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Sonia Vernanda
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Religion Arts Humanities Economics, Econometrics & Finance Languange, Linguistic, Communication & Media

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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.
Synergistic Alpha: A Deep Learning Framework for Forecasting Cryptocurrency Returns by Fusing On-Chain, Sentiment, and Market Data Gayatri Putri; Sonia Vernanda; Anies Fatmawati; Muhammad Faiz
Enigma in Economics Vol. 3 No. 2 (2025): Enigma in Economics
Publisher : Enigma Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61996/economy.v3i2.103

Abstract

The inherent volatility and unique economic characteristics of cryptocurrencies pose significant challenges to conventional asset-pricing models. This study investigates whether a synergistic fusion of the network’s fundamental data (on-chain metrics), market behavioral dynamics (social media sentiment), and historical market data can uncover statistically and economically significant predictive power when analyzed by advanced deep learning architectures. We developed a sophisticated forecasting and backtesting framework to predict the daily log returns of Bitcoin (BTC). The methodology is grounded in rigorous time-series analysis, beginning with Augmented Dickey-Fuller tests to ensure data stationarity. We constructed a multi-modal dataset from specified, high-frequency sources (Kaiko, Glassnode, and a custom-built FinBERT sentiment model) spanning January 1, 2018, to December 31, 2023. We systematically compared the performance of a state-of-the-art Transformer model against Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and robust econometric baselines, including GARCH(1,1) and ARIMA. The models were evaluated not only on statistical accuracy (such as Root Mean Squared Error and Directional Accuracy) but also on their economic significance via a realistic trading backtest that incorporates transaction costs. The fully integrated Hybrid Transformer model demonstrated superior forecasting accuracy, achieving the highest Directional Accuracy (61.25%). More importantly, in a transaction-cost-aware backtest, a trading strategy guided by this model yielded an annualized Sharpe Ratio of 1.58, significantly outperforming a buy-and-hold benchmark (Sharpe Ratio: 0.72). The strategy generated a statistically significant Jensen's Alpha of 0.18 (p < 0.01), indicating substantial risk-adjusted excess returns. Feature importance analysis via SHAP confirmed that social media sentiment and the NVT Signal were the most influential predictors beyond past returns. In conclusion, the findings provide strong evidence that the cryptocurrency market exhibits exploitable inefficiencies. The fusion of on-chain, sentiment, and market data, when processed by attention-based neural networks, uncovers a statistically and economically significant predictive edge. This work challenges the semi-strong form of market efficiency for digital assets and suggests that alpha is derivable from the complex, high-dimensional data footprints unique to this asset class, providing a robust framework for quantitative investment strategies.
The Afterlife of Objects: A Material Culture Analysis of Contested Artifacts in Diasporic Communities Shasa Indriyani; Sonia Vernanda; Abdul Malik
Enigma in Cultural Vol. 3 No. 2 (2025): Enigma in Cultural
Publisher : Enigma Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61996/cultural.v3i2.112

Abstract

This study investigated the complex "afterlife" of contested cultural artifacts, specifically focusing on the Indonesian keris (ceremonial dagger) held in Dutch museum collections and their significance within the Indonesian diaspora in the Netherlands. In an era of escalating repatriation debates, the profound and evolving role these objects play in the identity formation, collective memory, and cultural negotiation of diasporic communities remains a critical yet underexplored dimension. This research addressed this gap by examining how such artifacts, physically distant from their origin, continue to live vibrant, meaningful, and often contentious lives within the communities they represent. A mixed-methods approach was employed, grounded in ethnographic and material culture studies frameworks. The research was conducted between 2023 and 2024 in Amsterdam and The Hague. Data were collected through 45 in-depth, semi-structured interviews with first, second, and third-generation members of the Indonesian diaspora. This qualitative data was supplemented by a quantitative survey (n=250) to assess broader community attitudes towards the keris, museums, and cultural heritage. Thematic analysis was used for interview transcripts, while descriptive and inferential statistics were applied to survey data. The findings revealed a multifaceted and dynamic relationship with the keris. Four primary themes emerged from the qualitative data: 1) The artifact as a tangible anchor to an "imagined homeland" and ancestral lineage; 2) Significant generational shifts in meaning, moving from personal heirloom to a politicized symbol of post-colonial identity; 3) The museum as a dual site of connection and contestation; and 4) The emergence of a "digital afterlife," where online archives and social media create new forms of access and community engagement. Survey data corroborated these themes, with 88% of respondents viewing the keris as a vital symbol of their cultural identity, yet 65% expressing feelings of ambivalence or sadness regarding their location in Dutch museums. In conclusion, contested artifacts like the keris are not static relics but dynamic agents in the ongoing process of diasporic identity construction. Their afterlife is characterized by a continuous re-negotiation of meaning across generations and platforms. For diasporic communities, these objects serve as powerful conduits for memory, heritage, and political consciousness, complicating simplistic narratives of ownership and repatriation. The study concluded that understanding this diasporic dimension is essential for museums and policymakers engaging in ethical stewardship and decolonization efforts.
Co-Authors Abdul Malik Ahmad Badruddin Anies Fatmawati Darlene Sitorus Delia Tamim Gayatri Putri Gladys Putri Ifah Shandy Mary-Jane Wood Nurwijayanti Shasa Indriyani