cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Andhika Rafi Hananto
Contact Email
andhikarh90@gmail.com
Phone
+6282314736799
Journal Mail Official
support@jdmdc.com
Editorial Address
Graha Permata Estate, Jl. HM Bahrun Blok H9, Sokayasa, Berkoh, Kec. Purwokerto Tim., Kabupaten Banyumas, Jawa Tengah 53146
Location
Kab. banyumas,
Jawa tengah
INDONESIA
Journal of Digital Market and Digital Currency
Published by Meta Bright Indonesia
ISSN : -     EISSN : 30480981     DOI : https://doi.org/10.47738/jdmdc
Core Subject : Economy, Science,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance
Journal of Digital Market and Digital Currency publishes high-quality research on: Digital Marketing Digital Currencies Cryptocurrency Trends Blockchain Applications Fintech Innovations Our goal is to provide a platform for researchers, practitioners, and policymakers to share innovative findings, discuss emerging trends, and address the challenges and opportunities presented by the Journal of Digital Market and Digital Currency.
Arjuna Subject : Bisnis, Menejemen, dan Akutansi (semua kategori) - Bisnis, Menejemen, dan Akutansi (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol. 3 No. 1 (2026): Regular Issue March 2026" : 5 Documents clear
Assessing the Impact of Credit Score and Employment Stability on Loan Approval Using Logistic Regression Mohammed, El Felhi; Mirlam, Maram Saleh
Journal of Digital Market and Digital Currency Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v3i1.53

Abstract

This study investigates the determinants of loan approval decisions using a Logistic Regression approach based on applicants’ financial and employment characteristics. The dataset consists of key predictors, including income, credit score, loan amount, years employed, and points, which were analyzed to assess their influence on loan approval outcomes. Data preprocessing was conducted through z-score normalization, and the dataset was divided into training (80%) and testing (20%) subsets. The Logistic Regression model demonstrated exceptional predictive performance, achieving perfect values across all evaluation metrics, including Accuracy (1.000), Precision (1.000), Recall (1.000), F1-score (1.000), and ROC-AUC (1.000). These results indicate that the model was able to perfectly distinguish between approved and rejected loan applications. Further examination of model coefficients and odds ratios revealed that credit score and points were the most significant predictors positively influencing loan approval probability, while loan amount exhibited a negative relationship. The findings emphasize that creditworthiness and institutional scoring systems play a dominant role in financial decision-making, whereas income and employment history have a moderate but supportive influence. Although the model’s perfect performance highlights strong predictive capability, it may also reflect a highly structured or synthetic dataset, suggesting the need for validation using larger and more diverse samples. The study contributes to the growing literature on data-driven financial analytics by demonstrating that Logistic Regression remains a powerful and interpretable tool for assessing credit risk and improving loan approval transparency.
Causal Relationship Between AI R&D Investment and Stock Market Performance Using VAR and Granger Causality Models Salem, Abdel Badeeh M.; Aqel, Musbah J.
Journal of Digital Market and Digital Currency Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v3i1.54

Abstract

This study investigates the causal relationship between Artificial Intelligence (AI) R&D investment and stock market performance using a time-series econometric framework. Drawing on data from AI-driven firms between 2015 and 2024, the research applies Vector Autoregression (VAR) and Granger Causality models to explore whether innovation spending influences short-term financial outcomes. The analysis employs monthly aggregated data on AI R&D Spending and Stock Market Impact, supported by correlation analysis, impulse response estimation, and forecast error variance decomposition. The results indicate that AI R&D investment and market performance exhibit no statistically significant short-term causal linkage, as confirmed by non-significant Granger p-values (p > 0.05) and weak correlation (r = 0.13). The Impulse Response Function (IRF) shows a transient positive effect of R&D shocks on stock performance, peaking at approximately +0.12% before dissipating after the fourth period. Meanwhile, the Forecast Error Variance Decomposition (FEVD) reveals that more than 99% of the variance in R&D spending is explained by its own historical dynamics, suggesting minimal feedback from market reactions. These findings collectively imply that AI R&D investments operate on a long-term strategic horizon, while financial markets react within short-term informational cycles, creating a temporal disconnect between innovation effort and market recognition. The study contributes to the literature on innovation-finance dynamics by providing empirical evidence that technological progress and financial valuation evolve asynchronously, reflecting their inherently different timeframes and behavioral logics.
Deep Learning-Based Loan Approval Prediction Using Artificial Neural Network (ANN) and Feature Importance Analysis Armoogum, Sheeba; Dewi, Deshinta Arrova; Armoogum, Vinaye; Melanie, Nicolas; Kurniawan, Tri Basuki
Journal of Digital Market and Digital Currency Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v3i1.55

