Several works in the literature have focused on the analysis of key stylized facts of financial and cryptocurrency returns linked to fundamental problems of efficiency and predictability of financial and cryptocurrency markets, including heavy tails, absence of linear autocorrelations and volatility clustering. This paper provides a study of the above properties of Bitcoin and Ethereum markets using recently proposed robust, valid and statistically justified definitions of and methods for inference on market (in)efficiency, volatility clustering, and nonlinear dependence in return time series. In contrast to existing approaches, the inference methods used in the analysis are robust to heavy-tailedness, dependence and nonlinear dynamics of returns. The results of the study indicate that Bitcoin and Ethereum returns exhibit heavy tails, uncorrelatedness over time and volatility clustering largely similar to those in developed financial markets. The analysis has important implications for cryptocurrency pricing, market efficiency, econometric modeling, risk management, market participants and regulators.
Abstract With the introduction of spot Ethereum ETFs, Ethereum plays an increasingly important role in the cryptocurrency market. In this paper, we propose a Bayesian modelling framework incorporating a mixture copula for co-modelling Ethereum returns with Bitcoin or FTSE 100 returns. The mixture copula is designed as a combination of the Clayton copula and its three rotations, Frank, and Gaussian copulas. It provides substantial flexibility for handling a variety of dependency structures. The Bayesian approach offers the advantage of jointly estimating both the margins and copulas and simulating future returns in a coherent procedure. Using 10 different risk or risk-return measures, we provide updated empirical evidence on Ethereumâs role in both cryptocurrency and mixed portfolios. The analysis not only evaluates its diversification potential numerically but also sheds light on how the optimal allocations vary across distinct risk preferences and portfolio objectives. Moreover, based on the data of 2017â2024, we estimate that Ethereum futures has a hedging effectiveness on Bitcoin of about 30â40% across different risk preferences. Beyond these findings, the Bayesian mixture copula framework represents a methodological contribution to the modelling of complex dependence structures between financial returns. Taken together, our study delivers new insights that are particularly relevant in light of the evolving cryptocurrency landscape and the increasing integration of digital assets into mainstream investment practice.
Abstract This study empirically assesses the viability of Bitcoin as an alternative investment asset within the Egyptian context from 2011 to 2023. We conduct a comparative analysis of Bitcoinâs risk-return characteristics against traditional Egyptian investment vehicles: the EGX30 stock index, physical Gold, and the USD/EGP exchange rate. Utilizing historical daily data sourced from Coinbase, Bloomberg, Yahoo Finance, and the Central Bank of Egypt, we employ standard financial metrics including annualized returns, volatility (standard deviation), and Sharpe ratios. Correlation analysis is performed to evaluate Bitcoinâs diversification potential. Furthermore, we examine asset performance during significant periods of socio-economic stress: the 2011 Egyptian Revolution, the COVID-19 pandemic (2019-2020), and the EGP devaluation period (2022-2023). Our findings reveal Bitcoinâs exceptionally high volatility ( $$\sigma \approx 3.6\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>Ï</mml:mi> <mml:mo>â</mml:mo> <mml:mn>3.6</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> daily) and potential for substantial returns, yet yielding a surprisingly negative cumulative return over the entire sample period. Gold demonstrated characteristic stability ( $$\sigma \approx 1.0\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>Ï</mml:mi> <mml:mo>â</mml:mo> <mml:mn>1.0</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> daily), while the EGX30 offered moderate growth amidst volatility ( $$\sigma \approx 1.6\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>Ï</mml:mi> <mml:mo>â</mml:mo> <mml:mn>1.6</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> daily). Correlation analysis suggests limited diversification benefits between Bitcoin and traditional assets during certain periods. Event analysis highlights varying asset reactions, with Gold often acting as a safe haven, while Bitcoin exhibited mixed behavior. While Bitcoin presents diversification potential, its extreme volatility, negative long-term cumulative return within this sample period, and the prevailing regulatory uncertainty in Egypt necessitate careful consideration for investors seeking alternative assets in a challenging macroeconomic environment characterized by inflation and currency depreciation.
