arXiv subjects

Pietro Saggese

Publications and source records attributed to Pietro Saggese.

13 recordsLinked to original sources

arXiv2026

Voting Biases in Decentralized Autonomous Organization (DAO) Governance

Decentralized Autonomous Organizations (DAOs) use token-weighted voting to allocate resources, set protocol rules, and legitimate collective decisions. Yet, support in DAO voting is strikingly concentrated. What happens inside the ballot that produces this concentration? We study DAOs' governance at the proposal-choice level, linking each choice's voting-power share to three observable features: whether it expresses an approval-oriented stance, where it appears in the choice list, and whether it is selected by the proposal author. We find that (i) author-selected choices show the strongest and most robust association with voting-power share, with a 58.8% increase relative to non-author choices; (ii) approval-oriented choices retain a positive but slightly less consistent advantage (27.1%); and (iii) first-listed choices also attract systematically higher shares, consistent with position and order effects (7.7%). Results are robust across several specifications, which include subtracting an author's own voting power from computations. We use bias descriptively, to denote systematic associations rather than proven causal distortion. The results shift attention from proposal outcomes alone to the interface and social signals through which choices are presented. In DAO governance, ordering, author signals, and vote visibility should be treated as institutional design choices, not neutral implementation details.

cs.CY↗

arXiv2026

Stablecoins under Stress in a National Economy: Transaction-Level Evidence from Austrian Crypto-Asset Service Providers

Cryptoassets are increasingly entangled with the traditional financial system, and how this activity integrates into national economies and behaves under stress bears on financial stability and the design of public digital money. However, blockchain pseudonymity and the lack of geographic identifiers force existing work to rely on indirect proxies to infer and locate market participants. Here we use a regulatory registry that directly identifies the on-chain addresses of all crypto-asset service providers (CASPs) registered in Austria, reconstructing their on-chain transaction activity across Bitcoin, Ether, USDC, and USDT through May 2025, and separating retail-like from institutionally mediated flows. We find that Austrian CASPs intermediate roughly USD 30 billion with external counterparties and are integrated globally rather than domestically. In value, this activity is dominated by a few institutional counterparties; in number, by retail-like ones. Around three major shocks, the Terra-Luna collapse, the FTX bankruptcy, and the Silicon Valley Bank failure, the two groups respond through different mechanisms, and stablecoins do not act as a uniform safe haven. The clearest case is SVB, where retail-like deposits and institutional withdrawals are consistent with USDC's two-tiered redemption mechanism. These patterns are invisible in aggregate data. Registry-based, transaction-level measurement thus offers a reproducible, cross-jurisdictional basis for monitoring how cryptoasset markets transmit risk.

q-fin.GN↗

arXiv2025

A Unified Framework and Comparative Study of Decentralized Finance Derivatives Protocols

Decentralized Finance (DeFi) applications introduce novel financial instruments replicating and extending traditional ones through blockchain-based smart contracts. Among these applications, DeFi derivatives protocols enable the creation and trading of decentralized derivative instruments whose value depends on underlying cryptoassets, indices, or other reference variables. Despite their growing significance, however, they remain relatively understudied compared to other DeFi protocols, such as lending protocols and decentralized exchanges. This paper systematically analyzes DeFi derivatives protocols, categorized into perpetuals, options, and synthetics, with the aim of comparing their instrument structures, protocol mechanisms, operational dynamics, and economic agents. We provide a formal characterization of the main classes of decentralized derivative instruments and develop a protocol-agnostic framework that connects instrument-level specifications, market-state variables, and protocol-level mechanisms. We complement the analytical framework with numerical simulations that evaluate how derivative positions evolve under varying economic conditions, including changes in underlying asset prices, volatility, protocol-specific fees, and leverage. Overall, this study provides a structured analytical framework for understanding and comparing the design and functioning of decentralized finance derivatives protocols.

cs.GT↗

arXiv2025

Slaying the Dragon: The Quest for Democracy in Decentralized Autonomous Organizations (DAOs)

