In order to provide hedging strategies on the financial risks involved in such crises and also taking into consideration that two cryptocurrency prices have been impacted by Russia-Ukraine war uncertainties apart from the COVID-19 pandemic, we applied wavelet analysis along with the multivariate DCC-GARCH process to scrutinize the returnâvolatility causal relationship among gold price and six stock market indices, including three well-established emerging economy (EE) ones. We achieved a more balanced and complete picture by considering data for the time period July 28, 2016 to December 30, 2022. The events of analysis were crises in the Chinese market, a trade war between the USA and China), caused by the COVID-19 pandemic, after which came global recession â ˘ (a Russia-Ukraine war); next, part â Ł â the peak of the global energy crisis. The findings generally indicated that when a sudden shock sometimes like this happens (or in a pandemic), there is no one other than Ethereum for all investors in emerging and developed markets to find a safe haven or protect themselves, while Bitcoin acts as less safe. We also showed Gold as a hedge in Global Crises and as a Hedge and Weak Safe Haven Against Geopolitical Tension. Last, investors in the paired joint oil stock have a greater benefit but can gain only if they hold shorter-term investments. As for volatility, arguably, only bitcoin is to be observed as the least volatile among all other variables. Our findings suggested that stock markets are the source of volatility spillover to all others while prior work has established mixed evidence during the pandemic, the most crucial and recent periods, respectively.
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.
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.
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 correlationâgeneralized autoregressive conditional heteroskedasticity (DCCâGARCH) model, this research reveals significant timeâvarying interdependencies between EPU and financial markets, with a specific focus on the Bitcoin sector. This paper extends the literature by examining EPUâs influence on trading volume and volatility spillovers during different market regimes and crisis events, including the COVIDâ19 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 indicatorsâcovering ... | Find, read and cite all the research you need on Tech Science Press
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
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 Bitcoinâs 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 Ethereumâs transition to Proof of Stake (PoS) consensus triggered an immediate and sustained decrease in the persistence of Bitcoinâs 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.
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.
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.
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 2022â2023 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 cryptocurrencyâs 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 investorsâ increased expectations of specific strongholds in the cryptocurrency market, as well as increased investment.
This paper examines cryptocurrency shock transmission to financial markets and the macroeconomy using a Bayesian structural VAR with Pandemic Priors from 2015 to 2024. By affecting overall risk appetite, cryptocurrency price shocks generate positive financial market spillovers, accounting for 18% of equity and 27% of commodity price fluctuations. Real economic effects are significant in driving investment but remain limited, contributing only 4% to unemployment and 6% to industrial production variance. However, cryptocurrency shocks explain 18% of price-level forecast error variance at long horizons. Narrative analysis reveals sentiment and technology as primary shock drivers. These findings demonstrate cryptocurrencyâs deep financial system integration with important inflation implications for monetary policy.
This study examines how decentralized finance (DeFi) platforms coordinate capital and liquidity through algorithmic mechanisms. Using reproducible on-chain data from the DeFiLlama API, the analysis constructs a structural econometric framework linking micro-level choice, production efficiency, and network spillovers. A sequence of models-conditional logit, nested logit, nested CES, and spatial error-captures how algorithmic inputs, digital capital, and inter-protocol dependencies shape efficiency and systemic behavior. Results show that DeFi protocols exhibit strong internal substitution between algorithmic and traditional inputs, while cross-protocol linkages produce measurable spatial effects in efficiency and growth. The findings highlight how decentralized systems can self-organize productive coordination without central intermediaries, contributing to ongoing debates on financial autonomy, digital liquidity, and algorithmic governance.
