This study constructs a machine learning-driven multi-factor model for Ethereum quantitative trading, combining traditional technical indicators (RSI, MACD), on-chain metrics (gas usage, active addresses), and X platform social sentiment to predict short-term returns. Backtesting from Q4 2021 to Q3 2024, using online learning and genetic algorithms for dynamic factor updates, yields a 97% annualized return, a Sharpe ratio of 2.5, and an information ratio of 1.2, outperforming Ethereum's raw returns. Simulated trading in Q4 2024 (bull market) achieves a 33% quarterly return with an 18% maximum drawdown, while Q1 2025 (bear market) records a -10% quarterly return with a 12% drawdown, confirming robustness. Technical and sentiment factors drive performance, though a 22% maximum drawdown in backtesting highlights volatility risks. An optimal Z-score threshold (±1.0) and 4-hour trading frequency balance profitability and costs. Future enhancements include high-frequency mainnet data integration and advanced risk management to strengthen model resilience in Ethereum's volatile market.
This research investigates the effect of social media sentiment on the cryptocurrency market, particularly focusing on Bitcoin and Ethereum. Using TensorFlow as a machine learning tool, we developed a sentiment index from 66,582 Reddit posts about Bitcoin and 23,231 about Ethereum, collected in 2022. The sentiment scores, ranging from -1 (negative) to 1 (positive), were categorized into positive, neutral, and negative classes and analyzed alongside daily return and volatility metrics for both cryptocurrencies using a Vector Autoregression (VAR) model. Our study identifies significant impacts of social media sentiment on cryptocurrency markets. Specifically, Bitcoinâs returns show a heightened sensitivity to negative sentiment, whereas Ethereumâs returns remain unaffected by any sentiment type. However, the volatility of both cryptocurrencies is affected by neutral sentiment. These findings highlight distinct behavioral patterns across cryptocurrencies and uncover a bidirectional relationship between market dynamics and social media sentiment. This study offers novel insights into how public perception influences digital asset markets, thereby contributing to the behavioral finance literature and providing practical implications for investors and policymakers.
Maksym Lazirko, Deniz Appelbaum, Miklos A. Vasarhelyi
Cryptocurrency exchanges face increasing pressure to demonstrate reserve adequacy following platform failures, yet current Proof of Reserves (PoR) systems suffer from incomplete verification approaches that examine either on-chain or off-chain assets separately. This study introduces the Double-Helix Framework, a verification methodology that integrates on-chain blockchain analysis with off-chain consensus algorithms to provide complete assessment of exchange financial positions. The framework employs parallel verification strands that simultaneously validate blockchain-recorded transactions and off-chain financial information, creating a unified assessment mechanism that addresses the verification gaps in existing PoR systems. The framework's integration of traditional auditing principles with distributed ledger verification creates new possibilities for regulatory compliance and investor protection in digital asset management. This framework has implications for accounting practice, suggesting that comprehensive cryptocurrency audits require verification approaches that extend to on-chain, off-chain, and intersecting transactions that have varying degrees of separation between ledgers.
Ignacio Ariel Del Monte, Juan de Lucio, Miguel Angel Sicilia Urban
This systematic review examines risk of Impermanent Loss (IL) in Automated Market Makers (AMMs) within the Decentralized Finance (DeFi) ecosystem, employing the PRISMA-S methodology. Our comprehensive search across the Web of Science and Scopus databases identified 38 relevant studies published between January 2020 and September 2024. The review reveals a predominant focus on Constant Product Market Makers (CPMMs), which comprise 55.7% of all mentions, underscoring their central role in DeFi markets. There are 9 underlying causes affecting IL risk and the most important ones are price volatility, asset imbalance, and risk/return management. According to our categorization, the most commonly used Mitigation Strategies are Investment Strategies, Decentralized Tools and Technologies, and Design and Management of Liquidity Pool, Hedging Strategies and Context Strategies. IL risk research is calculated equally theoretically and empirically (9 references for each) and there are 6 research papers that calculate in a mixed way. Only 13 research papers employ market data in their reviews and 7.9% of all papers measure IL risk quantitatively. We seek to focus our studies on a more detailed treatment of the risk of IL that will result in improvements to liquidity providers in DeFi.
