Speakers

Alla Petukhina Organizer, HTW Berlin

Christina Erlwein-Sayer Organizer, HTW Berlin

Bernhard Haslhofer Organizer, Complexity Science Hub Vienna

Barbara Będowska-Sójka Organizer, Poznań University of Economics and Business

Pietro Saggese Organizer, IMT Lucca

Simon Trimborn Organizer, University of Amsterdam

Marcus Wunsch Organizer, ZHAW

On-Chain Realities: Empirical Evidence on Incentives, Manipulation, and Market Structure in DeFi

Claudio Tessone , University of Zurich

Abstract

The DeFi ecosystem has matured into complex sociotechnical systems where protocol design, economic incentives, and user behaviour interact in measurable — and often counterintuitive — ways. This talk presents empirical and analytical perspectives across several dimensions of decentralised finance: from consensus-layer staking dynamics and the circulation of value through DeFi protocols, to the forensic reconstruction of manipulation events and the real prevalence of MEV across execution environments. Together, these studies illustrate how rigorous on-chain analytics can expose the gap between protocol design intent and actual economic behaviour, with implications for mechanism design, market integrity, and the future of decentralised finance.

Biography

Claudio J. Tessone is Professor of Blockchain and Distributed Ledger Technologies at the University of Zurich and co-founder and Chairman of the UZH Blockchain Center — the largest academic blockchain initiative in Switzerland and ranked 1st in Europe by CoinDesk's University Blockchain Rankings. He leads the BDLT research group, whose work spans cryptoeconomics, blockchain analytics and forensics, consensus mechanisms, and the design and analysis of blockchain-based systems. His research adopts an interdisciplinary approach to modelling complex socio-economic and sociotechnical systems, combining large-scale empirical analysis with formal methods. He also directs several educational programmes, including the UZH CAS in Blockchain and the International Summer School: Deep Dive into Blockchain

Inequality in the Age of Pseudonymity

Aviv Yaish , Yale University

Abstract

Inequality measures such as the Gini coefficient inform and motivate policymaking, and are increasingly applied to digital platforms. We analyze how these measures perform in pseudonymous settings that are common in the digital age. A key challenge in such environments is the ability of actors to create fake identities under fictitious false names, also known as ``Sybils.'' While actors may do this to preserve privacy, we show that it can hamper inequality measurement: it is impossible for measures satisfying the literature's canonical set of desired properties to assess the inequality of an economy harboring Sybils. We characterize the class of all Sybil-proof measures, and prove they must satisfy relaxed versions of the established properties. Furthermore, we show that the imposed structure restricts the ability to assess inequality at a fine-grained level. We then apply our results to prove that popular measures are not Sybil-proof, with the famous Gini coefficient being but one example out of many. Finally, we examine the dynamics leading to the creation of Sybils in digital and traditional settings.

Biography

Aviv Yaish is a Postdoctoral Associate at Yale University, where he makes and breaks distributed systems by bridging economic theory and practical systems security. His approach is driven by a philosophy of constructive deconstruction: pushing systems to their limits is key to making them robust. His contributions have been recognized by several communities: security (CCS Distinguished Paper), economics (Annual CBER Best Paper), and industry (bounties from the Ethereum Foundation and Flashbots). He is currently a member of the Initiative for Cryptocurrencies & Contracts (IC3), and is a junior external faculty at Vienna's Complexity Science Hub. He earned his Ph.D. in Computer Science from the Hebrew University, where he served as the sole lecturer for two courses and received a teaching excellence award. During his studies, he was also a research consultant at Matter Labs. His honors include the AIANI and Jabotinsky fellowships, and inclusion in CBER's Rising Stars and the Hebrew University's 40 Under 40 lists.

LLMs as verification oracles for Solidity

Massimo Bartoletti , University of Cagliari

Abstract

Ensuring the correctness of smart contracts is critical, as even subtle flaws can lead to severe financial losses. While bug detection tools able to spot common vulnerability patterns can serve as a first line of defense, most real-world exploits and losses stem from errors in the contract business logic. Formal verification tools address this challenge, but their impact remains limited by steep learning curves and restricted specification languages. Recent works have begun to explore the use of large language models (LLMs) for security-related tasks such as vulnerability detection and test generation. Yet, a fundamental question remains open: can LLMs aid in assessing the validity of arbitrary contract-specific properties? In this talk, we present the first systematic empirical evaluation of GPT-5, a state-of-the-art reasoning LLM, in this role. We benchmark its performance on a large dataset of verification tasks, compare its outputs against those of established formal verification tools, and assess its practical effectiveness in real-world auditing scenarios. Our study combines quantitative metrics with qualitative analysis, and shows that recent reasoning-oriented LLMs - although lacking soundness guarantees - can be surprisingly effective at predicting the (in)validity of complex properties, suggesting a new frontier in the convergence of AI and formal methods for secure smart contract development and auditing.

