Blockchain Papers

Mazen Hasan Basha

No abstract is available for this record.

Jingrui Li, Divykumar Patel

No abstract is available for this record.

Bruno Biais, Jean Rochet, Stéphane Villeneuve

No abstract is available for this record.

Erhan Uluceviz

No abstract is available for this record.

Alshaikh A. Shokeralla

No abstract is available for this record.

K. S. Dhanya, Anindya Nag

No abstract is available for this record.

Aubain Nzokem

The paper presents two series representations of a L{\'e}vy process for the Generalized Tempered Stable (GTS) distribution: a series representation generated by the inverse tail integral and a short noise representation. Both series representations are used to simulate the daily returns of Bitcoin and Ethereum. The Q-Q plot analysis shows smooth linear patterns, indicating strong agreement between the empirical and theoretical GTS distributions.

William C. Johnson

No abstract is available for this record.

Saeed Mohammadi Dashtaki, Mehdi Hosseini Chagahi, Aein Bahadori, Behzad Moshiri · 6 authors

Cryptocurrency markets are highly volatile and sentiment-driven, posing challenges to traditional forecasting methods. This paper presents Hard and Soft Information Fusion (HSIF), a novel Transformer-based dual-stream model that combines market data and social sentiment using Financial Bidirectional Encoder Representations from Transformers (FinBERT), a financial sentiment analysis tool, and a bidirectional cross-attention mechanism. Evaluations on multi-year Bitcoin data show that HSIF achieves 97.48% accuracy and a 26.64% return, outperforming Long Short-Term Memory (LSTM)-based and other multimodal models. The results highlight the effectiveness of domain-specific sentiment embeddings and cross-modal attention in enhancing trend prediction accuracy for volatile cryptocurrency markets.

Renhong Wu, Yuantao Fang, Md. Alamgir Hossain

This study aims to explore the dynamic relationships among economic policy uncertainty (EPU), Bitcoin trading activity, and the NASDAQ index over the period from January 2, 2014, to March 21, 2023. Employing the dynamic conditional correlation–generalized autoregressive conditional heteroskedasticity (DCC‐GARCH) model, this research reveals significant time‐varying interdependencies between EPU and financial markets, with a specific focus on the Bitcoin sector. This paper extends the literature by examining EPU’s influence on trading volume and volatility spillovers during different market regimes and crisis events, including the COVID‐19 pandemic. The results demonstrate that the correlations between EPU and both Bitcoin and the NASDAQ index are dynamic and sensitive to market phases, with stronger effects observed during bull markets and heightened volatility during the pandemic. The findings provide new empirical evidence on the evolving role of EPU in shaping investor behavior and financial asset comovement. This study offers practical implications for investors, regulators, and policymakers, especially in designing risk management strategies under uncertainty.

Ahmed El Youssefi, Abdelaaziz Hessane, Imad Zeroual, Yousef Farhaoui

This study provides a systematic investigation into the influence of feature selection methods on cryptocurrency price forecasting models employing technical indicators. In this work, over 130 technical indicators—covering ... | Find, read and cite all the research you need on Tech Science Press

Luís Costa, Elisabete Vieira, Mara Madaleno

Purpose: This work analyzes whether cryptocurrencies significantly influence Euronext stock returns. Design/methodology/approach: To this end, this quantitative research analyzes companies from 4 Euronext financial markets between 2017 and 2022 using the panel data methodology. The Generalized Method of Moments (GMM) methodology was also used to make the analysis more robust. Findings: This study concluded that Bitcoin and Ethereum positively and statistically significantly influence Euronext stock returns. Their notoriety caused them to lose the safe haven characteristics they displayed in a more embryonic phase and led them to be influenced by the same systemic factors that affect the stock market. Originality/value: The results of this study are immensely important for private and institutional investors investing in Euronext stocks and looking to diversify their portfolios. Keywords: Stock Returns; Euronext; Bitcoin; Ethereum; Cryptocurrencies DOI: https://doi.org/10.58869/EJABM11(1)/06

Gabriel Borrego Rold aacute n

No abstract is available for this record.

Ismail Jirou, Ikram Jebabli, Amine Lahiani

No abstract is available for this record.