This paper explores the causal relationship between the U.S. trade policy uncertainty and cryptocurrency returns using the quantile Granger causality test. Unlike traditional approaches that focus on average effects, this method captures asymmetric causal dynamics across the entire conditional distribution. The analysis employs two established indices of trade policy uncertainty developed by Caldara et al. (2020) and by Baker et al. (2016), ensuring robustness and mitigating potential biases from relying on a single measure. The empirical results indicate that changes in cryptocurrency prices consistently Granger cause movements in trade policy uncertainty across most quantiles, suggesting that cryptocurrencies may serve as early indicators of shifts in economic policy sentiment. In contrast, the effect of trade policy uncertainty on cryptocurrency returns is most pronounced in the tails of the distribution, highlighting a stronger influence during periods of extreme market conditions. These findings highlight the importance of accounting for nonlinear and asymmetric effects in assessing the interaction between economic policy uncertainty and cryptocurrency markets.
This study investigates Granger-causality relationships between crypto-assets (Bitcoin and Ethereum) and traditional financial assets (stock indices and exchange rates) in BRICS-T countries over the 2016â2024 period. The findings highlight significant interlinkages: bidirectional causality exists between Bitcoin and Russia's stock market, and between Ethereum and both Brazil's stock market and the USD/INR exchange rate. Unidirectional causality is observed from Bitcoin to the stock markets of Brazil, India, and China, while the USD/TRY exchange rate influences Bitcoin. Similarly, Ethereum affects the stock markets of Russia, India, and South Africa, while the USD/TRY exchange rate also Granger-causes Ethereum. These results indicate a growing synchronization between crypto-assets and conventional financial markets. The presence of both unidirectional and bidirectional causalities emphasizes the increasing integration of global financial systems and highlights the importance for investors to consider cross-market interactions when making decisions. Crypto-assets are no longer isolated but are embedded in broader financial dynamics.
Investment advisory services are now commonly offered by consulting firms with financial experts, typically for a monthly fee. Financial markets require specialized knowledge, but advancements in artificial intelligence have revolutionized this field. Deep learning algorithms, especially Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), are widely used to predict asset price trends in nonlinear time-series data. However, they demand large datasets and are prone to overfitting. Recently, combining deep learning with reinforcement learning has shown promise, though it requires intensive research and computational resources. This study introduces the BTC-PDPR (Bitcoin Price Direction Prediction Robot) model, which predicts Bitcoin's daily price direction using the Random Forest Regressor. As an ensemble-based machine learning model, it works effectively with smaller datasets and identifies key technical indicators influencing price trends. The model achieved a 99.20% accuracy rate on data from March 2018 to the present. It runs efficiently in Google Colab (v5e1 configuration), producing results in just 22 seconds. This paper outlines the methodology, reviews relevant studies from 2017 to 2024, highlights gaps in the literature, and emphasizes the studyâs contributions to the field.
This study examined the dynamic interconnectedness and portfolio implications within the cryptocurrency ecosystem, focusing on five representative digital assets across the core functional categories: Layer 1 cryptocurrencies (Bitcoin (BTC) and Ethereum (ETH)), decentralized finance (Uniswap (UNI)), stablecoins (Dai), and crypto infrastructure tokens (Maker (MKR)). Using the Extended Joint Connectedness Approach within a Time-Varying Parameter VAR framework, the analysis captured time-varying spillovers of return shocks and revealed a heterogeneous structure of systemic roles. Stablecoins consistently acted as net absorbers of shocks, reinforcing their defensive profile, while governance tokens, such as MKR, emerged as persistent net transmitters of systemic risk. Foundational assets like BTC and ETH predominantly absorbed shocks, contrary to their perceived dominance. These systemic roles were further translated into portfolio design, where connectedness-aware strategies, particularly the Minimum Connectedness Portfolio, demonstrated superior performance relative to traditional variance-based allocations, delivering enhanced risk-adjusted returns and resilience during stress periods. By linking return-based systemic interdependencies with practical asset allocation, the study offers a unified framework for understanding and managing crypto network risk. The findings carry practical relevance for portfolio managers, algorithmic strategy developers, and policymakers concerned with financial stability in digital asset markets.