Biography

Massimo Bartoletti is Associate Professor at the Department of Mathematics and Computer Science of the University of Cagliari. His research activity concerns the development of tools and techniques for the specification, analysis and verification of software, with a special emphasis on security and formal methods. Massimo Bartoletti is founder of the laboratory Blockchain@Unica, one of the first academic research group on blockchain technologies in Italy, director of the node of the Cyber Security National Lab for the University of Cagliari, and core member of the CINI working group on Blockchain. The laboratory is currently investigating several aspects of blockchain technologies, among which custom Domain-Specific Languages for smart contracts. He is principal investigator of R&D projects on blockchain technologies, and program committee member of top-tier conferences on blockchain technologies, including ACM CCS - blockchain track and Financial Cryptography. He is the organizer of the DLT Workshop series, of the Scientific School on Blockchain & DLT series, and of the Workshop on Distributed Ledger Technologies and Formal Methods series. Massimo Bartoletti has published over 50 scientific papers on blockchain technologies since 2016, and it has presented his research in top-tier conferences like ACM CCS, Financial Cryptography, Computer Security Foundations, IEEE Security and Privacy Europe, and ESEC/SIGSOFT FSE.

Market Efficiency in Prediction Markets - A Comparison with Derivatives

Lorenzo Schönleber , Collegio Carlo Alberto / University of Turin

Abstract

We study pricing efficiency in decentralized prediction markets by comparing market-implied probabilities from Polymarket with benchmarks derived from option-implied risk-neutral distributions extracted from the derivatives market. We study Bitcoin and Ethereum prediction bets and find that, although Polymarket prices broadly track option-implied benchmarks, they show systematic mispricing driven by behavioral factors and market frictions. Mispricing is most pronounced in tail events, during periods of high volatility, major macroeconomic shocks, and reflects the influence of sentiment, attention, and blockchain-specific risks. These results reveal both efficiency and behavioral distortions in prediction markets.

Biography

Lorenzo Schönleber is an Assistant Professor at the Collegio Carlo Alberto and the University of Turin. His areas of interest include asset pricing, particularly option-implied information, and decentralized finance (DeFi). A central part of his current work aims to integrate insights from derivatives into DeFi.

Olivier Guéant , Université Paris Cité

Abstract

Biography

Olivier Guéant is Full Professor of Applied Mathematics at Université Paris Cité and a member of the LPSM (Laboratoire de Probabilités, Statistiques et Modélisation). He is co-director of the M2MO programme, formerly known as DEA Laure Elie. After a PhD on mean field games under the supervision of Pierre-Louis Lions, he worked in mathematical finance and published papers on a wide range of topics including market microstructure, market making, option pricing, portfolio optimization, and decentralized finance.

Informer in Algorithmic Investment Strategies on High-Frequency Bitcoin Data

Robert Ślepaczuk , University of Warsaw

Abstract

We investigate the usage of the Informer architecture for building automated trading strategies for high-frequency Bitcoin data. Three strategies using the Informer model with different loss functions: Root Mean Squared Error (RMSE), Generalized Mean Absolute Directional Loss (GMADL), and Quantile loss, are proposed and evaluated against the Buy and Hold benchmark and two benchmark strategies based on technical indicators. The evaluation is conducted using data of various frequencies: 5-min, 15-min, and 30-min intervals, over the 6 different periods. Although the Informer-based model with Quantile loss did not outperform the benchmark, two other models achieved better results. The performance of the model using RMSE loss worsens when used with higher frequency data, while the model that uses the novel GMADL loss function benefits from higher frequency data. When trained on 5-min intervals, it beats all the other strategies on most of the testing periods. The primary contribution of this study is the application and assessment of the RMSE, GMADL, and Quantile loss functions with the Informer model to forecast future returns, subsequently using these forecasts to develop automated trading strategies. The research provides evidence that employing an Informer model trained with the GMADL loss function can result in superior trading outcomes compared to the Buy&Hold approach.

