The new frontier of personalized portfolio management: quantitative methods with LangChain

This paper explores the integration of advanced computational techniques and Large Language Models (LLMs) in portfolio management, aiming to overcome the limitations of traditional robo-advisors and mean-variance optimization (MVO). We present a novel framework that incorporates Monte Carlo simulati...

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Main Author: Cheam, Caleb Zhong Wei
Other Authors: Ng Wee Keong
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/175212
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1752122024-04-26T15:41:36Z The new frontier of personalized portfolio management: quantitative methods with LangChain Cheam, Caleb Zhong Wei Ng Wee Keong School of Computer Science and Engineering AWKNG@ntu.edu.sg Computer and Information Science Mathematical Sciences Monte Carlo Portfolio optimization Risk management This paper explores the integration of advanced computational techniques and Large Language Models (LLMs) in portfolio management, aiming to overcome the limitations of traditional robo-advisors and mean-variance optimization (MVO). We present a novel framework that incorporates Monte Carlo simulations, Geometric Brownian Motion, and machine learning methods like clustering algorithms and differential evolution to enhance portfolio optimization. Our methodology leverages the power of LLMs to process unstructured data and provide personalized investment advice, reflecting a shift from conventional financial advisory methods toward more adaptive and investor-centric models. The research demonstrates how combining modern computational tools and AI can address specific investor preferences, improve risk management, and increase the transparency of investment strategies. We use a series of experiments to validate the effectiveness of our proposed solutions in achieving superior portfolio allocations compared to traditional methods. The findings suggest that our integrated approach not only aligns more closely with individual investor profiles but also enhances the robustness and efficiency of portfolio management in dynamic market conditions. Bachelor's degree 2024-04-21T11:43:11Z 2024-04-21T11:43:11Z 2024 Final Year Project (FYP) Cheam, C. Z. W. (2024). The new frontier of personalized portfolio management: quantitative methods with LangChain. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175212 https://hdl.handle.net/10356/175212 en SCSE23-0205 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
Mathematical Sciences
Monte Carlo
Portfolio optimization
Risk management
spellingShingle Computer and Information Science
Mathematical Sciences
Monte Carlo
Portfolio optimization
Risk management
Cheam, Caleb Zhong Wei
The new frontier of personalized portfolio management: quantitative methods with LangChain
description This paper explores the integration of advanced computational techniques and Large Language Models (LLMs) in portfolio management, aiming to overcome the limitations of traditional robo-advisors and mean-variance optimization (MVO). We present a novel framework that incorporates Monte Carlo simulations, Geometric Brownian Motion, and machine learning methods like clustering algorithms and differential evolution to enhance portfolio optimization. Our methodology leverages the power of LLMs to process unstructured data and provide personalized investment advice, reflecting a shift from conventional financial advisory methods toward more adaptive and investor-centric models. The research demonstrates how combining modern computational tools and AI can address specific investor preferences, improve risk management, and increase the transparency of investment strategies. We use a series of experiments to validate the effectiveness of our proposed solutions in achieving superior portfolio allocations compared to traditional methods. The findings suggest that our integrated approach not only aligns more closely with individual investor profiles but also enhances the robustness and efficiency of portfolio management in dynamic market conditions.
author2 Ng Wee Keong
author_facet Ng Wee Keong
Cheam, Caleb Zhong Wei
format Final Year Project
author Cheam, Caleb Zhong Wei
author_sort Cheam, Caleb Zhong Wei
title The new frontier of personalized portfolio management: quantitative methods with LangChain
title_short The new frontier of personalized portfolio management: quantitative methods with LangChain
title_full The new frontier of personalized portfolio management: quantitative methods with LangChain
title_fullStr The new frontier of personalized portfolio management: quantitative methods with LangChain
title_full_unstemmed The new frontier of personalized portfolio management: quantitative methods with LangChain
title_sort new frontier of personalized portfolio management: quantitative methods with langchain
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/175212
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