The rapid development of cryptocurrencies, stablecoins, and central bank digital currencies (CBDCs) has transformed the global monetary landscape and accelerated the transition toward a cashless society. While critics argue that digital currencies threaten financial stability due to volatility, disintermediation, energy consumption, and regulatory concerns, this paper contends that the increasing competition among digital and fiat currencies can generate significant economic benefits. By examining the evolution of cryptocurrencies, the emergence of stablecoins, the global adoption of CBDCs, and the case of Zimbabwe's hyperinflation, this study argues that currency competition encourages governments to pursue more disciplined fiscal and monetary policies, strengthens policy credibility, and helps anchor inflation expectations. Greater monetary credibility also expands policymakers' ability to respond effectively to future economic downturns. Although digital currencies present important risks, many of these challenges can be mitigated through technological innovation, appropriate regulation, and institutional development. Overall, this paper concludes that a wellmanaged transition toward a cashless society can promote competition, innovation, and long-term economic resilience rather than undermine financial stability.
Although stablecoins occupy a segment of digital-asset markets in which price stability is central by design, their temporary departures from reference values may reveal important information about latent risk and market stress. In this paper, we examine whether bubble and crash signals extracted from traditional cryptocurrencies and stablecoins improve volatility, Value-at-Risk, and Expected Shortfall forecasting and, in connection with these forecasting gains, contribute to the assessment of cross-asset contagions. The analysis applies the Bubble Crash–GARCH models, in which extreme price phases are identified through the Phillips, Shi, and Yu real-time monitoring procedure and incorporated into the conditional mean of returns through event-based dummy variables. For stablecoins, extreme episodes are not inferred from price dynamics in isolation but from deviations between the observed price and the asset-specific reference value. The empirical investigation focuses on Bitcoin, Ethereum, Tether’s USD-pegged (USDT), and Tether Gold and evaluates asset-specific bubble–crash effects and bidirectional contagion channels between traditional cryptocurrencies and stablecoins, using Bitcoin and Tether as the leading representatives of the two market segments. The findings indicate that accounting for bubble and crash episodes leads to more accurate volatility forecasts than standard GARCH benchmarks. For Value-at-Risk and Expected Shortfall, the bubble–crash specifications can improve tail risk forecasting at several tail probability levels through more accurate coverage, lower quantile loss, and stronger ESR backtesting performance. The results also reveal different degrees of price exuberance across the two asset categories: while extreme price dynamics are more evident among traditional cryptocurrencies, deviations from fundamentals are rare for stablecoins. Among stablecoins, USDT exhibits limited but detectable exuberance, whereas Tether Gold does not display extreme price episodes. However, when such deviations occur, as in the case of USDT, they generate significant contagion effects on major cryptocurrencies. Notably, extreme episodes originating in USDT have a stronger impacts on Bitcoin and Ethereum than the reverse spillovers from traditional cryptocurrencies to USDT. Overall, the evidence suggests that stablecoins are not merely passive instruments within the digital-asset ecosystem. Even temporary deviations from their reference values contain valuable information for risk forecasting and contagion monitoring.
Large language models (LLMs) have shown strong potential for financial analysis and trading, but direct trading remains challenging because the predictive capabilities required can vary across assets, decision fields, and market conditions. Existing LLM-based trading systems either coordinate human-defined external experts or adopt conventional internal Mixture-of-Experts (MoE) routers that do not directly evaluate how individual experts contribute to trading decisions. Moreover, these routers receive no direct signal indicating when an inactive expert has become more suitable as market conditions change. We find that native router scores poorly reflect how much individual experts improve trading decisions, frequently leaving better alternatives unselected. We further reveal that token-specific expert usefulness exhibits a compact low-dimensional structure. Based on these findings, we propose TradingMoE, a trading-oriented sparse MoE that augments a frozen dense LLM with lightweight residual experts. We introduce a Query-Key router that represents the expertise required by each token under the current market context as a low-dimensional query and matches it with learnable expert keys. We further propose a sparse expert selection update mechanism that samples a few inactive experts during training and estimates whether they should replace the weakest expert in the current Top-k route. This mechanism enables the router to update expert selection as market conditions change while preserving sparse computation. Experiments against 22 baselines on stock and cryptocurrency markets show that TradingMoE improves cumulative return over the best-performing baselines by 30.89% and 30.7%, respectively. Rolling paper-trading experiments further demonstrate that its advantage persists under forward-only deployment.
