Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal Journal of Digital Market and Digital Currency Journal of Current Research in Blockchain International Journal Research on Metaverse
Doan, Minh Luan
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Volatility and Risk Assessment of Blockchain Cryptocurrencies Using GARCH Modeling: An Analytical Study on Dogecoin, Polygon, and Solana Doan, Minh Luan
Journal of Digital Market and Digital Currency Vol. 2 No. 1 (2025): Regular Issue March
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i1.25

Abstract

This study analyzed the volatility and risk profiles of three prominent blockchain-based cryptocurrencies—Dogecoin, Polygon, and Solana—using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. Volatility, a key risk metric for cryptocurrencies, was modeled through the GARCH(1,1) framework, which effectively captured the time-varying nature of price fluctuations. The analysis revealed that Dogecoin exhibited the highest volatility and risk, primarily driven by its speculative market behavior and social media influence. Polygon and Solana, while also volatile, demonstrated more stability, with their risk profiles reflecting the technological advancements and broader use cases within their respective blockchain ecosystems. The study also incorporated Value at Risk (VaR) and Conditional Value at Risk (CVaR) metrics to assess the potential downside risks for each cryptocurrency. Dogecoin had the highest potential for extreme losses, followed by Polygon and Solana. The GARCH model successfully identified the volatility persistence in these assets, showing that past market conditions heavily influenced future volatility. This research contributes to the literature on cryptocurrency volatility by applying the GARCH(1,1) model to analyze digital assets with varying market characteristics. The findings emphasize the need for robust risk management strategies tailored to the unique behaviors of individual cryptocurrencies. Limitations of the study included the use of historical data and the focus on only three cryptocurrencies, suggesting opportunities for future research. Potential areas for further study include the incorporation of additional variables, such as macroeconomic indicators, and the exploration of alternative volatility models, such as EGARCH or TGARCH, to better capture the complexities of cryptocurrency markets. These insights provide valuable guidance for investors, risk managers, and policymakers navigating the volatile and evolving landscape of blockchain-based digital assets.
Sentiment Classification of Bitcoin-Related Tweets Using VADER: Analyzing Temporal Sentiment Trends in Cryptocurrency Markets Doan, Minh Luan
Journal of Current Research in Blockchain Vol. 2 No. 2 (2025): Regular Issue June 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i2.31

Abstract

This study explores the intricate relationship between public sentiment and Bitcoin market dynamics, leveraging sentiment analysis of Twitter data to uncover patterns in emotional discourse surrounding cryptocurrency. By analyzing sentiment trends from 2013 to 2019, the research reveals a cyclical interplay between positive and negative sentiment, often aligning with Bitcoin’s dramatic price movements. Positive sentiment peaks coincide with periods of market optimism, driven by narratives of technological innovation and mainstream adoption, while negative sentiment troughs reflect moments of fear, uncertainty, and doubt (FUD) during market corrections. Despite the observed alignment, the correlation between sentiment and Bitcoin prices remains weak, underscoring the complexity of market behavior and the influence of external factors such as macroeconomic trends and regulatory developments. The findings highlight the potential of sentiment analysis as a complementary tool for market prediction, offering valuable insights into the emotional undercurrents that shape cryptocurrency markets. This study contributes to a deeper understanding of the socio-economic and psychological dimensions of Bitcoin, providing a foundation for future research in sentiment-driven market analysis.
Predicting the Success of Virtual-Themed Animated Movies Using Random Forest Regression Doan, Minh Luan
International Journal Research on Metaverse Vol. 1 No. 3 (2024): Regular Issue December
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v1i3.16

Abstract

This paper presents a study using Random Forest Regression to predict the success of virtual-themed animated movies, with a focus on revenue and popularity. The dataset included 100 animated films, featuring attributes such as runtime, vote average, and genres. The objective was to identify the key factors influencing movie success. The model achieved an R² of 0.85 for predicting popularity, with vote average being the most significant predictor (importance score = 0.50), followed by runtime (importance score = 0.25). However, predicting revenue was more challenging, with the model achieving an R² of 0.65 and RMSE of 100, indicating that external factors like marketing and competition play a significant role. The findings reveal that audience reception, as captured by vote average, is crucial for predicting both popularity and revenue. The novelty of this research lies in its focus on virtual-themed animated movies and the use of machine learning to identify success factors in this niche genre. The study contributes to understanding the dynamics of movie success, offering valuable insights for filmmakers and production companies. Future research should explore the inclusion of external factors and advanced techniques to improve revenue prediction accuracy.
Co-Authors