Live Graph Lab: Towards Open, Dynamic and Real Transaction Graphs with NFT

Numerous studies have been conducted to investigate the properties of large-scale temporal graphs. Despite the ubiquity of these graphs in real-world scenarios, it's usually impractical for us to obtain the whole real-time graphs due to privacy concerns and technical limitations. In this paper, we introduce the concept of {\it Live Graph Lab} for temporal graphs, which enables open, dynamic and real transaction graphs from blockchains. Among them, Non-fungible tokens (NFTs) have become one of the most prominent parts of blockchain over the past several years. With more than \$40 billion market capitalization, this decentralized ecosystem produces massive, anonymous and real transaction activities, which naturally forms a complicated transaction network. However, there is limited understanding about the characteristics of this emerging NFT ecosystem from a temporal graph analysis perspective. To mitigate this gap, we instantiate a live graph with NFT transaction network and investigate its dynamics to provide new observations and insights. Specifically, through downloading and parsing the NFT transaction activities, we obtain a temporal graph with more than 4.5 million nodes and 124 million edges. Then, a series of measurements are presented to understand the properties of the NFT ecosystem. Through comparisons with social, citation, and web networks, our analyses give intriguing findings and point out potential directions for future exploration. Finally, we also study machine learning models in this live graph to enrich the current datasets and provide new opportunities for the graph community. The source codes and dataset are available at https://livegraphlab.github.io.Comment: Accepted by NeurIPS 2023, Datasets and Benchmarks Trac

A Social Network Approach to Analyzing Token Properties and Abnormal Events in Decentralized Exchanges

The properties of tokens within the Ethereum blockchain, such as their current prices, trade volumes, and potential future values, have been subjects of numerous studies. The complex interaction of the variables related to tokens makes analyzing them challenging. Employing social networks, a powerful tool for modeling connections within groups or communities, can provide valuable guidance. This study mainly focuses on creating and examining networks related to two major decentralized exchanges: Uniswap Version 2 and SushiSwap. We discovered that the distribution of links to nodes follow a power law making them scale-free networks. Additionally, during our analysis, we made an intresting discovery: the centrality of tokens in exchange graphs provide valuable insights into their value and significance in the world of cryptocurrencies. By observing changes in centrality over time, we uncovered noteworthy events in the cryptocurrency domain, that shows the potential of this networks for extracting information about the exchanges

Smart Contracts Software Metrics: a First Study

© 2018 The Author(s).Smart contracts (SC) are software codes which reside and run over a blockchain. The code can be written in different languages with the common purpose of implementing various kinds of transactions onto the hosting blockchain, They are ruled by the blockchain infrastructure and work in order to satisfy conditions typical of traditional contracts. The software code must satisfy constrains strongly context dependent which are quite different from traditional software code. In particular, since the bytecode is uploaded in the hosting blockchain, size, computational resources, interaction between different parts of software are all limited and even if the specific software languages implement more or less the same constructs of traditional languages there is not the same freedom as in normal software development. SC software is expected to reflect these constrains on SC software metrics which should display metric values characteristic of the domain and different from more traditional software metrics. We tested this hypothesis on the code of more than twelve thousands SC written in Solidity and uploaded on the Ethereum blockchain. We downloaded the SC from a public repository and computed the statistics of a set of software metrics related to SC and compared them to the metrics extracted from more traditional software projects. Our results show that generally Smart Contracts metrics have ranges more restricted than the corresponding metrics in traditional software systems. Some of the stylized facts, like power law in the tail of the distribution of some metrics, are only approximate but the lines of code follow a log normal distribution which reminds of the same behavior already found in traditional software systems.Submitted Versio

