A Comparative Analysis of Portfolio Optimization Using Mean-Variance,
  Hierarchical Risk Parity, and Reinforcement Learning Approaches on the Indian
  Stock Market
Jaydip Sen Aditya Jaiswal Anshuman Pathak Atish Kumar Majee Kushagra Kumar Manas Kumar Sarkar Soubhik Maji
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.