Blockchain Papers

Jianzheng Shi, Zhiyuan Wang, Ding Ding, Yue Wang · 7 authors

This study examines whether retail social media sentiment and community attention explain daily net capital flows into U.S. spot Bitcoin exchange-traded funds (ETFs), and whether issuer brand visibility conditions that relationship. We construct a balanced panel of N=10 ETFs over T=514 trading days (January 2024 to January 2026) and combine it with 162,819 cleaned Reddit posts to derive three AI-driven discourse variables: engagement-weighted sentiment, community attention, and a novel issuer-specific BrandScore. Entity fixed-effects regressions show that neither aggregate sentiment nor BrandScore level alone significantly predicts fund-level flows; however, the Sentiment × BrandScore interaction is significant (β^=2.930, p=0.038), indicating that sentiment becomes economically meaningful only when attached to a visible issuer. This interaction survives two-way (entity + date) fixed effects (p=0.012) and winsorization (p=0.004). Panel quantile regressions reveal distributional heterogeneity in the brand-sentiment channel. Rolling 90-day window estimation confirms the mechanism is episodic, with the interaction achieving significance in 62.8% of subsample windows. These results provide suggestive evidence for a brand-filtered sentiment transmission mechanism in digital asset markets.

Edward N. Udo, Goodness E. Mbakara

Abstract : This article investigates the efficacy of implementing an AI-powered automated trading system on the blockchain using advanced machine learning algorithms and smart contract technology. The work addresses the challenges of cryptocurrency market volatility, the need for real-time decision making and the limitations of traditional trading approaches that often result in suboptimal returns and exposure to increased risk. This work develops a comprehensive trading platform that combines Long Short-Term Memory (LSTM) neural networks, Q-Learning reinforcement learning algorithms and blockchain-based smart contracts to create an autonomous, intelligent trading system. The methodology follows a multi-layered approach that integrates real-time market data collection from CoinGecko and Snowtrace APIs, advanced AI model training using TensorFlow.js, and smart contract deployment on the Avalanche C-Chain using Hardhat and OpenZeppelin libraries. LSTM model is used for price prediction and Q-Learning agent is used for trading strategy optimization, while comprehensive risk management is implemented using Value at Risk (VaR) calculations, portfolio rebalancing algorithms and automated stop-loss mechanisms. The trading execution is facilitated through direct integration with Pangolin DEX smart contracts to ensure decentralized and trustless trade execution. The performance of the system is evaluated using a sophisticated backtesting engine with Monte Carlo simulations, comparing the AI-driven strategy against traditional buy-and-hold approaches. The performance metrics used were Sharpe ratio, maximum drawdown, win rate, and total return. The AI-powered token prediction system demonstrates a superior performance due to its ability to process complex, non-linear market patterns and adapt to changing market conditions through reinforcement learning, and execute trades with minimal latency through blockchain integration. The findings are expected to provide cryptocurrency traders and institutional investors with a robust and automated trading solution that leverages the benefits of both artificial intelligence and blockchain technology for improved investment outcomes and risk management.

Ariston Karagiorgis, Antonis Ballis

ABSTRACT This paper examines the relationship between skewness and kurtosis in Bitcoin spot and futures markets using high‐frequency data. We document a strong convex skewness–kurtosis relationship consistent with theoretical moment restrictions. Trading activity is positively associated with realized kurtosis, particularly in futures markets, though sensitive to specification and driven by extreme‐return episodes. Allowing the relationship to evolve over time reveals substantial curvature variation, indicating state‐dependent higher‐moment dynamics. The close co‐movement of results across markets suggests patterns reflect broad market‐wide conditions. The empirical framework is reduced‐form and results should be interpreted as conditional associations rather than causal effects.