While the literature features a number of proposals to defend against transaction manipulation attacks, existing proposals are still not integrated within large blockchains, such as Bitcoin, Ethereum, and Cardano. Instead, the user community opted to rely on more practical but ad-hoc solutions (such as Mempool.space) that aim at detecting censorship and transaction displacement attacks by auditing discrepancies in the mempools of so-called observers. In this paper, we precisely analyze, for the first time, the interplay between mempool auditing and the ability to detect censorship and transaction displacement attacks by malicious miners in Bitcoin and Ethereum. Our analysis shows that mempool auditing can result in mis-accusations against miners with a probability larger than 25% in some settings. On a positive note, however, we show that mempool auditing schemes can successfully audit the execution of any two transactions (with an overwhelming probability of 99.9%) if they are consistently received by all observers and sent at least 30 seconds apart from each other. As a direct consequence, our findings show, for the first time, that batch-order fair-ordering schemes can offer only strong fairness guarantees for a limited subset of transactions in real-world deployments.
Bintang Sahala Marpaung, Annaria Magdalena Marpaung, Petrosina Chece
Accurate stock price forecasting is vital for investors in formulating rational investment decisions within capital markets. This study analyzes the impact of Bitcoin, interest rates, and exchange rates on the stock prices of firms in the oil and gas mining sub-sector listed on the Indonesia Stock Exchange over the period 2018â2023. Employing a quantitative research design, the study utilizes secondary data and applies panel data regression analysis using EViews 9. The sample consists of eight firms selected from a population of eighteen companies through purposive sampling. The empirical results reveal that Bitcoin exerts a statistically significant partial effect on stock prices, whereas interest rates and exchange rates do not demonstrate a significant individual impact. Furthermore, the joint analysis indicates that Bitcoin, interest rates, and exchange rates collectively have no significant influence on stock prices. These findings suggest that investors should carefully assess stock price movements and broader market dynamics when making investment decisions, while firms are encouraged to enhance their financial performance to improve investment attractiveness.
High-frequency crypto forecasting requires systems that are accurate, explainable, and designed for human decision-making. Bitcoin presents a unique challenge for Human-Centred AI (HCAI) due to its volatility and sensitivity to heterogeneous technical, fundamental, and sentiment signals. This paper presents an explainable multimodal framework for Bitcoin forecasting at 15-minute resolution. We align five modalitiesâmarket data, on-chain metrics, the Fear & Greed Index (FGI), news, and Redditâonto a unified, leakage-safe 15-minute grid. We evaluate tree-based, sequential, and Multimodal Fusion Block (MFB) models for next-interval log-return prediction using chronological splits. Results show that while short-horizon prediction remains challenging, multimodal features consistently improve over structured baselines, particularly during event-driven periods. To ensure transparency, the framework integrates a dual-layer explanation system: SHapley Additive exPlanations (SHAP) attributions combined with large language model (LLM) narratives, ensuring outputs are both technically faithful and human-accessible. This work unlocks the âblack boxâ of complex predictive architectures, transforming opaque multimodal signals into transparent, actionable decision support for high-frequency trading.
Abstract Automatic trading systems cope with the needs of put out emotional biases from the trading operation of public assets. These systems place orders based on a price model that forecasts the future price of an asset. Those systems, developed by edge funds and institutional investors, are not available to the public, and extensive research in this field is worth the effort. In this research, we developed a short-term price model based on a neural network and used it to forecast the near-future price direction. More in depth, we introduced the feature extraction process and parametric labeling strategy to build an ML ready dataset that includes more than 400 cryptocurrencies. The model is then validated by building a trading strategy on the two most capitalized cryptos at the time of writing: Bitcoin and Ethereum. The validation uses a trading simulation that spans six years of historical data for Bitcoin and Ethereum, including both retrospective (backtest) and prospective (forward test) evaluations. The results demonstrate that the neural network-based model exhibits a very good generalization to patterns found in historical data, enabling predictions in future data within the trading simulation. In addition, a comprehensive analysis of the importance of features was conducted to enhance the interpretability and performance of the model. Finally, we test our model in a simulated trading session; it shows that, with a simple buy-only strategy plus a stop loss, the trading system limits the draw dawn during bear markets.
