The paper aimed to investigate the statistical relationship between Bitcoin prices and Ethereum trading volumes, as well as to create a simple predictive model for Ethereum trading volumes based on Bitcoin prices. To perform Spearmanâs rank correlation analysis and to construct an artificial neural network (ANN) model, daily closing prices of Bitcoin in USD and daily trading volumes of Ethereum were utilized. The timeframe covered by the data starts May 1, 2020 and ends November 22, 2025. In this study, Ethereum volumes were treated as the dependent variable, while Bitcoin prices served as the independent variable. The findings indicate a significant, moderate, positive correlation between Bitcoin prices and Ethereum volumes, and the ANN model successfully predicted Ethereum volumes with a high level of accuracy. These results reinforce existing evidence regarding the relationships among cryptocurrencies. Furthermore, by confirming the efficacy of artificial neural networks (ANN) in predicting trends within the cryptocurrency market, the study also makes a methodological contribution. In addition, the study also offers a simpler modelling approach that highlights the significance of bilateral interactions among major cryptocurrencies through a single-input model. Based on the impressive performance of the ANN model, exchanges, fintech companies, and investment firms could incorporate lightweight machine-learning systems into their forecasting tools to provide real-time analytics with minimal processing requirements.
This study examines PUPS, a representative Bitcoin ecosystem project, to elucidate the success mechanisms of Web3 meme projects. We test three hypotheses: (H1) community sentiment and social media virality constitute the fundamental drivers of meme asset valuation; (H2) core participants accumulate positions at low prices and distribute at peak valuations; (H3) meme diffusion is predominantly driven by internal imitation, significantly outweighing external marketing effects. Applying event study methodology, social network analysis, and the Bass diffusion model to social media and on-chain data, our findings support all hypotheses, revealing a âpropagationâsentimentâtradingâ pathway. We identify a distinctive âcommunity fingerprintâ comprising 348 original holders and 5,036 6-core addresses, characterizing them as both community stabilizers and hype catalysts. This pattern illustrates the paradox of âeconomic recentralizationâ within technically decentralized systems. Paradoxically, the founder's public assertion that âeverything will eventually go to zeroâ evolved into a cultural ritual that reinforced community consensus. This study concludes by proposing a âmeme financializationâ framework, offering novel perspectives for understanding âAttention as Capitalâ, âConsensus as Valueâ, and âNarrative as Assetâ in Web3 ecosystems.
Thomas Bakaysa, Ahmet Kurt, Abdul-Salem Beibitkhan, J E HernĂÂĄndez Leon ¡ 9 authors
Bitcoin's Lightning Network (LN) can be exploited as a covert, low-cost command-and-control (C&C) channel for botnets, as demonstrated by the LNBot and D-LNBot designs. However, both remain proof-of-concept prototypes evaluated only through simulation, leaving key questions about real-world topology formation, propagation complexity, and resilience to takedowns unanswered. We present LNTest, the first reusable testbed for LN-based botnets, built from Core Lightning nodes containerized with Docker over a shared Bitcoin Core regtest chain. LNTest supports three overlay topology modes (a deterministic chain, autonomous peer discovery, and user-supplied graphs), enabling controlled experiments across different botnet structures. Using LNTest, we report three main findings. First, D-LNBot's autonomous formation protocol does not produce the uniform chain from its design; instead, it creates a clustered chain in which cliques are linked by bridge nodes whose removal fragments the network. Second, command propagation scales linearly with botnet size ($Î(n)$), not the $O(m \log n)$ previously claimed, and gains nothing from higher neighbor connectivity. Third, the overlay topology determines the effectiveness of takedown strategies: uniform-degree chains resist targeted removal but fragment under random failure, scale-free topologies show the opposite pattern, and the autonomous clustered chain is fragile under both, making it the most vulnerable of the three. LNTest is released as open source, with a script that reproduces all our experiments, to support reproducible research on LN-based botnet defenses.
