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All Journal Eureka Herba Indonesia Enigma in Economics
Sonia Vernanda
Department of Mathematics Education, Emerald Education Center, Samarinda, Indonesia
Humanities Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Medicine & Pharmacology

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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.
Adaptive Quantile Calibration of Daily and Weekly Cycle-Low Forecasts in Bitcoin, S&P 500 Futures, and Gold Muhammad Faiz; Sonia Vernanda
Enigma in Economics Vol. 4 No. 1 (2026): Enigma in Economics
Publisher : Enigma Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61996/economy.v4i1.131

Abstract

Background. Market-cycle forecasts are vulnerable to hindsight because a low becomes identifiable only after subsequent price confirmation. Objective. This study evaluated whether an adaptive, confirmation-aware interval could attain at least 80% chronological forecast precision for daily cycle lows (DCLs) and weekly cycle lows (WCLs) in Bitcoin, S&P 500 futures, and gold. Methods. The Adaptive Quantile-Calibrated Cycle Window used only the latest 20 completed cycles. Its lower endpoint was the empirical 10th percentile of prior low-to-low durations, and its upper endpoint was the 90th percentile of prior-low-to-next-confirmation durations. Forecasts originating from 1 January 2021 through 14 July 2026 were evaluated sequentially, and the retrospective protocol was externally preregistered. Results. Fixed clocks achieved 70.9% DCL precision and 55.6% WCL precision. The adaptive interval achieved 109/127 DCL hits (85.8%; 95% CI 78.7%–90.8%) and 27/27 WCL hits (100.0%; 95% CI 87.5%–100.0%). Mean window width increased from 14.7 to 32.8 days for DCL and from 4.0 to 11.7 weeks for WCL. A wider 5th–95th percentile band produced 93.7% DCL precision with a 95% lower confidence bound of 88.1%. Conclusion. Adaptive interval calibration exceeded the 80% point target, but the gain depended on materially wider windows and a small WCL sample; prospective replication remains necessary.
Multi-Target Complementary Systems Pharmacology of a Zingiberaceae Jamu Formula (Curcuma zanthorrhiza, Curcuma longa and Zingiber officinale) in Reversing Lipotoxicity and Fibrogenesis Transcriptomics in MASH Khalil Jibran; Isadora Selestine; Sonia Vernanda
Eureka Herba Indonesia Vol. 7 No. 2 (2026): Eureka Herba Indonesia
Publisher : HM Publisher in collaboration with CMHC Research Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37275/ehi.v7i2.150

Abstract

Background: Metabolic dysfunction-associated steatohepatitis (MASH) is characterized by lipotoxicity, lobular inflammation and progressive fibrogenesis. Single-target agents show limited efficacy, underscoring the potential of traditional Indonesian Zingiberaceae Jamu combining Curcuma zanthorrhiza, Curcuma longa and Zingiber officinale, whose systems-level multi-target mechanisms remain undefined against human transcriptomes. Objective: To decipher the multi-target polypharmacological complementarity of the Zingiberaceae Jamu triad against human MASH pathophysiology using clinical biopsy RNA-Seq, network pharmacology and molecular docking. Methods: Human liver biopsy RNA-Seq transcriptomics (NCBI Gene Expression Omnibus, GEO, GSE135251; N = 216: 10 healthy controls, 51 non-alcoholic fatty liver [NAFL], 155 MASH fibrosis stages F0–F4) were integrated with bioactive metabolite profiling (Lipinski Rule of Five and Veber oral bioavailability), target prediction, protein–protein interaction (PPI) topology, KEGG enrichment and AutoDock Vina molecular docking. Results: Biopsy analysis identified 1,842 differentially expressed genes (DEGs; 1,024 upregulated, 818 downregulated), with upregulation of fibrogenic (COL1A1, TIMP1, ACTA2, TGFB1) and inflammatory genes (CCL2, CXCL8, TNF) alongside suppression of fatty acid oxidation regulators (PPARA, CPT1A, ACOX1). Intersection of 24 bioavailable phytochemicals with MASH DEGs yielded 126 shared targets (PPI: 126 nodes, 1,142 edges, average degree 18.13). Six master hub genes were identified: PPARG (degree 84), TNF (78), AKT1 (76), TGFBR1 (68), RELA (64) and MMP9 (58). Docking predicted spontaneous binding: curcumin to PPARG (ΔG = −9.4 kcal/mol) and AKT1 (−8.8); xanthorrhizol to TNF-α (−8.2); 6-shogaol to TGFBR1 (−8.6) and MMP9 (−8.4). Conclusion: The findings are consistent with a tri-pillar polypharmacological complementarity in the Zingiberaceae Jamu formula and establish a rational mechanistic framework for standardized phytopharmaceutical development against human MASH.
Co-Authors Anies Fatmawati Gayatri Putri Isadora Selestine Khalil Jibran Muhammad Faiz Muhammad Faiz