In this study, we evaluated the returns and return volatility of a Brazilian stablecoin linked to fertilizers during periods preceding its discontinuation. In light of the safe haven literature, we also tested the correlation between this stablecoin and a traditional cryptocurrency, Bitcoin, and modeled its behavior during periods of Bitcoinâs extreme returns. In terms of methodology, we employ GARCH-family models (including DCC-GARCH) to analyze daily data from 1 December 2022 to 16 January 2025. We also employ an analysis using Large Language Models (LLMs), evaluating the stablecoin time series considering the period of its discontinuation. The results indicated that as the discontinuation date approached, the stablecoin exhibited statistically significant lower returns and higher volatility. While the DCC-GARCH indicated no correlation between the assets, we found that the stablecoinâs returns exhibited a negative relationship with Bitcoinâs extreme returns, challenging its potential efficacy as a safe haven. This article offers practical contributions for digital asset investors, indicating that even physically backed stablecoins, designed for stability, are subject to significant volatility, idiosyncratic risks, and potential discontinuation.
AbstractContemporary blockchain architectures face a critical impasse defined herein as the "Tetra-Lemma"âa four-dimensional optimization problem comprising decentralization, security, scalability, and thermodynamic sustainability. Proof-of-Work networks confront diminishing security budgets due to the exhaustion of block subsidies, while Proof-of-Stake systems risk validator centralization. This paper establishes a Unified Monetary-Supply Framework that resolves these structural conflicts by synthesizing the deterministic "Customized Halving" schedule with the probabilistic regeneration logic of the Proof of Rinne (PoR). We demonstrate that by enforcing a "Thermodynamic Statute of Limitations" on dormant assets, the protocol functions as a Non-Equilibrium Thermodynamic Engine. This architecture transforms entropic asset attritionâtraditionally viewed as systemic lossâinto a regenerative security budget. Using Rincoin as a case study, the model proves that a high-frequency blockchain can maintain a deflationary supply curve while anchoring the effective circulation at a permanent target equilibrium, offering a rigorous blueprint for a closed-loop, regenerative digital economy over a secular horizon. Key Quantitative Findings Asymptotic Convergence: While the effective circulating supply may experience a temporary peak (approx. 27 million RIN), the Dual-Layer Temporal Architecture ensures stabilization below the 21 million threshold (specifically converging to 20.88 million RIN). Perpetual Stability: Beyond the initial mining and transition phases (spanning 443â703 years), the PoR mechanism ensures the indefinite maintenance of the effective circulating supply. This transcends the finite lifecycle of traditional PoW assets by establishing a permanent, self-sustaining regenerative cycle. Thermodynamic Equilibrium: Mathematical verification of the "Golden Ratio" between Reserve, Unrecovered Loss, and Actual Circulation (approx. 77 : 70 : 21). Publication StatusThis manuscript (v1.6.1) serves as the foundational theoretical framework for the Rincoin protocol. Future iterations will formalize the consensus mechanisms required to govern these algorithmic parameters. Integrity & Provenance ArchitectureThe scientific integrity and existence of this document are secured by a Triple-Verification Layer: 1. Academic Provenance: Indexed via Zenodo (DOI: 10.5281/zenodo.17141922). 2. Thermodynamic Timestamping: Anchored to the Bitcoin blockchain via OpenTimestamps. 3. Identity Assurance: Digitally signed by the author via a third-party certification authority (GMO Sign). Note: Verification data and the "Certificate of Authenticity" are available in the supplementary files. CorrespondencePrimary Author: Michiru Tokino (also known as Aevust in the decentralized infrastructure community). Academic Inquiries: edu@aevust.org Community Governance: @aevustus (Discord) / @aevust (X/Telegram) Keywords: Rincoin, Proof of Rinne (PoR), non-equilibrium thermodynamic engine, phase transition of value, dual-layer architecture, customized halving, thermodynamic statute of limitations, regenerative crypto-economics, blockchain tetra-lemma.