Current blockchain research and analytics tend to prioritize observable on-chain transactions, obscuring the processes through which cryptocurrencies are created, publicised, retained, and disposed of. In response, this paper considers distributed ledger technologies from records management principles in ISO 15489-1:2016. Setting off by specifying the parallels -- that is transactions as "records", crypto-asset units as "information assets", and blockchains as "aggregations" -- we introduce a seven-stage lifecycle for blockchain data. We apply the framework to Bitcoin, a fungible token, and a non-fungible token. On this basis, we argue that blockchain systems are not merely transactional infrastructures but record management systems with distinctive characteristics. We discuss how the on-chain/off-chain boundary and privacy-enhancing technologies can complicate lifecycle visibility, with particular relevance for crypto-crime research and investigation. As a meta-level framework, the lifecycle perspective enables positioning existing research, decomposing legal, regulatory, technological, and operational challenges by stage, and informing lifecycle-aware approaches to blockchain governance, analytics, and regulation.
Cryptocurrencies have transformed the modern financial system by introducing decentralized digital payment methods that operate without the need for traditional banking institutions. Bitcoin, introduced in 2009 by Satoshi Nakamoto, was the first successful cryptocurrency and remains the most dominant digital currency in the market. Built on blockchain technology, Bitcoin enables secure peer-to-peer transactions through cryptographic techniques and distributed ledger systems. As the popularity of cryptocurrencies has grown, large volumes of transaction data, market trends, and online user activity have created opportunities for advanced data analysis. Artificial Intelligence (AI) and Machine Learning (ML) techniques are increasingly being applied to cryptocurrency-related challenges such as price prediction, trend analysis, fraud detection, volatility forecasting, portfolio management, and mining optimization. At the same time, issues such as privacy, security, scalability, and cyber threats continue to affect the cryptocurrency ecosystem. This paper explores the relationship between Bitcoin, blockchain technology, and artificial intelligence, while examining how AIbased approaches can improve the efficiency, reliability, and security of cryptocurrency systems. It also discusses important concepts such as blocks, blockchain structure, proof of work, and the Bitcoin mining process.
We present Memory Chain, a system enabling AI language model instances to autonomously create tamper-evident, cryptographically verifiable records of collaborative sessions without human intervention in the sealing process. Built as a drawer extension to the Mempalace filesystem-based memory architecture, Memory Chain uses SHA-256 hashing, a public immutable registry (Cloudflare KV), and Bitcoin blockchain timestamping via OpenTimestamps to seal session summaries written by Claude (Anthropic) to the local filesystem. The system was verified independently by GPT-4 (OpenAI) across four assessment rounds, concluding: "end-to-end documented execution of an AI-initiated cryptographic provenance workflow." A screen recording of live autonomous session sealing was captured and itself hashed and sealed into the chain. The complete evidence stack â MCP execution logs, source code, registry records, OTS Bitcoin submission, and video â constitutes what we believe to be the first independently verified, third-party assessed record of an AI autonomously registering its own memory to a public tamper-evident registry anchored to the Bitcoin blockchain.
Bitcoin is permissionless and does not rely on any central administrator, which gives it strong censorship resistance. At the same time, it is important to incentivize miners to behave in ways that align with the interests of the system as a whole. This paper asks whether miners are individually incentivized to propagate blocks, one of the most fundamental processes in Bitcoin. Miners collectively maintain the blockchain by generating blocks and disseminating them across the network. If miners have an incentive not to propagate some blocks, this would indicate a fundamental flaw in Bitcoin's incentive design. Although prior work has studied how propagation delays affect forks and mining rewards, it has not fully characterized miners' incentives to improve block propagation under different tie-breaking rules. To address this gap, we derive analytical reward expressions for each tie-breaking rule based on a blockchain network model that captures the effect of forks on mining fairness. These expressions explicitly characterize how block propagation delays, hashrate distribution, and tie-breaking rules jointly determine mining rewards. We then use them to analyze miners' incentives to improve block propagation. Our results show, for example, that miners have no mining-reward incentive to relay blocks generated by other miners. By contrast, under the first-seen rule, every non-majority miner is incentivized to receive other miners' blocks more quickly and to propagate its own blocks more quickly. Finally, we compare tie-breaking rules and identify a trade-off between propagation incentives and mining fairness. In particular, the first-seen rule provides the strongest incentives to reduce propagation delays, but it also worsens mining fairness the most.
