Blockchain security is threatened by selfish mining, where a miner (operator) deviates from the protocol to increase their revenue. Selfish mining is exacerbated by adverse conditions: rushing (network propagation advantage for the selfish miner), varying block rewards due to block contents, called miner extractable value (MEV), and petty-compliant miners who accept bribes from the selfish miner. The state-of-the-art selfish-mining-resistant blockchain protocol, Colordag, does not treat these adverse conditions and was proven secure only when its latency is impractically high. We present MAD-DAG, Mutually-Assured-Destruction Directed-Acyclic-Graph, the first practical protocol to counter selfish mining under adverse conditions. MAD-DAG achieves this thanks to its novel ledger function, which discards the contents of equal-length chains competing to be the longest. We analyze selfish mining in both Colordag and MAD-DAG by modeling a rational miner using a Markov Decision Process (MDP). We obtain a tractable model for both by developing conservative reward rules that favor the selfish miner to yield an upper bound on selfish mining revenue. To the best of our knowledge, this is the first tractable model of selfish mining in a practical DAG-based blockchain. This enables us to obtain a lower bound on the security threshold, the minimum fraction of computational power a miner needs in order to profit from selfish mining. MAD-DAG withstands adverse conditions under which Colordag and Bitcoin fail, while otherwise maintaining comparable security. For example, with petty-compliant miners and high levels of block reward variability, MAD-DAG's security threshold ranges from 11% to 31%, whereas both Colordag and Bitcoin achieve 0% for all levels.
South Korea faces the dual challenge of managing growing distributed solar energy surpluses and the high energy demand of industries like Bitcoin mining. Leveraging mining operations as a flexible load to monetize this `net-metering surplus' presents a viable synergy, but requires a robust site selection methodology. Traditional GIS-based Multi-Criteria Decision Analysis (MCDA) struggles with subjective weighting and integrating heterogeneous spatial data (areal-level and lattice-level). This thesis develops and implements a Two-Stage Hierarchical Optimization framework to overcome these limitations. Stage 1 (Areal-Level) employs a cost-benefit optimization to determine the optimal number ($K^*$) and combination of regions, maximizing a final adjusted net profit by balancing surplus power revenue against detailed land and non-linear infrastructure costs. Stage 2 (Point-Level) then uses a GIS-based sliding window search within these selected regions, applying topographic (slope $< 6.0^\circ$) and land-use constraints at a 30m resolution to identify physically constructible `unit sites'. The model identified an optimal configuration of $K^*=3$ regions (Yongin, Damyang, Miryang) yielding a maximum potential net profit of approximately \$307 million. Crucially, the Stage 2 screening revealed that Yongin, the most profitable region, was also the most physically constrained, 87\% of sites filtered out. This research contributes a scalable, objective framework for energy infrastructure siting that effectively integrates multi-scale spatial data. It provides a data-driven strategy for policymakers and grid operators (like Korea Electric Power Corporation) to monetize curtailed renewables and enhance grid stability.
Abstract We present a spatial analysis of Bitcoin-accepting merchants using BTC Map, a global crowdsourced dataset built on OpenStreetMap, to provide ground-level evidence on Bitcoinâs payment ecosystem. While prior research emphasizes macroeconomic drivers, our analysis of approximately 11,000 merchants shows that local adoption is more strongly shaped by community dynamics and sectoral niches. Acknowledging quality variance in crowdsourced data, we focus on verified regional clusters. We find a global concentration of adoption in the hospitality sector, localised clusters driven by grassroots initiatives rather than national policy and significant presence in alternative healthcare and IT services. These findings highlight the limits of top-down interventions such as El Salvadorâs legal tender law and underscore the role of social networks in sustaining adoption. By contrasting spatial micro-level evidence with national studies, this work positions merchant data as a key lens for understanding Bitcoinâs evolving role as a medium of exchange.