Francisco Angulo De Lafuente, V. F. Veselov, Richard Goodman
This definitive research memoria presents a comprehensive, mathematically verified paradigm for neural communication with Bitcoin mining Application-Specific Integrated Circuits (ASICs), integrating five complementary frameworks: thermodynamic reservoir computing, hierarchical number system theory, algorithmic analysis, network latency optimization, and machine-checked mathematical formalization. We establish that obsolete cryptocurrency mining hardware exhibits emergent computational properties enabling bidirectional information exchange between AI systems and silicon substrates. The research program demonstrates: (1) reservoir computing with NARMA-10 Normalized Root Mean Square Error (NRMSE) of 0.8661; (2) the Thermodynamic Probability Filter (TPF) achieving 92.19% theoretical energy reduction; (3) the Virtual Block Manager achieving +25% effective hashrate; and (4) hardware universality across multiple ASIC families including Antminer S9, Lucky Miner LV06, and Goldshell LB-Box. A significant contribution is the machine-checked mathematical formalization using Lean 4 and Mathlib, providing unambiguous definitions, machine-verified theorems, and reviewer-proof claims. Key theorems proven include: independence implies zero leakage, predictor beats baseline implies non-independence (the logical core of TPF), energy savings theoretical maximum, and Physical Unclonable Function (PUF) distinguishability witnesses. Vladimir Veselov's hierarchical number system theory explains why early-round information contains predictive power. This work establishes a new paradigm: treating ASICs not as passive computational substrates but as active conversational partners whose thermodynamic state encodes exploitable computational information.
AbstractContemporary blockchain architectures face a critical impasse defined herein as the "Tetra-Lemma"âa four-dimensional optimization problem comprising decentralization, security, scalability, and thermodynamic sustainability. Proof-of-Work networks confront diminishing security budgets, while Proof-of-Stake systems risk validator centralization. This paper presents a Unified Monetary-Supply Framework designed to resolve these structural conflicts. By deriving a closed-form solution for supply dynamics that integrates a deterministic "Customized Halving Mechanism" with probabilistic asset attrition models, we demonstrate a mathematical convergence that maintains thermodynamic security over a secular horizon. Key Quantitative Findings: Asymptotic Convergence: While effective circulating supply may experience a temporary peak (approx. 27 million RIN), all evaluated models are engineered to stabilize below the 21 million threshold (specifically converging to 20.88 million RIN). Secular Stability: The framework secures a deflationary emission schedule mirroring Bitcoinâs scarcity model over a multi-century horizon of 443â703 years. Publication Status & RoadmapThis manuscript (v1.5.0) is maintained as a Living Research Document. It serves as the foundational theoretical framework for the Rincoin protocol. Future iterations will formalize the consensus mechanisms required to govern these algorithmic parameters. Integrity & Provenance ArchitectureThe scientific integrity and existence of this document are secured by a Triple-Verification Layer: 1. Academic Provenance: Indexed via Zenodo (DOI: 10.5281/zenodo.17141922). 2. Thermodynamic Timestamping: Anchored to the Bitcoin blockchain via OpenTimestamps. 3. Identity Assurance: Digitally signed by the author via a third-party certification authority (GMO Sign). Note: Verification data and the "Certificate of Authenticity" are available in the supplementary files. CorrespondencePrimary Author: Michiru Tokino (also known as Aevust in the decentralized infrastructure community). Academic Inquiries: edu@aevust.org Community Governance: @aevustus (Discord) / @aevust (X/Telegram)
The delisting of Binance USD (BUSD) constitutes a major regulatory intervention in the stablecoin market and provides a unique opportunity to examine how targeted regulation affects liquidity allocation, market concentration, and short-run systemic risk in crypto-asset markets. Using daily data for 2023 and a linear and nonlinear Local Projections event-study framework, this paper analyzes the dynamic market responses to the BUSD delisting across major stablecoins and cryptocurrencies. The results show that liquidity displaced from BUSD is reallocated primarily toward USDT and USDC, leading to a measurable increase in stablecoin market concentration, while decentralized and algorithmic stablecoins absorb only a limited share of the shock. At the same time, Bitcoin and Ethereum experience temporary liquidity contractions followed by a relatively rapid recovery, suggesting conditional resilience of core crypto-assets. Overall, the findings document how a regulatory-induced exit of a major stablecoin reshapes short-run market dynamics and concentration patterns, highlighting potential trade-offs between regulatory enforcement and market structure. The paper contributes to the literature by providing the first empirical analysis of the BUSD delisting and by illustrating the usefulness of Local Projections for studying regulatory shocks in cryptocurrency markets.