Beacon Kit: Ecosystem epoch heartbeat @ the world game (s). Block-time arbitrage tokenized commodity index, adaptive procedural template @ system of federated DeFi cryptocurrency quantum - AI systems consensus
The rapid growth of cryptocurrencies and increasing instability in traditional financial systems have significantly transformed global investment behaviour in recent years. In developing countries experiencing economic crises and currency depreciation, investors increasingly seek alternative financial assets that can preserve value and generate higher returns. Sri Lanka has recently experienced severe economic instability characterised by inflation, foreign-exchange shortages, sovereign debt problems, and rapid depreciation of the Sri Lankan rupee. Under these conditions, interest in cryptocurrency investment has increased, particularly among younger and technologically aware investors. Therefore, this study examines whether fiat currency devaluation shifts investment from the stock market to the cryptocurrency market among university students in Sri Lanka. The study adopts a quantitative research approach and uses primary data collected through a structured questionnaire from 150 final-year undergraduate students at the University of Sri Jayewardenepura. Stratified random sampling was used to select respondents from the Faculty of Humanities and Social Sciences, the Faculty of Management Studies and Commerce, and the Faculty of Applied Sciences. Descriptive statistics, chi-square analysis, and binary logistic regression were employed to analyse the relationship between rupee depreciation and cryptocurrency investment behaviour. The findings reveal that depreciation of the Sri Lankan rupee significantly influences investment decisions among university students. Most respondents perceived cryptocurrency investment as more profitable than stock-market investment during periods of economic uncertainty. The chi-square analysis identified significant relationships between cryptocurrency investment behaviour and age, income, stock-market investment, and perceptions of rupee depreciation. Furthermore, the binary logistic regression results confirmed that rupee depreciation positively and significantly affects cryptocurrency investment, whereas stock-market investment had a negative relationship with cryptocurrency investment behaviour. The study concludes that economic instability, declining confidence in fiat currency, and increasing awareness of digital financial systems encourage university students in Sri Lanka to shift their investment preferences from the traditional stock market to cryptocurrency.
Cryptocurrency time-series forecasting is a challenging task because market data usually exhibit high noise, strong volatility, non-stationarity, nonlinear dynamics, and long-range dependencies. In addition, multivariate market indicators often contain redundant or weakly informative variables, which may reduce forecasting accuracy and model interpretability. To address these issues, this study proposes BSFinformer, a Boruta-SHAP enhanced Finformer framework for multivariate cryptocurrency time-series forecasting. The proposed framework first applies a leakage-aware Boruta-SHAP feature selection strategy to identify informative market variables and remove redundant features. To avoid temporal information leakage, feature selection is performed only on the training set, and the selected feature subset is then applied unchanged to the validation and test sets. The selected features are subsequently fed into an improved Finformer model that integrates temporal embedding, sequence decomposition, and sparse self-attention to capture local fluctuations, trend evolution, and long-range temporal dependencies. Experiments are conducted on three cryptocurrency assets, namely Bitcoin, Dogecoin, and Binance Coin, using chronological train–validation–test splits. The proposed model is compared with classical forecasting models and recent long-sequence forecasting baselines, including LSTM, Transformer, Informer, Autoformer, DLinear, PatchTST, TimesNet, and iTransformer. Experimental results show that BSFinformer achieves competitive forecasting performance in terms of MSE and MAE. Ablation experiments further demonstrate the contributions of Boruta-SHAP feature selection, temporal embedding, sequence decomposition, and sparse self-attention. These results indicate that feature-selected temporal modeling can improve forecasting accuracy and interpretability for multivariate cryptocurrency market data.