Vera Mita Nia, Hermanto Siregar, Roy Sembel, Nimmi Zulbainarni
This study explores how surprise shocks in Indonesiaâs macroeconomic environmentâspecifically interest rates, inflation, and exchange ratesâaffect the returns and volatility of key financial assets, including gold, Bitcoin (BTC), stocks (JKSE), and government bonds. Utilizing the EGARCH(1,1) model, this research demonstrates that gold exhibits enduring resilience as a safe-haven during periods of rising inflation and interest rate fluctuations. In contrast, Bitcoin is marked by pronounced speculative dynamics, showing persistent, asymmetric, and extreme volatility, yet delivering attractive gains when market conditions are strong. The findings indicate that stocks and bonds are particularly susceptible to changes in macroeconomic variables, thereby illustrating the vulnerabilities typical of emerging markets. Through portfolio optimization employing the Mean-Variance approach, gold dominates the optimal asset allocation, while Bitcoin provides notable diversification benefits. The results of backtesting using the Kupiec and Basel Traffic Light procedures confirm that GARCH-family risk estimations are robust and meet international regulatory standards. Furthermore, analysis of the Sharpe ratio and cumulative returns reveals that Mean-Variance portfolios consistently outperform equally weighted alternatives by delivering higher risk-adjusted returns and lower overall volatility. By integrating advanced econometric methods with real-world macroeconomic shocks in an Indonesian context, this research offers practical insights for both investors and policymakers addressing asset allocation under uncertainty, while laying the groundwork for future work involving broader asset universes and sophisticated modeling techniques.
The objective of this article is to identify the impact of corporate investments in Bitcoin on the stability of the cryptocurrency market, with particular emphasis on the investment strategy of MicroStrategy (currently Strategy). The first section of the paper outlines the operational mechanisms of Bitcoin, including its consensus system and the blockchain technology that underpins its security and decentralisation. The second section examines the structure of the cryptocurrency market, identifying its key participants and the mechanisms driving its volatility and dynamics. The third section is dedicated to an analysis of MicroStrategyâs quarterly reports for 2023 to 2024. The final section presents the conclusions, which indicate that the companyâs aggressive acquisition strategy is associated with significant financial risk. The analysed data suggest that continued exposure to the highly volatile cryptocurrency market may lead to serious challenges for the firm, potentially undermining its longâterm financial stability. Consequently, this may pose systemic risks to the broader cryptocurrency market, particularly by exerting substantial downward pressure on the supply side.
We examine the qualifying attributes of decentralized finance (DeFi) as a financial asset class. To achieve this objective, we perform analysis on the relationship (using both level and percentage-change data) between DeFi valuation and selected influencing variables, namely total value locked (TVL), Bitcoin (BTC) value, and market variables. A suite of long-panel data econometric methods is employed on a multi-frequency (daily, weekly, and monthly) panel dataset comprising 16 major DeFi protocols from January 2022 to December 2023. Our empirical design aims to be a comprehensive assessment and triangulation. There are several key findings. First, while there is evidence of cointegration suggesting a possible long-run relationship, this relationship is found to be inconsistent across different variables and time frequencies. However, the impulse response analysis suggests that shocks from the influencing variables do not have a permanent impact. Second, Bitcoin value is found to be the most important influencing factor (positive and highly significant), reflecting strong cryptocurrency market sentiment and aligning with previous research on spillover effects from major cryptocurrencies (Čoiman et al., 2022; Yousaf et al., 2022).
