Resilient Portfolio Optimization using Traditional and Data-Driven Models for Cryptocurrencies and Stocks

Abstract

Constructing resilient portfolios is of crucial and utmost importance to investment management. This study compares traditional and data-driven models for building resilient portfolios and analyzes their performance for stocks (S&P 500) and highly volatile cryptocurrency markets. The study investigates the performance of traditional models, such as mean-variance and constrained optimization, and a recently proposed data-driven resilient portfolio optimization model for stocks. Moreover, the study analyzes these methods with evolving S&P CME bitcoin futures index and the Crypto20 index. These analyses highlight the need for further investigation into traditional and data-driven approaches for resilient portfolio optimization, including higher-order moments, particularly under varying market conditions. This study provides valuable insights for investors and portfolio managers aiming to build resilient portfolios that could be used in different market environments.