Impact of Network Connectedness

학위논문(박사) -- 서울대학교대학원 : 공과대학 산업공학과, 2022. 8. 이재욱.금융 자산은 언제나 리스크에 노출되어 있다. 이 리스크의 크기와, 각 자산이 리스크에 대해 얼마나 보상받는 지를 정확히 측정하는 것은 자산의 특성을 이해하는 데 중요한 문제이다. 자산가격결정모형 (asset pricing model)은 자산의 리스크와 그 보상을 통해서 금융 자산의 수익률을 설명하려 하는 모형이다. 본 연구에서는 여러 자산가격결정모형의 형태 중 팩터 모델에 집중하였다. 팩터 모델은 초과 수익률을 팩터와 베타로 분리해서 설명하는 모델이다. 전통적인 팩터 모델들은 거시 금융 변수나 기업 변수 등을 통하여 팩터와 베타를 추정하는데, 이 때 자산 간의 연결관계를 고려하는 연구는 많이 진행되지 않았다. 금융 자산들은 서로 영향을 주는 관계에 있기 때문에 각각의 수익률 또한 개별적이 아니라 자산 간의 그래프 구조를 고려하며 동시에 평가되어야 한다. 본 논문은 팩터 모델에 자산 간의 연결 구조를 반영하기 위한 인공지능 기반 실증적 자산가격결정모형을 제안한다. 이를 위해 먼저 그래프 인공신경망 (GNN)을 바탕으로 한 멀티 팩터 모델을 개발하였다. 이 때 모델의 구조를 결정하는 것 만큼이나 중요한 것은 자산 간 그래프 구조를 어떻게 정의할 것인가라는 문제이다. GNN은 그 입력 변수로서 잘 정의된 그래프 구조를 요구하지만 자산 간의 연결 구조는 명확하게 정의되지 않았기 때문에, 본 연구에서는 자산 간의 연결성을 피어슨 상관계수를 이용하여 추정하고 이를 특정 임계값을 통해 0과 1로 이진화 시키는 방식을 사용했다. 제안한 모델의 구조는 베타를 추정하는 부분과 팩터를 추정하는 부분으로 나뉘어지는데, 각각 기업 변수와, 수익률을 이용해서 추정한다. 1957년부터 미국에 상장된 주식들을 대상으로 한 실증 실험 결과, 제안한 모델은 설명력과 예측 성능 측면에서 벤치마크 모델들보다 우수한 성능을 보였다. 또한 통계적 성능 이외에도 팩터의 경제적 의미를 측정하는 면에서, 제안한 모델로부터 추정한 팩터가 가장 효율적인 확률적 할인요소 (stochastic discount factor)를 추정할 수 있다는 점 역시 확인하였다. 자산가격결정모형의 가장 중요한 목적은 수익률이지만, 변동성 또한 금융 자산의 움직임을 설명하는 데 중요한 성질이다. 많은 사전 연구에서 밝혀졌듯 수익률과 변동성 사이에는 상관관계가 존재하기 때문에 변동성은 수익률을 설명하는 요인이 될 수 있다. 자산가격결정모형에서와 마찬가지로 자산들 간의 연결 구조를 고려하는 것은 변동성 예측에서도 성능 향상에 큰 영향을 미칠 수 있다. 변동성 분석에서는 여러 자산의 변동성이 서로 영향을 미치는 것을 스필오버 (spillover)라 부른다. 본 논문에서는 스필오버 효과를 직접적으로 반영하는 변동성 예측 모델을 개발하였다. 제안한 모델은 변동성의 측면에서 자산 간 연결 구조를 변동성 스필오버 지수로 구성한 인접행렬로 정의하며, 모델의 구조로는 시공간적 그래프 인공신경망 (spatial-temporal GNN)를 사용하였다. 글로벌 시장 지수들에 대한 실증 실험을 통해서 제안한 모델은 단기와 중기 변동성 예측에서 벤치마크 모델에 비해 가장 좋은 예측 성능을 보이고, 다른 시장에 큰 영향을 주는 시장을 이용하여 다른 시장에 대한 예측 성능을 크게 높일 수 있음을 보였다. 자산가격결정모형에 변동성을 직접적으로 반영하기 위해서는 모형 내에서 변동성이 어떻게 정의되는가를 먼저 살펴보아야 한다. 변동성은 자산가격결정모형 내에서 잔차의 표준편차로 해석할 수 있다. 그러나 시계열 기반 방법론을 사용하여 추정하는 기존의 자산가격결정모형은 시간에 따라 불변하는 변동성을 가정한다. 본 논문에서는 시간에 따라 변하는 변동성을 예측 모델을 이용하여 추정하고, 이를 팩터 모델의 손실함수에 정규화로 사용함으로써 시간에 따라 변화하는 변동성의 특성을 반영하는 팩터 모델을 제안하였다. 미국 상장 주식에 대한 실증 실험 결과 제안한 모델은 시간 불변 변동성 조건을 완화하지 않은 모델에 비해 변동서이 낮은 시기에서 통계적 성능이 큰 폭으로 상승함을 확인하였다. 현재 무시할 수 없는 규모로 성장한 가상화폐 시장에는 구조적으로 확실하게 연결된 자산이 존재한다. 같은 블록체인 상에 존재하는 토큰들은 해당 블록체인 위에서 발행되고 거래되므로 네트워크 구조 상으로 연결성을 지닌다. 본 연구에서는 앞서 진행된 연구에 대한 응용으로, 명확히 구조적으로 연결된 자산들이 초과 수익률을 설명할 수 있는 측정 가능한 공통된 팩터를 가짐을 보이고자 했다. 연구의 대상을 이더리움 블록체인 상의 토큰들로 제한하여 실증 실험을 진행한 결과, EIP-1559 적용 이후에 이더리움 가스 수익률이 시장 수익률과 함께 토큰의 수익률을 설명할 수 있는 팩터로서 작용함을 보였다. 또한, 이더리움 가스 수익률은 토큰의 변동성에 영향을 주는 요소로, 토큰 변동성 예측에도 도움을 줄 수 있는 요소임을 스필오버 기반 변동성 예측 모델을 통해 확인하였다. 본 논문은 자산 간의 연결성을 고려한 자산가격결정모형을 구성하였으며, 이를 통해서 금융 자산들이 갖는 그래프 구조가 실질적으로 수익률에 영향을 미침을 확인할 수 있다. 이 연구결과는 향후 새로운 금융 시장에 대해서도 적용 가능한 확장성 있는 모델이며, 금융 자산의 평가에 있어 여러 자산을 동시에 상관관계를 고려하며 평가해야 한다는 함의점을 제공하고 있다.Financial assets are always exposed to risks. It is important to evaluate the risk properly and figure out how much each asset is compensated for its risk. Asset pricing model explains the behavior of financial asset return by evaluating the risk and risk exposure of asset return. We focused on factor model structure among asset pricing models, which explains excess return through factor and beta coefficients. While conventional factor models estimate factor or beta through various macroeconomic variables or firm-specific variables, there exist fewer studies considering the connectedness between assets. Since financial assets have connected dynamics, asset returns should be priced simultaneously considering the graph structure of assets. In this dissertation, we proposed the AI-based empirical asset pricing model to reflect the connected structure between assets in the factor model. We first proposed the graph neural network-based multi-factor asset pricing model. As important as the structure of the model in constructing an asset pricing model that reflects the structure of the connection between assets is, how to define the connectivity. Graph neural network requires a well-defined graph structure. We defined the connectedness between assets as the binary converted Pearson correlation coefficients of asset returns by the cutoff value. The proposed model consists of a beta estimation part and a factor estimation part, where each part is estimated with firm characteristics and excess returns, respectively. The empirical analysis of U.S equities reveals that the proposed model has more explanatory power and prediction ability than benchmark models. In