Blockchain is one of the most consequential innovations since the world wide web. Although blockchain is argued to remove, displace, or redistribute expertise, there is little understanding of the role of expertise in blockchain ecosystems, and more generally the expertise that fuels the development of new technologies by means of open, fluid, and heterogeneous knowledge contributions. An empirical study of the social organization of the Ethereum community, the second largest blockchain ecosystem after Bitcoin, reveals the contrasting tensions involved in setting up a system of decentralized expertise. The alternate community mantras ârough consensus, running codeâ and âwide consensus, better code?â suggest that the Ethereum community enacts expertise centralization and decentralization practices simultaneously to create a fragile balance between individualized accountabilities and a generalized sense of diffused participation. These practices unfold along a continuum of routine operations punctuated by critical events and are both essential for navigating the uncertainties of decentralized organizations. The study contributes to research on new forms of expertise occasioned by emerging technologies, and in particular to our understanding of blockchain expertise. The studyâs relational perspective on expertise adds to research on the dynamics of knowledge de/centralization in online communities.
Since its inception, Bitcoin has been positioned as a revolutionary alternative to national currencies, attracting immense public and academic interest. This paper presents a critical evaluation of this claim, suggesting that Bitcoin faces significant structural barriers to qualifying as money. It synthesizes critiques from two distinct schools of economic thought - Post-Keynesianism and the Austrian School - and validates their conclusions with rigorous technical analysis. From a Post-Keynesian perspective, it is argued that Bitcoin does not function as money because it is not a debt-based IOU and fails to exhibit the essential properties required for a stable monetary asset (Vianna, 2021). Concurrently, from an Austrian viewpoint, it is shown to be inconsistent with a strict interpretation of Mises's Regression Theorem, as it lacks prior non-monetary value and has not achieved the status of the most saleable commodity (Peniaz and Kavaliou, 2024). These theoretical arguments are then supported by an empirical analysis of Bitcoin's extreme volatility, hard-coded scalability limits, fragile market structure, and insecure long-term economic design. The paper concludes that Bitcoin is more accurately characterized as a novel speculative asset whose primary legacy may be the technological innovation it has spurred, rather than its viability as a monetary standard.
S Thillainayagi, Paolo Pavan, S Shashank, L V Preetham ¡ 5 authors
Bitcoin is known for its high volatility and speculative trading behavior.Predicting Bitcoin prices is valuable for investors, traders, and financial analysts.The study uses historical price data, technical indicators, and/or sentiment analysis.Machine learning and statistical models like ARIMA, Linear Regression, and LSTM are applied.Deep learning models, especially LSTM, show better accuracy in capturing time-series patterns
Regardless of oneâs opinion, Bitcoinâs presence in the global economy is growing. However, Bitcoinâs emerging role and its implications are greatly under-researched, particularly in the context of government interest in Bitcoin. Nonetheless, increased private investment in Bitcoin and increased government interest in formally incorporating Bitcoin into existing economic systems, suggest a new development within the global economy that must be investigated. Through qualitative text analysis of pro-Bitcoin narratives presented in digital media platforms and official government policies and public statements, this thesis explores how private and government interest in Bitcoin is explained and framed within these contexts. This study finds that there are many important nuances within pro-Bitcoin narratives in the context of private interest that challenge and expand contemporary thinking. It presents new insights into government interest in Bitcoin, particularly concerning its intended role and future, suggesting it will have a presence in efforts beyond finance. Finally, this thesis suggests that despite converging attitudes in private and governmental pro-Bitcoin narratives, diverging attitudes reflect curious implications concerning distrust and dissatisfaction in government efforts. Ultimately, this study reflects that Bitcoin is a dynamic and non-traditional development that requires continuous research to better understand its present and future role in global systems.
