Financial portfolio optimization
Financial markets provide platforms where businesses can gather funds from individual investors and investors can in turn gain from the growth of businesses. The objectives of an investment are always maximizing its return and minimizing its risk by allocating limited funds among a range of assets....
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2021
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| Online Access: | https://hdl.handle.net/10356/149903 |
| _version_ | 1811695373714980864 |
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| author | Chen, Nannan |
| author2 | Ponnuthurai Nagaratnam Suganthan |
| author_facet | Ponnuthurai Nagaratnam Suganthan Chen, Nannan |
| author_sort | Chen, Nannan |
| collection | NTU |
| description | Financial markets provide platforms where businesses can gather funds from individual investors and investors can in turn gain from the growth of businesses. The objectives of an investment are always maximizing its return and minimizing its risk by allocating limited funds among a range of assets. This can be characterized as a multi-objective optimization problem (MOP).
Multi-objective evolutionary algorithm (MOEA) is an effective tool to identify multiple Pareto-optimal solutions which represent best possible trade-offs for different risk-return preferences among different objectives. Each solution defines a portfolio. Investors with different risk tolerance can thus choose different portfolios to fit their needs. In addition, clustering technique is applied before MOEA to gather the assets with high correlation. The portfolio risk is thus reduced by holding combinations of assets from different clusters. In this report, I adapt the clustering technique and integrate into the framework of MOEAs to enhance the diversity of the evolutionary process. I can thus obtain Pareto-optimal solutions which are close to global optimums. As a result, empowered by the above combination techniques, investors can find portfolios that fulfil their investment requirements.
Since each MOEA has its own advantages and disadvantages in a particular situation, I choose four MOEAs that are either representative or promising as a technique. Experimentally, I am particularly interested in stock markets since it is one major financial market. With extensive experiments on real-world datasets, I show the performance of each MOEA by analyzing each Pareto-optimal solution. The experimental framework developed in this report can be easily applied to other financial markets. |
| first_indexed | 2024-10-01T07:22:27Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/149903 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:22:27Z |
| publishDate | 2021 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1499032023-07-07T18:04:21Z Financial portfolio optimization Chen, Nannan Ponnuthurai Nagaratnam Suganthan School of Electrical and Electronic Engineering EPNSugan@ntu.edu.sg Engineering::Electrical and electronic engineering::Computer hardware, software and systems Financial markets provide platforms where businesses can gather funds from individual investors and investors can in turn gain from the growth of businesses. The objectives of an investment are always maximizing its return and minimizing its risk by allocating limited funds among a range of assets. This can be characterized as a multi-objective optimization problem (MOP). Multi-objective evolutionary algorithm (MOEA) is an effective tool to identify multiple Pareto-optimal solutions which represent best possible trade-offs for different risk-return preferences among different objectives. Each solution defines a portfolio. Investors with different risk tolerance can thus choose different portfolios to fit their needs. In addition, clustering technique is applied before MOEA to gather the assets with high correlation. The portfolio risk is thus reduced by holding combinations of assets from different clusters. In this report, I adapt the clustering technique and integrate into the framework of MOEAs to enhance the diversity of the evolutionary process. I can thus obtain Pareto-optimal solutions which are close to global optimums. As a result, empowered by the above combination techniques, investors can find portfolios that fulfil their investment requirements. Since each MOEA has its own advantages and disadvantages in a particular situation, I choose four MOEAs that are either representative or promising as a technique. Experimentally, I am particularly interested in stock markets since it is one major financial market. With extensive experiments on real-world datasets, I show the performance of each MOEA by analyzing each Pareto-optimal solution. The experimental framework developed in this report can be easily applied to other financial markets. Bachelor of Engineering (Electrical and Electronic Engineering) 2021-06-10T04:38:16Z 2021-06-10T04:38:16Z 2021 Final Year Project (FYP) Chen, N. (2021). Financial portfolio optimization. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/149903 https://hdl.handle.net/10356/149903 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering::Computer hardware, software and systems Chen, Nannan Financial portfolio optimization |
| title | Financial portfolio optimization |
| title_full | Financial portfolio optimization |
| title_fullStr | Financial portfolio optimization |
| title_full_unstemmed | Financial portfolio optimization |
| title_short | Financial portfolio optimization |
| title_sort | financial portfolio optimization |
| topic | Engineering::Electrical and electronic engineering::Computer hardware, software and systems |
| url | https://hdl.handle.net/10356/149903 |
| work_keys_str_mv | AT chennannan financialportfoliooptimization |