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Personalized Investment Recommendations Using Recommendation Systems

Sadriu, Lorik LU (2023) NEKN02 20231
Department of Economics
Abstract
This paper presents a Deep Learning-based Hybrid Recommendation System (DLHR) designed specifically for institutional investors with public portfolio holdings on the Stockholm Stock Exchange. The objective is to provide personalized investment recommendations, complement existing portfolios, and explore untapped cross-selling opportunities. We evaluate the DLHR's effectiveness using selected metrics and compare it to widely used baseline methods. We utilize the mean-variance spanning tests to assess the potential for expanding the investment opportunity set. We find that investors seeking to maximize the Sharpe Ratio would have benefitted from adding the recommended stocks. Our results demonstrate that leveraging deep learning techniques... (More)
This paper presents a Deep Learning-based Hybrid Recommendation System (DLHR) designed specifically for institutional investors with public portfolio holdings on the Stockholm Stock Exchange. The objective is to provide personalized investment recommendations, complement existing portfolios, and explore untapped cross-selling opportunities. We evaluate the DLHR's effectiveness using selected metrics and compare it to widely used baseline methods. We utilize the mean-variance spanning tests to assess the potential for expanding the investment opportunity set. We find that investors seeking to maximize the Sharpe Ratio would have benefitted from adding the recommended stocks. Our results demonstrate that leveraging deep learning techniques enables effective identification and exploitation of untapped cross-selling opportunities, leading to enhanced portfolio performance and improved risk management. Thus, we conclude that implementing the DLHR approach on the Swedish stock market can provide personalized investment recommendations for institutional investors. This research highlights the practicality of employing machine learning techniques within the financial sector. (Less)
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author
Sadriu, Lorik LU
supervisor
organization
course
NEKN02 20231
year
type
H1 - Master's Degree (One Year)
subject
keywords
Recommendation systems, Deep learning, Institutional investors, Investment decision-making, Mean-variance spanning test, Cross-Selling
language
English
id
9119216
date added to LUP
2023-11-24 08:57:34
date last changed
2023-11-24 08:57:34
@misc{9119216,
  abstract     = {{This paper presents a Deep Learning-based Hybrid Recommendation System (DLHR) designed specifically for institutional investors with public portfolio holdings on the Stockholm Stock Exchange. The objective is to provide personalized investment recommendations, complement existing portfolios, and explore untapped cross-selling opportunities. We evaluate the DLHR's effectiveness using selected metrics and compare it to widely used baseline methods. We utilize the mean-variance spanning tests to assess the potential for expanding the investment opportunity set. We find that investors seeking to maximize the Sharpe Ratio would have benefitted from adding the recommended stocks. Our results demonstrate that leveraging deep learning techniques enables effective identification and exploitation of untapped cross-selling opportunities, leading to enhanced portfolio performance and improved risk management. Thus, we conclude that implementing the DLHR approach on the Swedish stock market can provide personalized investment recommendations for institutional investors. This research highlights the practicality of employing machine learning techniques within the financial sector.}},
  author       = {{Sadriu, Lorik}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Personalized Investment Recommendations Using Recommendation Systems}},
  year         = {{2023}},
}