Alfredo Medina Hernandez

Sovereign AI-Native Multi-Asset Trading, Execution, and Financial nfrastructure Charter Sovereign AI‑Native Financial Execution Infrastructure PARRALAX‑AIHFTFUND is a multi‑asset, AI‑native financial organism engineered to operate across traditional and blockchain‑based markets. It provides a unified execution layer where autonomous agents can observe markets, interpret structure, execute trades, manage risk, govern portfolios, issue digital assets, coordinate token economies, and maintain verifiable proof‑of‑computation. This repository contains the core infrastructure, protocol stack, and governance architecture for building sovereign, agent‑driven financial systems. Mission To build a sovereign AI‑native financial infrastructure capable of coordinating autonomous trading agents, multi‑asset execution, fund governance, risk control, digital‑asset creation, and market intelligence across both traditional and blockchain‑native markets. The system exists to move beyond bots, dashboards, and scripts. Its purpose is to become a real execution organism for financial markets. Vision PARRALAX‑AIHFTFUND aims to create a long‑horizon financial intelligence layer where AI agents can: Observe and interpret global market structure Execute trades across heterogeneous venues Manage risk and exposure Govern portfolios and internal policy Issue and manage digital assets Coordinate internal token economies Maintain proof‑of‑computation and decision lineage Operate across crypto, fiat, equities, FX, derivatives, AI tokens, NFTs, and future asset classes Build market memory over time The system is designed to evolve as markets evolve. Foundational Premise Modern markets are: Machine‑driven Fragmented Multi‑asset Tokenized Agent‑mediated A serious financial infrastructure must therefore operate across: Traditional finance (equities, FX, derivatives, funds) Decentralized finance (DEXs, AMMs, on‑chain liquidity) Tokenized and synthetic assets AI‑native markets Autonomous agent economies High‑speed execution environments Governance‑controlled fund structures Programmable financial instruments PARRALAX‑AIHFTFUND is built to bridge old‑world and new‑world markets. What PARRALAX‑AIHFTFUND Is A sovereign trading infrastructure framework An AI‑native market execution system A multi‑asset financial operating layer A protocol stack for autonomous trading agents A fund governance and charter framework A digital‑asset issuance and management environment A blockchain‑compatible coordination layer A risk‑aware execution engine A compute‑receipt and proof‑trace system A foundation for future AI‑managed financial organisms It is built for real execution, not passive analysis. What PARRALAX‑AIHFTFUND Is Not Not a research repo Not a toy trading bot Not a simulation Not a dashboard Not a signal script collection Not a crypto hype project Not a single‑asset system Not a prediction‑only model Research supports the system. Research does not define the system. Status Active development. Core modules stabilizing. Execution layer expanding. Governance and digital‑asset subsystems in progress. PARRALAX‑AIHFTFUND is an AI‑native financial execution framework designed to coordinate autonomous agents across traditional and blockchain‑based markets. The system provides a unified operating layer for multi‑asset execution, risk management, fund governance, digital‑asset issuance, and verifiable compute‑traceability. System Mission To construct a sovereign financial intelligence layer capable of continuous operation across heterogeneous markets, enabling agents to observe market conditions, interpret structure, execute trades, manage exposure, and maintain internal governance. Operational Scope The system is engineered to function across: Traditional finance (equities, FX, derivatives, funds) Decentralized finance (DEXs, AMMs, on‑chain liquidity) Tokenized and synthetic assets AI‑native markets and agent economies Governance‑controlled fund structures High‑speed execution environments Programmable financial instruments System Definition PARRALAX‑AIHFTFUND comprises: A sovereign trading and execution infrastructure A multi‑asset financial operating layer A protocol stack for autonomous trading agents A governance and charter framework A digital‑asset issuance and management environment A blockchain‑compatible coordination layer A risk‑aware execution engine with compute receipts Non‑Scope The system is not a research‑only repository, simulation toy, dashboard, signal script collection, or prediction‑only model. It is infrastructure‑first and execution‑oriented.

Ya-Wei Yang, Zih-Ying Lin, Yun-Chen Tang

This research examines 42 countries and investigates the relationship between geopolitical risk and global non-fungible token (NFT) investor attention. We use Google search volumes related to NFTs across different regions as a proxy for such attention. Our findings indicate that geopolitical risk positively impacts global NFT investor attention, suggesting that investors in countries with higher geopolitical risk may pay more attention to the NFT market. We further explore the effects across different NFT segments and find that geopolitical risk particularly influences investor attention in the metaverse segment. This positive nexus is further amplified during the Russia-Ukraine war and the COVID-19 pandemic.

