Aim: This study examines whether and how the disposition effect shapes Ethereum investors’ selling decisions. It asks whether investors are more likely to realize gains than losses, whether this asymmetry strengthens during high-volatility periods, and whether it weakens around major protocol upgrades, including the Merge, Shapella, and Dencun. Methodology: The study builds a high-frequency address-day panel for 2020–2024 using public on-chain data and labeled centralized-exchange deposit clusters as conservative proxies for sell decisions. Rolling cost bases are reconstructed under FIFO and value-weighted rules, and unrealized gains and losses are linked to realized sales through discrete-time logit and Cox hazard models. The design also includes event windows and robustness checks. Findings: The framework is designed to identify three mechanisms: asymmetric realization of gains over losses, stronger gain realization under high volatility, and attenuation around major protocol-upgrade events. Implications: The study offers a transparent design for analyzing behavioral bias in crypto-asset markets with verifiable blockchain data. It is relevant to exchanges, regulators, and market designers concerned with investor behavior and risk management. Originality/value: The article extends behavioral finance to Ethereum by using public ledger data rather than brokerage records and by integrating behavioral bias, volatility regimes, and protocol events in one framework.
We document stable cross-asset patterns in cryptocurrency limit-order-book microstructure: the same engineered order book and trade features exhibit remarkably similar predictive importance and SHAP dependence shapes across assets spanning an order of magnitude in market capitalization (BTC, LTC, ETC, ENJ, ROSE). The data covers Binance Futures perpetual contract order books and trades on 1-second frequency starting from January 1st, 2022 up to October 12th, 2025. Using a unified CatBoost modeling pipeline with a direction-aware GMADL objective and time-series cross validation, we show that feature rankings and partial effects are stable across assets despite heterogeneous liquidity and volatility. We connect these SHAP structures to microstructure theory (order flow imbalance, spread, and adverse selection) and validate tradability via a conservative top-of-book taker backtest as well as fixed depth maker backtest. Our primary novelty is a robustness analysis of a major flash crash, where the divergent performance of our taker and maker strategies empirically validates classic microstructure theories of adverse selection and highlights the systemic risks of algorithmic trading. Our results suggest a portable microstructure representation of short-horizon returns and motivate universal feature libraries for crypto markets.
We formulate and solve stochastic control problems that model the core yield-generating strategy of the Ethena protocol, a decentralized finance (DeFi) stablecoin that earns yield by combining a long position in staked Ethereum (stETH) with an equal-sized short position in ETH perpetual futures. The combined position is delta-neutral with respect to the ETH spot price, yet earns carry from two sources: staking rewards on the stETH leg, and funding-rate payments received from long perpetual holders when the perpetual trades at a premium to spot. A key feature of our model is that the control -- the rate of simultaneously buying stETH and shorting the perpetual -- exerts two distinct types of price impact. \textit{Permanent} impact shifts the mid-market prices of both legs, compressing the basis and permanently eroding future funding income. \textit{Temporary} impact reflects execution slippage on each leg. We study both an infinite-horizon discounted problem and a finite-horizon problem in which the protocol maximizes total wealth up to a fixed date $T$, subject to a terminal cost for liquidating any remaining position. In both cases the optimal control is obtained explicitly.