This systematic review synthesises empirical research on individual-level cryptocurrency adoption, distinguishing adoption intention, actual adoption and use, and continuance intention and use. We searched Scopus and Web of Science for English-language empirical studies published between 2019 and 2025 and synthesised findings using a structured narrative approach. Eighty-five studies were included, with reported sample sizes summing to 56,054 participants. No formal study-level risk-of-bias assessment was conducted. The literature was dominated by cross-sectional quantitative studies and technology-adoption frameworks, particularly UTAUT, TAM, TPB, and DOI. Evidence was strongly concentrated on adoption intention (n = 75), whereas actual adoption and use (n = 16) and continuance intention and use (n = 8) were examined much less frequently. Across studies, adoption was associated with psychological, technological, social, economic, knowledge-related, institutional, and individual factors, with no single determinant consistently dominating across outcomes. The synthesis further distinguished direct predictors, mediating mechanisms, moderators, drivers, and barriers. The evidence base is limited by its reliance on self-reported, cross-sectional designs and uneven coverage of realised and continued engagement. Future research should more clearly specify adoption outcomes and use longitudinal, behavioural, and post-adoption designs.
While much has been written about the volatility of digital assets, academic scholarship has largely overlooked how blockchain technologies have been adopted and reimagined by LGBTQ+ communities. This article addresses that gap through a digital ethnography of queer NFT communities active during the crypto craze of 2022, combining online participant observation with semi-structured interviews. Drawing on José Esteban Muñoz’s concept of queer futurity, it examines how queer users imagined blockchain as a speculative platform for alternative economic and social possibility—despite the financial risks embedded in the technology’s libertarian and capitalist structures. The article interrogates the utopian rhetoric of inclusion, decentralisation, and wealth redistribution that was deployed within these communities to justify their interest in and holdings of non-fungible tokens (NFTs) and cryptocurrency. Queer leaders leveraged the blockchain to foster inclusive digital communities and promote wealth circulation amongst LGBTQ+ individuals, while community members embraced the technology as a risky opportunity for queer economic mobility. The article positions blockchain as a contested site where competing futurities collide—offering the illusion of liberation and the reproduction of existing inequalities. It argues that while queer users sought to make the blockchain ‘queer from the start,’ their efforts were ultimately constrained by the capitalist logics that underpin the technology.
This article develops the concept of the “Manufacture of Deviance* 2.0” as an extension of the economic model proposed in The Economic Policy of Online Media: Manufacture of Dissent. It argues that the transition from traditional mass media to decentralised digital platforms has produced not simply a transformation in the distribution of violent imagery, but a new economic relationship between attention, taboo and atrocity. Traditional broadcast media developed institutional and ethical mechanisms intended to restrict representations of real torture, mutilation and death. The online attention economy partially reverses this logic: what is forbidden, disturbing, transgressive or difficult to access can acquire additional informational value precisely because of its exceptional character. As audiences become saturated with conventional news, entertainment and political controversy, increasingly extreme material can compete successfully for the scarce resource of human attention. Violence consequently becomes capable of generating multiple currencies simultaneously: advertising value, subscriptions, donations and cryptocurrency, but also views, followers, notoriety, group membership, prestige and social recognition. The article traces a genealogy from the spectacle of public punishment and execution, through the controversial appearance of uncensored atrocity footage on traditional television, to gore communities, extremist propaganda, cartel execution videos, closed social-media groups and contemporary networks of digitally performed vigilantism. Particular attention is given to the evolution of so-called “pedophile