addition, the most efficient stochastic discount factor can be estimated from the estimated factors. While return is the main object of asset pricing, volatility is also important property for explaining the behavior of financial assets. Volatility can be the factor in explaining return since many studies point out that return and volatility are correlated. As with the asset pricing model, considering the connected structure between assets in volatility prediction can be of great help in explaining the dynamics of assets. In the volatility analysis, what affects between volatility is called spillover. In this aspect, we proposed the volatility prediction model that can directly reflect this spillover effect. We estimated the graph structure between asset volatility using the volatility spillover index and utilized the spatial-temporal graph neural network structure for model construction. From the empirical analysis of global market indices, we confirm that the proposed model shows the best performance in short- and mid-term volatility forecasting. To include volatility in the asset pricing discussion, it is necessary to focus on how volatility is defined in the asset pricing model. In the asset pricing model, volatility can be interpreted as the variance of the residual of the model. However, asset pricing models with time-series estimation mostly have time-unvarying volatility constraints. We constructed an asset pricing model with time-varying volatility by estimating variability using the prediction model and reflecting it in the training loss of the asset pricing model. We identify that the proposed model can improve the statistical performance during the low volatility period through an empirical study of U.S equities. Currently, there are clearly structurally connected assets in the cryptocurrency market, which has grown to a scale that cannot be ignored. All of the same blockchain-based tokens are issued and traded on that blockchain, so they have strong structural connectivity. We tried to identify that an observable factor for explaining excess return exists in such connected tokens as an application of previous studies. We limited the analysis target to Ethereum-based tokens and showed that the Ethereum gas price became a factor for the macroeconomic factor model after the application of EIP-1559. Furthermore, we applied the volatility spillover index-based volatility prediction model using gas return and showed that gas return can increase the prediction performance of certain tokens' volatility.Chapter 1 Introduction 1 1.1 Motivation of the Dissertation 1 1.2 Aims of the Dissertation 10 1.3 Organization of the Dissertation 13 Chapter 2 Graph-based multi-factor asset pricing model 14 2.1 Chapter Overview 14 2.2 Preliminaries 17 2.2.1 Graph Neural Network 17 2.2.2 Graph Convolutional Network 18 2.3 Methodology 19 2.3.1 Multi-factor asset pricing model 19 2.3.2 Proposed method 21 2.3.3 Forward stagewise additive factor modeling 23 2.4 Empirical Studies 24 2.4.1 Data 24 2.4.2 Benchmark models 24 2.4.3 Empirical results 28 2.5 Chapter Summary 33 Chapter 3 Volatility prediction with volatility spillover index 37 3.1 Chapter Overview 37 3.2 Preliminaries 41 3.2.1 Realized Volatility 41 3.2.2 Volatility Spillover Measurements 42 3.2.3 Benchmark Models 45 3.3 Empirical Studies 50 3.3.1 Data 50 3.3.2 Descriptive Statistics 51 3.3.3 Proposed Method 52 3.3.4 Empirical Results 54 3.4 Chapter Summary 61 Chapter 4 Graph-based multi-factor model with time-varying volatility 64 4.1 Chapter overview 64 4.2 Preliminaries 67 4.2.1 Local-linear regression for time-varying parameter estimation 67 4.3 Methodology 68 4.3.1 Time-varying volatility implied loss function 68 4.3.2 Proposed model architecture 70 4.4 Empirical Studies 72 4.4.1 Data 72 4.4.2 Benchmark Models 72 4.4.3 Empirical Results 73 4.5 Chapter Summary 79 Chapter 5 Macroeconomic factor model and spillover-based volatility prediction for ERC-20 tokens 82 5.1 Chapter Overview 82 5.2 Preliminaries 85 5.3 Methodology 86 5.3.1 Relation analysis 86 5.3.2 Factor model analysis 89 5.3.3 Volatility prediction with volatility spillover index 90 5.4 Empirical Studies 90 5.4.1 Data 90 5.4.2 Empirical Results 98 5.5 Chapter Summary 102 Chapter 6 Conclusion 105 6.1 Contributions 105 6.2 Future Work 108 Bibliography 109 국문초록 130박