This chapter explores how Decentralized Autonomous Organizations (DAOs), a novel institutional form based on blockchain technology, challenge traditional centralized governance structures. DAOs govern projects ranging from finance to science and digital communities. They aim to redistribute decision-making power through programmable, transparent, and participatory mechanisms. This chapter outlines both the opportunities DAOs present, such as incentive alignment, rapid coordination, and censorship resistance, and the challenges they face, including token concentration, low participation, and the risk of de facto centralization. It further discusses the emerging intersection of DAOs and artificial intelligence, highlighting the potential for increased automation alongside the dangers of diminished human oversight and algorithmic opacity. Ultimately, we discuss under what circumstances DAOs can fulfill their democratic promise or risk replicating the very power asymmetries they seek to overcome.

cs.CY↗

arXiv2025

Towards Verifiability of Total Value Locked (TVL) in Decentralized Finance

Total Value Locked (TVL) aims to measure the aggregate value of cryptoassets deposited in Decentralized Finance (DeFi) protocols. Although blockchain data is public, the way TVL is computed is not well understood. In practice, its calculation on major TVL aggregators relies on self-reports from community members and lacks standardization, making it difficult to verify published figures independently. We thus conduct a systematic study on 939 DeFi projects deployed in Ethereum. We study the methodologies used to compute TVL, examine factors hindering verifiability, and ultimately propose standardization attempts in the field. We find that 10.5% of the protocols rely on external servers; 68 methods alternative to standard balance queries exist, although their use decreased over time; and 240 equal balance queries are repeated on multiple protocols. These findings indicate limits to verifiability and transparency. We thus introduce ``verifiable Total Value Locked'' (vTVL), a metric measuring the TVL that can be verified relying solely on on-chain data and standard balance queries. A case study on 400 protocols shows that our estimations align with published figures for 46.5% of protocols. Informed by these findings, we discuss design guidelines that could facilitate a more verifiable, standardized, and explainable TVL computation.

q-fin.GN↗

arXiv2024

Assessing the Solvency of Virtual Asset Service Providers: Are Current Standards Sufficient?

Entities like centralized cryptocurrency exchanges fall under the business category of virtual asset service providers (VASPs). As any other enterprise, they can become insolvent. VASPs enable the exchange, custody, and transfer of cryptoassets organized in wallets across distributed ledger technologies (DLTs). Despite the public availability of DLT transactions, the cryptoasset holdings of VASPs are not yet subject to systematic auditing procedures. In this paper, we propose an approach to assess the solvency of a VASP by cross-referencing data from three distinct sources: cryptoasset wallets, balance sheets from the commercial register, and data from supervisory entities. We investigate 24 VASPs registered with the Financial Market Authority in Austria and provide regulatory data insights such as who are the customers and where do they come from. Their yearly incoming and outgoing transaction volume amount to 2 billion EUR for around 1.8 million users. We describe what financial services they provide and find that they are most similar to traditional intermediaries such as brokers, money exchanges, and funds, rather than banks. Next, we empirically measure DLT transaction flows of four VASPs and compare their cryptoasset holdings to balance sheet entries. Data are consistent for two VASPs only. This enables us to identify gaps in the data collection and propose strategies to address them. We remark that any entity in charge of auditing requires proof that a VASP actually controls the funds associated with its on-chain wallets. It is also important to report fiat and cryptoasset and liability positions broken down by asset types at a reasonable frequency.

