Follow blockchain research across journals, conferences, and preprint repositories.
2,329 results · page 9 of 98
W LIU, Xiaohan Bao, Xing Han, Youwei Li
No abstract is available for this record.
Andres Romo, Ricardo Soto, Emanuel Vega, Broderick Crawford · 6 authors
In recent years, computational intelligence techniques have significantly contributed to the automation and optimization of trading strategies. Despite the increasing sophistication of predictive models, classical technical indicators such as dual Simple Moving Averages (2-SMA) remain popular due to their simplicity and interpretability. This work proposes an adaptive trading system that combines the 2-SMA strategy with a learning-based metaheuristic optimizer known as the Learning-Based Linear Balancer (LB2). The objective is to dynamically adjust the strategy’s parameters to maximize returns in the highly volatile cryptocurrency market. The proposed system is evaluated through simulations using historical data of the BTCUSDT futures contract from the Binance platform, incorporating real-world trading constraints such as transaction fees. The optimization process is validated over 34 training/test splits using overlapping 60-day windows. Results show that the LB2-optimized strategy achieves an average return on investment (ROI) of 7.9% in unseen test periods, with a maximum ROI of 17.2% in the best case. Statistical analysis using the Wilcoxon Signed-Rank Test confirms that our approach significantly outperforms classical benchmarks, including Buy and Hold, Random Walk, and non-optimized 2-SMA. This study demonstrates that hybrid strategies combining classical indicators with adaptive optimization can achieve robust and consistent returns, making them a viable alternative to more complex predictive models in crypto-based financial environments.
Satyaban Sahoo, Deepti Singh
This study employs novel quantile time-frequency connectedness approach to explore the dynamic connectedness among sustainable assets (sustainable, green bond, and clean energy index), traditional assets (traditional index and crude oil), and cryptocurrency. This method assesses the impact of uncertain events on asset relationships. Findings indicate median connectedness of 36.94% in the short run and 4.81% in the long run, with short-term dynamics dominating system transmission. The traditional index is the primary transmitter of short-run shocks, while the green bond index leads in long-run shocks. Diversification across asset classes is recommended for effective hedging and optimal returns during extreme market conditions.
Timothy Dombrowski
This paper provides a systematic review and synthesis of two converging financial literatures: mortgage-backed securities (MBS) and decentralized finance (DeFi). I trace the evolution of MBS research from the 1970s through the 2008 financial crisis to present day, while examining how blockchain innovations create new possibilities for real estate finance. The methodology combines traditional literature review techniques with bibliometric analysis, utilizing Google Scholar and Google Trends data to document the shifting research landscape and public interest in these topics over time. The findings reveal that while MBS research peaked following the 2008 financial crisis, DeFi and real estate tokenization research show more recent growth trajectories since 2016. The synthesis highlights how blockchain technology offers potential improvements in transparency, transaction costs, and liquidity for real estate markets, while acknowledging significant regulatory and governance challenges. This review contributes to understanding the current state of research at the intersection of traditional real estate finance and emerging blockchain applications, providing a foundation for future empirical investigations.
Geul Lee, Doojin Ryu
ABSTRACT This study investigates how overconfident cryptocurrency traders influence the connection between returns and risk premia, proxied by option‐adjusted credit spreads. Using daily data from January 2021 to February 2025, we uncover asymmetry and state dependence: returns decline when spreads widen, particularly during crashes, yet they do not recover when spreads narrow. Equity indices exhibit more balanced co‐movements. The asymmetry strengthens in high‐volatility periods and persists after we control for broad market returns and after we substitute a composite crypto index for individual cryptocurrencies. These findings indicate a distinctive pricing mechanism in cryptocurrency markets shaped by overconfident behaviour and credit‐spread dynamics.