This paper investigates the role of artificial intelligence (AI) stocks and AI cryptocurrencies in portfolio diversification, reflecting on the rising interest in technology-oriented assets. While much research has focused on the diversification, hedging, and safe-haven properties of digital assets, such as Bitcoin and Ethereum, this study focuses on whether AI cryptocurrencies and AI stocks provide untapped diversification potential. Using mean-variance, risk parity, and higher-order moments approaches, we construct portfolios that combine AI stocks, AI cryptocurrencies, and traditional assets under various optimization frameworks. The findings reveal that the mean-variance framework is more conservative in allocating to AI cryptocurrencies, while the higher-order moments approach accommodates for greater flexibility. Seemingly, investors may benefit from expanding their asset pool to incorporate AI stocks and AI cryptocurrencies. Across most portfolio settings, gold and commodities dominate allocations, followed by AI stocks, with AI cryptocurrencies receiving only marginal weights owing to their high volatility. However, allocations to AI cryptocurrencies increase as investor risk tolerance increases, thereby highlighting their potential for risk-seeking portfolios. Overall, the results indicate that AI stocks and AI cryptocurrencies can enhance portfolio diversification and improve risk-return outcomes. These results offer valuable insights for investors seeking to optimize their portfolios, through exposure to emerging technology-driven assets while balancing traditional risk considerations. ⢠The study explores the diversification potential of AI Stocks and AI Cryptocurrencies to a traditional portfolio. ⢠Dominated by NVIDIA and Tesla, AI stocks perform better than AI cryptocurrencies. ⢠AI cryptocurrencies offer limited diversification benefits while significantly increasing portfolio risk. ⢠Unlike AI stocks, AI cryptocurrencies are not dominated by a single player in portfolio diversification. ⢠Allocation to AI cryptocurrencies is highly sensitive to investor risk aversion, particularly driven by their explosive market behaviour.
The environmental footprint of cryptocurrency networks, particularly the electricity-intensive Bitcoin (BTC) blockchain, has raised growing concern among policymakers, investors, and environmental organizations. This study examines how major global environmental events and climate policy announcements influence Bitcoinâs return and risk dynamics, linking digital asset markets to sustainability debates. Thirteen events between 2010 and 2024âincluding multilateral agreements (e.g., the Paris Agreement), COP summits, extreme weather disasters, and national policy interventionsâare analyzed using an event study framework integrated with the Capital Asset Pricing Model (CAPM) and GARCH-based volatility modelling. We hypothesize that highly visible policy events generate stronger short-run abnormal returns than climate disasters, while disasters produce more persistent effects on volatility. Results confirm this distinction: events such as the U.S. Paris Agreement withdrawal triggered immediate and significant reactions, whereas major weather disasters induced longer-term volatility adjustments. While overall systematic risk remained stable, event-specific responses revealed shifts in Bitcoinâs sensitivity to global equity markets. Climate-related signals shape speculative digital asset markets, with implications for sustainable finance, climate risk assessment, and regulatory policy design. Climate-related news can shape investor perceptions of energy-intensive digital assets, with implications for environmental policy design, sustainable finance strategies, and climate risk assessment. For policymakers, the results highlight the potential of environmental signals to influence speculative markets, supporting the case for integrating financial market behaviour into environmental management and regulatory planning.
Taki E. M. Abedesselam, Fabio Giacomelli, Francesco Pasquale, Michele Salvi
We study a random process over graphs inspired by the way payments are executed in the Lightning Network, the main layer-two solution on top of Bitcoin. We first prove almost tight upper and lower bounds on the time it takes for a payment failure to occur, as a function of the number of nodes and the edge capacities, when the underlying graph is complete. Then, we show how such a random process is related to the edge-betweenness centrality measure and we prove upper and lower bounds for arbitrary graphs as a function of edge-betweenness and capacity. Finally, we validate our theoretical results by running extensive simulations over some classes of graphs, including snapshots of the real Lightning Network.
