Artificial intelligence/machine learning for wealth management on mobile device

Traditionally, portfolio management involves balancing a portfolio with different assets using statistical methods of analysis. These analyses are typically performed by portfolio managers or expert investors. For the amateur investor, the level of research required to form a solid understanding...

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Main Author: Sim, Eccles Jia Xuen
Other Authors: Ng Wee Keong
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2022
Subjects:
Online Access:https://hdl.handle.net/10356/156611
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1566112022-04-21T04:58:25Z Artificial intelligence/machine learning for wealth management on mobile device Sim, Eccles Jia Xuen Ng Wee Keong School of Computer Science and Engineering AWKNG@ntu.edu.sg Engineering::Computer science and engineering Traditionally, portfolio management involves balancing a portfolio with different assets using statistical methods of analysis. These analyses are typically performed by portfolio managers or expert investors. For the amateur investor, the level of research required to form a solid understanding of assets can be unmanageable. In the absence of time, tools, or level of information to match the experts, this project explores artificial intelligence solutions that may aid in reducing the analytical gap between amateur investors and financial experts. Our goal is to create an application that is intuitive to an amateur investor while maintaining the technicalities required for deep valuations of portfolio assets. Apart from the ability to learn and predict optimal allocations of portfolios, the application provides supplementary features automating the analysis of a portfolio using standard modern portfolio theory (MPT) frameworks. The mobile application is developed using the Dart programming language along with the Flutter Framework. A variant of the deep reinforcement learning algorithm known as proximal policy optimization (PPO) is used as the agent to learn an investor’s portfolio and suggest optimal stock allocations for maximized returns. It is imperative to note that this mobile application is a proof of concept and is not financial advice. Keywords: Analysis; Reinforcement Learning; Mobile Application; Amateur Investor Bachelor of Engineering (Computer Science) 2022-04-21T04:58:24Z 2022-04-21T04:58:24Z 2022 Final Year Project (FYP) Sim, E. J. X. (2022). Artificial intelligence/machine learning for wealth management on mobile device. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156611 https://hdl.handle.net/10356/156611 en SCSE21-0080 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::Computer science and engineering
spellingShingle Engineering::Computer science and engineering
Sim, Eccles Jia Xuen
Artificial intelligence/machine learning for wealth management on mobile device
description Traditionally, portfolio management involves balancing a portfolio with different assets using statistical methods of analysis. These analyses are typically performed by portfolio managers or expert investors. For the amateur investor, the level of research required to form a solid understanding of assets can be unmanageable. In the absence of time, tools, or level of information to match the experts, this project explores artificial intelligence solutions that may aid in reducing the analytical gap between amateur investors and financial experts. Our goal is to create an application that is intuitive to an amateur investor while maintaining the technicalities required for deep valuations of portfolio assets. Apart from the ability to learn and predict optimal allocations of portfolios, the application provides supplementary features automating the analysis of a portfolio using standard modern portfolio theory (MPT) frameworks. The mobile application is developed using the Dart programming language along with the Flutter Framework. A variant of the deep reinforcement learning algorithm known as proximal policy optimization (PPO) is used as the agent to learn an investor’s portfolio and suggest optimal stock allocations for maximized returns. It is imperative to note that this mobile application is a proof of concept and is not financial advice. Keywords: Analysis; Reinforcement Learning; Mobile Application; Amateur Investor
author2 Ng Wee Keong
author_facet Ng Wee Keong
Sim, Eccles Jia Xuen
format Final Year Project
author Sim, Eccles Jia Xuen
author_sort Sim, Eccles Jia Xuen
title Artificial intelligence/machine learning for wealth management on mobile device
title_short Artificial intelligence/machine learning for wealth management on mobile device
title_full Artificial intelligence/machine learning for wealth management on mobile device
title_fullStr Artificial intelligence/machine learning for wealth management on mobile device
title_full_unstemmed Artificial intelligence/machine learning for wealth management on mobile device
title_sort artificial intelligence/machine learning for wealth management on mobile device
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/156611
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