Lennart Schwertfeger, Bodo Vogt

This paper demonstrates practical arbitrage trading on the cryptocurrency market. It provides guidance on how to build a high-frequency trading system that benefits from exhibiting arbitrage opportunities. It reveals the algorithm of the trading bot that incorporates the order placement and execution strategy between decentral and central cryptocurrency exchanges. The arbitrage algorithm is implemented on two different blockchains that interact with Uniswap and Balancer folks. Current research explores arbitrage opportunities with back-testing models, the paper focuses on trades with realized arbitrage trades. Practical arbitrage includes all operational costs, liquidity constraints, direct effects on markets, and competition with peer arbitrage traders.

Fernando Arruda de Faria

I replicate and extend the cross-sectional trend-factor methodology of Liebi, Stulz, and Tsyvinski (2024) on a contemporaneous out-of-sample period and a liquidity-restricted coin universe. Using 141 USDT spot pairs over 128 weekly observations from November 2023 to April 2026, an Elastic-Net cross-sectional regression aggregating 29 technical indicators generates a long-short portfolio with a mean weekly return of 3.82% (Newey-West t = 5.03) and an annualized Sharpe ratio of 4.54. The strategy delivers market-neutral alpha of 3.82% per week (t = 7.09) with a CAPM beta of 0.020. Three findings warrant emphasis. First, value-weighting destroys the alpha entirely, confirming concentration in smaller, dispersed names. Second, twelve of the top fifteen Elastic-Net coefficients are negative, indicating that the underlying pattern is short-term reversal rather than trend continuation. Third, returns are heavily regime-dependent, with Sharpe ratios near 1.0 in trending markets and above 7.0 in dispersive regimes. Cross-sectional dispersion-harvesting alpha persists in cryptocurrency markets, but its empirical realization is sharply sensitive to universe liquidity, weighting scheme, rebalance horizon, and market regime.

Ding Ding, Yang Li, Poh Ling Neo, Zhiyuan Wang · 5 authors

This paper develops a tractable theoretical framework to study how network participation shapes the boom–bust dynamics of non-fungible token (NFT) prices. We model NFT pricing under network effects and heterogeneous consumers, and show that prices and participation are jointly determined in equilibrium. The model implies a critical participation threshold that separates expansion from contraction regimes: above this threshold, positive feedback between participation and valuation generates self-reinforcing growth, while below it, weakening network benefits lead to contraction. We provide empirical evidence using data from the aggregate NFT market and prominent collections including Bored Ape Yacht Club (BAYC) and CryptoPunks. Reduced-form regressions show a positive association between prices and network participation, with stronger effects at the collection level than in the aggregate market. Threshold estimation further provides evidence consistent with regime-dependent dynamics, with clearer tipping behaviour in well-defined NFT communities than in the aggregate market. These findings suggest that NFT valuation is closely tied to network structure and participation dynamics. More broadly, this paper contributes a unified framework that links participation, price formation, and threshold behaviour in NFT markets.

Sen Yang, Aviv Yaish, Arthur Gervais, Fan Zhang

Permissionless Proof-of-Stake (PoS) economic security is predicated on the high cost of violating consensus safety or liveness. We show that liquid staking introduces additional risks that are not captured by standard PoS economic security arguments. Through an empirical study of Ethereum data, we find that the operational performance of liquid staking pools is positively associated with subsequent normalized liquid staking token (LST) returns. Motivated by this, we present a cross-layer attack: a low-stake adversary can manipulate the consensus protocol to degrade a target pool's performance and take application-layer positions that profit if the market reprices the corresponding \gls{LST} in-line with the historically observed association. To make the consensus layer manipulation concrete, we develop a deep reinforcement learning (DRL) framework to automatically discover attack strategies. Our evaluation shows that the learned strategies can recover near-optimal theoretical attacks and uncover new manipulation behaviors that significantly degrade target pool performance. We further characterize feasible application-layer monetization channels and analyze leveraged shorting in detail using Monte Carlo simulations, showing that such attacks can be profitable with over one-half probability for LSTs of major staking pools. Our findings reveal a previously overlooked attack surface in PoS systems with liquid staking and expose a gap between consensus and economic security.