AI-powered Fraud Detection in Decentralized Finance: A Project Life Cycle Perspective

In recent years, blockchain technology has introduced decentralized finance (DeFi) as an alternative to traditional financial systems. DeFi aims to create a transparent and efficient financial ecosystem using smart contracts and emerging decentralized applications. However, the growing popularity of DeFi has made it a target for fraudulent activities, resulting in losses of billions of dollars due to various types of frauds. To address these issues, researchers have explored the potential of artificial intelligence (AI) approaches to detect such fraudulent activities. Yet, there is a lack of a systematic survey to organize and summarize those existing works and to identify the future research opportunities. In this survey, we provide a systematic taxonomy of various frauds in the DeFi ecosystem, categorized by the different stages of a DeFi project's life cycle: project development, introduction, growth, maturity, and decline. This taxonomy is based on our finding: many frauds have strong correlations in the stage of the DeFi project. According to the taxonomy, we review existing AI-powered detection methods, including statistical modeling, natural language processing and other machine learning techniques, etc. We find that fraud detection in different stages employs distinct types of methods and observe the commendable performance of tree-based and graph-related models in tackling fraud detection tasks. By analyzing the challenges and trends, we present the findings to provide proactive suggestion and guide future research in DeFi fraud detection. We believe that this survey is able to support researchers, practitioners, and regulators in establishing a secure and trustworthy DeFi ecosystem.Comment: 38 pages, update reference