1989). Our study focuses on the implementation and evaluation of the choice of portfolio constraints This methodology is closely related to the shrinkage approach and has gained widespread employment in both the academic and practitioner communities to manage the typical operational limits of MV optimization, deriving from the overfitting of input data of limited predictive value (Frost and Savarino 1988; Grauer and Shen 2000; Jagannathan and Ma 2003; Behr et al 2013). From an operational point of view, the issue of portfolio constraints assumes great significance in the perspective of asset managers with the aim of creating more stable portfolios that are less subject to the effects of input estimation errors in the optimization process or to extreme weights, related to the dominance of a single factor in the covariance matrix.
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