2023/05/27 by Jaydip Sen, Sen, Jaydip, Aditya Jaiswal +11
Decision Sciences · Economics, Econometrics and Finance · #FOS: Computer and information sciences #FOS: Economics and business #Financial Markets and Investment Strategies #Machine Learning (cs.LG) #Portfolio Management (q-fin.PM) #Risk and Portfolio Optimization #Stock Market Forecasting Methods
paper · pdf · doi:10.48550/arxiv.2305.17523
openalex publication_date 2023/05/27 · openalex created_date 2023/05/31 · openalex updated_date 2026/07/28
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.