Real-Time Portfolio Management System Utilizing Machine Learning Techniques

Prakash K. Aithal

doi:10.1109/ACCESS.2023.3263260

Real-Time Portfolio Management System Utilizing Machine Learning Techniques

2023-01-01

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DOI：10.1109/ACCESS.2023.3263260

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There are 1641 companies listed on the National Stock Exchange of India. It is undoubtedly infeasible for a retail investor to invest in all the stocks. It is a well-known fact that the portfolio's return is an average return of all its constituent stocks, and risk will be less than or equal to the maximum risk of all the portfolio components. This paper is unique as it elaborates on the entire portfolio selection, optimization, and management process. Portfolio selection is accomplished through the K-Means algorithm. Optimization is achieved utilizing the genetic algorithm, and a sliding window is applied for portfolio management. Four different ways of portfolio calculation, namely, equally-weighted portfolio, global minimum variance portfolio, market cap-weighted portfolio, and maximum Sharpe ratio portfolio, are applied. The results depict that all three optimized portfolios outperform the Nifty index. The dataset for the study is obtained from globaldatafeeds.in.

Keywords:

Portfolio selection

portfolio optimization

portfolio management

real-time

K-means algorithm

metaheuristic algorithms

maximum sharpe ratio portfolio

global minimum variance portfolio

equally-weighted portfolio

sliding window

Real-Time Portfolio Management System Utilizing Machine Learning Techniques

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