Lucas Mussoi Almeida

This dissertation presents an empirical analysis of decentralized finance through three distinct studies. By harnessing the power of on-chain data, this research delves into the mechanics of DeFi, exploring how we assess financial risk and measure market efficiency. Furthermore, it directly addresses the significant economic exter nalities of the sector by measuring the annualized energy draw of Bitcoin’s global mining industry. The first article, Risk forecasting comparisons in decentralized fi nance: An approach in constant product market makers (this research was presented at the Annual Conference of the Banco Central do Brasil (2024) and subsequently published in the Journal of Economics and Business, Volume 133, 2025; 2024 JCR Impact Factor: 3.4), pioneers by comparing risk measures between centralized and decentralized exchanges. By employing a vast dataset from Uniswap V2 Liquid ity Pool (LP) and conducting a meticulous comparative analysis of Value-at-Risk (VaR) and Expected Shortfall (ES) forecasts, using both parametric Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models and the non para metric DeepAR neural network, it demonstrates that liquidity provision generally presents a statistically significant lower risk profile than an equivalent buy and hold strategy. A critical exception exists for stablecoin pairs, where protocol fees become the primary risk driver. This research provides a crucial empirical foundation for developing sophisticated, model informed risk management tools in Decentralized Finance (DeFi). The second article, Pricing efficiency in cryptocurrencies: the case of centralized and decentralized markets (published in the Journal of Economics and Business, Volume 133, 2025; 2024 JCR Impact Factor: 3.4), offers a comparative analysis of market efficiency between liquidity pool mechanisms and traditional order book systems. Utilizing Asymmetric Multifractal Detrended Fluctuation Analysis (asym metric MF-DFA) and the Thermal Optimal Path (TOP) method on data from Binance and Uniswap V2, it reveals that algorithmic LP can achieve superior weak form market efficiency compared to Centralized Exchanges (CEX) order books. The study conclusively identifies the Decentralized Exchanges (DEX) as the lead market in price discovery, transmitting signals to its centralized counterpart with an average lag of under 24 hours. This efficiency premium, driven by radical transparency and high velocity arbitrage, intensified significantly following the Ethereum 2.0 upgrade. The third article, Bitcoins halving events and the fractal nature of mining energy consumption, investigates the long term impact of Bitcoins programmed monetary policy on its mining energy consumption behavior. Applying asymmetric MF-DFA to data from the Cambridge Centre for Alternative Finance, it uncovers the com plex, multifractal nature of Bitcoins energy dynamics, showing an evolution from persistent, heterogeneous behavior before halving events toward more efficient and random consumption characteristics after each subsequent halving. The analysis provides the first documented evidence of a significant cross-chain effect, showing that Ethereum’s transition to Proof of Stake (PoS) consensus triggered an immediate and sustained decrease in the persistence of Bitcoin’s energy consumption patterns. This finding reveals previously unrecognized interconnectivity between seemingly independent networks and creates new pathways for assessing the environmental relationships within blockchain ecosystems.

Murray A. Rudd, Dennis Porter

We refine a bottom-up, quantity-clearing framework of Bitcoin price formation that couples its fixed 21-million-coin cap with plausible demand growth and execution behavior. This approach relies on first-principles economic supply-and-demand dynamics rather than assumptions about anticipated Bitcoin price appreciation, its price history, or its potential effectiveness in demonetizing other asset classes. We considered five key high-level factors that may affect price determination: level of market demand; intertemporal investment preferences; fiat-denominated withdrawal sensitivity; initial liquid supply; and daily withdrawal levels from liquid supply. With a goal of both increasing understanding of the impacts of price drivers and developing probabilistic forecasts, we show two models: (1) a baseline to assess the impacts of parameter changes, alone and in combination, on Bitcoin price trajectories and liquid supply over time and (2) a Monte Carlo simulation that incorporates uncertainty across a range of uncertain parameterizations and presents probabilistic price and liquid supply forecasts to 2036. Our baseline model highlighted the importance of liquid supply and withdrawal sensitivity in price impacts. The Monte Carlo simulation results suggest a 50% likelihood that Bitcoin price will exceed USD 5.17 M by April 2036. Generally, prices from the low single millions to the low tens of millions per Bitcoin by 2036 emerge under broad parameter sets; hyperbolic paths to higher price levels are relatively rare and concentrate when liquid supply falls near or below BTC 2 M and withdrawal sensitivity is low. Our results help locate where right-tail risk and disorderly market outcomes concentrate and suggest that policy tools are available to help guide trajectories.