Biography

Associate Professor at the Faculty of Economic Sciences, University of Warsaw (UW), Head of the Department of Quantitative Finance and Machine Learning (DoQFaML), and Head of the Quantitative Finance Research Group (QFRG) https://www.wne.uw.edu.pl/en/faculty/structure/departments/quantitative-finance Robert Ślepaczuk received his PhD from the University of Warsaw in 2005 and obtained his habilitation in economics and finance from the Poznań University of Economics in 2020. He founded the Quantitative Finance Research Group (QFRG) in 2011. Since 2023, he has served as Associate Professor at the University of Warsaw and Head of the Department of Quantitative Finance and Machine Learning. linkedin profile His research focuses on algorithmic trading, quantitative investment strategies, financial econometrics, derivatives pricing, volatility modelling, and machine learning applications in finance. In particular, his work addresses algorithmic trading systems across multiple asset classes, volatility indicators based on high-frequency option data (such as the VIX), and the modelling and forecasting of financial markets. He has more than 20 years of experience in algorithmic trading and quantitative investment strategies, combining academic research with practical industry experience. Previously, he served as Investment Director of the quantitative fund management division at Union Investment TFI, where he specialized in the design and implementation of automated investment strategies for futures and options across various asset classes, including volatility products and cryptocurrencies. Currently, his practical work focuses on auditing algorithmic investment strategies used by hedge funds. googlescholar Scopus Robert Ślepaczuk holds an Investment Adviser license (no. 316) and a Securities Broker license (no. 2245). He is the author of numerous scientific publications and has led or participated in many research projects funded by national and international institutions. He has supervised more than 100 bachelor’s and master’s theses and currently supervises eight PhD students. Teams from the University of Warsaw under his supervision have repeatedly achieved top results in the Rotman European and International Trading Competitions (RETC and RITC).

No Questions Asked: Effects of Transparency on Stablecoin Liquidity During the Collapse of Silicon Valley Bank

Walter Hernandez , University College London

Abstract

Fiat-pegged stablecoins are by nature exposed to spillover effects during market turmoil of Traditional Finance (TradFi). Particularly, the collapse of Silicon Valley Bank (SVB) in March 2023 presents a natural experiment for analyzing how disclosed adverse reserve exposure transmits through Decentralized Exchange (DEX) liquidity for USDC and USDT. The former with a higher reporting frequency and transparency than the latter. Therefore, we conduct an empirical study that adapts the Marginal Cost of Immediacy (MCI) measure to Uniswap's Automated Market Maker (AMM) protocol, examining top USDC and USDT liquidity pools, and then conducting Difference-in-Differences (DiD) estimation on MCI and analysis on Total Value Locked (TVL) in USD, as well as measuring liquidity concentration across different providers. Results show that the SVB event reduced USDC's TVL, diminishing its market dominance over USDT while raising USDC's liquidity cost between 600% to 1000% relative to USDT. Our findings are consistent with a flight-to-safety behavior and with conditional effects of disclosure on stability: USDC's frequent and detailed disclosures led to violent market reactions in Decentralized Finance (DeFi) liquidity provision, while USDT, whose exposure remained unverified, experienced comparatively muted effects.

Biography

Walter Hernandez Cruz is a final PhD candidate in Computer Science at the University College London (UCL)'s Centre for Blockchain Technologies (CBT), and part of the Financial Computing and Analytics Group at the department of Computer Science at University College London. He is also a Science Fellow at Exponential Science foundation, based in Switzerland. As part of his PhD and as a Science Fellow at Exponential Science, he is currently researching, but not limited to, topics around Decentralized Finance (DeFi), Automated Market Makers (AMM), Stablecoins, and Natural Language Processing (NLP) applied to Distributed Ledger Technology (DLT) domain. His most recent work includes analyzing stablecoins behavior in DeFi during market shocks in the banking sector, the evolution of Distributed Ledger Technologies (DLTs) using NLP, and comparing AMMs performance across blockchain platforms. Walter also contributes to the UK Centre for Blockchain Technologies

The (In-)Finite Money Glitch

Co-Pierre Georg , Frankfurt School of Finance & Management

Abstract

The infinite money glitch is the strategy where a firm issues convertible debt to purchase crypto assets-primarily Bitcoin-which increases its stock's volatility, in turn boosting hedge fund demand for its convertible bonds and enabling the company to issue even more debt to buy more crypto assets in a self-reinforcing cycle. I build a three-date equilibrium model in which convertible issuance, hedging motives, and an inelastic Bitcoin market jointly determine pricing and deal size. Convertible-arbitrage hedge funds dynamically short and cover the stock as it moves, which transmits Bitcoin shocks into realized equity volatility and raises the value of the conversion option in subsequent issues, supporting further issuance and purchases of Bitcoin. The model shows that, if conversion fails at maturity, forced Bitcoin sales can trigger large drawdowns when sell-side impact is steep, tradable supply is thin, and prior issuance was large. This yields non-monotonic effects: looser intermediation lifts prices near term but increases expected liquidation later, with testable implications for the implied-realized volatility gap, stock-loan tightness, and free-float measures.