Aktham Maghyereh, Basel Awartani

This study examines the latent common volatility factor in cryptocurrency markets using daily data for ten major cryptocurrencies from January 2018 to September 2025. It estimates the common volatility factor (COVOL) within the factor-volatility framework of Engle and Campos-Martins (2023) and it identifies its determinants using machine learning and SHAP analysis. Results reveal a statistically significant common volatility factor that intensifies during major macroeconomic events and crypto-specific shocks. Bitcoin exhibits the highest exposure, while global financial stress and investor sentiment are found to be the primary drivers. This paper provides the first direct estimation of a common volatility factor in cryptocurrency markets, demonstrating their increasing integration with global financial conditions and offering important implications for risk management and portfolio diversification.

Angelo Ranaldo, Ganesh Viswanath-Natraj, Junxuan Wang

Abstract We conduct the first comprehensive study of blockchain currencies—stablecoins pegged to fiat currencies and traded on decentralized exchanges (DEXs). Using transaction-level data linked to wallet characteristics, we show that prices in these markets are generally efficient, though constrained by blockchain frictions such as gas fees and Ether volatility. DEX rates closely track traditional currency markets through arbitrage and informed trading. Traders with substantial market share and access to primary markets exert greater price impact, reflecting informational advantages. While blockchain markets may improve access for customers excluded from traditional venues, their scalability depends on addressing frictions inherent to decentralized trading.

Kenzo Arai

We propose a framework for quantifying Credit Valuation Adjustment (CVA) in tokenized securities settled atomically via stablecoins on distributed ledger technology (DLT). We introduce the Settlement Currency Valuation Adjustment (SCVA), a new adjustment term inspired by the Collateral Cost Adjustment (CCA) of Fujii and Takahashi (2013). Using a two-state Markov depeg model, we derive a semi-analytical SCVA expression. Under USDC 2023 parameters, SCVA exceeds the settlement-period CVA reduction by a factor of approximately 3. Cross-stablecoin analysis reveals that the regulatory design of the settlement currency determines whether atomic settlement yields a net CVA benefit.

Drissia Ennagoura, Kamal El Kehal, Abdelhamid Berdai, Safae Merzouk · 8 authors

Cryptocurrency price prediction is challenging due to strong nonlinearity and high volatility. This paper comparatively evaluates three forecasting models for Ethereum (ETH): SARIMAX with exogenous technical indicators, Long Short-Term Memory (LSTM) networks, and Facebook Prophet. Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), and Exponential Moving Average (EMA) are incorporated to enhance signal quality. Empirical results reveal clear trade-offs between predictive accuracy, profitability, and risk. SARIMAX achieves the highest directional accuracy (75.00%) with limited profitability, while LSTM yields the highest cumulative profit (23.84%) at the cost of higher drawdown. Prophet provides a balanced compromise between accuracy and risk. The study contributes by jointly evaluating statistical forecasting accuracy and trading-oriented performance metrics, offering practical insights into model suitability for different investor risk profiles.

Ningyu Zhou

Cryptocurrency prices often move with narratives and investor sentiment. This paper builds a multilingual crypto sentiment index, ML-CryptoSI, using daily news text in six languages and Binance market data for BTC and ETH. We first aggregate language-level daily sentiment and then use PCA to extract the common component across languages. Next, we test whether ML-CryptoSI predicts next-day returns and volatility proxies after controlling for lagged market conditions, liquidity, and day-of-week fixed effects. The results show that ML-CryptoSI has incremental information for returns, especially for ETH, and the effect is stronger on high news-intensity days. In contrast, the evidence for volatility prediction is weak in this short sample. Overall, the findings suggest that the common factor in multilingual news sentiment matters for short-run crypto pricing and is state dependent.

