Improving Portfolio Selection by Balancing Liquidity-Risk-Return: Evidence from Stock Markets

Abstract

The Modern Portfolio Theory was mathematically structured on the basis of the risk-return tradeoff: in other words, the riskier the investment, the greater the required potential return. Traditional portfolio optimization models, however, implicitly consider that all assets can be traded at any time and in any quantity, which is unrealistic. The aim was to propose a two-stage method that includes the prior classification of liquidity based on the bid-ask spread and a mathematical optimization model that uses liquidity as a defined participation constraint. Simulations were carried out using twenty years of data from the American (NYSE) and Brazilian (B3) stock exchanges. The results showed that the method developed offers a broader range of the alternatives that comprise the MV model with a more realistic approach to liquidity. The proposed method can form portfolios that respect the risk-return rules once the investor’s risk profile has been defined, making it a useful recommendation tool for institutional investors. From a conservative point of view, the developed method also showed the potential for reducing uncertain sales by 10.3% on average.