This paper investigates the intersection of artificial intelligence (AI) agentsâautonomous software entities capable of adapting, learning, and executing multi-step operationsâand decentralized finance (DeFi) ecosystems. It highlights how the adaptive decision-making capabilities, flexible governance frameworks, and data-driven optimization strategies of AI agents reshape market coordination and organizational architectures. Drawing on a qualitative analysis of 306 major crypto AI agents, the study introduces a typology that maps their diverse application areas, including algorithmic trading, portfolio management, sentiment-driven communities, and immersive entertainment. To further conceptualize the role of AI in decentralized governance, the paper develops a quadrant-based framework that distinguishes four archetypal system configurations: Traditional Decentralized Autonomous Organization (DAO) Tools, Maximally Distributed Agency, Closed Systems, and AI Dictatorships. These configurations, defined by varying degrees of autonomy and decentralization, reveal critical trade-offs between transparency, efficiency, adaptability, and control. This framework serves as a lens to theorize how AI agents reconfigure trust mechanisms, power dynamics, and decision-making processes in decentralized ecosystems. Grounded in economic and socio-technical theory, the paper positions AI agents as transformative intermediaries in tokenized environments. While demonstrating their capacity to streamline operations, enhance decision quality, and enrich user engagement, the study also addresses the governance risks posed by algorithmic control and systemic opacity. Taken together, the conceptual and empirical insights lay a foundation for ongoing interdisciplinary inquiry into the evolving role of AI agents in decentralized finance. ⢠Introduces a typology of 306 AI agents across key DeFi application areas ⢠Maps AI agent roles in trading, governance, community, and entertainment ⢠Develops a governance framework for AI agent autonomy and decentralization ⢠Shows how AI agents reduce transaction costs and reshape market structures ⢠Highlights risks of opacity, misalignment, and centralization in DeFi AI use
Asad Ul Islam Khan, Rasim Ăzcan, Mohamed Abbas Ibrahim
In this paper, we use the Empirical Bayes estimation and multiple linear regression approach to examine the impact of the top 5 cryptocurrenciesâ crash risks on the G-7 and China equity marketsâ crash risks. MATLAB was used to calculate the crash risks, while Stata software was employed for the econometric analysis. Three crash risk measures are usedto validate the robustness of the results: (i) the relative frequency of the number of crash days in the market, (ii) the monthly returnsâ skewness, and (iii) the down-to-up volatility. Our findings indicate that overall crash risks of the top 5 cryptocurrencies are positively related with G-7 and Chinese stock marketsâ crash risk. This suggests that the crash risk transmits from the crypto to the equity markets and the crashes in crypto can serve as a predictor in the stock markets. Furthermore, there is a negative correlation between the historical crash risks of the G-7 stock market and the present crash risks of the same stock market. This suggests that past stock market crashes can serve as a predictive factor for assessing the current risk of a stock market crash.
Elie Bouri, Amin Sokhanvar, Harald Kinateder, Serhan ĂiftçioÄlu
⢠Reveals significant positive predictability in the stock marketâcryptocurrency nexus. ⢠U.S. tech and semiconductor stocks and Nvidia predict cryptocurrency returns and vice versa. ⢠Mutual returns predictability is significant across several quantiles and lags. ⢠It generally holds when controlling for the U.S. dollar index and treasury market. ⢠A trading strategy based on the cross-quantilogram outperforms a benchmark strategy. This study examines the directional return predictability between the technology sector of U.S. stock market and three major cryptocurrencies (Bitcoin, Ethereum, and Dogecoin). Using daily data from August 7, 2015, to February 8, 2024, and the cross-quantilogram approach in both static and dynamic settings, the results reveal significant positive predictability in the stock marketâcryptocurrency nexus. The technology sector, semiconductors subsector, and Nvidia Corporation exert predictive power over cryptocurrency returns and vice versa across several quantiles and lags. When controlling for the impact of other financial variables, namely, U.S. dollar and U.S. treasury markets, the return predictability holds, especially for the two largest cryptocurrencies, Bitcoin and Ethereum, which reflects their importance and tighter connections with the U.S. technology sector. A trading strategy based on the results of the cross-quantilograms outperforms a benchmark strategy (i.e., always long position in either stocks or cryptocurrency), which underlines the practical implications of our main findings, particularly in terms of the significant return interactions between U.S. technology/semiconductors stocks and large cryptocurrencies.
This study presents a novel methodology for multi-step Bitcoin (BTC) price prediction by combining advanced stacking-based architectures with temporal attention mechanisms. The proposed Temporal Attention-Enhanced Stacking Network (TAESN) integrates the complementary strengths of diverse machine learning algorithms while emphasizing critical temporal features, leading to substantial improvements in forecasting accuracy over traditional methods. Comprehensive experimentation and robust evaluation validate the superior performance of TAESN across various BTC prediction horizons. Additionally, the model not only demonstrates enhanced predictive accuracy but also offers interpretable insights into the temporal dynamics underlying cryptocurrency markets, contributing to both practical forecasting applications and theoretical understanding of market behavior.