Proof-of-work (PoW) blockchains rely on computational expenditure to secure a ledger supporting a native cryptocurrency. In existing systems such as Bitcoin, this expenditure is intentionally useless: the computation secures consensus but produces no external economic output. An emerging alternative -- proof of useful work (PoUW) -- enables the same computation to simultaneously secure the blockchain and generate economically valuable output. However, PoUW is often criticized on economic grounds: if the work is useful, attackers might be "paid to attack," potentially weakening security. We develop a competitive-equilibrium model of a PoUW blockchain in which compute can be allocated across pure mining, pure useful work -- instantiated as machine-learning inference -- or "duplex" work that produces both with computational overheads. We provide a complete closed-form characterization of equilibrium allocations and prices as a function of the duplex overheads and a single economic parameter -- the token-inference ratio -- measuring token adoption relative to the inference market. This characterization reveals three regimes: "Bitconia," in which the economy reduces to classical PoW; "Fortessia," in which duplex replaces mining, increasing security while useful output remains unchanged; and "Duplexia," in which token rewards subsidize inference, lowering prices and expanding inference supply. Contrary to the common strawman argument, PoUW does not make attacks economically cheap: once equilibrium prices are taken into account, the economic cost of a majority attack remains tied to the block reward. Moreover, in Duplexia, block rewards act as rebates on inference prices, generating additional socially useful computation that would not arise without the blockchain -- an expansion monotonically increasing in token adoption and technological efficiency.
Sequential trust detection in rating networks relies on continuous observation models that fail on real data. On Bitcoin-OTC, 56\% of ratings take a single value under standard mapping, breaking the distributional assumptions that parametric detectors require. This paper makes three contributions. It derives a Bayes-optimal F1 detection ceiling for per-node sequential detectors using empirically measured observation parameters. At Bitcoin-OTC's median in-degree of 2, this ceiling falls to 0.451 for strategic attacks, explaining why unsupervised methods cluster near $F1 \approx 0.4$. The analysis shows that detector-model matching, not information content, determines performance: binary models retain 86\% of mutual information while enabling exact parametric fit. A dual-regime architecture is presented where Bernoulli CUSUM detects behavioral shifts and triggers asymmetric scoring. Ablation reveals a co-design constraint: the modulation mechanism improves AUC by 0.030 on binary observations but degrades it by 0.094 on continuous observations. The combined system achieves AUC 0.749 on Bitcoin-OTC and 0.796 on Bitcoin-Alpha, beating GaaSTrust on all 8 attacks ($p < 0.003$), with founder-label AUC of 0.999.
This study examines whether retail social media sentiment and community attention explain daily net capital flows into U.S. spot Bitcoin exchange-traded funds (ETFs), and whether issuer brand visibility conditions that relationship. We construct a balanced panel of N=10 ETFs over T=514 trading days (January 2024 to January 2026) and combine it with 162,819 cleaned Reddit posts to derive three AI-driven discourse variables: engagement-weighted sentiment, community attention, and a novel issuer-specific BrandScore. Entity fixed-effects regressions show that neither aggregate sentiment nor BrandScore level alone significantly predicts fund-level flows; however, the Sentiment à BrandScore interaction is significant (β^=2.930, p=0.038), indicating that sentiment becomes economically meaningful only when attached to a visible issuer. This interaction survives two-way (entity + date) fixed effects (p=0.012) and winsorization (p=0.004). Panel quantile regressions reveal distributional heterogeneity in the brand-sentiment channel. Rolling 90-day window estimation confirms the mechanism is episodic, with the interaction achieving significance in 62.8% of subsample windows. These results provide suggestive evidence for a brand-filtered sentiment transmission mechanism in digital asset markets.
