Perception and Clustering Analysis towards Cryptocurrency Investment Decision using Machine Learning

Sittivangkul, Krit; Arreeras, Tosporn; Tiwong, Sunida

doi:10.1109/dasa54658.2022.9764969

Cited by 1 publication

(1 citation statement)

References 2 publications

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“…Burggraf (2021) developed a hierarchical risk parity approach to manage portfolio risk of cryptocurrencies. Sittivangkul et al (2022) used K-means clustering analysis to group people for their investment interests in cryptocurrencies. However, ML approach has been rarely used for risk assessment, let alone cryptocurrency investments from a household perspective, which underscores the importance of this study.…”

Section: Machine Learning Approach For Household Cryptocurrency Inves...mentioning

confidence: 99%

Investigating risk assessment in post-pandemic household cryptocurrency investments: an explainable machine learning approach

Lin

2023

J Asset Manag

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This study provides an applicable methodological approach applying artificial intelligence (AI)-based supervised machine learning (ML) algorithms in risk assessment of post-pandemic household cryptocurrency investments and identifies the best performed ML algorithm and the most important risk assessment determinants. The empirical findings from analyzing 13 determinants from 1,000 dataset collected from major cryptocurrency communities online suggest that the logistic regression (LR) algorithm outperforms the remaining six ML algorithms by using performance metrics, lift chart, and ROC chart. Moreover, to make the ML algorithm results explainable and tackle the “black box” issue, the top five most important determinants are discovered, which are the interaction between investment amount and investment duration, investment amount, perception of traditional investments, cryptocurrency literacy, and perception of cryptocurrency volatility. The present study contributes to the literature on risk assessment, especially on the household cryptocurrency investments in the post-pandemic era and the body of knowledge on explainable supervised ML algorithms.

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Section: Machine Learning Approach For Household Cryptocurrency Inves...mentioning

confidence: 99%

Investigating risk assessment in post-pandemic household cryptocurrency investments: an explainable machine learning approach

Lin

2023

J Asset Manag

View full text Add to dashboard Cite

show abstract

Perception and Clustering Analysis towards Cryptocurrency Investment Decision using Machine Learning

Cited by 1 publication

References 2 publications

Investigating risk assessment in post-pandemic household cryptocurrency investments: an explainable machine learning approach

Investigating risk assessment in post-pandemic household cryptocurrency investments: an explainable machine learning approach

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