hunter” subcultures associated with Maxim “Tesak” Martsinkevich and Occupy Pedophilia, in which humiliation and violence could be transformed into reproducible performances possessing recognisable scripts, symbols and gestures. Rather than assuming the existence of a single organisation commissioning such violence, the article proposes a more disturbing possibility: networked media can reproduce violent behaviour without central command because attention, imitation, belonging and social currency themselves operate as incentives. Against this background, the article examines the 2026 Youth Hill case in Plovdiv, Bulgaria, involving teenagers accused of participating in the fatal assault and humiliation of a 37-year-old man after he had allegedly been lured to a meeting. The case is treated cautiously, distinguishing established information, prosecutorial allegations and media reporting from the broader theoretical interpretation developed here. It nevertheless provides a disturbing case through which to investigate the migration of violent spectacle from the screen into physical behaviour and its subsequent return to the screen as content. The article argues that this process resembles, in technologically transformed form, the public scaffold: the condemned sinner, the righteous crowd, ritual humiliation and spectacular punishment return within a global digital square in which spectators can simultaneously watch, judge, distribute and reward the spectacle. The Manufacture of Deviance 2.0 therefore describes a potentially advanced stage of the attention economy: not merely the monetisation of disagreement and outrage, but the conversion of transgression, cruelty and ultimately human suffering into communicative value.
Open access
4 source records
Populism, Right-Wing Movements
Crime, Deviance, and Social Control
Terrorism, Counterterrorism, and Political Violence
Cheuk Hang Au, Po-Hsu Shieh, Vladimir Nurbaev, Kris M. Y. Law · 5 authors
Digital platforms face a fundamental paradox: while expanding service variety is a dominant competitive strategy, it risks inducing a “paradox of choice” that confuses and deters users. This tension manifests with extreme clarity in the nascent, high-complexity market of cryptocurrency exchanges, creating a pressing empirical puzzle. To resolve this, we adopt the Stimulus-Organism-Response (SOR) perspective in a three-stage mixed-method study to investigate how platforms can strategically manage this trade-off. Our qualitative exploration (Study 1) established a capital flow schema called “inflow, roll, and go” and identified key complexity-reduction mechanisms. A subsequent survey (n = 190, Study 2) validated that perceived innovativeness and scalability are critical stimuli for service variety, which in turn drives user continuance intention. A final survey (n = 140, Study 3) confirmed that users prioritise services that bridge to the traditional financial system, forming a minimal viable structure with a variety of functions. Our meta-inferences make several key contributions, including the resolution of the service variety paradox by introducing a theoretical distinction between value-adding “real-variety” and confusing “pseudo-variety” and the development of a strategic roadmap that guides exchanges in navigating the tension between service expansion and user confusion, offering actionable insights for platform strategy in any high-velocity digital market.
The fluctuation characteristics of financial time series have always been one of the research hotspots in the academic community. Generally speaking, financial return series have the characteristics of volatility clustering, fat tails, conditional heteroskedasticity, asymmetric shocks, etc. The above phenomena can be explained from the perspective of dynamic conditional variance by GARCH models and their extensions. This paper first introduces the basic ideas of ARCH and GARCH models, with a focus on the issue of volatility clustering of financial returns. Then, it reviews the relevant research from three aspects: model evolution, application scenarios, and practical value. It also analyzes the role of GARCH-type models in capturing volatility persistence, asymmetric impact, and risk transmission through applications in cryptocurrencies, energy assets, and high-frequency financial data. The study shows that GARCH-type models capture the volatility clustering feature of financial returns well, but there is still room to improve the modeling of extreme risk, the handling of high-dimensional assets, and model interpretability.