Erdinç Akyıldırım, Ahmet Faruk Aysan, Oğuzhan Çepni, Shaen Corbet
This study investigates the influence of news-based sentiment on the returns of Decentralized Finance (DeFi) coins using a sample of 27 coins from January 2017 to March 2022. Our results indicate that news sentiment significantly impacts DeFi returns, with negative sentiment exerting a stronger influence than positive sentiment. Transaction volume and network security also emerge as critical drivers of DeFi coin returns. Smaller coins are more sensitive to news sentiment, showing greater return volatility. The impact of news-based sentiment is more pronounced during weekdays, likely due to reduced participation by institutional investors and trading algorithms. These findings have important implications for investors and policymakers, suggesting multiple pathways for market manipulation under specific conditions. • We investigate the relationship between DeFi coins and news-based sentiment. • Negative sentiment has a greater impact on returns. • Transaction volume and network security drive returns. • Smaller DeFi coins are more susceptible to news sentiment and greater return volatility. • DeFi returns’ sensitivity to news-media sentiment is significantly elevated during weekdays.
Myriam Ben Osman, Héla Mzoughi, Khaled Guesmi, Kamel Naoui
No abstract is available for this record.
Mateus Gonzalez de Freitas Pinto
We investigate the high-frequency dynamics of Bitcoin and Ethereum perpetual futures traded on Binance from January 2020 to December 2024. After a thorough discussion of the stylized facts and particularities of Bitcoin perpetual futures, based on previous research in futures markets, we evaluate the fit of two competing models of market microstructure: the Mixture of Distributions Hypothesis (MDH) and the Intraday Trading Invariance Hypothesis (ITIH). Using intraday data at different levels of aggregation, we investigate the relationship between return volatility per transaction and trade size. We find evidence favoring the MDH in the crypto futures market.
Tuan Tran, Duc A. Tran
No abstract is available for this record.
Remy Jonkam Oben, Mehdi Seraj, Şerife Zihni Eyüpoğlu
Purpose This study investigates volatility and returns spillovers among US technology stocks, decentralized finance (DeFi) tokens and conventional cryptocurrencies, while also examining strategies for optimal portfolio allocation. Design/methodology/approach The study analyses daily financial market data from October 05, 2020 to February 09, 2024 by employing the Diebold and Yilmaz (2012) and dynamic conditional correlations generalized auto-regressive conditional heteroscedasticity (DCC-GARCH) models. Findings Empirical findings showed that the US technology stocks were highly interconnected both in returns and volatilities (same as the crypto assets), while technology stock-crypto asset market connections were quite low. Moreover, the technology stocks (crypto assets) were generally net volatility and return receivers (transmitters). Overall, market connectedness was high (65.6% for volatility and 77.2% for return). Portfolio optimization results showed that technology stock-crypto asset (all-DeFi, all-cryptocurrency, all-technology stock and DeFi-cryptocurrency) portfolios were attractive to risk-averse (risk-neutral and risk-seeking) investors. Originality/value This is the first study to comprehensively analyze volatility and return connectedness and provide insights into portfolio optimization across traditional technology, DeFi and cryptocurrency markets. The insights from this study will aid in risk management, optimal portfolio diversification and formulation of regulations and policies to promote market stability.
Zhijie Yu
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.
Havva Koç
Cryptocurrency markets, characterized by high volatility, limited regulation, and rapid digitalization, represent an area that requires in-depth analysis of investor behavior and market dynamics. In this context, herd behavior emerges as a phenomenon where investors follow collective movements instead of making individual decisions, leading to market anomalies. The dynamics of cryptocurrency markets differ from those of traditional financial markets, necessitating alternative analytical methods to understand irrational investment decisions within these markets. This study aims to investigate herd behavior in cryptocurrency markets and its impact on market volatility. It identifies that during periods of high market volatility, investors tend to exhibit more irrational movements, following herd behavior, which disturbs market equilibrium.
Margherita Renieri, Letterio Galletta, Alberto Lluch Lafuente, Aleksander Junge · 5 authors
Automated Market Makers ( AMM s) are one of the most used Decentralized Finance services enabling users to exchange crypto-assets directly without intermediaries. However, current protocols impose significant constraints on the liquidity levels required for transactions. In this paper, we propose a liquidity-saving mechanism designed to minimize the liquidity required by AMM services. Our mechanism delays the transactions violating the liquidity constraints in a queue, and, when certain conditions are met, it selects from the queue a feasible transaction sequence that fulfills the constraints and executes them atomically on the blockchain. We provide an operational semantics of such a mechanism that precisely characterizes the interactions between users and AMM s and the conditions when the liquidity-saving mechanism is triggered. Moreover, we show that our mechanism allows for novel liquidity saving behavior for multi-party exchange, multi- AMM arbitrage, and enhances user intent compared to traditional AMM s. Finally, to validate our approach, we develop a simulator and experiment with various application scenarios, yielding insights into the practical implications of our mechanism.