q-fin.GN↗

arXiv2024

Detecting Financial Bots on the Ethereum Blockchain

The integration of bots in Distributed Ledger Technologies (DLTs) fosters efficiency and automation. However, their use is also associated with predatory trading and market manipulation, and can pose threats to system integrity. It is therefore essential to understand the extent of bot deployment in DLTs; despite this, current detection systems are predominantly rule-based and lack flexibility. In this study, we present a novel approach that utilizes machine learning for the detection of financial bots on the Ethereum platform. First, we systematize existing scientific literature and collect anecdotal evidence to establish a taxonomy for financial bots, comprising 7 categories and 24 subcategories. Next, we create a ground-truth dataset consisting of 133 human and 137 bot addresses. Third, we employ both unsupervised and supervised machine learning algorithms to detect bots deployed on Ethereum. The highest-performing clustering algorithm is a Gaussian Mixture Model with an average cluster purity of 82.6%, while the highest-performing model for binary classification is a Random Forest with an accuracy of 83%. Our machine learning-based detection mechanism contributes to understanding the Ethereum ecosystem dynamics by providing additional insights into the current bot landscape.

cs.CR↗

arXiv2024

Investigating Similarities Across Decentralized Financial (DeFi) Services

We explore the adoption of graph representation learning (GRL) algorithms to investigate similarities across services offered by Decentralized Finance (DeFi) protocols. Following existing literature, we use Ethereum transaction data to identify the DeFi building blocks. These are sets of protocol-specific smart contracts that are utilized in combination within single transactions and encapsulate the logic to conduct specific financial services such as swapping or lending cryptoassets. We propose a method to categorize these blocks into clusters based on their smart contract attributes and the graph structure of their smart contract calls. We employ GRL to create embedding vectors from building blocks and agglomerative models for clustering them. To evaluate whether they are effectively grouped in clusters of similar functionalities, we associate them with eight financial functionality categories and use this information as the target label. We find that in the best-case scenario purity reaches .888. We use additional information to associate the building blocks with protocol-specific target labels, obtaining comparable purity (.864) but higher V-Measure (.571); we discuss plausible explanations for this difference. In summary, this method helps categorize existing financial products offered by DeFi protocols, and can effectively automatize the detection of similar DeFi services, especially within protocols.

q-fin.ST↗

arXiv2023

The Governance of Decentralized Autonomous Organizations: A Study of Contributors' Influence, Networks, and Shifts in Voting Power

We present a study analyzing the voting behavior of contributors, or vested users, in Decentralized Autonomous Organizations (DAOs). We evaluate their involvement in decision-making processes, discovering that in at least 7.54% of all DAOs, contributors, on average, held the necessary majority to control governance decisions. Furthermore, contributors have singularly decided at least one proposal in 20.41% of DAOs. Notably, contributors tend to be centrally positioned within the DAO governance ecosystem, suggesting the presence of inner power circles. Additionally, we observed a tendency for shifts in governance token ownership shortly before governance polls take place in 1202 (14.81%) of 8116 evaluated proposals. Our findings highlight the central role of contributors across a spectrum of DAOs, including Decentralized Finance protocols. Our research also offers important empirical insights pertinent to ongoing regulatory activities aimed at increasing transparency to DAO governance frameworks.

cs.SI↗

arXiv2022

Disentangling Decentralized Finance (DeFi) Compositions

We present a measurement study on compositions of Decentralized Finance protocols, which aim to disrupt traditional finance and offer services on top of distributed ledgers, such as Ethereum. DeFi compositions may impact the development of ecosystem interoperability, are increasingly integrated with web technologies, and may introduce risks through complexity. Starting from a dataset of 23 labeled DeFi protocols and 10,663,881 associated Ethereum accounts, we study the interactions of protocols and associated smart contracts. From a network perspective, we find that decentralized exchanges and lending protocols have high degree and centrality values, that interactions among protocol nodes primarily occur in a strongly connected component, and that known community detection methods cannot disentangle DeFi protocols. Therefore, we propose an algorithm to decompose a protocol call into a nested set of building blocks that may be part of other DeFi protocols. With a ground truth dataset we have collected, we can demonstrate the algorithm's capability by finding that swaps are the most frequently used building blocks. As building blocks can be nested, i.e., contained in each other, we provide visualizations of composition trees for deeper inspections. We also present a broad picture of DeFi compositions by extracting and flattening the entire nested building block structure across multiple DeFi protocols. Finally, to demonstrate the practicality of our approach, we present a case study that is inspired by the recent collapse of the UST stablecoin in the Terra ecosystem. Under the hypothetical assumption that the stablecoin USD Tether would experience a similar fate, we study which building blocks and, thereby, DeFi protocols would be affected. Overall, our results and methods contribute to a better understanding of a new family of financial products.

