Crypto Assets

Partial multivariate transformer as a tool for cryptocurrencies time series prediction ArXiv ID: 2512.04099 “View on arXiv” Authors: Andrzej Tokajuk, Jarosław A. Chudziak Abstract Forecasting cryptocurrency prices is hindered by extreme volatility and a methodological dilemma between information-scarce univariate models and noise-prone full-multivariate models. This paper investigates a partial-multivariate approach to balance this trade-off, hypothesizing that a strategic subset of features offers superior predictive power. We apply the Partial-Multivariate Transformer (PMformer) to forecast daily returns for BTCUSDT and ETHUSDT, benchmarking it against eleven classical and deep learning models. Our empirical results yield two primary contributions. First, we demonstrate that the partial-multivariate strategy achieves significant statistical accuracy, effectively balancing informative signals with noise. Second, we experiment and discuss an observable disconnect between this statistical performance and practical trading utility; lower prediction error did not consistently translate to higher financial returns in simulations. This finding challenges the reliance on traditional error metrics and highlights the need to develop evaluation criteria more aligned with real-world financial objectives. ...

HODL Strategy or Fantasy? 480 Million Crypto Market Simulations and the Macro-Sentiment Effect ArXiv ID: 2512.02029 “View on arXiv” Authors: Weikang Zhang, Alison Watts Abstract Crypto enthusiasts claim that buying and holding crypto assets yields high returns, often citing Bitcoin’s past performance to promote other tokens and fuel fear of missing out. However, understanding the real risk-return trade-off and what factors affect future crypto returns is crucial as crypto becomes increasingly accessible to retail investors through major brokerages. We examine the HODL strategy through two independent analyses. First, we implement 480 million Monte Carlo simulations across 378 non-stablecoin crypto assets, net of trading fees and the opportunity cost of 1-month Treasury bills, and find strong evidence of survivorship bias and extreme downside concentration. At the 2-3 year horizon, the median excess return is -28.4 percent, the 1 percent conditional value at risk indicates that tail scenarios wipe out principal after all costs, and only the top quartile achieves very large gains, with a mean excess return of 1,326.7 percent. These results challenge the HODL narrative: across a broad set of assets, simple buy-and-hold loads extreme downside risk onto most investors, and the miracles mostly belong to the luckiest quarter. Second, using a Bayesian multi-horizon local projection framework, we find that endogenous predictors based on realized risk-return metrics have economically negligible and unstable effects, while macro-finance factors, especially the 24-week exponential moving average of the Fear and Greed Index, display persistent long-horizon impacts and high cross-basket stability. Where significant, a one-standard-deviation sentiment shock reduces forward top-quartile mean excess returns by 15-22 percentage points and median returns by 6-10 percentage points over 1-3 year horizons, suggesting that macro-sentiment conditions, rather than realized return histories, are the dominant indicators for future outcomes. ...

Uncertain Regulations, Definite Impacts: The Impact of the US Securities and Exchange Commission’s Regulatory Interventions on Crypto Assets ArXiv ID: 2412.02452 “View on arXiv” Authors: Unknown Abstract This study employs an event study methodology to investigate the market impact of the U.S. Securities and Exchange Commission’s (SEC) classification of crypto assets as securities. It explores how SEC interventions influence asset returns and trading volumes, focusing on explicitly named crypto assets. The empirical analysis highlights significant adverse market reactions, notably returns plummeting 12% over one week post-announcement, persisting for a month. We demonstrate that the severity of market reaction depends on sentiment and asset characteristics such as market size, age, volatility, and illiquidity. Further, we identify significant ex-ante trading volume effects indicative of pre-announcement informed trading. ...

Liquidity Jump, Liquidity Diffusion, and Crypto Wash Trading ArXiv ID: 2411.05803 “View on arXiv” Authors: Unknown Abstract We develop a new framework to detect wash trading in crypto assets through real-time liquidity fluctuation. We propose that short-term price jumps in crypto assets results from wash trading-induced liquidity fluctuation, and construct two complementary liquidity measures, liquidity jump (size of fluctuation) and liquidity diffusion (volatility of fluctuation), to capture the behavioral signature of wash trading. Using US stocks as a benchmark, we demonstrate that joint elevation in both liquidity metrics indicates wash trading in crypto assets. A simulated regulatory treatment that removes likely wash trades confirms this dynamic: it reduces liquidity diffusion significantly while leaving liquidity jump largely unaffected. These findings align with a theoretical model in which manipulative traders amplify both the level and variance of price pressure, whereas passive investors affect only the level. Our model offers practical tools for investors to assess market quality and for regulators to monitor manipulation risk on crypto exchanges without oversight. ...

Dynamic Latent-Factor Model with High-Dimensional Asset Characteristics ArXiv ID: 2405.15721 “View on arXiv” Authors: Unknown Abstract We develop novel estimation procedures with supporting econometric theory for a dynamic latent-factor model with high-dimensional asset characteristics, that is, the number of characteristics is on the order of the sample size. Utilizing the Double Selection Lasso estimator, our procedure employs regularization to eliminate characteristics with low signal-to-noise ratios yet maintains asymptotically valid inference for asset pricing tests. The crypto asset class is well-suited for applying this model given the limited number of tradable assets and years of data as well as the rich set of available asset characteristics. The empirical results present out-of-sample pricing abilities and risk-adjusted returns for our novel estimator as compared to benchmark methods. We provide an inference procedure for measuring the risk premium of an observable nontradable factor, and employ this to find that the inflation-mimicking portfolio in the crypto asset class has positive risk compensation. ...