Crypto currency is one of most interesting financial innovation of 21st century. Crypto currency trading not only involve financial literacy while trading but also there are psychological factors affecting the decision of traders. Keeping in view the psychological factors and investors’ decision, this research study is designed to investigate the complex interplay between psychological triggers and market dynamics in the cryptocurrency sector in Pakistan, specifically examining how these elements coalesce to drive investor behavior and market volatility. While traditional financial models often attribute asset fluctuations to technological or fundamental shifts, this study posits that cryptocurrency markets are fundamentally driven by human perception and emotional reactivity. Utilizing a quantitative methodological approach, data was collected from a sample of 175 experienced traders to analyze the impact of emotional states, market sentiment, and behavioral discipline on trading outcomes. The empirical results, derived through multiple linear regression analysis, reveal that the model possesses a high level of explanatory power, accounting for 56% of the variance in emotional trading behavior (R2=0.56R2=0.56). Market sentiment emerged as the primary determinant of impulsive trading (β=0.48β=0.48), demonstrating that external social cues often exert a stronger influence on decision-making than internal emotional states. Among specific psychological variables, Fear, Uncertainty, and Doubt (FUD) were identified as the most significant predictors of rash choices (β=0.34β=0.34), while the Fear of Missing Out (FOMO) also demonstrated a substantial, though secondary, effect (β=0.21β=0.21). Conversely, the study found that trading experience and the application of systematic strategies serve as vital moderating factors that decrease emotional reactivity and enhance behavioral stability (β=−0.19β=−0.19). The findings contribute to the fields of behavioral finance and digital economics by illustrating that the volatility inherent in digital assets is a systemic byproduct of individual psychological biases aggregated through digital narratives. The research concludes that achieving a sustainable financial ecosystem requires moving beyond purely technical regulations. Instead, it advocates for the implementation of behaviorally-informed safeguards, such as algorithmic "cooling-off" periods and sentiment-aware trading tools, to mitigate the risks associated with reactive investing. Ultimately, this work provides a blueprint for a more resilient digital financial future by prioritizing human factors in market governance.
Initial Coin Offerings (ICOs) have emerged as an innovative mechanism for raising capital, particularly for blockchain-based projects. However, the lack of regulatory oversight and the prevalence of low-quality information raise important questions about what truly drives ICO success. While existing literature focuses predominantly on technical and signalling variables, the role of investor decision-making remains theoretically underdeveloped and empirically underexplored. This paper addresses this gap by pursuing two objectives. First, we identify the drivers of ICO success using a probit model applied to an original sample of 535 ICOs conducted between January 2016 and May 2021. Second, we investigate investor decision-making patterns using a novel dataset of 200 active crypto-forum participants over the same period. Our results have three main findings, though with modest statistical strength than initially estimated. (I) Marketing channels are the most consistent predictor of ICO success across the sample period, clearing conventional significance thresholds only in the pooled sample (z = 1.90, p<0.10), with each additional channel raising the probability of soft-cap achievement by approximately 1.0 percentage point. (II) Team presentation and video presentation show no meaningful influence on success in any period. (III) Whitepaper availability is not statistically significant even in pooled sample, reinforcing rather than qualifying its irrelevance as a predictor; the number of accepted cryptocurrency price speculation rather than project fundamentals, consistent with mood and sentiment dominating information-based decision making in ICO markets, though this finding should be read alongside the data limitations discussed in 3.B. These findings contribute to the behavioural finance literature by providing an operational definition of ‘investor mood’ and demonstrating its empirical relevance in crypto markets. We conclude that understanding investor mood is not a secondary question but a necessary complement to technical analysis of ICO success.