ABSTRACT This paper employs deep learning and machine learningâbased NLP models to investigate the impact of the news sentiment on the Bitcoin price. The lagged Bitcoin variables, news indicators, macroeconomic, and financial factors were taken into account to explain the importance of news sentiment on the Bitcoin price. Moreover, FinBERTâbased sentiment scores and semantic features extracted from over 650,000 financial news headlines were integrated with financial and macroeconomic variables. The importance scores of the investigation showed that Bitcoin was largely explained by its lagged price movements, which suggests the speculative nature of the cryptocurrency. However, the investigation also revealed that Bitcoin was significantly influenced by the news sentiment score. In other words, the paper indicates that the movements in the Bitcoin price can be predominantly explained by the news sentiment. Advanced hybrid models (all ML and DL models with the addition of variables obtained with the FinBERT model) were optimized using Optuna and RandomizedSearchCV. The FinBERTâLSTM model achieved the best prediction accuracy. Nevertheless, the main findings indicated that the response of the Bitcoin price to negative news was much stronger than to positive and neutral news. This finding suggests that the asymmetric relationship between the Bitcoin price and news sentiment was evident. GARCHâbased volatility and whatâif scenario analyses further demonstrated that negative sentiment leads to sharper fluctuations in the Bitcoin price. The paper provides important implications for policymakers, portfolio managers, investors, and academics.
This study investigates the application of the Light Gradient Boosting Machine (LGBM) model for both deterministic and probabilistic forecasting of Bitcoin realized volatility. Utilizing a comprehensive set of 69 predictors -- encompassing market, behavioral, and macroeconomic indicators -- we evaluate the performance of LGBM-based models and compare them with both econometric and machine learning baselines. For probabilistic forecasting, we explore two quantile-based approaches: direct quantile regression using the pinball loss function, and a residual simulation method that transforms point forecasts into predictive distributions. To identify the main drivers of volatility, we employ gain-based and permutation feature importance techniques, consistently highlighting the significance of trading volume, lagged volatility measures, investor attention, and market capitalization. The results demonstrate that LGBM models effectively capture the nonlinear and high-variance characteristics of cryptocurrency markets while providing interpretable insights into the underlying volatility dynamics.
This study explores Bitcoin's value formation through the Granular Interaction Thinking Theory-Value Theory (GITT-VT). Rather than stemming from material utility or cash flows, Bitcoin's value arises from informational attributes and interactions of multiple factors, including cryptographic order, decentralization-enabled autonomy, trust embedded in the consensus mechanism, and socio-narrative coherence that reduce entropy within decentralized value-exchange processes. To empirically assess this perspective, a Bayesian linear model was estimated using daily data from 2022 to 2025, operationalizing four informational value dimensions: Store-of-Value (SOV), Autonomy (AUT), Social-Signal Value (SSV), and Hedonic-Sentiment Value (HSV). Results indicate that only SSV exerts a highly credible positive effect on next-day returns, highlighting the dominant role of high-entropy social information in short-term pricing dynamics. In contrast, SOV and AUT show moderately reliable positive associations, reflecting their roles as low-entropy structural anchors of long-term value. HSV displays no credible predictive effect. The study advances interdisciplinary value theory and demonstrates Bitcoin as a dual-layer entropy-regulating socio-technological ecosystem. The findings offer implications for digital asset valuation, investment education, and future research on entropy dynamics across non-cash-flow digital assets.
Once a playground for tech enthusiasts, the crypto space has shifted to a financial field that is increasingly on policymakersâ radar due to the increasing adoption of crypto-assets, and also some significant crypto-related collapses. In this context, it is crucial to propose monitoring frameworks to assess the potential integration of the crypto sphere into traditional financial systems. We propose the use of the TVP-VAR approach as a strategic instrument for policymakers to analyze the connectedness between major financial markets and relevant crypto systems, such as the emerging centralized finance sector and the increasingly relevant decentralized finance ecosystem. Our findings indicate that the financial integration between the crypto space and traditional financial markets remains weak. Nonetheless, we report a very slight increase in connectedness since 2020, suggesting that while the crypto space is still far from being fully integrated, it has begun to establish modest but persistent links with conventional financial markets. ⢠We examine dynamic connectedness between crypto and global equity markets. ⢠TVP-VAR shows cryptoâTradFi integration remains weak but rising since 2020. ⢠DeFi and broad crypto indices transmit more spillovers than Bitcoin or CeFi. ⢠Results highlight regulatory priority on DeFi and full-market monitoring.
