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

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