Decentralised finance (DeFi) is a relatively new trend in finance that uses blockchain, smart contracts, and distributed ledger technology to offer financial services in a decentralised manner. Although scholars have made many theoretical advances in decentralised finance in recent years, knowledge of its theoretical structure and future research areas remains limited. This is why this study provides a bibliometric analysis of 1002 articles on DeFi published in Scopus between 2012 and 2026. The analysis uses performance analysis and a science mapping approach based on citation analysis, co-authorship, bibliographic coupling and keyword co-occurrence analysis. The results reveal a remarkably high annual growth rate of 39.34% and DeFi’s dynamism and interdisciplinary nature. The three main countries involved in DeFi research are the USA, China, and the UK. Management Science, Energy Economics and Technological Forecasting and Social Change became the main scientific journals for disseminating knowledge about DeFi. Analysis of thematic changes showed a transition of scientific interests from blockchain and cryptocurrencies to new topics, like artificial intelligence, sustainability, governance, and financial inclusion. Overall, the current study provides a better understanding of the intellectual, conceptual, and social basis of DeFi and highlights possible research areas in the use of artificial intelligence in DeFi, decentralised governance, and sustainable digital financial system development.
This informative document explores the evolving digital asset landscape, covering cryptocurrency, NFTs, blockchain technology, Web3, and emerging market trends. It provides readers with practical insights into digital ownership, market developments, and the importance of research when evaluating opportunities in the growing blockchain economy. Collective Shift
Technological innovations are often perceived as something alien, terrifying, and monstrous. Blockchain technology that creates shared “blocks” of information, which are interconnected and verified by the network comes as no exception. Two main features of blockchain (1) the absence of a gatekeeper organisation controlling the data, and (2) the fact that the information is rather hard to corrupt and hack, makes the technology very attractive and versatile. It is also what makes it appear frightening, especially for the traditionally centralised and hierarchical disciplines like law. As there is no one to control the data and the access to it, blockchains open a whole world of new possibilities with cryptocurrencies being one of the most popular examples.Approaching blockchain technologies in the context of J. J. Cohen’s monster theory demonstrates that they can be perceived as modern monsters. Our inability to understand the technology and the way it works makes this particular monster both fearful and desired (thesis 6), and law reacts to the fears that circulate in the society. Thus, blockchain technologies are often banned by law in a similar way as in medieval narratives dragons were banished by saints and heroes. Building on Cohen’s thesis 7, which argues that monsters show how we (mis)interpret our surroundings, this article will employ the historical perspective upon the fear of the monstrous to create a better understanding of the legal policies surrounding blockchains. By comparing current legal decisions concerning blockchain technology with the strategies of dealing with monsters, offered by medieval chronicles and collections of wonders (including William of Malmesbury and William of Newburgh), we will analyse the modern way of controlling monsters – or controlling the fear of them.
This perspective examines whether nuclear fusion can provide a scalable, low-carbon power source for rapidly growing AI-driven data center demand. As large language models, cloud computing, and cryptocurrency mining accelerate electricity consumption growth, data centers are projected to account for a substantially larger share of U.S. and global electricity use in the coming decades, creating significant pressure on grid reliability and decarbonization goals. We evaluate the technical and economic alignment between data center load profiles and nuclear power, particularly fusion, through a comparative analysis of capacity factors, levelized cost of electricity, grid interconnection constraints, and deployment pathways. Unlike intermittent renewables, nuclear fission and fusion offer high-capacity-factor, firm baseload generation suited to AI training and inference workloads that require continuous, reliable power. Preliminary techno-economic analysis suggests that several Nth-of-a-kind fusion concepts, particularly magnetic confinement systems, may become cost-competitive with firmed renewable systems and advanced fission for hyperscale data center applications. Co-location of fusion plants with data centers further reduces transmission bottlenecks, improves resilience, and aligns with emerging hyperscaler procurement strategies. We also assess recent regulatory developments and argue that fusion's favorable safety profile and reduced waste burden improve its long-term social and political viability relative to fission. We conclude that fusion represents a strategically important pathway for sustainably powering next-generation computing infrastructure and should be prioritized in both policy and industrial deployment planning.
