--- title: Crypto & DeFi url: https://www.ml-quant.com/topics/crypto-defi/ site: ML-Quant (https://www.ml-quant.com) updated: 2026-10-09 license: Summaries CC BY 4.0; links go to the original sources index: https://www.ml-quant.com/llms.txt --- # Crypto & DeFi Crypto assets, DeFi, stablecoins and blockchain markets. 303 papers featured; 21 in the last 12 months. Papers featured per quarter: 2023 Q2 13, 2023 Q3 29, 2023 Q4 31, 2024 Q1 35, 2024 Q2 37, 2024 Q3 33, 2024 Q4 14, 2025 Q1 40, 2025 Q2 37, 2025 Q3 12, 2025 Q4 11, 2026 Q1 0, 2026 Q2 0, 2026 Q3 2, 2026 Q4 9 ## Most cited - [Factors Influencing Cryptocurrency Prices: Evidence from Bitcoin, Ethereum, Dash, Litcoin, and Monero](https://www.ml-quant.com/papers/arxiv/2511.22782/): 227 citations. The study examines the price factors influencing five major cryptocurrencies from 2010-2018, highlighting the roles of market conditions, long-term appeal, and the SP500 index. - [Decentralised Finance and Automated Market Making: Execution and Speculation](https://www.ml-quant.com/papers/ssrn/4144743/): 49 citations. The study investigates automated market makers, particularly constant product market makers, and develops two optimal trading strategies using stochastic optimal control tools, as demonstrated with Uniswap v3 data. - [Decentralised Finance and Automated Market Making: Predictable Loss and Optimal Liquidity Provision](https://www.ml-quant.com/papers/arxiv/2309.08431/): 47 citations. The study outlines the wealth dynamics of strategic liquidity providers in constant product markets, proposes an optimal liquidity provision strategy, and uses Uniswap v3 data to show its effectiveness. - [The Influence of ChatGPT on Artificial Intelligence Related Crypto Assets: Evidence from a Synthetic Control Analysis](https://www.ml-quant.com/papers/doi/10-1016-j-frl-2023-103993/): 38 citations. New method proposed to compute security prices without third party involvement. - [The Shape of Money Laundering: Subgraph Representation Learning on the Blockchain with the Elliptic2 Dataset](https://www.ml-quant.com/papers/arxiv/2404.19109/): 31 citations. The article presents Elliptic2, a dataset of Bitcoin subgraphs, to help understand and combat money laundering in cryptocurrency. - [Price Discovery in Cryptocurrency Markets](https://www.ml-quant.com/papers/arxiv/2506.08718/): 29 citations. Centralized markets typically lead in Ethereum price discovery compared to decentralized exchanges, affecting liquidity, arbitrage, and market efficiency. - [Deep learning and NLP in cryptocurrency forecasting: Integrating financial, blockchain, and social media data](https://www.ml-quant.com/papers/arxiv/2311.14759/): 29 citations. The study uses Machine Learning and Natural Language Processing to predict Bitcoin and Ethereum prices using Twitter and Reddit data, improving forecasting accuracy. - [Axioms for Automated Market Makers: A Mathematical Framework in FinTech and Decentralized Finance](https://www.ml-quant.com/papers/arxiv/2210.01227/): 26 citations. Mathematical Framework: The research introduces a new framework for Automated Market Makers, suggesting a unique fee structure and a novel AMM that reduces divergence loss. - [A comparison of cryptocurrency volatility-benchmarking new and mature asset classes](https://www.ml-quant.com/papers/arxiv/2404.04962/): 25 citations. The paper analyzes the factors influencing cryptocurrency volatility from 2020 to 2022, highlighting that positive market returns, positive signed volatility, and negative daily leverage increase price volatility. - [A Data Engineering Framework for Ethereum Beacon Chain Rewards: From Data Collection to Decentralization Metrics](https://www.ml-quant.com/papers/arxiv/2402.11170/): 25 citations. A study of consensus reward data from the Ethereum Beacon chain offers insights into reward distribution and evolution, aiding in the assessment and refinement of blockchain systems' decentralization, security, and efficiency. - [A dataset of Uniswap daily transaction indices by network](https://www.ml-quant.com/papers/arxiv/2312.02660/): 25 citations. The study explores the effect of Layer 2 solutions on DeFi by analyzing millions of transactions from Uniswap, offering insights into adoption, scalability, and decentralization in the DeFi sector. - [Decentralized Finance: Protocols, Risks, and Governance](https://www.ml-quant.com/papers/arxiv/2312.01018/): 22 citations. Protocols, Risks, Governance: The article discusses the benefits of decentralized finance (DeFi) over traditional finance, the function of smart contracts, and the associated risks, highlighting the need for more research on scalability and auditing. ## Latest - [Fundamentals in Cryptocurrency Valuation: Evidence from DeFi Markets](https://www.ml-quant.com/papers/ssrn/7566845/) (2026-10-09): Revenue surprises on 95 DeFi protocols predict abnormal token returns, with one-standard-deviation surprises associated with 28.8 basis points higher returns over three days when tokens have direct revenue claims. - [Predicting Crypto Contagion: Machine Learning Forecasts of Return