A Comparative Analysis of Portfolio Optimization Using Mean-Variance, Hierarchical Risk Parity, and Reinforcement Learning Approaches on the Indian Stock Market

TL;DR

This paper compares three portfolio optimization methods—mean-variance, hierarchical risk parity, and reinforcement learning—using Indian stock market data, evaluating their performance based on returns, risks, and Sharpe ratios.

Contribution

It provides a comparative analysis of traditional and machine learning-based portfolio optimization approaches applied to the Indian stock market.

Findings

Reinforcement learning-based portfolios achieved higher returns.

Hierarchical risk parity offered better risk management.

Mean-variance portfolios showed moderate performance.

Abstract

This paper presents a comparative analysis of the performances of three portfolio optimization approaches. Three approaches of portfolio optimization that are considered in this work are the mean-variance portfolio (MVP), hierarchical risk parity (HRP) portfolio, and reinforcement learning-based portfolio. The portfolios are trained and tested over several stock data and their performances are compared on their annual returns, annual risks, and Sharpe ratios. In the reinforcement learning-based portfolio design approach, the deep Q learning technique has been utilized. Due to the large number of possible states, the construction of the Q-table is done using a deep neural network. The historical prices of the 50 premier stocks from the Indian stock market, known as the NIFTY50 stocks, and several stocks from 10 important sectors of the Indian stock market are used to create the environment for training the agent.