Ethan Hicks, Joseph Oglio, Mikhail Nesterenko, Gokarna Sharma
We study financial transaction confirmation finality in Bitcoin as a function of transaction amount and user risk tolerance. A transaction is recorded in a block on a blockchain. However, a transaction may be revoked due to a fork in the blockchain, the odds of which decrease over time but never reach zero. Therefore, a transaction is considered confirmed if its block is sufficiently deep in the blockchain. This depth is usually set empirically at some fixed number such as six blocks. We analyze forks under varying network delays in simulation and actual Bitcoin data. Based on this analysis, we establish a relationship between block depth and the probability of confirmation revocation due to a fork. We use prospect theory to relate transaction confirmation probability to transaction amount and user risk tolerance.
Blockchain technologies underpin an expanding ecosystem of decentralized applications, financial systems, and infrastructure. However, the fundamental networking layer that sustains these systems, the peer-to-peer (P2P) layer, of all but the top few ecosystems remains largely opaque. In this paper, we present the first longitudinal, cross-network measurement study of 36 public blockchain networks. Over 9 months (since late 2024), we deployed 15 active crawlers, sourced data from two additional community crawlers, and conducted hourly connectivity probes (e.g., pings and protocol-level handshakes) to observe the evolving state of these networks. Furthermore, by leveraging Ethereum's discovery protocols, we inferred metadata for an additional 19 auxiliary networks that utilize the Ethereum peer discovery protocol. We also explored Internet-wide scans, which only require probing each protocol's default ports with a simple, network-specific payload. This approach allows us to rapidly identify responsive peers across the entire address space without having to implement custom discovery and handshake logic for every blockchain. We validated this method on Bitcoin and similar networks with known ground truth, then applied it to Cardano, which we could not crawl directly. Our study uncovers dramatic variation in network size from under 10 to more than 10,000 active nodes. We quantify trends in IPv4 versus IPv6 usage, analyze autonomous systems and geographic concentration, and characterize churn, diurnal behavior, and the coverage and redundancy of discovery protocols. These findings expose critical differences in network resilience, decentralization, and observability. Beyond characterizing each network, our methodology demonstrates a general framework for measuring decentralized networks at scale. This opens the door for continued monitoring, benchmarking, and more transparent assessments of blockchain infrastructure across diverse ecosystems.
Crypto enthusiasts claim that buying and holding crypto assets yields high returns, often citing Bitcoin's past performance to promote other tokens and fuel fear of missing out. However, understanding the real risk-return trade-off and what factors affect future crypto returns is crucial as crypto becomes increasingly accessible to retail investors through major brokerages. We examine the HODL strategy through two independent analyses. First, we implement 480 million Monte Carlo simulations across 378 non-stablecoin crypto assets, net of trading fees and the opportunity cost of 1-month Treasury bills, and find strong evidence of survivorship bias and extreme downside concentration. At the 2-3 year horizon, the median excess return is -28.4 percent, the 1 percent conditional value at risk indicates that tail scenarios wipe out principal after all costs, and only the top quartile achieves very large gains, with a mean excess return of 1,326.7 percent. These results challenge the HODL narrative: across a broad set of assets, simple buy-and-hold loads extreme downside risk onto most investors, and the miracles mostly belong to the luckiest quarter. Second, using a Bayesian multi-horizon local projection framework, we find that endogenous predictors based on realized risk-return metrics have economically negligible and unstable effects, while macro-finance factors, especially the 24-week exponential moving average of the Fear and Greed Index, display persistent long-horizon impacts and high cross-basket stability. Where significant, a one-standard-deviation sentiment shock reduces forward top-quartile mean excess returns by 15-22 percentage points and median returns by 6-10 percentage points over 1-3 year horizons, suggesting that macro-sentiment conditions, rather than realized return histories, are the dominant indicators for future outcomes.
