Machine learning for value investing
With the gradual elimination of the impact of the epidemic, financial markets have also shown a gradual recovery trend. At this time, how to obtain a higher return on investment under the premise of low risk has become an important knowledge. As a classic investment strategy, value investment relies...
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| Format: | Thesis-Master by Coursework |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/173598 |
| _version_ | 1811692782777008128 |
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| author | Jin, Zhenyu |
| author2 | Wang Lipo |
| author_facet | Wang Lipo Jin, Zhenyu |
| author_sort | Jin, Zhenyu |
| collection | NTU |
| description | With the gradual elimination of the impact of the epidemic, financial markets have also shown a gradual recovery trend. At this time, how to obtain a higher return on investment under the premise of low risk has become an important knowledge. As a classic investment strategy, value investment relies on in-depth analysis of the company's fundamentals, which has lower risks than other investment strategies. And machine learning has unique advantages in data processing. This study thus proposes an innovative machine learning-based value investing model. Through the analysis of the annual financial data (revenue, profit, assets, liabilities and other financial indicators) of thousands of listed companies by using ridge regression, aim to predict the future stock price of enterprises. Through in-depth analysis of ten years of financial data of these companies, we not only verify the correlation between financial characteristics and future stock prices, but also confirm the feasibility of machine learning models in the field of value investing. In the end, we filtered out some companies that are likely to achieve higher returns in the future, providing a strong reference for investors. This study can be seem as a successful and effective attempt in the intersection of value investment and machine learning, which provides a solid theoretical support for the subsequent in-depth exploration of value investment and financial decision-making. |
| first_indexed | 2024-10-01T06:41:16Z |
| format | Thesis-Master by Coursework |
| id | ntu-10356/173598 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:41:16Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1735982024-02-23T15:44:12Z Machine learning for value investing Jin, Zhenyu Wang Lipo School of Electrical and Electronic Engineering ELPWang@ntu.edu.sg Engineering With the gradual elimination of the impact of the epidemic, financial markets have also shown a gradual recovery trend. At this time, how to obtain a higher return on investment under the premise of low risk has become an important knowledge. As a classic investment strategy, value investment relies on in-depth analysis of the company's fundamentals, which has lower risks than other investment strategies. And machine learning has unique advantages in data processing. This study thus proposes an innovative machine learning-based value investing model. Through the analysis of the annual financial data (revenue, profit, assets, liabilities and other financial indicators) of thousands of listed companies by using ridge regression, aim to predict the future stock price of enterprises. Through in-depth analysis of ten years of financial data of these companies, we not only verify the correlation between financial characteristics and future stock prices, but also confirm the feasibility of machine learning models in the field of value investing. In the end, we filtered out some companies that are likely to achieve higher returns in the future, providing a strong reference for investors. This study can be seem as a successful and effective attempt in the intersection of value investment and machine learning, which provides a solid theoretical support for the subsequent in-depth exploration of value investment and financial decision-making. Master's degree 2024-02-19T00:46:56Z 2024-02-19T00:46:56Z 2023 Thesis-Master by Coursework Jin, Z. (2023). Machine learning for value investing. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/173598 https://hdl.handle.net/10356/173598 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering Jin, Zhenyu Machine learning for value investing |
| title | Machine learning for value investing |
| title_full | Machine learning for value investing |
| title_fullStr | Machine learning for value investing |
| title_full_unstemmed | Machine learning for value investing |
| title_short | Machine learning for value investing |
| title_sort | machine learning for value investing |
| topic | Engineering |
| url | https://hdl.handle.net/10356/173598 |
| work_keys_str_mv | AT jinzhenyu machinelearningforvalueinvesting |