Real-Time Portfolio Management System Utilizing Machine Learning Techniques
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 eq...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10087290/ |
| _version_ | 1797849316354686976 |
|---|---|
| author | Prakash K. Aithal M. Geetha Dinesh U Basri Savitha Parthiv Menon |
| author_facet | Prakash K. Aithal M. Geetha Dinesh U Basri Savitha Parthiv Menon |
| author_sort | Prakash K. Aithal |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T18:41:54Z |
| format | Article |
| id | doaj.art-8edc9a1dff8546b9a000c164e9d364d2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T18:41:54Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8edc9a1dff8546b9a000c164e9d364d22023-04-10T23:01:26ZengIEEEIEEE Access2169-35362023-01-0111325953260810.1109/ACCESS.2023.326326010087290Real-Time Portfolio Management System Utilizing Machine Learning TechniquesPrakash K. Aithal0https://orcid.org/0000-0002-4304-9512M. Geetha1https://orcid.org/0000-0002-6150-7601Dinesh U2https://orcid.org/0000-0002-0304-4725Basri Savitha3Parthiv Menon4https://orcid.org/0000-0002-9902-0014Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaDepartment of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaDepartment of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaManipal Institute of Management, Manipal Academy of Higher Education, Manipal, IndiaDepartment of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaThere 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.https://ieeexplore.ieee.org/document/10087290/Portfolio selectionportfolio optimizationportfolio managementreal-timeK-means algorithmmetaheuristic algorithms |
| spellingShingle | Prakash K. Aithal M. Geetha Dinesh U Basri Savitha Parthiv Menon Real-Time Portfolio Management System Utilizing Machine Learning Techniques IEEE Access Portfolio selection portfolio optimization portfolio management real-time K-means algorithm metaheuristic algorithms |
| title | Real-Time Portfolio Management System Utilizing Machine Learning Techniques |
| title_full | Real-Time Portfolio Management System Utilizing Machine Learning Techniques |
| title_fullStr | Real-Time Portfolio Management System Utilizing Machine Learning Techniques |
| title_full_unstemmed | Real-Time Portfolio Management System Utilizing Machine Learning Techniques |
| title_short | Real-Time Portfolio Management System Utilizing Machine Learning Techniques |
| title_sort | real time portfolio management system utilizing machine learning techniques |
| topic | Portfolio selection portfolio optimization portfolio management real-time K-means algorithm metaheuristic algorithms |
| url | https://ieeexplore.ieee.org/document/10087290/ |
| work_keys_str_mv | AT prakashkaithal realtimeportfoliomanagementsystemutilizingmachinelearningtechniques AT mgeetha realtimeportfoliomanagementsystemutilizingmachinelearningtechniques AT dineshu realtimeportfoliomanagementsystemutilizingmachinelearningtechniques AT basrisavitha realtimeportfoliomanagementsystemutilizingmachinelearningtechniques AT parthivmenon realtimeportfoliomanagementsystemutilizingmachinelearningtechniques |