Multiobjective portfolio optimization: Forecasting and evaluation under investment horizon heterogeneity

This study designs a methodological framework to forecasting allocation weights and evaluating multiobjective portfolios considering the investment horizon heterogeneity. The investment horizon heterogeneity may induce the terrible data loss problem under traditional asset return definition, while in the real world, there are many portfolio makers whose investment horizons are not consistent. The methodological framework has three parts. First, this paper gives a novel multitimescale analysis (MTA) tool as a computation procedure to decompose the raw return series, and the decomposed subreturn series could represent the information for a portfolio maker with specific investment horizon and has the same data length as the raw return series. Second, proposed methodological framework uses a time‐varying parameter GAS‐D‐Vine‐Copula model to construct the joint distribution of subreturn series of multiassets in a portfolio. Third, due to the stochastic dominance consistency issue, this paper applies three different utility functions as the outputs of a portfolio strategy and two cost functions as the inputs of a portfolio strategy in an efficiency evaluation model. The empirical example of US aviation stock market data from 2013 to 2021 reveals that the Mean‐Skewness‐Volatility‐HMCR‐LPM multiobjective has the greatest numbers of optimal strategy timings for portfolio makers with 2‐, 3–5‐, and 10–50‐day‐length investment horizons. The investment horizon of 1‐day length is the least efficient, and the investment horizon between 10‐ and 50‐day length is the most efficient. The proposed methodology indicates that more multiobjectives in a portfolio strategy are not necessarily better, and there is an optimal range of investment horizons for portfolio makers.