The Triadic Stress Index (TSI) takes a network index whose four factors were first observed in soil microbiome co-occurrence networks and applies it, without alteration, to the correlation network of financial assets. We test it on five markets spanning 2006-2026 (equities including banking crises and the AI sector, cryptocurrencies, commodities, foreign exchange and sovereign debt), against three independent definitions of a crisis episode, at a fixed alarm budget, out of sample, with block-bootstrap intervals and a Holm correction across the family of tests. The benchmarks are the Absorption Ratio, the industry standard used by MSCI and central banks; the effective rank and the Vendi score, the sharpest spectral measures available; Ollivier-Ricci curvature; and the global and local balance indices of signed correlation networks. Three comparisons favour the index. It carries a per-node decomposition, diag(A^3), naming which asset is carrying the concentration with no parameter to select, and scores 0.97-0.99 against 0.33-0.84 for the only published per-node alternative, whereas spectral attribution must first choose how many components to read and collapses under a standard but wrong choice. Its alarms are the cleanest of anything tested, 4.0% of them with no matching episode against 14.7% for the effective rank and roughly 59% for the Absorption Ratio. And it beats the Absorption Ratio on detection by 0.273 in F1 out of sample, p<0.0005. The remaining comparisons are ties. Against the effective rank and the Vendi score the index ties in every scheme and both samples, and the margin over the Absorption Ratio narrows under the strictest labelling. On real matrices the far simpler node degree reproduces the attribution. A lead-lag analysis puts the peak cross-correlation at zero lag: this is a coincident state index, not a forecast.
This study investigates whether the macroscopic statistical maturity of cryptocurrencies implies dynamical equivalence with traditional equity markets. We analyze high-frequency data (2020--2025) using the Complexity--Entropy Causality Plane (CECP) and directed horizontal visibility graphs (directed HVG) to uncover complex temporal patterns and time-directed structures in the return series. While conventional stylized facts show striking convergence across all assets, structural diagnostics reveal a compelling paradox: cryptocurrencies appear more locally random than the equity benchmark during ordinary periods, yet exhibit significantly stronger directional time-irreversibility around high-visibility return events. The absolute-return results show that large cryptocurrency fluctuations tend to begin abruptly and remain elevated afterward. Separate analyses of positive returns and negative-return magnitudes show that this pattern is shared across cryptocurrencies on the upside but varies across assets on the downside. We conclude that statistical maturity is only skin-deep; the underlying dynamical processes of mature cryptocurrencies remain fundamentally distinct from traditional benchmarks.
Cryptocurrency exchange-traded products (ETPs) listed on European exchanges provide a regulated environment for studying intraday market anomalies. We study four Bitcoin and Ethereum ETPs traded on Xetra and Nasdaq Stockholm over the period January 2024 - December 2025 using one-minute bars. As a benchmark, we adopt an extreme value theory approach in which anomalous bars are defined as returns falling below a threshold estimated by fitting a generalised Pareto distribution to left-tail exceedances. We then propose three new binary anomaly indicators. The first, a cross-venue divergence anomaly, identifies venue-specific price divergence between the two exchanges. The second is a no-recovery anomaly that identifies extreme price drops followed by little or no recovery over the next ten active bars. The third is a momentum-reversal anomaly that identifies extreme price drops following positive short-term momentum. Although each anomaly type represents fewer than 1% of one-minute bars, statistical analysis using Mann-Whitney U tests shows that anomaly observations exhibit significantly higher effective spreads, higher values of liquidity-related ratios, and more pronounced order-flow imbalances than non-anomalous bars. Furthermore, employing an out-of-sample prediction methodology with four classifiers - random forest, logistic regression, extreme gradient boosting, and light gradient boosting machine - shows that all four anomaly types are predictable one bar ahead, with AUC-ROC values of up to 0.82. Permutation importance indicates that short-term volatility and drawdown measures are generally more useful for prediction than microstructure variables.