Biography

Co-Pierre Georg is Professor of Practice in Digital Finance and Technology and Director of the Frankfurt School Blockchain Center at Frankfurt School of Finance & Management. His research spans financial stability, blockchain protocol design, tokenomics, CBDCs, and crypto markets, with publications in leading journals including the Journal of Financial Economics, Journal of Economic Theory, Operations Research, Research Policy, and Nature Physics. Previously, he held the DSI/NRF Dual Research Chair in Blockchain Technologies and SARB Chair in Financial Stability Studies at the University of Cape Town, where he founded the Financial Innovation Hub and a FinTech Master's program; earlier roles include economist at the Deutsche Bundesbank Research Centre and Associate Professor at EDHEC Business School. Funded by initiatives like Ripple's UBRI and the Algorand Foundation, his work bridges academia and industry on crypto economics, privacy in distributed systems, and digital property rights. He held visiting positions at MIT, Oxford, Columbia, and Princeton.

Early warning systems fo cryptocurrency markets: Predicting 'zombie' assets using machine learning

Barbara Będowska-Sójka , Poznań University of Economics and Business

Abstract

Tokenized Governance: Delegation, Agency, and Cycles

Michele Fabi , Télécom Paris

Abstract

Token-based platform governance has evolved into a mediated voting system, in which users outsource voting to specialized agents. While this extit{vote delegation} can improve governance efficiency, it also reintroduces agency conflicts. We develop a dynamic principal-agent model in which agents are initially unknown and exert effort to build reputation. However, as perceived quality (reputation) rises, users rationally reduce active oversight. This decline in monitoring increases agents' returns to opportunism and misconduct, eventually leading to governance failures and agent turnover. To mitigate these governance cycles, we show that designs that slow reputation accumulation and use token-based compensation improve the platform's stability.

Biography

Michele Fabi is an Assistant Professor at Télécom Paris, affiliated with CREST and the Institut Polytechnique de Paris. His work applies game theory to financial economics to analyze topics such as blockchain ecosystems, energy markets. startup finance, and the digitalization of money.

A DeFi Update, Expectations, Governance & Markets

Daniel Liebau , ESSEC Business School, Singapore

Abstract

Biography

Where Does MEV Really Come From? Revisiting CEX-DEX Arbitrage on Ethereum

Janos Tapolcai , Budapest University of Technology and Economics

Abstract

A central question of the Ethereum ecosystem is where Maximal Extractable Value (MEV) revenue originates and to what extent it stems from harming unsuspecting users. It is acceptable if MEV arises from arbitrages between centralised and decentralised exchanges (CEX--DEX). Yet theoretical models have significantly underestimated the scale of these arbitrages, while empirical studies have highlighted their importance -- though these remain conservative estimates, constrained by numerous debatable heuristic assumptions. Revisiting the theoretical model, we found that CEX–DEX arbitrages require trading volumes on the order of the total activity of major liquidity pools and yield profits comparable to MEV. Most prior AMM models utilised the Black–Scholes (BS) stochastic differential equation (SDE) -- i.e., geometric Brownian motion -- and assumed continuous price trajectories where asset prices move in small increments only. We argue that BS underestimates arbitrage profits by ignoring price jumps, which are precisely the points at which arbitrage opportunities tend to arise. To address this gap, we present an extended discrete-time AMM model in which the price process is the sum of a diffusive component and stochastic jumps that can have arbitrary noise distributions. Although mathematically more involved this framework allows us to employ a general discrete-time SDE and compute the stationary probability distribution via function iteration with geometric convergence. We further prove that the resulting mispricing process is an ergodic Markov chain. We implement our model in C++, collect spot prices and AMM exchange data from the Ethereum blockchain and fit the model parameters to the observed prices. The estimates derived from our model closely match empirical observations and provide a natural theoretical explanation for several fundamental questions in the blockchain ecosystem.

Biography

János Tapolcai received the M.Sc. degree in technical informatics and the Ph.D. degree in computer science from the Budapest University of Technology and Economics (BME), Budapest, in 2000 and 2005, respectively, and the D.Sc. degree in engineering science from the Hungarian Academy of Sciences (MTA) in 2013. He is currently a Full Professor with the High-Speed Networks Laboratory, Department of Telecommunications and Artificial Intelligence, BME. He has authored over 200 scientific publications. He is a winner of the MTA Lendület Program and the Google Faculty Award in 2012, Microsoft Azure Research Award in 2018. He is a TPC member of leading conferences, e.g. IEEE INFOCOM 2012-2024, ACM Sigmetrics 2026 and the general chair of ACM SIGCOMM 2018.