cs.CR↗

arXiv2021

Who are the arbitrageurs? Empirical evidence from Bitcoin traders in the Mt. Gox exchange platform

We mine the leaked history of trades on Mt. Gox, the dominant Bitcoin exchange from 2011 to early 2014, to detect the triangular arbitrage activity conducted within the platform. The availability of user identifiers per trade allows us to focus on the historical record of 440 investors, detected as arbitrageurs, and consequently to describe their trading behavior. We begin by showing that a considerable difference appears between arbitrageurs when indicators of their expertise are taken into account. In particular, we distinguish between those who conducted arbitrage in a single or in multiple markets: using this element as a proxy for trade ability, we find that arbitrage actions performed by expert users are on average non-profitable when transaction costs are accounted for, while skilled investors conduct arbitrage at a positive and statistically significant premium. Next, we show that specific trading strategies, such as splitting orders or conducting arbitrage non aggressively, are further indicators of expertise that increase the profitability of arbitrage. Most importantly, we exploit within-user (across hours and markets) variation and document that expert users make profits on arbitrage by reacting quickly to plausible exogenous variations on the official exchange rates. We present further evidence that such differences are chiefly due to a better ability of the latter in incorporating information, both on the transactions costs and on the exchange rates volatility, eventually resulting in a better timing choice at small time scale intervals. Our results support the hypothesis that arbitrageurs are few and sophisticated users.

q-fin.GN↗

arXiv2021

Impact of Local Energy Markets on the Distribution Systems: A Comprehensive Review

In recent years extensive research has been conducted on the development of different models that enable energy trading between prosumers and consumers due to expected high integration of distributed energy resources. Some of the most researched mechanisms include Peer-to-Peer energy trading, Community Self-Consumption and Transactive Energy Models. Here, we present an exhaustive review of existing research around Local Energy Market integration into distribution systems, in particular benefits and challenges that the power grid can expect from integrating these models. We also present a detailed overview of methods that are used to integrate physical network constraints into the market mechanisms, their advantages, drawbacks, and scaling potential. We find that financial energy transactions do not directly reflect the physical energy flows imposed by the constraints of the installed electrical infrastructure. Therefore, ensuring the stable and reliable delivery of electricity, it is vital to understand the impact of these transactions on the grid infrastructure, e.g., impacts on the control, operation, and planning of electricity distribution systems.

eess.SY↗

arXiv2018

Network-based indicators of Bitcoin bubbles

The functioning of the cryptocurrency Bitcoin relies on the open availability of the entire history of its transactions. This makes it a particularly interesting socio-economic system to analyse from the point of view of network science. Here we analyse the evolution of the network of Bitcoin transactions between users. We achieve this by using the complete transaction history from December 5th 2011 to December 23rd 2013. This period includes three bubbles experienced by the Bitcoin price. In particular, we focus on the global and local structural properties of the user network and their variation in relation to the different period of price surge and decline. By analysing the temporal variation of the heterogeneity of the connectivity patterns we gain insights on the different mechanisms that take place during bubbles, and find that hubs (i.e., the most connected nodes) had a fundamental role in triggering the burst of the second bubble. Finally, we examine the local topological structures of interactions between users, we discover that the relative frequency of triadic interactions experiences a strong change before, during and after a bubble, and suggest that the importance of the hubs grows during the bubble. These results provide further evidence that the behaviour of the hubs during bubbles significantly increases the systemic risk of the Bitcoin network, and discuss the implications on public policy interventions.

physics.soc-ph↗