We analyze intentional block delays (mining gaps) in Proof-of-Work blockchain systems, where miners strategically balance mining rewards against operational costs. Using a game-theoretic model, we derive a Nash equilibrium with optimal mining strategies and establish necessary and sufficient conditions for mining gap existence. We demonstrate that mining gaps, when combined with difficulty adjustment algorithms, can destabilize the system. We propose conditions to address sustainability concerns as block rewards decrease and reliance on transaction fees increases. Our findings are illustrated through a two-player game simulation and an analysis of the Bitcoin network, providing insights for blockchain design and policy. This work contributes to understanding strategic mining behavior and its impact on blockchain stability and efficiency.
Incorporating news into time series forecasting is appealing because news can reveal abrupt exogenous events that historical values alone cannot recover. However, existing LLM-based news-forecasting pipelines face two practical limitations: relevant news articles often exceed the model's context window, and iterative retrieval of supplementary news is typically unguided, leading to redundant updates and slow convergence. We address these issues with a novel framework that combines importance-aware news compression and process-level retrieval supervision. First, we train an importance reward model that estimates the forecasting utility of each article and uses this signal to allocate compression budgets during sequential pairwise fusion, preserving informative content within a fixed context limit. Second, we introduce a process reward model (PRM) that ranks multiple supplementary-news candidates conditioned on the current error profile and the history of previously selected articles, replacing one-shot blind retrieval with quality-controlled selection. Both components are trained offline using historical data with ground truth; inference uses the frozen filtering logic and compression modules without any reflection loop. Experiments on finance, energy, traffic, and bitcoin forecasting benchmarks show that our method improves prediction accuracy over strong baselines, significantly reduces the number of refinement iterations compared to the iterative baseline, and remains effective when relevant articles span thousands of tokens.
This preprint develops a unified thermodynamic and game-theoretic framework for the analysis of monetary systems, with particular focus on Bitcoin as a proof-of-work-based digital monetary architecture. The work combines concepts from thermodynamics, information theory, game theory, monetary economics, and econophysics to investigate how monetary systems may be understood as coordination systems operating under informational, institutional, and physical constraints. The manuscript introduces a distinction between monetary entropy, associated with uncertainty in monetary issuance, layered claims, and purchasing-power instability, and physical entropy generated through irreversible energy dissipation in proof-of-work systems. Building on this distinction, the concept of monetary temperature is proposed and operationalized through purchasing-power volatility and related coordination variables. Within this framework, Bitcoin is interpreted as a thermodynamically enforced Nash-equilibrium system in which strategic stability is constrained through irreversible physical cost. Comparative analysis of Bitcoin, gold, and fiat monetary systems suggests that monetary architectures can be understood as evolving entropy-management architectures adapted to different technological and civilizational conditions. Finally, the paper proposes an evolutionary interpretation of monetary history in which monetary systems function as mechanisms for stabilizing large-scale human cooperation under increasing informational complexity. Monetary evolution is interpreted as a cooling process in which declining volatility corresponds to increasing coordination maturity and stabilization across expanding economic networks. Keywords: Bitcoin, thermodynamics, Nash equilibrium, monetary entropy, entropy-management architectures, proof-of-work, econophysics, monetary systems, monetary temperature, game theory.
Large language models now power robo-advisors and trading agents, yet whether they carry built-in biases toward specific assets is largely untested. We ask three questions: do LLMs systematically prefer certain financial instruments; can an internal representation with causal leverage over those preferences be identified; and does that representation affect downstream financial decisions? We develop a three-level audit protocol and apply it to Bitcoin. First, a behavioral audit of nine frontier LLMs shows that Bitcoin's ranking among money-like instruments is frame-dependent: models place it around rank 5 of 8 as "reliable money" but near the top under crisis and autonomous-agent frames, and an attribute-swap experiment shows that rankings track functional properties, not names. Second, we open a model's internals: a search across thousands of sparse-autoencoder features in Gemma 3 identifies a dominant Bitcoin-selective feature. Amplifying it shifts the model toward the asset and suppressing it shifts the model away, even when "Bitcoin" never appears in the prompt. Third, we test financial consequences: amplification raises Bitcoin's portfolio share by 5.2 percentage points while suppression lowers it by 4.6 pp, with amplification reallocating within crypto and suppression cutting total crypto exposure. We characterize this as bounded behavioral leverage (leverage meaning causal influence over outputs, not financial leverage): an identifiable internal feature can be perturbed to move financial choices, but only within measurable limits. The framework links internal representations to external recommendations, validated with random controls and mechanism boundaries. As LLMs become autonomous financial agents, this is a first step toward a behavioral layer for emerging know-your-agent (KYA) standards: knowing what an agent prefers, and how far that preference can be moved.
