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Navigating the Post-ETF Paradigm: An Integrative Multi-Factor Model for Projecting Bitcoin's 2025 Market Cycle Apex Abdul Malik; Ahmad Badruddin; Mary-Jane Wood; Sonia Vernanda; Gladys Putri; Ifah Shandy; Darlene Sitorus; Delia Tamim
Enigma in Economics Vol. 3 No. 1 (2025): Enigma in Economics
Publisher : Enigma Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61996/economy.v3i1.91

Abstract

Bitcoin’s market structure underwent a fundamental and irreversible transformation following the 2024 regulatory approval and launch of spot Exchange-Traded Funds (ETFs) in the United States. This event catalyzed an unprecedented wave of institutional adoption, signaling the asset's maturation from a fringe, retail-driven speculative vehicle into an emergent institutional-grade macro-asset. This study moves beyond traditional cyclical models, which are predicated on historical, pre-institutional market dynamics, to analyze Bitcoin's valuation within this profoundly evolved landscape. The primary objective is to project the potential price apex for Bitcoin in the 2024-2025 market cycle by developing and applying a transparent, replicable, and comprehensive multi-factor analytical framework. A multi-factorial, longitudinal analysis was conducted using a combination of publicly available data and simulated datasets from Q1 2022 to Q2 2025. The model is built upon a structured, semi-quantitative framework designed to synthesize three core analytical pillars: (1) Macroeconomic Environment, quantitatively assessing the impact of Federal Reserve interest rate policy, US Dollar Index (DXY) dynamics, and inflation trends through correlation analysis and sensitivity modeling. (2) On-Chain Intelligence, utilizing a suite of metrics from primary sources like Glassnode, including MVRV Z-Score, LTH-SOPR, and Illiquid Supply growth, while critically evaluating the continued validity of their historical thresholds. (3) Market & Flow Dynamics, which integrates technical analysis with a rigorous, quantitative assessment of spot ETF demand versus daily new supply, moving beyond subjective interpretations of price charts. A transparent weighting rubric was developed to integrate the findings from each pillar, mitigating subjective bias and ensuring the analytical synthesis is replicable. The synthesis of the model's components revealed a powerful confluence of bullish factors projected to intensify through late 2024 and into 2025. The Macroeconomic pillar scored moderately positive, forecasting a probable shift to monetary easing. The On-Chain pillar registered a strongly positive score, driven by a profound and persistent supply shock, evidenced by record illiquid supply growth and sustained exchange outflows, indicating strong holder conviction. The Market & Flow Dynamics pillar also scored strongly positive, with institutional demand via ETFs consistently outstripping newly mined supply by a significant multiple. The model's base-case scenario, derived from the weighted synthesis of these pillars, projects a Bitcoin price apex in the range of $150,000 to $200,000, with the most probable timing for this peak occurring between Q4 2024 and Q2 2025. In conclusion, the findings indicate that the 2024-2025 Bitcoin market cycle is fundamentally distinct from its predecessors, primarily driven by a structural, institutional-led demand shock that interacts with, and is amplified by, traditional macroeconomic tailwinds and established cyclical patterns. The projected price apex reflects a market structure that has matured, with future cycles likely to be more influenced by global liquidity conditions than the halving event alone. This research provides a robust, transparent, and theoretically grounded framework for valuing Bitcoin in its new role within the global financial system and offers a template for future analysis of digital assets as they integrate with traditional finance.
The Velocity of Relevance: Mapping the Structural Divergence Between Labor Market Signals and University Curricula in Indonesia via Text Mining and Network Analysis Bimala Putri; Delia Tamim; Hesti Putri
Open Access Indonesia Journal of Social Sciences Vol. 8 No. 6 (2025): Open Access Indonesia Journal of Social Sciences
Publisher : HM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37275/oaijss.v8i6.310

Abstract

The persistent disconnect between higher education outcomes and labor market demands, frequently termed the skills mismatch, remains a critical barrier to Indonesia's economic competitiveness in the Fourth Industrial Revolution. Traditional survey-based methodologies often lack the granularity to capture dynamic market shifts and technical nuances. This study employs a Big Data approach, utilizing automated web scraping to harvest N = 1,042,500 unique job advertisements from major Indonesian portals and N = 4,500 course syllabi from 50 top-tier Indonesian universities between 2023 and 2024. We applied Natural Language Processing, specifically Latent Dirichlet Allocation for topic modeling, and Social Network Analysis to calculate semantic overlap and centrality measures between industry demands and academic provision. We utilized the Overlap Coefficient to correct for corpus size imbalance. The analysis reveals a structural divergence: while 82% of job ads prioritize Digital Fluency and Agile Project Management, only 28% of curricula explicitly integrate these competencies. Network analysis identifies Data Analysis as a peripheral node in academic graphs but a central hub in industry networks with a Betweenness Centrality of 0.45. Conversely, theoretical constructs dominant in academia show weak linkage to employability clusters. In conclusion, the findings evidence a systemic velocity gap where industry requirements evolve three times faster than curriculum adaptation. We propose a dynamic, API-driven curriculum model to mitigate this asymmetry.
The Velocity of Relevance: Mapping the Structural Divergence Between Labor Market Signals and University Curricula in Indonesia via Text Mining and Network Analysis Bimala Putri; Delia Tamim; Hesti Putri
Open Access Indonesia Journal of Social Sciences Vol. 8 No. 6 (2025): Open Access Indonesia Journal of Social Sciences
Publisher : HM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37275/oaijss.v8i6.310

Abstract

The persistent disconnect between higher education outcomes and labor market demands, frequently termed the skills mismatch, remains a critical barrier to Indonesia's economic competitiveness in the Fourth Industrial Revolution. Traditional survey-based methodologies often lack the granularity to capture dynamic market shifts and technical nuances. This study employs a Big Data approach, utilizing automated web scraping to harvest N = 1,042,500 unique job advertisements from major Indonesian portals and N = 4,500 course syllabi from 50 top-tier Indonesian universities between 2023 and 2024. We applied Natural Language Processing, specifically Latent Dirichlet Allocation for topic modeling, and Social Network Analysis to calculate semantic overlap and centrality measures between industry demands and academic provision. We utilized the Overlap Coefficient to correct for corpus size imbalance. The analysis reveals a structural divergence: while 82% of job ads prioritize Digital Fluency and Agile Project Management, only 28% of curricula explicitly integrate these competencies. Network analysis identifies Data Analysis as a peripheral node in academic graphs but a central hub in industry networks with a Betweenness Centrality of 0.45. Conversely, theoretical constructs dominant in academia show weak linkage to employability clusters. In conclusion, the findings evidence a systemic velocity gap where industry requirements evolve three times faster than curriculum adaptation. We propose a dynamic, API-driven curriculum model to mitigate this asymmetry.