The Cryptocurrency Market in Transition before and after COVID-19: An Opportunity for Investors?

Nguyen, An Pham Ngoc; Tan, Tai; Bezbradica, Marija; Crane, Martin

doi:10.3390/e24091317

Cited by 18 publications

(8 citation statements)

References 121 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Hence, by comparing this theoretical distribution with the empirical data, we can identify key eigenvalues containing specific information on the data. This characteristic of the RMT supports the need to clean the effect of noise and trend in the data [56,58].…”

Section: Random Matrix Theorysupporting

confidence: 54%

Students’ Learning Behaviour in Programming Education Analysis: Insights from Entropy and Community Detection

Mai,

Crane,

Bezbradica

2023

Entropy

Self Cite

View full text Add to dashboard Cite

The high dropout rates in programming courses emphasise the need for monitoring and understanding student engagement, enabling early interventions. This activity can be supported by insights into students’ learning behaviours and their relationship with academic performance, derived from student learning log data in learning management systems. However, the high dimensionality of such data, along with their numerous features, pose challenges to their analysis and interpretability. In this study, we introduce entropy-based metrics as a novel manner to represent students’ learning behaviours. Employing these metrics, in conjunction with a proven community detection method, we undertake an analysis of learning behaviours across higher- and lower-performing student communities. Furthermore, we examine the impact of the COVID-19 pandemic on these behaviours. The study is grounded in the analysis of empirical data from 391 Software Engineering students over three academic years. Our findings reveal that students in higher-performing communities typically tend to have lower volatility in entropy values and reach stable learning states earlier than their lower-performing counterparts. Importantly, this study provides evidence of the use of entropy as a simple yet insightful metric for educators to monitor study progress, enhance understanding of student engagement, and enable timely interventions.

show abstract

Section: Random Matrix Theorysupporting

confidence: 54%

Students’ Learning Behaviour in Programming Education Analysis: Insights from Entropy and Community Detection

Mai,

Crane,

Bezbradica

2023

Entropy

Self Cite

View full text Add to dashboard Cite

show abstract

“…This finding is interesting because we believe this is related to the unique characteristics of cryptocurrencies that the cryptocurrency market can be volatile with as much as 30 percent fluctuation in prices in a day and even a tweet from some influencer could swing the prices (Subburaj 2021 ), and the debate of whether cryptocurrencies should be treated as long-term or short-term investments heats among household investors; thus, the investment duration may be less important than households’ perception of cryptocurrency volatility in terms of risk assessment, which is supported by our findings as well. As to the evolvement of investment duration along the pandemic time horizon, a tick-by-tick dataset with 34 cryptocurrencies has been investigated by Nguyen et al ( 2022 ) between February 2019 and April 2021, and the Pearson linear similarity was measured and a minimum spanning tree (MST) was constructed to reveal that cryptocurrency investment duration has become more uncertain, and the time-varying structure does exist in the cryptocurrency investment interactions. Our finding further supports the study and suggests that the investment duration could be a less vital factor than the interaction between the investment amount and investment duration for the post-pandemic times.…”

Section: Discussionmentioning

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

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.

show abstract

“…Artificial Intelligence (AI) has not only gathered considerable attention in technologies but has also begun to infuse the daily lives of ordinary people. As AI applications increasingly find their way into various facets of society such as smart cities (Pham, Mai, and Massey 2016), education Bezbradica 2019, 2023) and finance (Nguyen et al 2022(Nguyen et al , 2023, the acquisition and utilisation of AI skills have emerged as critical factors in ensuring the safe and effective use of these technologies. This paradigm shift towards AI-driven solutions extends beyond individual interactions, as AI applications now play an integral role in delivering public services, democratic practices (Pham, O'Sullivan, and Mai 2023), commercial activities, and beyond, across Europe.…”

Section: Introductionmentioning

confidence: 99%

Addressing Digital and AI Skills Gaps in European Living Areas: A Comparative Analysis of Small and Large Communities

Pham,

O'Sullivan,

Scantamburlo

et al. 2024

AAAI

View full text Add to dashboard Cite

As Artificial Intelligence (AI) continues to permeate various aspects of societies, understanding the disparities in AI knowledge and skills across different living areas becomes imperative. Small living areas have emerged as significant contributors to Europe's economy, offering an alternative to the bustling environment of larger cities for those seeking an improved quality of life. Nonetheless, they often encounter challenges related to digital infrastructure, access to financial resources, and digital skills gaps, limiting their economic and social growth prospects. This study investigates the digital and AI skills gaps in the context of small and large European living areas, shedding light on the potential hindrances to unleashing the full economic and social potentials of these regions in an AI-enabled economy. Drawing from a comprehensive dataset encompassing 4,006 respondents across eight EU countries, this research examines the current perceptions and understandings of AI and digital skills within two distinct population groups: residents of smaller living areas and their counterparts in larger communities. Through bivariate analysis, notable insights are revealed concerning trust in AI solutions and entities, self-assessed digital skills, AI Awareness, AI Attitudes and demography variables in both population groups. These insights may refer to the significance of addressing digital and AI skills gaps in fostering growth and preparedness for the AI-driven future. As AI becomes increasingly integral to various aspects of society, targeted interventions and policies are essential to bridge these gaps and enable individuals and communities to harness the transformative potential of AI-enabled economies.

show abstract

The Cryptocurrency Market in Transition before and after COVID-19: An Opportunity for Investors?

Cited by 18 publications

References 121 publications

Students’ Learning Behaviour in Programming Education Analysis: Insights from Entropy and Community Detection

Students’ Learning Behaviour in Programming Education Analysis: Insights from Entropy and Community Detection

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

Addressing Digital and AI Skills Gaps in European Living Areas: A Comparative Analysis of Small and Large Communities

Contact Info

Product

Resources

About