Stock picking with machine learning
In recent years, deep learning models based on neural networks have revolutionized the field of artificial intelligence. Unlike traditional machine learning approaches, deep learning models extract intricate features from raw data and perform end-to-end predictions, minimizing manual intervention an...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/176936 |
| _version_ | 1826112241289658368 |
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| author | Zhu, Bangyuan |
| author2 | Wang Lipo |
| author_facet | Wang Lipo Zhu, Bangyuan |
| author_sort | Zhu, Bangyuan |
| collection | NTU |
| description | In recent years, deep learning models based on neural networks have revolutionized the field of artificial intelligence. Unlike traditional machine learning approaches, deep learning models extract intricate features from raw data and perform end-to-end predictions, minimizing manual intervention and information loss associated with multi-step learning processes. However, when applied to multi-feature stock selection tasks, existing deep learning models may encounter limitations, necessitating the design of customized network structures for optimal effectiveness.
This research proposes a novel approach inspired by convolutional neural networks to address the challenges of stock selection. A feature extraction layer akin to a convolutional layer is constructed by developing custom operator functions. When integrated with batch normalization, pooling, and fully connected layers, it extracts features from volumetric and pricing data for return rate prediction.
The experimental results demonstrate the efficacy of the proposed model in enhancing stock selection accuracy and profitability metrics compared to conventional deep learning architectures. The study showcases the potential of customized network structures in improving predictive performance and decision-making processes in investment strategies. |
| first_indexed | 2024-10-01T03:03:44Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/176936 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:03:44Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1769362024-05-24T15:44:09Z Stock picking with machine learning Zhu, Bangyuan Wang Lipo School of Electrical and Electronic Engineering ELPWang@ntu.edu.sg Computer and Information Science Stock Deep learning Artificial intelligence In recent years, deep learning models based on neural networks have revolutionized the field of artificial intelligence. Unlike traditional machine learning approaches, deep learning models extract intricate features from raw data and perform end-to-end predictions, minimizing manual intervention and information loss associated with multi-step learning processes. However, when applied to multi-feature stock selection tasks, existing deep learning models may encounter limitations, necessitating the design of customized network structures for optimal effectiveness. This research proposes a novel approach inspired by convolutional neural networks to address the challenges of stock selection. A feature extraction layer akin to a convolutional layer is constructed by developing custom operator functions. When integrated with batch normalization, pooling, and fully connected layers, it extracts features from volumetric and pricing data for return rate prediction. The experimental results demonstrate the efficacy of the proposed model in enhancing stock selection accuracy and profitability metrics compared to conventional deep learning architectures. The study showcases the potential of customized network structures in improving predictive performance and decision-making processes in investment strategies. Bachelor's degree 2024-05-23T06:53:04Z 2024-05-23T06:53:04Z 2024 Final Year Project (FYP) Zhu, B. (2024). Stock picking with machine learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/176936 https://hdl.handle.net/10356/176936 en A3225-231 application/pdf Nanyang Technological University |
| spellingShingle | Computer and Information Science Stock Deep learning Artificial intelligence Zhu, Bangyuan Stock picking with machine learning |
| title | Stock picking with machine learning |
| title_full | Stock picking with machine learning |
| title_fullStr | Stock picking with machine learning |
| title_full_unstemmed | Stock picking with machine learning |
| title_short | Stock picking with machine learning |
| title_sort | stock picking with machine learning |
| topic | Computer and Information Science Stock Deep learning Artificial intelligence |
| url | https://hdl.handle.net/10356/176936 |
| work_keys_str_mv | AT zhubangyuan stockpickingwithmachinelearning |