Ozan Nadirgil

Abstract Introduction of cryptocurrency markets offered unique confidentiality and hedging benefits to investors, since they have been rapidly integrated into the financial system through forming substantial long- and short-term interdependencies with equity markets. Previous studies present contradicting and ambigious conclusions about the financial contagion between the equity and crypto markets, nonetheless they do not adequately pose light on the impacts of global events on the relationship between these markets. To address these concerns, this research examines the volatility spillovers between the equity and Decentralized Finance (DeFi) markets through simulating the dependencies with the aim of eliminating the bias and drawbacks of the traditional models by appling an optimized and original analytical framework composed of Deep Neural Network (DNN) and Time Varying Parameters Vector Auto Regression (TVP-VAR) models. Results identify substantial transmission from Bitcoin (BTC), Nasdaq 100 (NASDAQ), Shanghai Stock Exchange (SSE), and New York Stock Exchange (NYSE) to BNB, Euronext, Tether (TET), and Ethereum (ETH) and reflect the significant impact of the global important events on the financial spillovers. Model assessment results confirm the robust accuracy, fitness, reliability, and validity of the model, as well as the success of key parameter optimization.

Dingding Cao, Bianbian Jiao, Jingzong Yang, Yujing Zhong · 5 authors

The high-frequency issuance and short-cycle speculation of meme tokens in decentralized finance (DeFi) have significantly amplified rug-pull risk. Existing approaches still struggle to provide stable early warning under scarce anomalies, incomplete labels, and limited interpretability. To address this issue, an end-to-end warning framework is proposed for BSC meme tokens, consisting of four stages: dataset construction and labeling, wash-trading pattern feature modeling, risk prediction, and error analysis. Methodologically, 12 token-level behavioral features are constructed based on three wash-trading patterns (Self, Matched, and Circular), unifying transaction-, address-, and flow-level signals into risk vectors. Supervised models are then employed to output warning scores and alert decisions. Under the current setting (7 tokens, 33,242 records), Random Forest outperforms Logistic Regression on core metrics, achieving AUC=0.9098, PR-AUC=0.9185, and F1=0.7429. Ablation results show that trade-level features are the primary performance driver (Delta PR-AUC=-0.1843 when removed), while address-level features provide stable complementary gain (Delta PR-AUC=-0.0573). The model also demonstrates actionable early-warning potential for a subset of samples, with a mean Lead Time (v1) of 3.8133 hours. The error profile (FP=1, FN=8) indicates that the current system is better positioned as a high-precision screener rather than a high-recall automatic alarm engine. The main contributions are threefold: an executable and reproducible rug-pull warning pipeline, empirical validation of multi-granularity wash-trading features under weak supervision, and deployment-oriented evidence through lead-time and error-bound analysis.

Vivi Indah Bintari, Deasy Lestary Kusnandar, Risna Amalia Hamzah

This study aims to compare the investment performance of Decentralized Finance (DeFi), equities (IHSG), and gold during the 2021–2024 period, which represents a full market cycle characterized by high volatility and economic uncertainty. The research objective is to evaluate differences in return, risk (volatility), risk adjusted performance, and inter asset correlations to assess portfolio diversification potential. A quantitative comparative approach is employed using monthly secondary data, analyzed through descriptive statistics, non-parametric difference tests, and correlation analysis. The findings indicate statistically significant differences among the three investment instruments. Gold demonstrates the highest risk efficiency and consistently performs as a safe haven asset. Equities show moderate stability but relatively lower risk adjusted performance. In contrast, DeFi records the highest average returns, accompanied by extreme volatility and low efficiency. Correlation results reveal a strong positive relationship between gold and equities, while DeFi exhibits significant negative correlations with both assets, indicating diversification potential despite elevated systemic risk. This study concludes that gold remains the most resilient investment asset, equities serve as a balanced growth instrument, and DeFi should be positioned as a high risk speculative asset rather than a core portfolio component.