Operations research has long contributed to addressing energy and environmental challenges through mathematical modeling and decision-support methods. In particular, numerous studies have examined investment planning, capacity expansion, and policy design for renewable energy systems under uncertainty. As efforts to achieve carbon neutrality intensify worldwide, the expansion of renewable energy has become a critical policy and investment priority. However, the inherent variability of renewable power generation and the substantial upfront investment costs continue to hinder investment decisions and limit the adoption of renewable energy. To address the economic challenges associated with renewable energy penetration, recent studies have explored the use of cryptocurrency mining as a means of monetizing surplus renewable electricity. This study contributes to this emerging research stream by developing a real options model that captures the interaction between renewable energy investment and cryptocurrency mining under uncertainty. The numerical results show that cryptocurrency mining increases the value of renewable energy investment and accelerates investment by lowering the investment threshold. Moreover, the equilibrium determination of mining capacity reduces the renewable energy investment threshold by approximately 40.5% compared with the benchmark in which mining capacity is specified exogenously.
Velian Prapatoni, Rizky Parlika, Firza Prima Aditiawan
Cryptocurrency markets are characterized by high volatility, rapid price fluctuations, and substantial uncertainty, creating challenges for investment risk interpretation. This study develops a descriptive risk-interpretation framework, rather than a price-prediction or decision-optimization model, by integrating multi-scale volatility analysis with technical indicators. A quantitative descriptive design was applied to approximately one year of historical hourly price data for Bitcoin and Ethereum, covering open, high, low, close, volume, and percentage change attributes. The data were chronologically sorted, numerically cleaned and normalized, transformed into log returns, and analyzed through rolling standard deviation. Volatility was estimated across three explicitly defined horizons: short-term 7-period, medium-term 30-period, and long-term 90-period rolling windows. Moving Average (MA), Relative Strength Index (RSI), and Average True Range (ATR) were then incorporated to contextualize trend direction, momentum, and fluctuation intensity. The results show that volatility is strongly horizon-dependent: short-term movements responded more sharply to market shocks, whereas longer horizons produced smoother risk patterns. Across the analyzed Bitcoin and Ethereum hourly series, the reported 42.3% short-term and 21.7% medium-term increases were calculated as relative differences against long-term rolling volatility during identified high-uncertainty intervals, not as predictive accuracy measures. These findings indicate that combining rolling volatility with MA, RSI, and ATR can improve the transparency of descriptive cryptocurrency risk assessment. The framework may support preliminary interpretation for novice or risk-averse investors, although it does not empirically test investor comprehension or subsequent decision quality.
Mohammad Quthbul Widad, Rizky Parlika, Firza Prima Aditiawan
Although Bitcoin is acknowledged as the largest cryptocurrency by market capitalization and trading volume in the world's financial market, investors face a great deal of risk and uncertainty due to its exceptionally high volatility and non-linear price changes. To provide a data-driven foundation for risk reduction and forecasting support, accurate modeling techniques are crucial. This work attempts to provide a thorough comparative analysis mapping the precise accuracy–efficiency trade-off between Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models under a standardized Grid Search hyperparameter optimization pipeline using a recent Bitcoin closing-price dataset spanning from January 1, 2020, to January 1, 2026. The research methodology follows a structured data science pipeline, beginning with data acquisition from Yahoo Finance, followed by preprocessing using Min-Max Scaling fitted strictly on the training partition to eliminate data leakage. Model development involves an experimental approach where both LSTM and GRU neural controllers are tuned to extract optimal structural weights. The predictive precision of these models is rigorously evaluated using three standard metrics: MAE, RMSE, and MAPE, while processing throughput is measured via hardware execution times. The research findings indicate that the optimized LSTM model achieved superior one-step-ahead predictive precision with a MAPE of 2.32%, whereas the GRU model recorded a higher error rate of 3.94%. However, the GRU model demonstrated a significant advantage in computational efficiency, completing the training process 8.45 times faster than LSTM. In conclusion, while LSTM is recommended as a forecasting support tool for high-precision financial analysis, GRU remains a viable, parameter-efficient alternative for real-time monitoring on resource-constrained systems before real-world financial deployment.