Amid the growing debate over how cryptocurrencies are reshaping global finance, this study explores the nexus between Bitcoin, Brent Crude Oil, Gold and the U.S. Dollar Index. We used a time-varying vector autoregressive (tvVAR) model to examine the connection among these four assets during the Trump (2017â2020) and Biden (2021â2024) governments. The 48-week return forecast of the BitcoinâGold correlation was also conducted by using the Bayesian Structural Time Series (BSTS) model. Results indicate that Bitcoin was the most volatile asset, while the U.S. Dollar remained the least volatile under both regimes. Under Trump, U.S. Dollar significantly influenced Oil and Bitcoin while Bitcoin and Gold were negatively linked to Oil and positively associated with U.S. Dollar. An inverse relationship between Bitcoin and Gold also emerged. Under Biden, Bitcoin, Gold, and U.S. Dollar all significantly affected Oil with Bitcoin showing a positive impact. Bitcoin and Gold remained negatively correlated though not significantly, and the Dollar maintained positive ties with both. Forecasts show a positive link between Bitcoin and Gold in the coming year. However, Bitcoin does not exhibit consistent characteristics of a safe-haven asset during the U.S. presidential transitions examined, largely due to its high volatility and unstable correlations with a traditional safe-haven asset, Gold. This study contributes to the understanding of shifting relationships between digital and traditional assets across political regimes.
As investment portfolios become increasingly diversified and financial asset risks grow more complex, accurately forecasting the risk of multiple asset classes through mathematical modeling and identifying their heterogeneity has emerged as a critical topic in financial research. This study examines the volatility and tail risk of gold, crude oil, Bitcoin, and selected stock markets. Methodologically, we propose two improved Value at Risk (VaR) forecasting models that combine the autoregressive (AR) model, Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model, Extreme Value Theory (EVT), skewed heavy-tailed distributions, and a rolling window estimation approach. The modelâs performance is evaluated using the Kupiec test and the Christoffersen test, both of which indicate that traditional VaR models have become inadequate under current complex risk conditions. The proposed models demonstrate superior accuracy in predicting VaR and are applicable to a wide range of financial assets. Empirical results reveal that Bitcoin and the Chinese stock market exhibit no leverage effect, indicating distinct risk profiles. Among the assets analyzed, Bitcoin and crude oil are associated with the highest levels of risk, gold with the lowest, and stock markets occupy an intermediate position. The findings offer practical implications for asset allocation and policy design.
Cryptocurrency has gained increasing prominence in the recent past with its increased integration with established financial markets like stock markets, currency markets and so on. This study examines the integration of cryptocurrency and the Indian stock market. By employing time-varying parameter-vector autoregression (TVP-VAR), the study analyses the daily closing data of Sensex, Nifty 50, Bitcoin and Ethereum over a 41-month period from 1st April 2020 to 31st August 2023 to find out the total connectedness, pairwise connectedness and to identify the net receivers and transmitters. The findings reveal a moderate unidirectional transmission from the cryptocurrency market to the stock market, thereby confirming cryptocurrencies as net transmitters and stock market indices as net receivers. This study is of relevance to policymakers in devising an appropriate cryptocurrency regulatory framework and to investors in designing better portfolios.
This research examines the dynamic interaction between conventional financial assets, namely the US dollar, the S&P 500 index, gold and crude oil, and ten major green cryptocurrencies, focusing on their spillover linkages and hedging capacities during major global economic and geopolitical shocks. The study analyses daily data to uncover spillover effects using the innovative Quantile-Vector Autoregressive methodology developed by Cunado et al. (2023) . Results indicate that green cryptocurrencies significantly interact with other examined instruments. Algorand, Cardano, IOTA, TRON and Powerledger demonstrate the largest interactive effects, with the latter standing out as a consistent transmitter of influence across both crises, demonstrating that this sub-class of cryptocurrency is exhibiting elevated maturity. Traditional assets predominantly act as receivers of such risk dynamics from more speculative asset classes, with gold identified as an effective absorber of spillovers, especially in bear markets. Conversely, the US dollar and crude oil are identified as large transmitters of spillover impacts, a result found to be particularly influential in periods of geopolitical conflict. The study further reveals that green cryptocurrencies promoting trust, innovation, and renewable energy are more effectively connected with traditional investments than those focusing on financial services or business accessibility, presenting diversification opportunities during crises.
This study aims to analyze the impact of the U.S. Federal Reserve Systemâs monetary policy on major cryptocurrencies. Specifically, it explores whether the effects differ between volatile cryptocurrencies, such as Bitcoin and Ethereum, and the stablecoin Tether. To this end, we utilize an autoregressive distributed lag (ARDL) bounds testing approach, analyzing monthly data from January 2019 to April 2025. The empirical results indicate that the responses of volatile and stable cryptocurrencies to the Fedâs monetary policy differ. In the long term, the prices of Bitcoin and Ethereum tend to react positively to the Fedâs monetary policy changes, whereas Tetherâs prices experience a negative impact. We recommend novel policy implications in this study based on these empirical findings.