Spillovers](https://www.ml-quant.com/papers/ssrn/7578304/) (2026-10-09): The study forecasts time-varying return spillovers among cryptocurrencies using machine learning, finding monetary policy and funding rates drive connectedness and classifying events that shift spillover composition into contagion types. - [Reassessing Bitcoin’s Role under ETF-Driven Institutionalization: A Time-Frequency Connectedness Analysis](https://www.ml-quant.com/papers/ssrn/7566372/) (2026-10-09): The research shows that after U.S. Bitcoin ETF approval in January 2024, return and volatility linkages between Bitcoin and equities strengthened substantially, with volatility spillovers becoming markedly asymmetric from stocks to crypto. - [Anatomy of a run: the Terra Luna Crash](https://www.ml-quant.com/papers/repec/ehl-lserod-137575/) (2026-10-09): Granular blockchain data reveals the Terra crash resulted from subsidized money creation, real-time transaction visibility, and investor concentration amplifying financial fragility. - [Interest Rate Transmission in Decentralized Finance: Evidence from Aave Lending Protocol](https://www.ml-quant.com/papers/repec/abo-neswpt-sp0003/) (2026-10-09): The study finds that US risk-free rates predict stablecoin lending rates in decentralized finance, though transmission operates mainly through crypto-native factors like utilization. - [Active Liquidity On Chain: Evidence from PropAMMs Across Chains](https://www.ml-quant.com/papers/arxiv/2609.38056/) (2026-10-02): Proprietary AMMs earn 0.37 to 1.19 basis points within two seconds by repricing continuously and avoiding arbitrage, compared to passive AMMs losing 0.22 to 0.62 basis points. - [Not All LPs Are Equal: The Active-Passive Gap in Automated Market Maker Liquidity Provision](https://www.ml-quant.com/papers/arxiv/2609.37963/) (2026-10-02): Analysis of Uniswap pools shows passive liquidity providers underperform aggregate pool returns, with the gap wider on Ethereum than on layer-two blockchains. - [Oracle-Parametrized Constant Function Market Makers: From Price Feeds to Pricing Rules](https://www.ml-quant.com/papers/arxiv/2609.33799/) (2026-10-02): A framework shows when oracle-informed automated market makers reduce loss versus rebalancing and improve capital efficiency, with counterfactual backtests on SPY data. - [Liquidity Capacity and Token Survival in Cryptocurrency Markets](https://www.ml-quant.com/papers/ssrn/7536975/) (2026-10-02): The research shows that adjusted illiquidity—price impact scaled by market absorption capacity—predicts which cryptocurrency tokens survive better than volatility, improving out-of-sample forecasting of token death. - [ETH-TraceBench: A Large-Scale Event-Stream Benchmark for Ethereum DeFi under Temporal, Protocol, and Contract Shift](https://www.ml-quant.com/papers/arxiv/2609.23659/) (2026-09-25): Introduces a large-scale benchmark on 1.35 billion Ethereum transactions to evaluate DeFi representations under temporal, protocol, and contract drift, revealing model degradation on unseen pools. - [Stablecoins Meet the Mundell–Fleming Trilemma](https://www.ml-quant.com/papers/repec/fip-fednsr-103702/) (2026-09-25): Wallet-level stablecoin data shows crisis countries experience inflows during banking restrictions; this endogenizes capital mobility and tightens monetary policy constraints. - [Cash vs. Crypto in DeFi](https://www.ml-quant.com/papers/ssrn/4478395/) (2025-12-28): The article discusses how cryptocurrencies can improve societal functions compared to traditional currencies and emphasizes the innovations needed to build confidence in decentralized finance. - [Bitcoin's Price Alchemy: Unraveling the Influence of Macro Announcements on Volatility and Trading Volume in an Era of Rising Inflation](https://www.ml-quant.com/papers/ssrn/4478184/) (2025-12-28): Bitcoin's price volatility significantly reacts to FOMC and CPI announcements, showing unique patterns during inflation. - [The Impact of Bitcoin ETF Approval on Bitcoin's Hedging Properties Against Traditional Assets](https://www.ml-quant.com/papers/arxiv/2512.12815/) (2025-12-19): Changing Dynamics with Traditional Assets: In January 2024, the approval of a Bitcoin Spot ETF boosted interest from big investors and made Bitcoin more closely linked to stock markets, while its connections to gold remained steady and its relationship with the U.S. Dollar stayed negative. - [Market Effects of Order Flow in Crypto](https://www.ml-quant.com/papers/ssrn/4961610/) (2025-12-01): Analysis indicates that payment for order flow in crypto markets increases trading costs and reduces volumes, especially for assets beyond Bitcoin and Ethereum, after new tokens are introduced. - [DeXposure: A Dataset and Benchmarks for Inter-protocol Credit Exposure in Decentralized Financial Networks](https://www.ml-quant.com/papers/arxiv/2511.22314/) (2025-12-01): Credit Exposure Dataset: The DeXposure dataset offers a comprehensive resource for analyzing credit exposure in decentralized finance, featuring 43.7 million entries for financial machine learning research. - [Factors Influencing Cryptocurrency Prices: Evidence from Bitcoin, Ethereum, Dash, Litcoin, and