Francisco Angulo de Lafuente, Seid Mehammed Abdu, Nirmal Tej
This paper presents SiliconHealth, a comprehensive blockchain-based healthcare infrastructure designed for resource-constrained regions, particularly sub-Saharan Africa. We demonstrate that obsolete Bitcoin mining Application-Specific Integrated Circuits (ASICs) can be repurposed to create a secure, low-cost, and energy-efficient medical records system. The proposed architecture employs a four-tier hierarchical network: regional hospitals using Antminer S19 Pro (90+ TH/s), urban health centers with Antminer S9 (14 TH/s), rural clinics equipped with Lucky Miner LV06 (500 GH/s, 13W), and mobile health points with portable ASIC devices. We introduce the Deterministic Hardware Fingerprinting (DHF) paradigm, which repurposes SHA-256 mining ASICs as cryptographic proof generators, achieving 100% verification rate across 23 test proofs during 300-second validation sessions. The system incorporates Reed-Solomon LSB watermarking for medical image authentication with 30-40% damage tolerance, semantic Retrieval-Augmented Generation (RAG) for intelligent medical record queries, and offline synchronization protocols for intermittent connectivity. Economic analysis demonstrates 96% cost reduction compared to GPU-based alternatives, with total deployment cost of $847 per rural clinic including 5-year solar power infrastructure. Validation experiments on Lucky Miner LV06 (BM1366 chip, 5nm) achieve 2.93 MH/W efficiency and confirm hardware universality. This work establishes a practical framework for deploying verifiable, tamper-proof electronic health records in regions where traditional healthcare IT infrastructure is economically unfeasible, potentially benefiting over 600 million people lacking access to basic health information systems.
Christopher Blake, Chen Feng, Xuechao Wang, Qianyu Yu
A proof of the security of the Bitcoin protocol is made rigorous, and simplified in certain parts. A computational model in which an adversary can delay transmission of blocks by time $Î$ is considered. The protocol is generalized to allow blocks of different scores and a proof within this more general model is presented. An approach used in a previous paper that used random walk theory is shown through a counterexample to be incorrect; an approach involving a punctured block arrival process is shown to remedy this error. Thus, it is proven that with probability one, the Bitcoin protocol will have infinitely many honest blocks so long as the fully-delayed honest mining rate exceeds the adversary mining rate. This means that an adversary cannot censor future transactions of a user in perpetuity, which would render the protocol useless.
Christopher Blake, Chen Feng, Xuachao Wang, Qianyu Yu
Proof of work blockchain protocols using multiple hash types are considered. It is proven that the security region of such a protocol cannot be the AND of a 51\% attack on all the hash types. Nevertheless, a protocol called Merged Bitcoin is introduced, which is the Bitcoin protocol where links between blocks can be formed using multiple different hash types. Closed form bounds on its security region in the $Î$-bounded delay network model are proven, and these bounds are compared to simulation results. This protocol is proven to maximize cost of attack in the linear cost-per-hash model. A difficulty adjustment method is introduced, and it is argued that this can partly remedy asymmetric advantages an adversary may gain in hashing power for some hash types, including from algorithmic advances, quantum attacks like Grover's algorithm, or hardware backdoor attacks.