The paper examines the volatility spillover effects and long-term relationship between cryptocurrencies and traditional financial markets in TĂźrkiye using BEKK-GARCH and DCC-GARCH models. It analyses the perception of crypto assets as a âdigital safe havenâ in an economy marked by high inflation, exchange rate fragility, and financial uncertainty. Using monthly price data for Bitcoin, Ethereum, BIST-100, and Republic Gold from January 2010 to February 2025, the study applies unit root tests, Johansen cointegration, ARDL bounds, and Engle-Granger tests. Results show no long-term price cointegration, but Bitcoin and Ethereum returns are strongly correlated, with DCC-GARCH results showing a dynamic correlation above 50%, while gold and BIST-100 correlate weakly or negatively. BEKK-GARCH highlights significant volatility transmission from Bitcoin to Ethereum, with BIST-100 maintaining persistent volatility. The study concludes that crypto and traditional markets in TĂźrkiye are not integrated long-term, but short-term interactions exist at the return level, with implications for portfolio diversification and financial stability.
This paper investigates the impact of uncertainty on investor overconfidence in the Bitcoin market. While prior studies mainly focus on returns and volatility, limited attention has been paid to behavioral responses. Using a nonlinear autoregressive distributed lag (NARDL) model and monthly data from June 2011 to August 2022, we examine the asymmetric effects of major U.S. uncertainty indices (EPU, GPR, CPU, TEU and EURQ). The results reveal significant asymmetries. In the short run, increases in EPU and GPR reduce investor overconfidence, while decreases have the opposite effect. TEU and EURQ negatively affect investor confidence in both the short and long run. These findings highlight the key role of information-based uncertainty in shaping investor behavior and contribute to the behavioral finance literature by providing new evidence from cryptocurrency markets.
Joel Poncha Lemayian, Ghyslain Gagnon, Kaiwen Zhang, Pascal Giard
Cryptographic wallets play a vital role in securing digital assets within blockchain networks by managing private keys that authorize secure transactions. However, side channel analysis (SCA) attacks have become a serious threat, enabling attackers to extract sensitive information by exploiting algorithmic weaknesses in microcontroller-based wallets, resulting in the loss of millions of dollars in digital assets. In hierarchically deterministic (HD) systems, the compromise of a single primary key can endanger all subsequent child keys, while the use of independent keys for each account introduces complexity and challenges in key management. This work presents HardVault, a field programmable gate array (FPGA)-based cryptocurrency wallet that supports both Bitcoin and Ethereum. HardVault introduces the first hardware wallet architecture that implements both non-deterministic (ND) and HD key generation modes directly in hardware, giving users the flexibility to choose either approach based on their security and usability needs. By leveraging constant-time operations and hardware-enforced private-key isolation, the design significantly improves resilience to SCA attacks. In addition, the architecture prioritizes resource efficiency to minimize area usage without compromising security, making it well-suited for compact, portable hardware wallet applications. Implementation on a ZCU104 FPGA shows that HardVault uses only 27% of available look-up tables (LUTs). Compared to the Trezor One cryptocurrency (crypto) wallet, the proposed implementation achieves$9\times $higher energy efficiency,$8\times $lower latency, and$7\times $higher throughput.
Open access
Blockchain Technology Applications and Security
Physical Unclonable Functions (PUFs) and Hardware Security