Natalia Rostova

Welcome to my website. I am an Assistant Professor of Finance at EDHEC Business School (Campus Nice). I obtained my PhD in Finance from the École Polytechnique Fédérale de Lausanne (EPFL) and Swiss Finance Institute (SFI) in 2025.

My main research interests are in FinTech, Decentralized Finance, and Cryptocurrency Markets. I am interested in studying emerging financial technologies and the applications of blockchain technology and decentralized finance (DeFi) in traditional financial markets. In my research, I aim to provide transparency about the risks of these technologies for market participants. I am passionate about collecting and analyzing blockchain data.

You can find my CV here.

Publications

Relationship Capital and Financing Decisions, with E. Morellec and T. Geelen, 2024

Management Science, forthcoming

Abstract: Lending relationships matter for firm financing. In a model of debt dynamics, we study how lending relationships are formed and how they impact leverage and debt maturity choices, thereby rationalizing recent empirical findings and generating new testable predictions. In the model, lending relationships evolve through repeated interactions between firms and debt investors. Stronger lending relationships lead firms to adopt higher leverage ratios, issue longer term debt, and raise funds from non-relationship lenders via syndicated loans or bonds issues when relationship quality is sufficiently high. Debt contracts involving non-relationship investors have longer maturity than those exclusively issued to relationship investors.

Link

Working Papers

Economic Incentives in the Digital Art Market

Abstract: Non-fungible tokens (NFTs) are assets on a blockchain that represent ownership of digital art and are traded on NFT marketplaces. The NFT market on the Ethereum blockchain was monopolistic until the end of 2022, when a new marketplace entered and captured a significant market share. This paper collects transactions from these marketplaces to study the effects of increased competition on the incumbent marketplace, artists, and investors. While competition had positive effects by reducing transaction costs, increasing trading volume and attracting new users, it decreased the profits of artists, discouraged them from creating new artwork, and thereby reduced the supply of new assets. I also study user migration, multi-homing behavior, and market segmentation, and compares the results with the predictions from theories of platform economics.

Presentations: FutureFinTech Federated Conference in Luxembourg 2025, EPFL/UNIL PhD Seminar 2024, SFI Job Market Workshop 2024, SFI PhD Workshop Zurich 2024, St. Gallen Financial Economics Workshop 2024, University of Utah - David Eccles School of Business, University of Calgary - Haskayne School of Business, HEC Montreal, IE University

Draft

Decentralized Exchanges with Low Price Impact, with W. Huang and Z. Song, 2024

Abstract: This paper examines the impact of decentralized exchange (DEX) on market quality, focusing on price stability and transaction costs. We develop a model that shows that the introduction of a DEX can reduce price variation and transaction costs for assets with stable values, by reducing the price impact for large traders. To validate the model predictions, we collect and analyze trade-level data on stablecoins from both centralized and decentralized exchanges such as Binance, Curve and Uniswap.

Presentations: BIS Research Seminar 2023, Blockchain Kaigi 2024 at UZH Blockchain Center, Warwick Business School - Gillmore Centre Conference on DeFi & Digital Currencies 2024, CEPR Fintech and Digital Currencies RPN Workshop 2024, 3rd Bonn/Mannheim Workshop on Digital Finance, EDHEC Business School

Draft

How Algorithmic Stablecoins Fail, with G. Kurovskiy, 2023

Abstract: In May 2022, the $18.7-billion algorithmic stablecoin USD Terra (UST) and its $20-billion backing token Luna experienced a sudden and rapid collapse from $1 and $80, respectively, to nearly 0 in a matter of days. Using transaction-level data from the Terra blockchain and cryptocurrency exchanges, this paper investigates the UST-Luna price stabilization mechanism during the collapse and argues that several flaws in the design of UST impeded its price stabilization. Using a simple model, we demonstrate that a combination of these design features explains data patterns observed during the crash.

Presentations: SNB-CIF Conference on Cryptoassets and Financial Innovation 2023, the Edinburgh Economics of Financial Technology Conference 2023, SFI Research Days 2023, CEF Nice 2023

Draft

Early Detection of Scam Cryptocurrencies With AI, with G. Kurovskiy, 2024

Abstract: This paper proposes two methods for detecting fraudulent cryptocurrencies ("scams") at their launch: a neural network and a time-series language model, based solely on the information extracted from their smart contracts. The idea is based on the observation that scam issuers often replicate or reuse parts of code from previous scams. We collect words from smart contracts and transform them into tensors to make predictions regarding whether a token is a scam. The analysis of the accuracy of our models suggests that both are able to detect fraudulent tokens, though the time-series language model shows slightly better performance. An advantage of our approach is that, unlike the majority of other token characteristics such as management quality or token network, smart contract data is available at the token's launch. Our method thus serves as an early-stage fraud detection tool and might be particularly useful for retail traders who invest in small-capitalization tokens.

Presentations: CFC St. Moritz Conference Academic Research Track 2024

Draft