Portfolio Optimization: A Comparative Study

TL;DR

This paper compares three portfolio optimization methods—mean-variance, hierarchical risk parity, and autoencoder-based—evaluating their performance on Indian stock data from 2018 to 2022, highlighting their strengths in risk-adjusted returns and annual returns.

Contribution

It provides a comparative analysis of three distinct portfolio design approaches applied to Indian stock market data, highlighting their relative performance.

Findings

MVP achieved the best risk-adjusted returns on out-of-sample data.

Autoencoder-based portfolios outperformed in annual returns.

The study offers insights into the effectiveness of different portfolio optimization techniques.

Abstract

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.