Enhancing Law Enforcement Training: A Gamified Approach to Detecting Terrorism Financing ArXiv ID: 2403.13625 “View on arXiv” Authors: Unknown Abstract Tools for fighting cyber-criminal activities using new technologies are promoted and deployed every day. However, too often, they are unnecessarily complex and hard to use, requiring deep domain and technical knowledge. These characteristics often limit the engagement of law enforcement and end-users in these technologies that, despite their potential, remain misunderstood. For this reason, in this study, we describe our experience in combining learning and training methods and the potential benefits of gamification to enhance technology transfer and increase adult learning. In fact, in this case, participants are experienced practitioners in professions/industries that are exposed to terrorism financing (such as Law Enforcement Officers, Financial Investigation Officers, private investigators, etc.) We define training activities on different levels for increasing the exchange of information about new trends and criminal modus operandi among and within law enforcement agencies, intensifying cross-border cooperation and supporting efforts to combat and prevent terrorism funding activities. On the other hand, a game (hackathon) is designed to address realistic challenges related to the dark net, crypto assets, new payment systems and dark web marketplaces that could be used for terrorist activities. The entire methodology was evaluated using quizzes, contest results, and engagement metrics. In particular, training events show about 60% of participants complete the 11-week training course, while the Hackathon results, gathered in two pilot studies (Madrid and The Hague), show increasing expertise among the participants (progression in the achieved points on average). At the same time, more than 70% of participants positively evaluate the use of the gamification approach, and more than 85% of them consider the implemented Use Cases suitable for their investigations. ...

Liquidity Premium, Liquidity-Adjusted Return and Volatility, and Extreme Liquidity ArXiv ID: 2306.15807 “View on arXiv” Authors: Unknown Abstract We establish innovative liquidity premium measures, and construct liquidity-adjusted return and volatility to model assets with extreme liquidity, represented by a portfolio of selected crypto assets, and upon which we develop a set of liquidity-adjusted ARMA-GARCH/EGARCH models. We demonstrate that these models produce superior predictability at extreme liquidity to their traditional counterparts. We provide empirical support by comparing the performances of a series of Mean Variance portfolios. ...

DecentralizedFinance—A Systematic Literature Review and Research Directions ArXiv ID: ssrn-4016497 “View on arXiv” Authors: Unknown Abstract Decentralized Finance (DeFi) is the (r)evolutionary movement to create a solely code-based, intermediary-independent financial system—a movement which has grown Keywords: Decentralized Finance (DeFi), code-based finance, intermediary-independent, Crypto Assets Complexity vs Empirical Score Math Complexity: 0.8/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a systematic literature review that synthesizes existing academic work; it does not present novel mathematical models or algorithms, and its empirical focus is on analyzing prior research methods rather than conducting new data-driven backtests. flowchart TD A["Research Goal: Map the DeFi landscape and identify future directions"] --> B["Methodology: Systematic Literature Review"] B --> C["Data: 148 selected peer-reviewed papers"] C --> D["Computational Process: Thematic analysis of DeFi components and challenges"] D --> E["Outcome: Proposed DeFi taxonomy"] D --> F["Outcome: Identified research directions"]

The Markets in Crypto-Assets Regulation (MICA) and the EU DigitalFinanceStrategy ArXiv ID: ssrn-3725395 “View on arXiv” Authors: Unknown Abstract The European Commission published its new Digital Finance Strategy on 24 September 2020. One of the centrepieces of the Strategy is the draft Regulation on Mark Keywords: Digital Finance Strategy, EU Regulation, Crypto-Assets, Operational Resilience, European Commission, Cryptocurrency / Digital Assets Complexity vs Empirical Score Math Complexity: 0.5/10 Empirical Rigor: 0.0/10 Quadrant: Philosophers Why: This paper is a legal and policy analysis of EU regulation (MiCA), discussing regulatory frameworks, definitions, and supervisory cooperation without any mathematical modeling or empirical data. It focuses on regulatory challenges and proposed solutions in a theoretical, non-quantitative manner. flowchart TD A["Research Question"] --> B["Key Methodology"] B --> C["Data & Inputs"] C --> D["Computational Process"] D --> E["Key Findings/Outcomes"] A["Research Question<br/>How does MiCA align with<br/>EU Digital Finance Strategy?"] B["Methodology Steps<br/>- Policy analysis<br/>- Regulatory comparison<br/>- Impact assessment"] C["Data & Inputs<br/>- European Commission papers<br/>- MiCA draft texts<br/>- Academic literature"] D["Computational Process<br/>- Qualitative coding<br/>- Thematic analysis<br/>- Gap identification"] E["Key Findings<br/>1. MiCA is core to Digital Strategy<br/>2. Focus on operational resilience<br/>3. Defines crypto-asset categories<br/>4. Balances innovation vs protection"]