Real-Time Portfolio Management System Utilizing Machine Learning Techniques

被引：9
作者：
Aithal, Prakash K. [1 ]
Geetha, M. [1 ]
Acharya, U. Dinesh [1 ]
Savitha, Basri [2 ]
Menon, Parthiv [1 ]
机构：
[1] Manipal Acad Higher Educ, Manipal Inst Technol, Dept Comp Sci & Engn, Manipal 576104, India
[2] Manipal Acad Higher Educ, Manipal Inst Management, Manipal 576104, India
来源：
IEEE ACCESS | 2023年 / 11卷
关键词：
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; ALGORITHMS; SELECTION;
D O I：
10.1109/ACCESS.2023.3263260
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：32595 / 32608
页数：14
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