Aadi Singhi

This paper presents a Multi Agent Bitcoin Trading system that utilizes Large Language Models (LLMs) for alpha generation and portfolio management in the cryptocurrencies market. Unlike equities, cryptocurrencies exhibit extreme volatility and are heavily influenced by rapidly shifting market sentiments and regulatory announcements, making them difficult to model using static regression models or neural networks trained solely on historical data. The proposed framework overcomes this by structuring LLMs into specialised agents for technical analysis, sentiment evaluation, decision-making, and performance reflection. The agents improve over time via a novel verbal feedback mechanism where a Reflect agent provides daily and weekly natural-language critiques of trading decisions. These textual evaluations are then injected into future prompts of the agents, allowing them to adjust allocation logic without weight updates or finetuning. Back-testing on Bitcoin price data from July 2024 to April 2025 shows consistent outperformance across market regimes: the Quantitative agent delivered over 30\% higher returns in bullish phases and 15\% overall gains versus buy-and-hold, while the sentiment-driven agent turned sideways markets from a small loss into a gain of over 100\%. Adding weekly feedback further improved total performance by 31\% and reduced bearish losses by 10\%. The results demonstrate that verbal feedback represents a new, scalable, and low-cost approach of tuning LLMs for financial goals.

David Korsah, Lord Mensah, Kofi A. Osei, Godfred Amewu

Purpose This study seeks to: (1) examine the extent of interconnectedness prevailing between the cryptocurrency market, the stock market and the precious metals market. (2) Conduct thorough assessment of hedge and safe-haven qualities of broad range of precious metals and cryptocurrencies against returns on the African stock market. Design/methodology/approach This study applied two novel approaches that is Cross-quantilogram, an advanced statistical technique used to examine the relationship between quantiles of response variable and the quantiles of predictor variables, and TVP-VAR, a technique that captures the dynamic connectedness of variables under consideration. Findings It was found that the three markets are highly interconnected, particularly among assets under the respective financial markets. It was further revealed that the Johannesburg Stock Exchange (JSE) was the most resilient stock market, whereas Bitcoin, BNB, Silver (XAG) and Platinum (XPT) also exhibited notable resistance to shocks. Finally, the study found that cryptocurrencies and precious metals portrayed varying hedge and safe haven qualities under the various stock markets. Practical implications The high interdependency between the African stock market, cryptocurrencies and precious metals suggests that none of the markets is immune to shocks form the other market. The finding that cryptocurrencies and precious metals exhibit some degree of safe-haven and hedge potentials, albeit limited in certain stock markets, provides investors with alternative investment options during market downturns. Since most African stock markets, except the JSE, are net receivers of shocks, investors in these markets should exercise caution during periods of global financial uncertainty. Originality/value To the best of our knowledge, this study is the first to explore the dynamic interconnectedness between seven carefully selected African stock markets, three distinct cryptocurrencies and four precious metals, while also assessing the hedge and safe-haven potential of the cryptocurrencies and precious metals against stock market returns. Additionally, the study stands out in recent literature by employing two novel approaches: the TVP-VAR model, which captures the dynamic connectedness among variables, and the Cross-Quantilogram, an advanced statistical method that analyzes the relationship between the quantiles of the response and predictor variables, all within a single study.

Giovanni Arroyo, Lawrence Millen

No abstract is available for this record.

Daria Smuseva, Ivan Malakhov, Andrea Marin, Carla Piazza · 5 authors

No abstract is available for this record.

Chang Wang, Qunhong Sun, Qibin Huang, Yifan Hu · 5 authors

No abstract is available for this record.

Barbara Čeryová, Peter Árendáš

No abstract is available for this record.

Annette Hofmann, Chad Dulle

No abstract is available for this record.

Shubham Kumar Verma, Salah Boulaaras

This research primarily focuses on the volatility of cryptocurrency prices. In this study, the top five cryptocurrencies based on their market capitalization and data for the financial year 2022–2023 are considered for analysis via fractal dimensions and fractal functions, well-known tools in the subject of Fractal Geometry for measuring and modeling irregular and non-smooth phenomena. By integrating abnormal return with the capital asset pricing model, we ascertain the response of the acquiring cryptocurrency’s price. The study finds that BTC has consistency in its fluctuations, TETH has shown greater volatility over time, and ETH has shown some consistency; at [Formula: see text], XRP is riskier than the others. While BNB is easily predictable at [Formula: see text], it does not fluctuate in a systematic manner at [Formula: see text]. AAR indicates a short-term profit, while CAAR is decreasing, suggesting a long-term lack of profit. These reasons stem from investors’ increased expectations of specific strongholds in the cryptocurrency market, as well as increased investment.