Marco Dessalvi, Massimo Bartoletti, Alberto Lluch-Lafuente
Decentralized Finance (DeFi) has revolutionized financial markets by enabling complex asset-exchange protocols without trusted intermediaries. Automated Market Makers (AMMs) are a central component of DeFi, providing the core functionality of swapping assets of different types at algorithmically computed exchange rates. Several mainstream AMM implementations are based on the constant-product model, which ensures that swaps preserve the product of the token reserves in the AMM - up to a trading fee used to incentivize liquidity provision. Trading fees substantially complicate the economic properties of AMMs, and for this reason some AMM models abstract them away in order to simplify the analysis. However, trading fees have a non-trivial impact on users' trading strategies, making it crucial to develop refined AMM models that precisely account for their effects. In this work, we extend a foundational model of AMMs by introducing a new parameter, the trading fee ϕ ∈ (0,1], into the swap rate function. Fee amounts increase inversely proportional to ϕ. When ϕ = 1, no fee is applied and the original model is recovered. We analyze the resulting fee-adjusted model from an economic perspective. We show that several key properties of the swap rate function, including output-boundedness and monotonicity, are preserved. At the same time, other properties - most notably additivity - no longer hold. We precisely characterize this deviation by deriving a generalized form of additivity that captures the effect of swaps in the presence of trading fees. In particular, we prove that when ϕ < 1, executing a single large swap yields strictly greater profit than splitting the trade into smaller ones. Finally, we derive a closed-form solution to the arbitrage problem in the presence of trading fees and prove its uniqueness. All results are formalized and machine-checked in the Lean 4 proof assistant.
Existing decentralization measures are almost entirely origination-side, quantifying concentration in who mines or validates blocks. We introduce a spectral methodology measuring concentration on the destination side instead: where value ultimately flows once it leaves a validator wallet. Modeling wallet-to-wallet transfers as a Markov chain, we compute near-real-time steady-state probabilities via the Perron-Frobenius theorem to identify long-run terminal recipients. Applied to 76,855 Ethereum wallets from four years of mining data, fund flows collapse to just four terminal accounts. None of these fund destination accounts are among the network's three dominant identifiable revenue-earning miners.
On January 10, 2024 (July 23rd, 2024), the Commission approved the listing of eleven (eight) bitcoin-based (Ethereum-based) exchange-traded products (ETPs) in NYSE Arca, Nasdaq and Cboe BZX. Using these approvals as a natural experiment, we employ a program evaluation framework to study the impact of the introduction of crypto-based ETPs on the liquidity of spot bitcoin/Ethereum markets in crypto trading platforms. We use the most recently available blockchain data supplied by Kaiko. The estimation strategy identifies that while the level of liquidity has not changed, that the time series trading pattern has, and that successive returns are more (less) related. However, though returns are on average more correlated we find that the returns in the bitcoin and Ethereum spot market overall show improvements after the introduction of these ETPs globally as compared to before. The findings further shed light on the workings of different aspects of crypto asset markets.
Despite trading volumes in the tens of billions, NFT markets are illiquid: median quoted spreads of 48-200% far exceed equity levels, though execution-based measures covering nearly all sales yield effective costs of only 2-5%. Using over 410 million orderbook events-including, for the first time, comprehensive bid-side data-across six major collections, we document a distinctive institutional response: a two-tier orderbook in which collection-level floor bids, rather than token-specific orders, supply the dominant source of buy-side liquidity. A small number of algorithmic market makers provide these bids but face adverse selection inherent in collection-level offers, experiencing market-adjusted post-purchase returns of-3% to-7%. In panel regressions, collection identity absorbs over 30 percentage points of R-squared , dominating all observable spread determinants. A comparison of the same 10 000 assets under two market structures reveals that the native bilateral marketplace achieves tighter spreads (54% vs. 200%), suggesting that ease of bidding generates liquidity activity but not price efficiency.
NFTs provided an extraordinary real-time laboratory for bubble economics: returns were exceptionally right-skewed, illiquidity pervaded even the most active platforms, and a handful of trades drove aggregate performance. Investors extrapolating from realized returns without recognizing selection bias and survivorship faced a substantial risk of disappointment. As our data and simulations confirm, successful NFT investing during the bubble required an almost perfect confluence of timing, liquidity, and luck. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org .
