A collective portfolio selection approach for investment clubs

Yung Ming Li; Lien Fa Lin; Min Cheng Hung

doi:10.1016/j.im.2023.103909

A collective portfolio selection approach for investment clubs

Yung Ming Li^*, Lien Fa Lin, Min Cheng Hung

^*Corresponding author for this work

Institute of Information Management

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

Recently, with the popularity of social investing platforms, participating in an investment club has become a good choice for investors. Following financial experts in the investment club likely generates more profit as they have higher expertise in planning an investment portfolio. In this study, we propose a portfolio selection mechanism that combines collective intelligence extracted from investors’ opinions and LSTM stock price predictions to infer a club's investment preference and predict the profitability of the extracted investment targets. Based on a club's risk tolerance and investment preference, the proposed mechanism can create an appropriate stock portfolio for the investors in the club. Utilizing StockTwits and stock historical data, the experimental results verify that the proposed portfolio selection mechanism performs better than market indices and other benchmark approaches in the market.

Original language	English
Article number	103909
Journal	Information and Management
Volume	61
Issue number	2
DOIs	https://doi.org/10.1016/j.im.2023.103909
State	Published - Mar 2024

Keywords

Collective intelligence
Investment club
LSTM
Recommendation mechanism
Social network
Text mining

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10.1016/j.im.2023.103909

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title = "A collective portfolio selection approach for investment clubs",

abstract = "Recently, with the popularity of social investing platforms, participating in an investment club has become a good choice for investors. Following financial experts in the investment club likely generates more profit as they have higher expertise in planning an investment portfolio. In this study, we propose a portfolio selection mechanism that combines collective intelligence extracted from investors{\textquoteright} opinions and LSTM stock price predictions to infer a club's investment preference and predict the profitability of the extracted investment targets. Based on a club's risk tolerance and investment preference, the proposed mechanism can create an appropriate stock portfolio for the investors in the club. Utilizing StockTwits and stock historical data, the experimental results verify that the proposed portfolio selection mechanism performs better than market indices and other benchmark approaches in the market.",

keywords = "Collective intelligence, Investment club, LSTM, Recommendation mechanism, Social network, Text mining",

author = "Li, {Yung Ming} and Lin, {Lien Fa} and Hung, {Min Cheng}",

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year = "2024",

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AU - Lin, Lien Fa

AU - Hung, Min Cheng

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N2 - Recently, with the popularity of social investing platforms, participating in an investment club has become a good choice for investors. Following financial experts in the investment club likely generates more profit as they have higher expertise in planning an investment portfolio. In this study, we propose a portfolio selection mechanism that combines collective intelligence extracted from investors’ opinions and LSTM stock price predictions to infer a club's investment preference and predict the profitability of the extracted investment targets. Based on a club's risk tolerance and investment preference, the proposed mechanism can create an appropriate stock portfolio for the investors in the club. Utilizing StockTwits and stock historical data, the experimental results verify that the proposed portfolio selection mechanism performs better than market indices and other benchmark approaches in the market.

AB - Recently, with the popularity of social investing platforms, participating in an investment club has become a good choice for investors. Following financial experts in the investment club likely generates more profit as they have higher expertise in planning an investment portfolio. In this study, we propose a portfolio selection mechanism that combines collective intelligence extracted from investors’ opinions and LSTM stock price predictions to infer a club's investment preference and predict the profitability of the extracted investment targets. Based on a club's risk tolerance and investment preference, the proposed mechanism can create an appropriate stock portfolio for the investors in the club. Utilizing StockTwits and stock historical data, the experimental results verify that the proposed portfolio selection mechanism performs better than market indices and other benchmark approaches in the market.

KW - Collective intelligence

KW - Investment club

KW - LSTM

KW - Recommendation mechanism

KW - Social network

KW - Text mining

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DO - 10.1016/j.im.2023.103909

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SN - 0378-7206

VL - 61

JO - Information and Management

JF - Information and Management

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ER -

A collective portfolio selection approach for investment clubs

Abstract

Keywords

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