Gilles Brice M’bakob
No abstract is available for this record.
Tingsheng Feng, Zhihao Shen, Xi Zhao, Xiaoni Lu · 5 authors
Accurately predicting user trading behavior in decentralized exchanges is essential for investors to mitigate risks and optimize their trading strategies. While existing research primarily focuses on predicting trading behavior in stock markets, these methods often struggle to adapt to the distinct nature of cryptocurrency trading. Specifically, they face issues such as limited adaptivity to high-frequency and algorithmic trading, as well as an insufficient consideration of fine-grained real-time market participants' behavior.Thanks to the pending mechanism of blockchain, it becomes possible to capture traders' interactions before transactions are finalized, providing valuable insights into market state. However, accurately modeling and predicting trading behavior in decentralized exchanges presents challenges, including limited adaptability to high-frequency trading, a lack of fine-grained transaction data, and high computational costs. This work proposes CryptoMixer, a lightweight fine-grained market information-aware multilayer perceptron (MLP)-based model for high-frequency cryptocurrency trading behavior prediction. Specifically, to overcome the sparsity and asynchrony of user behavior data, CryptoMixer develops a Market Information Augmenter that aggregates historical transaction data of users. Furthermore, CryptoMixer designs a Market Information Mixer as well as a Two-stream MLP Fusion Mixer to capture fine-grained user trading behavior patterns. We evaluate CryptoMixer on real-world user trading datasets from the Uniswap decentralized finance platform. Experimental results demonstrate that CryptoMixer outperforms traditional prediction models while maintaining low computational overheads, providing a practical solution for real-time cryptocurrency trading behavior prediction. The code is available at https://github.com/aqua111000/CryptoMixer.
Apostolos Ampountolas
No abstract is available for this record.
Yutong Sun, Shangrong Jiang, Shouyang Wang
No abstract is available for this record.
Xinran Huang, Linzhi Tan, Haozhe Su, Jeremy Eng‐Tuck Cheah
ABSTRACT One of the critical risks associated with cryptocurrency assets is the so‐called downside risk, or tail risk. Conditional Value‐at‐Risk (CVaR) is a measure of tail risks that is not normally considered in the construction of a cryptocurrency portfolio. In this paper, we propose a new approach to portfolio construction based on a deep learning CVaR utility function. This approach is designed to address the issue of tail risk. We evaluate the performance of this approach in comparison to other portfolio construction techniques, including the naïve, minimum variance and mean‐variance portfolios. Our findings indicate that the proposed approach outperforms traditional optimisation models.
Bilgehan Teki̇n
Abstract This research examines the dynamics of the non-fungible tokens (NFT) market by utilizing key financial metrics such as Bitcoin prices, the Crypto Fear-Greed Index, and DeFi indicators. It analyzes NFT-USD values, the Crypto Fear-Greed Index, total value locked in DeFi, and Bitcoin interactions between February 2021–July 2023. Employing ordinary least squares regression, quantile regression, Johansen cointegration, and VECM Granger analysis, the study uncovers complex relationships shaping the NFT market. The findings reveal a positive correlation between Bitcoin prices and NFT values, a negative relationship between total value locked in DeFi and NFT values, and an inverse connection between the Crypto Fear-Greed Index and NFT values. Additionally, cointegration exists among the variables, and causality analysis indicates that Bitcoin influences total value locked, while shifts in the Crypto Fear-Greed Index reflect market sentiment changes. These insights contribute to a deeper understanding of behavioral finance by illustrating how psychological factors, such as investor sentiment and the bandwagon effect, interact with digital asset markets. From a practical perspective, the results emphasize the importance of recognizing these interdependencies for policymakers and market participants striving to foster innovation in the rapidly evolving NFT ecosystem. By aligning with the transformative potential of Blockchain and DeFi, this study provides strategic insights for optimizing resource allocation, enhancing market efficiency, and shaping regulatory frameworks within innovative financial landscapes.
