Portfolio constraints: An empirical analysis

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.