Petar Zhivkov, Juri D. Kandilarov

Hyperparameter optimization for cryptocurrency trading strategies encounters distinct challenges owing to continuous operation, volatility rates 3–4 times higher than equity indices, and price dynamics influenced by market sentiment. Bayesian optimization (Tree-Structured Parzen Estimator, TPE) and evolutionary algorithms (Differential Evolution, DE) are great for machine learning, but there are not many systematic comparisons for trading cryptocurrencies. This research evaluates Random Sampling, TPE, and DE through 36 factorial experiments, comprising 3 trading strategies (3, 4, and 5 hyperparameters) × 3 optimizers × 4 cryptocurrency pairs (BTC/USDT, ETH/USDT, INJ/USDT, SOL/USDT), resulting in 14,400 backtesting trials with walk-forward validation. TPE won 75% of strategy–asset pairs (9 of 12), reaching 90% of optimal performance within 13–17% of trial budgets. We find strategy-specific optimizer compatibility: mean-reversion strategies show DE underperformance independent of topology (−1% to −8%), whereas trend-following strategies show consistent DE competitiveness across assets (+13% to +37%). Most notably, for the same strategy, parameter space topology differs significantly between assets (trend following: 4.6% viable on BTC to 82% on ETH = 17.8×; mean reversion: 10.8% on ETH to 92% on SOL = 8.5×), indicating that topology results from strategy–asset interaction rather than intrinsic properties. Complete testing failures and widespread severe overfitting point to regime non-stationarity as a fundamental problem. Among the contributions are: (1) evidence shows that topological effects are dominated by optimizer–strategy compatibility (DE fails on mean-reversion strategies even in 92% viable spaces, but succeeds on trend-following strategies regardless of topology, spanning 13.6–82% viable spaces); (2) this is the first systematic Bayesian versus evolutionary comparison across 4 cryptocurrency assets; (3) parameter space topology emerges from strategy–asset interaction, varying up to 17.8-fold; and (4) single-period backtests inadequately identify parameter instability.

Hamid Najafi Bouyaghchi, Ameneh Farahani, Ismail A Mageed

The cryptocurrency market is volatile, which makes it very difficult to accurately predict. The Long Short-Term Memory (LSTM) is an approach to Predict Price Cryptocurrency (PPC) that uses price time series data. However, in this method, the prediction accuracy is dependent on the tuning of meta-parameters. Therefore, to tune these meta-parameters, an improved version of the optimization algorithms is needed that provides the task of selecting the optimal values of these parameters for price predictions. Therefore, in this study, the LSTM is combined with the classic version of the Differential Evolution (DE) algorithm, and the real data against the prediction results of the model presented in this study showed the appropriate accuracy of this model. Then, the classic version of the DE algorithm was modified to reduce its errors compared to previous algorithms. In this regard, coding was done in MATLAB version 2023b software, and the improved version was compared in terms of error rate with the Gray Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and the new Bald Eagle Search (BES) algorithm, which showed an accuracy of 86.94% for the improved model in this study.

O.O. Amam, M.T. Nwakuya, M.A. Ijomah

This study investigates the volatility behaviour of Ethereum (Coinbase) returns using the Generalized Autoregressive Heteroskedasticity GARCH (1,1) model under three distributional assumptions: Normal, Student-t, and the Generalized Error Distribution (GED). Cryptocurrency markets are characterized by extreme price swings, heavy-tailed behaviour, and persistent volatility, making traditional constant-variance models ineffective. Descriptive statistics reveal strong deviations from normality in Ethereum returns, with high kurtosis (7.8454) and an extremely large Jarque–Bera statistic (1797.182 with its p-value less than 5%), indicating excess tail risk and frequent extreme movements. Preliminary analysis reveal that the return series is stationary, free from serial correlation, but exhibits significant ARCH effects, justifying the use of conditional heteroskedasticity models. Empirical results show highly persistent volatility across all models, with α + β values close to unity: approximately 0.99 under the Normal distribution, 1.01 under the Student-t specification, and 0.994 under GED distribution. Model comparison reveals that heavy-tailed error structures outperform the Normal model, with GED achieving the lowest AIC (−3.781), SIC (−3.7629), HQC (−3.7743), and the lowest MAPE (114.6606). These findings demonstrate that flexible distributional assumptions greatly enhance the modelling of extreme and persistent volatility in Ethereum returns. The study emphasizes the importance of adopting heavy-tailed GARCH frameworks when analysing cryptocurrency risk and forecasting volatility.

Jonathan Lee Miller

We test price efficiency, which shows the fairness of trading for retail investors using the runs tests and variance ratio tests. We reject the hypothesis that Bitcoin prices are price efficient on most markets, but efficient on the Bitstamp BTC/USD. Coinbase departs from efficiency, indicating that fraud, later found by regulators, has significantly harmed retail investors. We also document barriers to trading of Bitcoin, which result in difficulties in arbitrage despite global price differences. My results predict the hack of the Bitfinex exchange, which caused it to close and harmed many people.

