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.
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.
Derived from original PMR research written by Bastien Buchwalter, Jean-Michel Maeso, and Vincent Milhau using AI and an editor
Quickly apply original, key PMR-published papers with Snapshots—a short article companion that distills PMR research into compressed, digestible takeaways, so you can put the paper’s core ideas to work in your investment process—fast. This Snapshot is based on an article about cleaning cryptocurrency data so researchers and investors can build more reliable investable universes. It presents a three-step protocol for fixing market-cap spikes, Bitcoin-dominance dips, and volume anomalies in CoinMarketCap data while preserving prices and returns and improving aggregate market indicators for analysis.
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.
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.
This study investigates the correlation between the audio features of top-charting music and Non-Fungible Tokens (NFT) market dynamics, presenting a novel perspective within the realm of behavioral finance. Drawing on the regulatory focus theory and existing research on music's affective influence, the authors argue that popular music, as a reflection of society's collective regulatory focus, can significantly impact trading behaviours in NFTs, an asset class known for its susceptibility to emotional drivers and speculative activity. By employing a Long Short-Term Memory (LSTM) machine learning model and permutation importance technique, the analysis demonstrates that specific musical attributes—such as danceability, loudness, and mode—exhibit predictive power over daily NFT trading volumes. The study not only provides evidence of music's capacity to signal shifts in trading behaviors, offering innovative insights into the drivers of digital asset markets, but introduces a new interdisciplinary approach focusing on the collective regulatory focus reflected in the music.
In this thesis, I investigate the existence, magnitude, and evolution of regional overnight return anomalies in Bitcoin. Using high-frequency BTC/USDT data spanning August 2017 to March 2026, I construct session and overnight return components for three regional pseudo-sessions, based on local stock exchange trading hours – Asia (TSE), Europe (Xetra/LSE), and the United States (NYSE/Nasdaq). I document that Bitcoin exhibits a significant overnight premium in the Asian pseudo-session over the full sample, which persists after controlling for realised volatility, trading volume, and illiquidity. No significant premium is found for Europe or the US over the full sample. Notably, holding Bitcoin exclusively during Asian trading hours would have produced a negative cumulative return over a period in which the price of Bitcoin appreciated significantly. Second, the premium is not constant over time; it is strongest during speculative, retail-dominated phases and compresses during bear markets, consistent with the Adaptive Markets Hypothesis. Third, I examine how the introduction of US spot Bitcoin ETFs affected the premium using a difference-in-differences analysis, finding that the overnight premium increases in the US region, while the Asian overnight premium collapses to statistical insignificance. Finally, I find that a simulated trading strategy based on the Asian overnight premium underperforms a passive buy-and-hold benchmark under standard retail fee structures, consistent with the limits-to-arbitrage hypothesis. The findings extend the overnight drift literature to a continuously traded asset and provide direct empirical evidence for geographically segmented price discovery in cryptocurrency markets.
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.
We analyze the market quality of centralized crypto exchanges and decentralized blockchain-based venues (DEXs) using a unique and comprehensive data set. Focusing on two fundamental aspects, transaction costs and deviations from the no-arbitrage condition, we estimate the causal effect of gas fees on DEX market quality. We show that these fixed costs impose a significant burden on relatively small trades and cause persistent arbitrage deviations. Conversely, DEXs offer more competitive transaction costs for larger trades, offering a more favorable environment for institutional investors. Furthermore, we provide causal evidence that innovations aimed at enhancing the flexibility of liquidity provision in DEX markets lead to sizeable improvements in market quality. This paper was accepted by Agostino Capponi, finance. Funding: A. Ranaldo acknowledges financial support from the Swiss National Science Foundation nccr–on the move [Grant 204721]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.07703 .
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.
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.
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.
The emergence of Bitcoin as a major alternative investment asset has driven the development of financial instruments like Bitcoin Spot Exchange-Traded Funds (ETFs), offering broader market access and deeper integration into global trading ecosystems. This study analyses the impact of the introduction of Bitcoin exchange-traded funds (ETFs) on spot Bitcoin prices by analysing how the introduction of ETFs and their trading volumes influence price dynamics. Employing high-frequency trading data and advanced econometric methods, the research highlights the short-term and long-term interplay between ETF inflows and Bitcoin market behaviour. The findings provide insights for investors, policymakers, and market participants navigating the cryptocurrency landscape, emphasising the feedback mechanisms between traditional derivative instruments and native digital trading. This study reveals that Bitcoin ETF inflows influence spot market price dynamics in the short term, driven by investor sentiment and market momentum. However, Bitcoin prices exhibit independence from ETF inflows over longer horizons, highlighting the dominant role of underlying market mechanisms. Higher-frequency data analysis underscores the rapid adjustments in Bitcoin trading, while advanced econometric models confirm a stable long-term equilibrium relationship between ETF inflows and Bitcoin prices. These insights offer critical implications for observers navigating the evolving cryptocurrency ecosystem.
We model endogenous trading and liquidity provision at a decentralized exchange (DEX) and demonstrate that increasing DEX trading fees can increase DEX trading volume. DEXs employ a mechanical pricing rule whereby price impacts decrease with inventory that DEXs acquire by offering fee revenues to investors. Consequently, higher DEX fees can incentivize higher inventory, thereby reducing price impacts. Moreover, the reduction of price impact can offset the increase in fees so that the marginal cost of DEX trading declines despite charging a higher trading fee. In turn, lower DEX marginal trading costs lead to an increase in DEX trading volume. This paper was accepted by Agostino Capponi, finance.
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.
Bastien Buchwalter, Jean-Michel Maeso, Vincent Milhau
We develop a reproducible three-step protocol to clean daily cryptocurrency data from CoinMarketCap, one of the most used data providers in academic research. The procedure targets three recurring anomalies that distort market-level indicators: (1) extreme market-cap spikes, (2) one-day and multiday dips in Bitcoin dominance, and (3) abnormal trading volumes. Using more than 28,000 cryptocurrencies from 2014 to 2024, we show that the method modifies only a small subset of data while improving the reliability of key market indicators. We do not adjust prices or returns, preserving actual trading conditions. As an application, we construct dynamic investable universes using cleaned data and realistic constraints based on market capitalization and volume. This exercise shows that cleaning and filtering jointly produce more reliable universes, reducing spurious extremes and making them suitable for empirical asset pricing research and portfolio construction.
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.
This paper investigates extreme risk in cryptocurrency markets by comparing Bitcoin and Ethereum daily returns with those of S&P 500 and SPY ETF. Using the Generalized Tempered Stable (GTS) distribution to model heavy tails and Quantile-Quantile (Q-Q) plots to assess fitness, we find that all assets deviate sharply from normal distribution. Within this framework, Ethereum exhibits a higher frequency of extreme returns than Bitcoin, highlighting differences in risk profiles even among leading cryptocurrencies.