This paper examines the directional connectedness between the returns of Bitcoin and Ethereum and the supply of stablecoins across different market conditions. Using a Quantile Vector Autoregression (QVAR) model, we analyze daily log-returns of major cryptocurrencies and changes in stablecoin supply from January 2021 to November 2024, capturing dynamics at the 5th, 50th, and 95th quantiles. Our findings show that the Total Connectedness Index (TCI) nearly triples under extreme conditions, with Bitcoin and Ethereum transitioning from passive roles in normal periods to dominant transmitters of influence during downturns. Stablecoins behave heterogeneously across regimes, with roles varying significantly even within the same subclass. Tether exhibits state-dependent behavior, acting as a net receiver of shocks in most conditions but emerging as a transmitter during bull markets. We also assessed the impact of the Terra-LUNA collapse, revealing a regime shift in the transmission of shocks: connectedness rises under normal and negative conditions but declines in positive markets. These patterns suggest that, under certain conditions, major cryptocurrencies can influence stablecoin issuance in distinct ways, leading to asymmetric adjustments in supply across individual stablecoins and shaping liquidity dynamics throughout the ecosystem. While we do not attempt to model the underlying mechanisms behind these shifts, our results point to the importance of monitoring state-dependent relationships and recognizing the diverse behaviors of stablecoins. The findings motivate the development of regime-sensitive monitoring tools and support ongoing policy discussions around stablecoin design, issuance frameworks, and market transparency.
ABSTRACT Stablecoins attract academic interest because of their valueâpegging mechanisms and price stability. This likely results in distinct market efficiency. This study compares stablecoins (USDC, Tether, Dai) with Bitcoin and Ethereum and assesses long memory through the Hurst exponent while addressing distortions caused by heavy tails and extreme events. Through shuffled and rankâorder series with a slidingâwindow approach, we provide the first reliable timeâvarying analysis. The results show that stablecoins exhibit inefficiency and antiâpersistence, with Tether being relatively more efficient. Their tail properties are highly sensitive to extreme events. In contrast, Bitcoin and Ethereum maintain stable weakâform efficiency even during the COVIDâ19 pandemic. These differences are linked to stablecoins' US dollar pegging mechanisms and regulatory constraints. The findings of this study enable comparisons of market efficiency between stablecoins and unpegged cryptocurrencies and offer insights for regulation and investment decisions.
Anti-money laundering (AML) remains a critical challenge in cryptocurrency ecosystems, where blockchainâs transparency paradoxically coexists with pseudonymity. Traditional methods often fall short in modeling the temporal and structural complexity of transaction networks. This paper introduces ChronoWave-GNN, a graph neural framework designed from the theoretical perspective of time-frequency representation learning. By combining wavelet-based frequency decomposition with temporal encoding, our model captures nonstationary and multi-scale patterns inherent in illicit financial activity. This dual-domain perspective enhances the expressive capacity of graph representations without relying on modular patching. We validate our approach on the Elliptic dataset, where ChronoWave-GNN achieves a test accuracy of 0.9802 and F1-score of 0.9799, surpassing prior state-of-the-art results. These findings suggest that unifying temporal dynamics and spectral compression offers a principled and effective pathway for robust AML in decentralized financial systems.
Consensus protocols are crucial for a blockchain system as they are what allow agreement between the system's nodes in a potentially adversarial environment. For this reason, it is paramount to ensure their correct design and implementation to prevent such adversaries from carrying out malicious behaviour. Formal verification allows us to ensure the correctness of such protocols, but requires high levels of effort and expertise to carry out and thus is often omitted in the development process. In this paper, we present IsabeLLM, a tool that integrates the proof assistant Isabelle with a Large Language Model to assist and automate proofs. We demonstrate the effectiveness of IsabeLLM by using it to develop a novel model of Bitcoin's Proof of Work consensus protocol and verify its correctness. We use the DeepSeek R1 API for this demonstration and found that we were able to generate correct proofs for each of the non-trivial lemmas present in the verification.
