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

arXiv: https://arxiv.org/abs/2305.17523 · HTML: https://mlsanity.com/paper/2305.17523/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 65 references that are in the map.
- First posted on arXiv 27 May 2023.

## Builds on (most cited first, 24 of 24)

- [Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models](https://mlsanity.com/paper/2009.10819/) (2020)
- [A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing](https://mlsanity.com/paper/1912.07700/) (2019)
- [Stock Price Prediction Using CNN and LSTM-Based Deep Learning Models](https://mlsanity.com/paper/2010.13891/) (2020)
- [A Time Series Analysis-Based Stock Price Prediction Using Machine Learning and Deep Learning Models](https://mlsanity.com/paper/2004.11697/) (2020)
- [Accurate Stock Price Forecasting Using Robust and Optimized Deep Learning Models](https://mlsanity.com/paper/2103.15096/) (2021)
- [Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Timeseries](https://mlsanity.com/paper/2001.09769/) (2020)
- [Volatility Modeling of Stocks from Selected Sectors of the Indian Economy Using GARCH](https://mlsanity.com/paper/2105.13898/) (2021)
- [Robust Analysis of Stock Price Time Series Using CNN and LSTM-Based Deep Learning Models](https://mlsanity.com/paper/2011.08011/) (2020)
- [Profitability Analysis in Stock Investment Using an LSTM-Based Deep Learning Model](https://mlsanity.com/paper/2104.06259/) (2021)
- [Stock Portfolio Optimization Using a Deep Learning LSTM Model](https://mlsanity.com/paper/2111.04709/) (2021)
- [Design and Analysis of Robust Deep Learning Models for Stock Price Prediction](https://mlsanity.com/paper/2106.09664/) (2021)
- [Robust Portfolio Design and Stock Price Prediction Using an Optimized LSTM Model](https://mlsanity.com/paper/2204.01850/) (2022)
- [Stock Price Prediction Using Time Series, Econometric, Machine Learning, and Deep Learning Models](https://mlsanity.com/paper/2111.01137/) (2021)
- [Portfolio Optimization on NIFTY Thematic Sector Stocks Using an LSTM Model](https://mlsanity.com/paper/2202.02723/) (2022)
- [Hierarchical Risk Parity and Minimum Variance Portfolio Design on NIFTY 50 Stocks](https://mlsanity.com/paper/2202.02728/) (2022)
- [Precise Stock Price Prediction for Optimized Portfolio Design Using an LSTM Model](https://mlsanity.com/paper/2203.01326/) (2022)
- [Designing Efficient Pair-Trading Strategies Using Cointegration for the Indian Stock Market](https://mlsanity.com/paper/2211.07080/) (2022)
- [Machine Learning Applications in Misuse and Anomaly Detection](https://mlsanity.com/paper/2009.06709/) (2020)
- [Analysis of Sectoral Profitability of the Indian Stock Market Using an LSTM Regression Model](https://mlsanity.com/paper/2111.04976/) (2021)
- [A Comparative Study of Hierarchical Risk Parity Portfolio and Eigen Portfolio on the NIFTY 50 Stocks](https://mlsanity.com/paper/2210.00984/) (2022)
- [Machine Learning in Finance-Emerging Trends and Challenges](https://mlsanity.com/paper/2110.11999/) (2021)
- [Comprehensive Movie Recommendation System](https://mlsanity.com/paper/2112.12463/) (2021)
- [Stock Volatility Prediction using Time Series and Deep Learning Approach](https://mlsanity.com/paper/2210.02126/) (2022)
- [A Framework of Customer Review Analysis Using the Aspect-Based Opinion Mining Approach](https://mlsanity.com/paper/2212.10051/) (2022)

## Abstract (arXiv)

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.