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Real-Time Portfolio Management System Utilizing Machine Learning Techniques
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DOI：10.1109/ACCESS.2023.3263260.png)
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摘要
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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.
Keyword:
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
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
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