Although many academic studies have examined volatility spillovers and dynamic correlations between stock markets, they have largely overlooked the perspective of Small and Medium-Sized Enterprise (SME) markets. On this basis, this study explores the interconnectedness and volatility correlation between Decentralized Finance (DeFi) markets and SME markets. To understand the correlation between these markets, we empirically analyse six European SME market indices—the FTSE AIM All-Share Index (AIM), BIST SME Industrial Index (BISTSME), Euronext Growth All-Share Index (EURONEXT), First North All-Share Index (FIRSTNORTH), IBEX Medium Cap Index (IBEXC), and Scale All-Share Performance Index (SCALE)—alongside three cryptocurrencies: Aave (AAVE), Ethereum (ETH), and Uniswap (UNI); two stablecoins: Dai (DAI) and USD Coin (USDC); and one synthetic asset: Synthetix (SNX). The study employed BEKK-GARCH and DCC-GARCH to analyse the existence of spillover effects and correlations from October 5, 2020, to August 18, 2024. The findings indicate that AAVE, ETH, and UNI, in particular, transmit significant volatility to the EURONEXT and FIRSTNORTH markets. However, bidirectional volatility spillover was detected between EURONEXT and AAVE, ETH, UNI, USDC, and SNX, and FIRSTNORTH and AAVE, ETH, UNI, and SNX. This suggests volatility interdependence between these markets and the existence of potential risk contagion channels.
This research explores the relationship between abnormal investor attention and Bitcoin futures return by using several Google search keywords covering Bitcoin futures to measure investor attention in its futures market. The empirical findings show that abnormal investor attention significantly negatively correlates to Bitcoin futures return when the market declines. We further consider the effect of COVID-19 and Bitcoin market crash on such a correlation and present that the relation becomes more pronounced during the latter downward periods, but find only a weak effect on such a relation during the epidemic. Finally, we provide evidence after controlling for Bitcoin spot return and VIX that the negative relation between investor attention and Bitcoin futures return is still significant, especially during a Bitcoin crash.
This thesis investigates the design of automated market makers (AMMs) for trading tokenized derivatives in decentralized finance. Motivated by the limitations of existing AMMs, which only permit strictly positive prices, we introduce an invariant that also allows for negative prices. As a primary use case, the thesis develops an AMM for trading an offset token against tokenized euros. The thesis employs, on the one hand, a Monte Carlo–based simulation to evaluate the risk-adjusted returns of an AMM. The simulation includes two types of traders: arbitrage traders, who exploit price deviations between the AMM and the fair value, and noise traders, who represent demand for liquidity. On the other hand, we introduce KPIs such as impermanent loss and market depth. The goal of the thesis is to analyse whether these KPIs can be used to predict the risk-adjusted returns of an AMM.
This study investigates the relationship between dirty and clean cryptocurrencies and traditional stock index returns using the Quantile-Quantile (QQR) and Quantile-Quantile Granger Causality (QQGC) methods. The analyses were conducted using daily data from January 2018 to May 2025. QQR results show both positive and negative relationships between dirty and clean cryptocurrencies and the returns of the S&P 500, FTSE 100, TSX, and ASX indices at the low, medium, and high quantiles. According to the QQGC results, both dirty and clean cryptocurrencies showed predictive power for the returns of the S&P 500, FTSE 100, TSX, and ASX indices at different quantiles. Furthermore, it was found that both dirty and clean cryptocurrencies exhibit strong predictive power for S&P 500 and FTSE 100 returns, particularly in the middle quantiles. The results obtained reveal that distinguishing between dirty and clean cryptocurrencies under different market conditions provides important insights for investors' portfolio diversification strategies and risk management practices.
AbstractThis article investigates the economic and behavioral feasibility of creating personalizedcryptocurrencies (Fan Coins) linked to the performance of soccer players, using quantitative methods inEconometrics, focusing on box office revenue, sponsorships and sports betting. The analysis includes theThe cases of Diego Ribas (Flamengo), Neymar (Santos), and top scorers from São Paulo and Palmeiras.proposal is to analyze how reputation and sports performance can be transformed into digital assets withmeasurable value. Furthermore, it is proposed to use artificial intelligence systems to manage sponsorportfolios and a fan club application with a subscription system to foster a new sports business model basedon data and personalization.Keywords: Fan Coin; Cryptocurrency; Econometrics; Sports Economics; Game Theory;Behavioral Finance; Sports Betting; Artificial Intelligence; Financial Modeling; Digital Sponsorship
In this study, we employ an NFTs news attention (NFTATT) index to measure investor attention to non-fungible tokens (NFTs) and examine its impact on the price crash risk of Bitcoin futures listed on the Chicago Mercantile Exchange. Using a sample period spanning from February 2018 to June 2023, we document a negative relationship between NFTATT and Bitcoin price crash risk. Further analysis shows that the NFTATT index has a stronger mitigating effect on price crash risk when investor interest in NFTs is at a high level. Additionally, market sentiment, as measured by the crypto fear and greed index, tends to increase the likelihood of Bitcoin price crashes.
