Investment portfolio selection using machine learning techniques

The stock market is a crucial component of the financial market, influenced by various factors and exhibiting intrinsic patterns of change. Effective stock selec- tion is a key focus for researchers and practitioners. Traditional stock selection methods include multiple regression analysis and multi...

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Main Author: Wang, Siyang
Other Authors: Mao Kezhi
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2024
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Online Access:https://hdl.handle.net/10356/180887
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1808872024-11-01T15:45:54Z Investment portfolio selection using machine learning techniques Wang, Siyang Mao Kezhi School of Electrical and Electronic Engineering EKZMao@ntu.edu.sg Computer and Information Science Engineering Portfolio The stock market is a crucial component of the financial market, influenced by various factors and exhibiting intrinsic patterns of change. Effective stock selec- tion is a key focus for researchers and practitioners. Traditional stock selection methods include multiple regression analysis and multi-factor scoring, but these have limitations such as sensitivity to outliers and reliance on subjective weight assignment, respectively. This paper explores the application of several ensem- ble learning methods for stock selection and integrates these with the Markowitz portfolio theory to optimize investment portfolios, thereby enhancing investment returns. The main contributions of this work are: (1) Constructing a stock fea- ture indicator database, selecting key features using the RankIC method, and conducting effectiveness and correlation analyses; (2) Utilizing various machine learning models, including Decision Trees, Random Forests, AdaBoost, and XG- Boost, to evaluate their predictive power and generalization ability; (3) Optimiz- ing the Markowitz portfolio model based on model predictions and proposing an improved portfolio optimization method that maintains high investment re- turns while reducing overall risk. Empirical analysis validates the effectiveness of the proposed methods, offering new approaches and tools for stock selection and portfolio optimization. Master's degree 2024-11-01T10:53:11Z 2024-11-01T10:53:11Z 2024 Thesis-Master by Coursework Wang, S. (2024). Investment portfolio selection using machine learning techniques. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/180887 https://hdl.handle.net/10356/180887 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Computer and Information Science
Engineering
Portfolio
spellingShingle Computer and Information Science
Engineering
Portfolio
Wang, Siyang
Investment portfolio selection using machine learning techniques
description The stock market is a crucial component of the financial market, influenced by various factors and exhibiting intrinsic patterns of change. Effective stock selec- tion is a key focus for researchers and practitioners. Traditional stock selection methods include multiple regression analysis and multi-factor scoring, but these have limitations such as sensitivity to outliers and reliance on subjective weight assignment, respectively. This paper explores the application of several ensem- ble learning methods for stock selection and integrates these with the Markowitz portfolio theory to optimize investment portfolios, thereby enhancing investment returns. The main contributions of this work are: (1) Constructing a stock fea- ture indicator database, selecting key features using the RankIC method, and conducting effectiveness and correlation analyses; (2) Utilizing various machine learning models, including Decision Trees, Random Forests, AdaBoost, and XG- Boost, to evaluate their predictive power and generalization ability; (3) Optimiz- ing the Markowitz portfolio model based on model predictions and proposing an improved portfolio optimization method that maintains high investment re- turns while reducing overall risk. Empirical analysis validates the effectiveness of the proposed methods, offering new approaches and tools for stock selection and portfolio optimization.
author2 Mao Kezhi
author_facet Mao Kezhi
Wang, Siyang
format Thesis-Master by Coursework
author Wang, Siyang
author_sort Wang, Siyang
title Investment portfolio selection using machine learning techniques
title_short Investment portfolio selection using machine learning techniques
title_full Investment portfolio selection using machine learning techniques
title_fullStr Investment portfolio selection using machine learning techniques
title_full_unstemmed Investment portfolio selection using machine learning techniques
title_sort investment portfolio selection using machine learning techniques
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/180887
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