Monero](https://www.ml-quant.com/papers/arxiv/2511.22782/) (2025-12-01): The study examines the price factors influencing five major cryptocurrencies from 2010-2018, highlighting the roles of market conditions, long-term appeal, and the SP500 index. - [Denoising Complex Covariance Matrices with Hybrid ResNet and Random Matrix Theory: Cryptocurrency Portfolio Applications](https://www.ml-quant.com/papers/arxiv/2510.19130/) (2025-10-27): Builds a robust crypto covariance model using power laws and Random Matrix Theory with ResNet corrections to create stable, profitable portfolios across market regimes. - [Mechanism design and equilibrium analysis of smart contract mediated resource allocation](https://www.ml-quant.com/papers/arxiv/2510.05504/) (2025-10-09): The research introduces a design framework for smart contract-based resource allocation, aiming to balance efficiency and fairness in decentralized systems, validated through tests and a real-world case study. - [Smart Contract Adoption in Derivative Markets under Bounded Risk: An Optimization Approach](https://www.ml-quant.com/papers/arxiv/2510.07006/) (2025-10-09): The research combines structural theory with real-world validation to analyze the adoption of smart contracts under bounded risk, showing that while adoption choices are robust, their financial outcomes are fragile. - [Optimal automation under overdispersed discrete risk: thresholds and hysteresis in a Negative Binomial model](https://www.ml-quant.com/papers/arxiv/2510.05487/) (2025-10-09): The paper presents an optimization framework that merges dynamic Negative Binomial demand modeling with smart contract adoption, offering guidance for balancing inventory costs, service levels, and implementation expenses in high-variance demand scenarios. - [Improving Cryptocurrency Pump-and-Dump Detection through Ensemble-Based Models and Synthetic Oversampling Techniques](https://www.ml-quant.com/papers/arxiv/2510.00836/) (2025-10-03): The study uses SMOTE and advanced ensemble learning models like XGBoost and LightGBM to effectively detect pump and dump manipulation in cryptocurrency markets, enhancing market transparency and stability. - [How Exclusive are Ethereum Transactions? Evidence from non-winning blocks](https://www.ml-quant.com/papers/arxiv/2509.16052/) (2025-09-22): Dominant Source of Builder Revenues: The study shows that exclusive transactions, found only in blocks from one builder, make up 84% of total fees paid by transactions in winning blocks on Ethereum's blockchain. This makes them the main source of builder revenues. - [Banking 2.0: The Stablecoin Banking Revolution - How Digital Assets Are Reshaping Global Finance](https://www.ml-quant.com/papers/arxiv/2508.11395/) (2025-08-20): Stablecoins, combining cryptocurrency and traditional finance, are poised to transform the global financial system by addressing flaws in current fiat currencies and promoting a more globally connected financial system. - [Mapping Microscopic and Systemic Risks in TradFi and DeFi: a literature review](https://www.ml-quant.com/papers/arxiv/2508.12007/) (2025-08-20): The research offers a comparative analysis of systemic risks in traditional and decentralized finance, underlining their distinct features and the idea of 'crosstagion', where instability can spread between the two systems. - [Universal Patterns in the Blockchain: Analysis of EOAs and Smart Contracts in ERC20 Token Networks](https://www.ml-quant.com/papers/arxiv/2508.04671/) (2025-08-12): A study of 44 million Ethereum transactions reveals distinct patterns, showing differences between human and automated transaction behaviors in blockchain ecosystems. - [The Marginal Effects of Ethereum Network MEV Transaction Re-Ordering](https://www.ml-quant.com/papers/arxiv/2508.04003/) (2025-08-07): Two MEV builders, capable of manipulating transaction orders, now generate almost 80% of Ethereum blocks. This has led to frequent 'sandwich attacks' and indicates a need for changes like gas fee priority or private transaction pools. - [Mitigating Financial Frictions in Agriculture: A Framework for Stablecoin Adoption](https://www.ml-quant.com/papers/arxiv/2507.14970/) (2025-07-25): Stablecoins Solution: Fiat-collateralized stablecoins, a type of digital currency, could help solve financial issues in the global agricultural sector by reducing trade costs, increasing supply chain efficiency, and broadening credit access. - [A Comparative Analysis of Statistical and Machine Learning Models for Outlier Detection in Bitcoin Limit Order Books](https://www.ml-quant.com/papers/arxiv/2507.14960/) (2025-07-25): Research comparing statistical methods and machine learning for anomaly detection in cryptocurrency limit order books shows the Empirical Covariance model is the most effective, beating a standard Buy-and-Hold benchmark by 6.70%. - [A New Incentive Model For Content Trust](https://www.ml-quant.com/papers/arxiv/2507.09972/) (2025-07-17): The paper proposes a decentralized, incentive-based method for verifying digital content authenticity using smart contracts and digital identity to fight misinformation.