Non-Fungible Tokens (NFTs) enable the decentralized representation and exchange of real-world assets, supporting features like fractional ownership and programmable logic that underpin emerging digital finance ecosystems. However, the openness of NFT markets makes them susceptible to manipulation tactics like wash trading, where coordinated trades distort prices. This undermines valuation accuracy and erodes trust in decentralized finance. To counter these challenges, we propose NFTGuard, a unified framework for detecting wash trades and producing manipulation-resilient NFT price predictions. First, NFTGuard filters out manipulated transactions using a rule-based detector that identifies self-dealing and cyclic trading patterns prevalent in decentralized marketplaces. Second, NFTGuard prepares for price prediction by constructing a multi-modal representation that integrates temporal trading dynamics, transactional metadata, and asset-specific semantic signals. Third, NFTGuard performs prediction using a Multi-Layer Perceptron (MLP) mixing backbone that fuses these heterogeneous cues into manipulation-resilient forecasts. Experiments on real-world NFT datasets from platforms like Rarible and Opensea show that NFTGuard achieves a 90.9% F1-score in detecting wash trades and improves price prediction accuracy by over 10% compared to the baselines.
This paper measures price differences between Hegic option quotes on Arbitrum and a model-based benchmark built on Black--Scholes model with regime-sensitive volatility estimated via a two-regime MS-AR-(GJR)-GARCH model. Using option-level feasible GLS, we find benchmark prices exceed Hegic quotes on average, especially for call options. The price spread rises with order size, strike, maturity, and estimated volatility, and falls with trading volume. By underlying, wrapped Bitcoin options show larger and more persistent spreads, while Ethereum options are closer to the benchmark. The framework offers a data-driven analysis for monitoring and calibrating on-chain option pricing logic.
Cryptocurrencies market capitalization has surpassed $4 trillion in 2025, attracting individual and institutional traders seeking investment and speculation. However, volatility of cryptocurrencies prices makes profitable strategies a huge challenge, especially with respect to the variance of returns. In this context, this paper presents an innovative strategy based on the counterintuitive concept from Game Theory called Parrondo’s Paradox. The presented strategy results in improved capital gains (returns) when compared to traditional buy & hold. Also, the strategy is proven to work in daily, weekly and minute-by-minute timeframes. With the empirical results shown in this paper, the Parrondo’s Paradox framework can be used as a trading strategy by either individual or institutional investors.
Emmanuel L. C. VI M. Plan, Doan Binh Minh Do, Xuan Trung Pham, Lê Khánh Linh Vũ · 5 authors
Wash trading is a major issue in non-fungible token (NFT) markets that distorts transaction volumes and returns. In this work, we examined the effect of wash trading by focusing on two specific NFT collections. First, we implemented a multi-layered wash trading detection algorithm to identify wash trades. Using regression analysis, we then showed that weekly transaction volumes of a heavily wash-traded collection can be magnified by two orders of magnitude compared to a cleaner collection. Moreover, wash trading resulted in positive returns in a collection that has extensive wash trading; in contrast, wash trading in the cleaner collection was penalized with negative returns, suggesting heterogeneity in both incidence and profitability of wash trading. Our findings provide a better understanding on the impact of wash trading on NFT markets and highlight the need to improve security in NFT markets and other decentralized financial technology systems. In particular, by extending transaction-level detection to collection characteristics, we could assess the impact of wash trading. This approach is easily replicable and enables market stakeholder to identify inauthentic activity and policy makers to provide adequate safeguards for these products.
