GOAL ORIENTED SMART PORTFOLIO OPTIMIZATION
Portfolio optimization is a critical aspect of investment strategy, which aims to maximize returns while minimizing risk. This paper presents a comprehensive framework for portfolio optimization, encompassing risk assessment, data preprocessing, and optimization methodologies over a dataset of the N...
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| Format: | Article |
| Language: | English |
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University of Kragujevac
2025-03-01
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| Series: | Proceedings on Engineering Sciences |
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| Online Access: | https://pesjournal.net/journal/v7-n1/52.pdf |
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| author | Ayush Deshpande Rohan Gore Sankirti Shiravale Shubham Chougule Samarth Bahirgonde |
| author_facet | Ayush Deshpande Rohan Gore Sankirti Shiravale Shubham Chougule Samarth Bahirgonde |
| author_sort | Ayush Deshpande |
| collection | DOAJ |
| description | Portfolio optimization is a critical aspect of investment strategy, which aims to maximize returns while minimizing risk. This paper presents a comprehensive framework for portfolio optimization, encompassing risk assessment, data preprocessing, and optimization methodologies over a dataset of the Nifty500 index. Leveraging the investment risk profiling guide from the Chartered Financial Accountant (CFA) Institute, users are categorized into risk profiles ranging from "Low" to "Very high". Historical stock data from the NIFTY500 list is preprocessed which includes data cleansing and volatility analysis to categorize stocks into corresponding risk classes. Basic portfolios are generated using equal-weighted strategies followed by generating optimized portfolios by applying different optimization techniques: Monte Carlo simulation, Efficient Frontier, and the Black-Litterman model. These methodologies provide portfolios with optimized weights, which are further refined through a discrete allocation process. The algorithms are compared using volatility, returns, and Sharpe ratio as metrics. The effectiveness of the optimized portfolios is evaluated through backtesting against Nifty50 prices over a three-month window. This comprehensive approach offers investors a robust framework for constructing well-balanced portfolios tailored to their risk preferences and investment goals. |
| format | Article |
| id | doaj-art-4065b26633c04d088da5dc297bb536ec |
| institution | DOAJ |
| issn | 2620-2832 2683-4111 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | University of Kragujevac |
| record_format | Article |
| series | Proceedings on Engineering Sciences |
| spelling | doaj-art-4065b26633c04d088da5dc297bb536ec2025-08-20T02:56:55ZengUniversity of KragujevacProceedings on Engineering Sciences2620-28322683-41112025-03-017149550610.24874/PES07.01D.006GOAL ORIENTED SMART PORTFOLIO OPTIMIZATIONAyush Deshpande 0https://orcid.org/0000-0003-4525-8980Rohan Gore 1https://orcid.org/0009-0000-5937-5032Sankirti Shiravale 2https://orcid.org/0000-0002-9804-3978Shubham Chougule 3https://orcid.org/0009-0001-9254-6398Samarth Bahirgonde 4https://orcid.org/0009-0004-5292-7585Marathwada Mitra Mandal’s College of Engineering Pune, Pune, India Marathwada Mitra Mandal’s College of Engineering Pune, Pune, India Marathwada Mitra Mandal’s College of Engineering Pune, Pune, India Marathwada Mitra Mandal’s College of Engineering Pune, Pune, India Marathwada Mitra Mandal’s College of Engineering Pune, Pune, India Portfolio optimization is a critical aspect of investment strategy, which aims to maximize returns while minimizing risk. This paper presents a comprehensive framework for portfolio optimization, encompassing risk assessment, data preprocessing, and optimization methodologies over a dataset of the Nifty500 index. Leveraging the investment risk profiling guide from the Chartered Financial Accountant (CFA) Institute, users are categorized into risk profiles ranging from "Low" to "Very high". Historical stock data from the NIFTY500 list is preprocessed which includes data cleansing and volatility analysis to categorize stocks into corresponding risk classes. Basic portfolios are generated using equal-weighted strategies followed by generating optimized portfolios by applying different optimization techniques: Monte Carlo simulation, Efficient Frontier, and the Black-Litterman model. These methodologies provide portfolios with optimized weights, which are further refined through a discrete allocation process. The algorithms are compared using volatility, returns, and Sharpe ratio as metrics. The effectiveness of the optimized portfolios is evaluated through backtesting against Nifty50 prices over a three-month window. This comprehensive approach offers investors a robust framework for constructing well-balanced portfolios tailored to their risk preferences and investment goals.https://pesjournal.net/journal/v7-n1/52.pdfinvestmentportfolio optimizationportfolio buildingstockfinancial risk |
| spellingShingle | Ayush Deshpande Rohan Gore Sankirti Shiravale Shubham Chougule Samarth Bahirgonde GOAL ORIENTED SMART PORTFOLIO OPTIMIZATION Proceedings on Engineering Sciences investment portfolio optimization portfolio building stock financial risk |
| title | GOAL ORIENTED SMART PORTFOLIO OPTIMIZATION |
| title_full | GOAL ORIENTED SMART PORTFOLIO OPTIMIZATION |
| title_fullStr | GOAL ORIENTED SMART PORTFOLIO OPTIMIZATION |
| title_full_unstemmed | GOAL ORIENTED SMART PORTFOLIO OPTIMIZATION |
| title_short | GOAL ORIENTED SMART PORTFOLIO OPTIMIZATION |
| title_sort | goal oriented smart portfolio optimization |
| topic | investment portfolio optimization portfolio building stock financial risk |
| url | https://pesjournal.net/journal/v7-n1/52.pdf |
| work_keys_str_mv | AT ayushdeshpande goalorientedsmartportfoliooptimization AT rohangore goalorientedsmartportfoliooptimization AT sankirtishiravale goalorientedsmartportfoliooptimization AT shubhamchougule goalorientedsmartportfoliooptimization AT samarthbahirgonde goalorientedsmartportfoliooptimization |