Abstract

The increasing demand for efficient and objective credit evaluation has motivated the adoption of artificial intelligence in financial decision-making. This study proposes a deep learning-based loan approval prediction model using an Artificial Neural Network (ANN) combined with feature importance analysis to enhance interpretability. The dataset, consisting of 2,000 loan application records with both financial and demographic attributes, was preprocessed through normalization and one-hot encoding to ensure consistent feature representation. The ANN model was trained using three hidden layers (64–32–16 neurons) with the ReLU activation function and optimized using Adam with early stopping to prevent overfitting. Experimental results demonstrate that the proposed ANN model achieves an accuracy of 92%, with a precision of 0.91, a recall of 0.93, and a ROC-AUC of 0.95, indicating excellent classification capability. The Permutation Feature Importance analysis revealed that Credit Score, Income, and Loan Amount are the most significant predictors influencing loan approval decisions. These findings confirm that the ANN model can capture complex non-linear relationships among financial attributes while maintaining transparency through explainable AI techniques. The proposed approach contributes both theoretically and practically by combining predictive power with interpretability, offering a reliable and explainable framework for automating loan evaluation in modern financial institutions.
The Impact of Financial News Sentiment on Market Index Volatility through Event-Driven Analysis Using Random Forest and Linear Regression Models Latina, Ellen Joy; Abdurahman, Mar Jane
Journal of Digital Market and Digital Currency Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v3i1.56

Abstract

This study investigates the impact of financial news sentiment on market index volatility using an event-driven analytical approach combined with machine learning models. Two predictive algorithms, Linear Regression and Random Forest Regressor, were employed to evaluate how sentiment polarity, market event type, trading volume, and sector classification influence short-term index fluctuations. The results demonstrate that both models have limited explanatory power, as reflected by low and negative R² values (−0.0147 and −0.1479), indicating that sentiment polarity alone cannot adequately capture market volatility. Feature importance analysis revealed that Trading Volume (0.48) and Market Event Type (0.31) are the most influential predictors, while Sentiment Score (0.14) contributes marginally. These findings suggest that market volatility is primarily volume-driven and event-reactive, with sentiment serving as a secondary amplifier rather than a direct causal factor. The study concludes that combining sentiment analysis with quantitative and temporal indicators may improve the modeling of complex market dynamics in future research.
Market Regime Detection in Bitcoin Time Series Using K-Means Clustering and Hidden Markov Models Haryani, Calandra A.; Chandra; Tarigan, Riswan Efendi
Journal of Digital Market and Digital Currency Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v3i1.57

Abstract

The rapid growth of cryptocurrency markets has created new challenges in understanding and predicting the structural dynamics of digital asset prices. Bitcoin, as the most traded blockchain-based currency, exhibits extreme volatility, nonlinear patterns, and complex regime shifts that traditional financial models cannot adequately capture. This study proposes a hybrid analytical framework that integrates K Means clustering with the Hidden Markov Model to identify and model multiple market regimes in Bitcoin time series data. The Bitcoin dataset used in this research contains minute-level records that were preprocessed to extract key indicators, namely logarithmic returns and rolling volatility, which represent the short-term dynamics of market behavior. The K Means algorithm was first employed to segment the data into three distinct clusters that correspond to bullish, bearish, and sideways regimes, followed by the application of the Hidden Markov Model to estimate probabilistic transitions between these regimes over time. The results reveal that the hybrid K Means and Hidden Markov Model approach achieves superior performance compared to a standalone model, as indicated by a higher log likelihood and a lower Bayesian Information Criterion value. The transition probability matrix shows that bullish and bearish regimes are highly persistent, while the sideways regime acts as a transitional buffer that connects both market extremes. The empirical findings confirm that Bitcoin prices evolve through persistent and probabilistically determined regimes rather than random fluctuations. The proposed framework provides a more comprehensive understanding of cryptocurrency market dynamics and offers practical value for investors, risk analysts, and policymakers in designing adaptive trading and risk management strategies within blockchain-based financial ecosystems.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2026 2026


Reset
Filter By Issues
All Issue Vol. 3 No. 1 (2026): Regular Issue March 2026 Vol. 2 No. 4 (2025): Regular Issue December 2025 Vol. 2 No. 3 (2025): Regular Issue September 2025 Vol. 2 No. 2 (2025): Regular Issue June 2025 Vol. 2 No. 1 (2025): Regular Issue March Vol. 1 No. 1 (2024): Regular Issue June 2024 Vol. 1 No. 3 (2024): Regular Issue December Vol. 1 No. 2 (2024): Regular Issue September