<ns3:p>The increasing integration of Artificial Intelligence (AI) and Natural Language Processing (NLP) in financial markets has revolutionized the predictive modeling of asset prices. In cryptocurrency markets, where price movements are largely driven by investor sentiment, sentiment analysis has emerged as a valuable tool for understanding market behavior. This study investigates the correlation between sentiment polarity extracted from FinBERT and FinancialBERTâtwo pre-trained NLP models optimized for financial text analysisâand the price fluctuations of Bitcoin (BTC), Ethereum (ETH), and Ripple (XRP). The research explores the role of sentiment indicators as leading signals for price trends by examining their correlation across different time lags (immediate, 12-hour, and 24-hour periods).The study utilizes a hybrid sentiment model which uses FinBert and FinancialBert using time lagged correlation models, aggregating sentiment scores from multiple financial news sources retrieved via the MediaStack API, while historical cryptocurrency prices were obtained from the CoinGecko API. A dataset of 1,300 news articles over 90 days was analyzed, revealing that Ethereum exhibited the strongest sentiment-price correlation (0.3819, increasing to 0.3900 after 24 hours), followed by Bitcoin (0.2899 to 0.2919) and XRP (0.1005 to 0.1205). These findings suggest that market sentiment has a delayed impact on price movements, with Ethereum being the most responsive to sentiment fluctuations. This research highlights the potential of AI-driven sentiment analysis as a supplement to traditional financial indicators, offering new opportunities for algorithmic trading and risk assessment in decentralized finance (DeFi) markets. Future research should explore real-time applications, multilingual sentiment tracking, and hybrid predictive models to enhance the accuracy of sentiment-based cryptocurrency forecasting.</ns3:p>
The environmental impact of cryptocurrencies has attracted increasing scrutiny, largely due to the high energy consumption of blockchain networks. However, empirical research on the causal relationship between cryptocurrency trading activity and carbon emissions remains scarce. This study addresses this gap by analysing the dynamic interplay between cryptocurrency trading and COâ emissions for Bitcoin, Ethereum, and Binance Coin, using monthly data from January 2015 to September 2024. Employing the Toda-Yamamoto augmented Granger causality approach, we apply logarithmic transformations to ensure data stationarity and address integration and endogeneity concerns. Our results reveal a bidirectional Granger causality between Bitcoin trading and COâ emissions, suggesting a feedback loop between market activity and environmental impact. For Ethereum, we find a similar albeit weaker bidirectional causality from trading to emissions, while no significant causal link is detected for Binance Coin, likely reflecting its more energy-efficient consensus mechanism. These findings highlight the disproportionate environmental burden of proof-of-work cryptocurrencies and underscore the need for targeted regulatory responses. We recommend the adoption of carbon-sensitive crypto policies, such as mandatory energy usage disclosures and incentives for transitioning to sustainable consensus mechanisms. This study advances the environmental finance literature by providing robust empirical evidence on the links between digital asset markets and carbon emissions.
This study investigates the heterogeneous responses of Bitcoin (BTC), gold (GOLD), and green bonds (GBOND) to geopolitical risk (GPR) shocks across different market regimes and investment horizons. Using a triadic empirical framework that encompasses wavelet quantile-on-quantile regression (QQR), wavelet cross-quantilogram (WCQ), and advanced portfolio optimization strategies, our analysis captures asymmetric dependence, tail risks, and time-frequency dynamics from January 2015 to December 2024. Our results show that BTC consistently has strong hedging potential at lower quantiles, particularly during short-term stress, whereas GOLD and GBOND offer greater stability over medium- and long-term horizons. Conditional expected shortfall (CES) and extreme downside correlation (EDC) analyses highlight BTCâs resilience to extreme downside risks, whereas GOLD and GBOND serve primarily as long-term defensive assets. Portfolio optimization confirms BTCâs critical role in diversification under minimum correlation and connectedness strategies, and GBOND dominates variance-minimizing portfolios. These findings offer practical guidance for constructing robust, adaptive portfolios under geopolitical uncertainty.