Diversified Crypto Assets Portfolio Optimization Using K-Means Clustering Algorithm And The Efficient Frontier

Rizki Setiawan*, Muhammad Saiful Hakim

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The objective of this study is to examine the possibility of utilizing the machine learning approach (K-means clustering) for finding efficient frontier based on the risk and return association. Using 84 crypto coins included in CMC Crypto 200, this research applies k-means clustering to group the research data. The result show that crypto coins are grouped into 6 clusters. Eight crypto coins selected to construct portfolio investment based on Sharpe ratio and market capitalization for portfolio optimization stage. Our testing model shown that all of our portfolio model outperforms the market especially compared with CMC Crypto 200 Index performance.

Original languageEnglish
Title of host publicationProceedings of 2023 IEEE Technology and Engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350384659
DOIs
Publication statusPublished - 2023
Event2023 IEEE Technology and Engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023 - Bengaluru, India
Duration: 15 Dec 202316 Dec 2023

Publication series

NameProceedings of 2023 IEEE Technology and Engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023

Conference

Conference2023 IEEE Technology and Engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023
Country/TerritoryIndia
CityBengaluru
Period15/12/2316/12/23

Keywords

  • efficient frontier
  • k-means clustering
  • portfolio optimization

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