The rapid evolution of digital assets transforms cryptocurrencies into one of the most volatile and data-rich financial markets. Their nonlinear and unpredictable nature limits the effectiveness of traditional forecasting models, motivating the use of machine learning methods to identify hidden patterns and short-term price movements. This study compares the performance of Logistic Regression (LR), Random Forest (RF), XGBoost, Support Vector Classifier (SVC), K-Nearest Neighbors (KNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models in predicting the daily price directions of Bitcoin (BTC), Ethereum (ETH), and Ripple (XRP). Extensive data preprocessing and feature engineering are performed, integrating a broad set of technical indicators to enhance model generalization and capture temporal market dynamics. The results show that XGBoost achieves the highest classification accuracy of 55.9% for BTC and 53.8% for XRP, while LR provides the best result for Ethereum with an accuracy of 54.4%. In trading simulations, XGBoost achieves the strongest performance, generating a cumulative return of 141.4% with a Sharpe ratio of 1.78 for Bitcoin and 246.6% with a Sharpe ratio of 1.59 for Ripple, whereas LSTM delivers the best results for Ethereum with a 138.2% return and a Sharpe ratio of 1.05. Compared to recent studies, the proposed approach attains slightly higher accuracy, while demonstrating stronger robustness and profitability in practical backtesting. Overall, the findings confirm that through rigorous preprocessing machine learning-based strategies can effectively capture short-term price movements and outperform the conventional buy-and-hold benchmark, even under a simple rule-based trading framework.
Abstract This paper investigates the optimization of data sampling and target labeling techniques to enhance algorithmic trading strategies in cryptocurrency markets, focusing on Bitcoin (BTC) and Ethereum (ETH). Traditional data sampling methods, such as time bars, often fail to capture the nuances of the continuously active and highly volatile cryptocurrency market and force traders to wait for arbitrary points in time. To address this, we propose an alternative approach using information-driven sampling methods, including the CUSUM filter, range bars, volume bars, and dollar bars, and evaluate their performance using tick-level data from January 2018 to June 2023. Additionally, we introduce the Triple Barrier method for target labeling, which offers a solution tailored for algorithmic trading as opposed to the widely used next-bar prediction. We empirically assess the effectiveness of these data sampling and labeling methods to craft profitable trading strategies. The results demonstrate that the innovative combination of CUSUM-filtered data with Triple Barrier labeling outperforms traditional time bars and next-bar prediction, achieving consistently positive trading performance even after accounting for transaction costs. Moreover, our system enables making trading decisions at any point in time on the basis of market conditions, providing an advantage over traditional methods that rely on fixed time intervals. Furthermore, the paper contributes to the ongoing debate on the applicability of Transformer models to time series classification in the context of algorithmic trading by evaluating various Transformer architectures—including the vanilla Transformer encoder, FEDformer, and Autoformer—alongside other deep learning architectures and classical machine learning models, revealing insights into their relative performance.
The aim of this dissertation is to test the applicability of two strategies – Dollar Cost Average (DCA) and Lump-Sum (LS) – in the context of the crypto market. We tested these strategies on three assets, namely Bitcoin, Ethereum and Ripple. We developed a simulation using daily historical data recorded over a period of nine years. We then calculated performance ratios and created an AR-GARCH model to analyse their properties and predictive capacity more effectively. Our empirical results show that all assets are highly volatile and exhibit heavy tails and asymmetry. Additionally, they are moderately to highly correlated with each other. We also presented proof of higher Sharpe and Sortino ratios for DCA strategies, with Bitcoin performing better than the other two assets. The results also show that Bitcoin has low-to-moderate shock sensitivity and high persistence; Ethereum has low shock sensitivity and high persistence; and Ripple has both high shock sensitivity and persistence. Furthermore, we observed the impact of strategy choice on volatility. When compared to DCA, LS lowered shock sensitivity in Bitcoin and Ripple, enhancing persistence, while having an insignificant effect on Ethereum. Finally, we demonstrate that our model exhibits superior predictive capacity with regard to Ripple compared to Bitcoin and Ethereum, and that all three assets are inefficient. These findings contribute to previous literature by providing novel empirical data and attesting to the attributes of cryptocurrencies. Furthermore, this thesis improves financial awareness and provides investors with valuable information.