Alisa Kalacheva, Pavel Kuznetsov, Igor Vodolazov, Yury Yanovich
The rise in cryptoasset valuations and the ease of creating new tokens have spurred an increase in illicit activities within the market. Decentralized exchanges (DEX) facilitate the trading of a vast array of tokens, including those with minimal liquidity, amplifying the risk of fraudulent schemes. Fraudulent practices take various forms, including counterfeit tokens, rug pulls, and pump-and-dump schemes, all lacking functional innovation and relying heavily on aggressive social media marketing. This study contributes to the identification and profiling of deceitful tokens on DEX platforms. Our approach involved compiling on new tokens with an active trading start and attracted competition to buy them in first blocks spanning multiple years from the Ethereum blockchain, tracking all associated purchase and sale transactions. Our analysis revealed that Uniswap V2 predominantly hosts the trading of new tokens, with an alarming discovery that over 98% of tokens minted daily exhibit fraudulent characteristics. Subsequently, a machine learning model was developed to predict the likelihood of a rug pull occurring shortly after trading commencement. Although the dataset labeling methodology and detection problem statement are exploratory, we demonstrate the economic significance of the proposed approach within trading pipeline. The findings highlight the importance of identifying fraudulent activities and emphasize the need for collaboration between decentralized exchanges and regulatory bodies to mitigate financial losses for investors.
Fenglin Wu, Yu-Shi Wang, Yu-fan Wan, Yu-fan Wan · 5 authors
No abstract is available for this record.
Abdulkadri Toyin Alabi, Abdullahi Ishola
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 CO₂ 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 CO₂ 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.
Rüya Kaplan Yıldırım, Turgay Münyas, Gülden Kadooğlu Aydın
Constructing an effective asset allocation strategy requires building well-diversified portfolios that maintain robust performance beyond the sample data. The classical Markowitz portfolio optimisation, while widely used, is known to suffer from issues such as estimation errors and sensitivity to multicollinearity, which can significantly distort the allocation process and reduce performance reliability. In order to surmount the aforementioned challenges, the incorporation of Machine Learning echniques, specifically Ridge regression, into the portfolio creation process has been effected. This has resulted in the provision of a hybrid model that combines the strengths of Markowitz optimisation and Ridge regression. The integration of these approaches within the hybrid model serves to mitigate the prediction risks while maintaining the diversification benefits inherent to the Markowitz framework. The model was trained using an 80/20 split and cross-validation was employed to prevent overfitting. The findings indicate that this integrated approach attains the maximum Sharpe ratio, thereby significantly enhancing risk-adjusted returns and portfolio stability when applied to cryptoasset returns. The findings emphasise the merits of integrating classical optimisation methodologies with machine learning to develop more robust and adaptive asset allocation strategies. By analysing the impact of high-volatility cryptoassets on portfolio performance, it makes important contributions to both the literature and practical portfolio strategies for investors.
Senior Financial Markets Dealer, Nassau, The Bahamas, Vladyslav Yakymashko
This article investigates the phenomenon of volatility clustering in the cryptocurrency markets, focusing on Bitcoin (BTC) and Ethereum (ETH), through empirical time-series analysis. The study employs quantitative methods, including GARCH modeling, to identify persistent patterns in the price fluctuations of the two leading digital assets. The analysis is based on trading data over an extended period, encompassing both phases of high market turbulence and periods of relative stability. Adopting an interdisciplinary approach that integrates behavioral finance, econometrics, and financial market theory, particular attention is given to identifying autocorrelation, memory effects, and the structure of market shocks. The findings demonstrate that volatility clustering in BTC and ETH significantly differs from similar phenomena in traditional financial markets, largely due to their speculative nature, asset novelty, and the influence of both institutional and retail participants. The identified patterns enhance risk profiling for crypto assets and may be applied in hedging strategies, automated trading algorithm development, and investment portfolio optimization. Additionally, the study highlights the importance of accounting for both micro- and macroeconomic factors influencing market behavior. The article is intended for researchers in digital finance, risk managers, analysts, investors, and anyone examining unstable assets in conditions of high uncertainty and a rapidly changing informational landscape.