Remote Procedure Call (RPC) services have become a primary gateway for users to access public blockchains. While they offer significant convenience, RPC services also introduce critical privacy challenges that remain insufficiently examined. Existing deanonymization attacks either do not apply to blockchain RPC users or incur costs like transaction fees assuming an active network eavesdropper. In this paper, we propose a novel deanonymization attack that can link an IP address of a RPC user to this user's blockchain pseudonym. Our analysis reveals a temporal correlation between the timestamps of transaction confirmations recorded on the public ledger and those of TCP packets sent by the victim when querying transaction status. We assume a strong passive adversary with access to network infrastructure, capable of monitoring traffic at network border routers or Internet exchange points. By monitoring network traffic and analyzing public ledgers, the attacker can link the IP address of the TCP packet to the pseudonym of the transaction initiator by exploiting the temporal correlation. This deanonymization attack incurs zero transaction fee. We mathematically model and analyze the attack method, perform large-scale measurements of blockchain ledgers, and conduct real-world attacks to validate the attack. Our attack achieves a high success rate of over 95% against normal RPC users on various blockchain networks, including Ethereum, Bitcoin and Solana.
We investigate how transparencyâcrypto exchanges' verification of trader identities through Know-Your-Customer (KYC) and their transmission of trader and transaction data to tax authoritiesâshapes the effectiveness of tax policies in cryptocurrency markets. Using regulatory events and cross-exchange price variation, we provide initial global evidence that transparency amplifies the capitalization of statutory crypto-tax liabilities into prices. In the United States, Bitcoin prices on exchanges subject to new tax reporting obligations fall by an average of 0.34 % following announcements that raise expectations of information transmission, even without changes in statutory tax liabilities. Across jurisdictions, price declines are significantly larger where reporting systems are more transparent, and in cross-sectional analysis, exchanges that both enforce KYC and transmit information show the strongest price sensitivity to local tax liabilities, particularly where capital controls constrain arbitrage. These findings reveal a transparencyâprivacy trade-off unique to crypto markets and demonstrate how digital assets provide rare opportunities to test classic tax-capitalization theories under conditions of anonymity and regulatory heterogeneity, with implications for the design of effective tax policies.
We investigate a multi-class machine learning (ML) framework to generate daily Bitcoin trading signalsâBuy, Sell, or Hold. Three algorithmsâXGBoost, LightGBM, and Random Forestâare compared with a naive buy-and-hold strategy. Using BTC/USD daily data (2015â2024), we apply a range of technical indicators across trend, momentum, volatility, and volume, later pruned by correlation analysis. A Âą1% threshold defines the "Hold" zone to avoid minor fluctuations. Empirical tests show that LightGBM outperforms other models and even surpasses buy-and-hold in final portfolio value. Our findings support the design of tri-class ML strategies tailored for high-volatility markets like cryptocurrency.
Reddit is in the minority of mainstream social platforms that permit posting content that may be considered to be at the edge of what is permissible, including so-called Not Safe For Work (NSFW) content. However, NSFW is becoming more common on mainstream platforms, with X now allowing such material. We examine the top 15 NSFW-restricted subreddits by size to explore the complexities of responsibly sharing adult content, aiming to balance ethical and legal considerations with monetization opportunities. We find that users often use NSFW subreddits as a social springboard, redirecting readers to private or specialized adult social platforms such as Telegram, Kik or OnlyFans for further interactions. They also directly negotiate image "trades" through credit cards or payment platforms such as PayPal, Bitcoin or Venmo. Disturbingly, we also find linguistic cues linked to non-consensual content sharing. To help platforms moderate such behavior, we trained a RoBERTa-based classification model, which outperforms GPT-4 and traditional classifiers such as logistic regression and random forest in identifying non-consensual content sharing, showing better performance in this specific task. The source code and model weights are publicly available at https://github.com/socsys/15NSFWsubreddits.