Decentralized finance has introduced new forms of market making through automated market makers, where users provide liquidity to decentralized exchanges such as Uniswap. In Uniswap v3, liquidity providers (LPs) can concentrate their liquidity within custom price ranges, improving capital efficiency but introducing exposure to impermanent loss and nonlinear portfolio risk. Managing this exposure dynamically poses a challenge as the LP position’s token composition changes continuously with market prices. This thesis investigates whether a Deep Reinforcement Learning (DRL) agent can effectively hedge a Uniswap v3 LP position using cryptocurrency futures. A simulated Uniswap environment is developed to model concentrated liquidity providing positions and the hedge positions tied to it. The hedging problem is framed as a sequential decision process, where the agent seeks to minimize downside portfolio variance while maintaining upside exposure. The Proximal Policy Optimization algorithm is applied to learn an adaptive hedging policy, which is evaluated against baseline strategies such as fixed-frequency rebalancing. The results show that the DRL-based hedges can outperform the baseline strategies, achieving higher average portfolio returns, with similar average drawdowns. However, the learned policies varied between agents, and although they reduced downside variance in many cases, they were also more likely to experience larger maximum drawdowns. These findings indicate that reinforcement learning offers a promising but complex approach for managing the nonlinear risks of concentrated liquidity provision in decentralized exchanges.
We investigate the predictability of cryptocurrency returns using a comprehensive set of macroeconomic and cryptocurrency-specific factors and a set of 12 machine learning models. To enhance interpretability, we employ SHAP analysis to quantify the marginal contribution of each factor to model outputs. We further assess the economic value of predictive signals by constructing long-short and long-only portfolios. Empirically, tree-based methods, particularly random forests, deliver the highest predictive accuracy and outperform neural network and linear benchmarks, with predictability substantially stronger than that documented in equity markets. Across models, the market-to-realized-value ratio, new addresses, and active addresses consistently emerge as the most influential predictors, with higher values associated with higher expected returns. Portfolio results show that neural network-based strategies achieve the highest cumulative performance, indicating meaningful investment gains. Overall, our findings demonstrate the value of machine learning for return forecasting in the cryptocurrency market and provide practical insights for investors and financial analysts operating in highly volatile and evolving cryptocurrency environments.
Crypto analysts have to deal with a variety of challenges, with the most important area being the price volatility of cryptocurrencies. Due to uncertain market trends, many studies have been conducted on forecasting techniques, and some of these techniques have been integrated with advanced analytical tools, including machine learning (ML) techniques. Making reliable predictions of the speculative behavior of financial assets, especially in non-stationary and highly volatile environments such as the cryptocurrency market, is a challenging task. In this study, ML techniques are used to identify influential features that affect the prices of cryptocurrencies, especially for Bitcoins. In addition, multivariate control charts are utilized for signal detection, allowing for a structured approach to develop trading strategies for seasonal market conditions. Unlike other studies that do not take seasonality into serious consideration when analyzing market fluctuations, the proposed approach explicitly accounts for it. The developed strategy is tested across various market conditions, including the final days of each year from 2019 to 2024, and demonstrates strong and consistent performance in all cases. By systematically identifying key on-chain features and analyzing them by means of control charts, this study develops a structured approach to anomaly-based trading strategies in Bitcoins. These discoveries address an extensive discussion on automated trading systems, demonstrating that feature selection, technical indicators, market seasonality, and halving impacts are important components in hinting at successful cryptocurrency exchange strategies.