Klaus Grobys, Davide Sandretto, Janne Äijö

• We examine the profitability of a cryptocurrency momentum strategy using 9 “survivor coins”. • The survivor cryptocurrency momentum portfolio (SCMP) does not generate significant payoffs. • SCMP does not leverage a plain momentum strategy based on a broader set of coins. • Significant payoffs documented for momentum strategies are an artefact of coins that are only temporarily accessible for trading. Motivated by the significant illiquidity observed in the cryptocurrency market—exemplified by phenomena such as "defaulted coins"—this study is the first to investigate a cryptocurrency-specific analog of currency momentum, as implemented among G10 currencies. We analyze nine free-floating cryptocurrencies that remained within the top 100 altcoins by market capitalization during the sample period, spanning January 2017 to August 2024. Using weekly data, we evaluate two cryptocurrency momentum strategies: one focused solely on survivor coins and another utilizing the largest 30 coins for a given year (referred to as "plain cryptocurrency momentum"). Our main findings are as follows: (a) Cryptocurrency momentum is not evident when applied to survivor coins; (b) plain cryptocurrency momentum is profitable only after the dataset is trimmed; (c) the profitability of trimmed plain cryptocurrency momentum does not result from leveraging survivor coin-based cryptocurrency momentum; (d) even after trimming, the profitability of plain cryptocurrency momentum is highly sample-dependent.

Chengyuan Xu

The decentralized finance market exhibits extreme volatility and complex nonlinear dynamics that pose significant challenges for accurate price prediction and risk management. Traditional time series models, including Long Short-Term Memory networks and Transformer architectures, struggle with either computational inefficiency in capturing long-rangedependencies or inadequate context retention across extended sequences. This research investigates the application of Structured State Space Models, particularly the Mamba architecture with selective state spaces, for modeling temporal dependencies in DeFi markets. The proposed framework addresses the limitations of conventional approaches by leveraging SSMs' linear-time complexity while maintaining superior long-sequence modeling capabilities through context-aware selective mechanisms. Our methodology integrates SSM architectures with DeFispecific features including on-chain transaction volumes, liquidity metrics, and market microstructure indicators. Experimental validation across multiple cryptocurrency pairs demonstrates that SSM-based models achieve competitive performance compared to attentionbaseTransformers while offering substantial computational advantages. The results indicate that selective state space mechanisms enable effective capture of both short-term volatility patterns and long-horizon price trends in decentralized markets. This work contributes to the emerginintersection of advanced sequence modeling techniques and blockchain-based financial systems, providing insights for algorithmic trading strategies and risk assessment frameworks in the rapidly evolving DeFi ecosystem.

Thomas Anderson, Sarah Mitchell

The integration of distributed ledger technology with financial markets has precipitated a paradigm shift in how algorithmic trading strategies are conceived, executed, and settled. This paper presents a comprehensive analysis of blockchain-based algorithmic trading systems, focusing specifically on the dual challenges of execution efficiency and cryptographic security. While traditional high-frequency trading relies on centralized exchanges and proprietary networks to minimize latency, decentralized trading protocols introduce novel constraints related to block generation intervals, consensus mechanisms, and network propagation delays. We examine the implementation of algorithmic strategies via smart contracts, evaluating the trade offs between on-chain transparency and the privacy requirements of institutional investors. Furthermore, the study investigates critical vulnerabilities inherent to decentralized exchanges, such as Miner Extractable Value and front-running attacks, and proposes mitigation strategies utilizing commit-reveal schemes and zero-knowledge proofs. By analyzing the performance metrics of automated market makers against order book models, we provide empirical evidence regarding the current limitations and potential scalability of blockchain-based trading environments. The findings suggest that while blockchain architectures offer superior settlement finality and auditability, significant advancements in layer-two scaling solutions and privacy preserving cryptographic protocols are requisite for these systems to compete with traditional financial infrastructure in terms of throughput and latency.