An experimental study on diversification in portfolio optimization. (1st November 2021)

• Record Type: Journal Article
• Authors: Martínez-Nieto, Luisa; Fernández-Navarro, Francisco; Carbonero-Ruz, Mariano; Montero-Romero, Teresa
• Is Part Of: Expert systems with applications. Volume 181(2021)
• Publication Date: 2021-11-01
• Subjects: Mean–variance portfolio -- Diversification strategies -- 1/N portfolio -- Hyper-parameter optimization -- Out-of-sample performance; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.115203
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: New diversification strategies, along with other naive strategies as 1/N portfolios, have been proposed in the literature as a method for overcoming concentration limitations of the mean–variance model. However, it is not clear whether these strategies outperform the classical mean–variance model in all scenarios. Motivated by these points, this manuscript contributes an experimental study in which 11 diversification and mean–variance strategies are compiled and compared with a complete repository of 10 portfolio time series problems with three different estimation windows (composing a total of 30 datasets) and then evaluated using four performance metrics. Additionally, a novel purely data-driven method for determining the optimal value of the hyper-parameter associated with each approach is also proposed. Unlike results previously found in the literature, the empirical results obtained in this study show that equally weighed models obtain the worst ranking in all evaluation metrics except for the stability index, which is hypothetically due to the hyper-parameter optimization raising the transaction cost debate. Highlights: An experimental study with eleven diversification and mean–variance strategies and thirty datasets. Equally-weighted models got the worst results for all experimental metrics except for Stability Index. A novel data-driven method to determine the optimal value of the hyper-parameters is proposed.