Portfolio Optimization: A Comparative Study
Jaydip Sen Subhasis Dasgupta
Portfolio optimization has been an area that has attracted considerable
attention from the financial research community. Designing a profitable
portfolio is a challenging task involving precise forecasting of future stock
returns and risks. This chapter presents a comparative study of three portfolio
design approaches, the mean-variance portfolio (MVP), hierarchical risk parity
(HRP)-based portfolio, and autoencoder-based portfolio. These three approaches
to portfolio design are applied to the historical prices of stocks chosen from
ten thematic sectors listed on the National Stock Exchange (NSE) of India. The
portfolios are designed using the stock price data from January 1, 2018, to
December 31, 2021, and their performances are tested on the out-of-sample data
from January 1, 2022, to December 31, 2022. Extensive results are analyzed on
the performance of the portfolios. It is observed that the performance of the
MVP portfolio is the best on the out-of-sample data for the risk-adjusted
returns. However, the autoencoder portfolios outperformed their counterparts on
annual returns.