Applied mathematics and machine learning for optimal portfolio allocation

This research explores asset allocation techniques, leveraging mathematical methods to optimise and analyse equity portfolios for the Singapore Exchange (SGX). From 2003 to the first quarter of 2024, the study implements and compares four allocation models alongside Fama-French three-factor and f...

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Main Author: Suresh Babu, Vignesh Raja
Other Authors: Wong Jia Yiing, Patricia
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/176941
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1769412024-05-24T15:45:40Z Applied mathematics and machine learning for optimal portfolio allocation Suresh Babu, Vignesh Raja Wong Jia Yiing, Patricia School of Electrical and Electronic Engineering EJYWong@ntu.edu.sg Engineering Portfolio allocation This research explores asset allocation techniques, leveraging mathematical methods to optimise and analyse equity portfolios for the Singapore Exchange (SGX). From 2003 to the first quarter of 2024, the study implements and compares four allocation models alongside Fama-French three-factor and five-factor models, integrating machine learning methods such as principal component regression and hierarchical agglomerative clustering to enhance the precision of factor models and risk parity strategies. Through backtesting, the three-year lookback period was deemed optimal for SGX, with analysis revealing the benefits of three and five-factor models in specific contexts. The analysis also revealed the most optimal performances with the Risk Parity and its three-factor variant when optimised for the dispersion risk measure of Mean Absolute Deviation and the downside risk measure of Semi-Standard Deviation. The findings are presented on an interactive application, offering insights into equity portfolio management strategies using value-at-risk metrics and scenario analysis to evaluate portfolio performance under various market conditions. Bachelor's degree 2024-05-20T00:48:48Z 2024-05-20T00:48:48Z 2024 Final Year Project (FYP) Suresh Babu, V. R. (2024). Applied mathematics and machine learning for optimal portfolio allocation. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/176941 https://hdl.handle.net/10356/176941 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 Engineering
Portfolio allocation
spellingShingle Engineering
Portfolio allocation
Suresh Babu, Vignesh Raja
Applied mathematics and machine learning for optimal portfolio allocation
description This research explores asset allocation techniques, leveraging mathematical methods to optimise and analyse equity portfolios for the Singapore Exchange (SGX). From 2003 to the first quarter of 2024, the study implements and compares four allocation models alongside Fama-French three-factor and five-factor models, integrating machine learning methods such as principal component regression and hierarchical agglomerative clustering to enhance the precision of factor models and risk parity strategies. Through backtesting, the three-year lookback period was deemed optimal for SGX, with analysis revealing the benefits of three and five-factor models in specific contexts. The analysis also revealed the most optimal performances with the Risk Parity and its three-factor variant when optimised for the dispersion risk measure of Mean Absolute Deviation and the downside risk measure of Semi-Standard Deviation. The findings are presented on an interactive application, offering insights into equity portfolio management strategies using value-at-risk metrics and scenario analysis to evaluate portfolio performance under various market conditions.
author2 Wong Jia Yiing, Patricia
author_facet Wong Jia Yiing, Patricia
Suresh Babu, Vignesh Raja
format Final Year Project
author Suresh Babu, Vignesh Raja
author_sort Suresh Babu, Vignesh Raja
title Applied mathematics and machine learning for optimal portfolio allocation
title_short Applied mathematics and machine learning for optimal portfolio allocation
title_full Applied mathematics and machine learning for optimal portfolio allocation
title_fullStr Applied mathematics and machine learning for optimal portfolio allocation
title_full_unstemmed Applied mathematics and machine learning for optimal portfolio allocation
title_sort applied mathematics and machine learning for optimal portfolio allocation
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/176941
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