This paper documents the installation, configuration, and operation of a full Bitcoin node in a Linux environment, from manual compilation of the source code to complete synchronization with the network. The technical phases of the process are described, the main files generated by Bitcoin Core are analyzed, and the effects of the parameters txindex, prune, dbcache, maxmempool, and maxconnections are empirically studied. System resources during the block download (IBD) mechanism are also documented, and the operational importance of each resource is explained. This paper provides a solid foundation for future research proposals on Bitcoin node performance or for the development of blockchain data query tools.
In this work, we utilize the blockchain transactions and financial instruments to pre-dict the Bitcoin price using machine learning. We use three models: Light Gradient Boosting Machine (LightGBM), Decision Tree Regressor and Random Forest Regressor applied on a feature set which includes lagged close prices, 14-day Simple Moving Av-erage (SMA), Relative Strength Index (RSI) and daily confirmed Bitcoin transactions. The data is temporally aligned and pre-processed to maintain temporal coherence, as well as for conversational fluency. Through the results assessment by means of RMSE MAE, MAPE and RÂČ, we can found that Random Forest model has results closer to best performance with values of: 264.81 (RMSE); 175.41(MAE); for MAPE is 0.27% and; RÂČ equals to 0.9958. Our findings also lend strong support for the effectiveness of simul-taneously considering not only blockchain-specific market variables but also tradi-tional financial predictors towards improved model performance and generalization. Our findings underscore the importance of raw blockchain transaction data for pre-dicting cryptocurrency prices, and present a new tool for data-based decision making in decentralized finance.
Ni Wayan Lasmi, Ni Kadek Ayu Puspita Dewi, Ni Putu Nina Eka Lestari, Ni Ketut Arniti
Penelitian ini bertujuan untuk menganalisis strategi promosi Bitcoin yang diterapkan oleh pelaku industri aset kripto, khususnya Indodax dan edukator kripto seperti Timothy Ronald, dalam mengatasi ketidakpercayaan masyarakat Indonesia terhadap aset kripto. Ketidakpercayaan ini masih menjadi hambatan besar dalam proses adopsi Bitcoin, terutama disebabkan oleh rendahnya literasi keuangan digital, kekhawatiran terhadap keamanan investasi, serta stigma negatif yang melekat akibat fluktuasi harga dan pemberitaan media yang tidak selalu objektif. Penelitian ini menggunakan pendekatan kualitatif deskriptif yang memungkinkan peneliti memahami secara mendalam persepsi, pengalaman, serta strategi komunikasi yang diterapkan dalam promosi Bitcoin. Teknik pengumpulan data dilakukan melalui wawancara mendalam terhadap beberapa kategori informan, yaitu pengguna aktif Indodax, pengguna baru yang terdorong oleh fear of missing out (FOMO), masyarakat yang masih skeptis terhadap Bitcoin, serta karyawan dari Indodax. Selain itu, peneliti juga melakukan observasi konten media sosial dan edukasi yang disampaikan oleh influencer kripto sebagai bentuk dokumentasi promosi. Data dianalisis menggunakan model interaktif Miles dan Huberman yang meliputi proses reduksi data, penyajian data, serta penarikan kesimpulan secara tematik. Hasil penelitian menunjukkan bahwa strategi promosi yang efektif tidak hanya bergantung pada iklan atau kampanye visual semata, melainkan lebih menekankan pada edukasi berkelanjutan dan peningkatan literasi keuangan masyarakat. Penyampaian informasi yang sederhana, jujur, serta berbasis pada pengalaman nyata pengguna terbukti dapat membangun kepercayaan publik. Pendekatan edukatif yang dilakukan melalui media sosial, video interaktif, webinar, dan kolaborasi dengan influencer terbukti mampu mengubah persepsi negatif menjadi ketertarikan, terutama di kalangan generasi muda. Dengan demikian, edukasi menjadi kunci utama dalam meningkatkan minat dan kepercayaan masyarakat terhadap penggunaan Bitcoin di Indonesia.