Why would a blockchain-based startup and its venture capital investors choose to finance by issuing tokens instead of equity? What would be their rates of return for each asset? This paper focuses on the liquidity difference between the two fundraising methods. I build a three-period model of an entrepreneur, two types of investors, and users. Some investors have unforeseen liquidity needs in the middle period that can only be met with tokens. The entrepreneur obtains higher payoff by issuing tokens instead of equity, and the payoff difference increases with investors risk-aversion and need for liquidity in the middle period, as well as the depth of the token market.
{"title":"Token vs Equity for Startup Financing","authors":"Guangye Cao","doi":"arxiv-2402.04662","DOIUrl":"https://doi.org/arxiv-2402.04662","url":null,"abstract":"Why would a blockchain-based startup and its venture capital investors choose\u0000to finance by issuing tokens instead of equity? What would be their rates of\u0000return for each asset? This paper focuses on the liquidity difference between\u0000the two fundraising methods. I build a three-period model of an entrepreneur,\u0000two types of investors, and users. Some investors have unforeseen liquidity\u0000needs in the middle period that can only be met with tokens. The entrepreneur\u0000obtains higher payoff by issuing tokens instead of equity, and the payoff\u0000difference increases with investors risk-aversion and need for liquidity in the\u0000middle period, as well as the depth of the token market.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"144 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139769464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Daniel Celeny, Loïc Maréchal, Evgueni Rousselot, Alain Mermoud, Mathias Humbert
Along with the increasing frequency and severity of cyber incidents, understanding their economic implications is paramount. In this context, listed firms' reactions to cyber incidents are compelling to study since they (i) are a good proxy to estimate the costs borne by other organizations, (ii) have a critical position in the economy, and (iii) have their financial information publicly available. We extract listed firms' cyber incident dates and characteristics from newswire headlines. We use an event study over 2012--2022, using a three-day window around events and standard benchmarks. We find that the magnitude of abnormal returns around cyber incidents is on par with previous studies using newswire or alternative data to identify cyber incidents. Conversely, as we adjust the standard errors accounting for event-induced variance and residual cross-correlation, we find that the previously claimed significance of abnormal returns vanishes. Given these results, we run a horse race of specifications, in which we test for the marginal effects of type of cyber incidents, target firm sector, periods, and their interactions. Data breaches are the most detrimental incident type with an average loss of -1.3% or (USD -1.9 billion) over the last decade. The health sector is the most sensitive to cyber incidents, with an average loss of -5.21% (or USD -1.2 billion), and even more so when these are data breaches. Instead, we cannot show any time-varying effect of cyber incidents or a specific effect of the type of news as had previously been advocated.
{"title":"Prioritizing Investments in Cybersecurity: Empirical Evidence from an Event Study on the Determinants of Cyberattack Costs","authors":"Daniel Celeny, Loïc Maréchal, Evgueni Rousselot, Alain Mermoud, Mathias Humbert","doi":"arxiv-2402.04773","DOIUrl":"https://doi.org/arxiv-2402.04773","url":null,"abstract":"Along with the increasing frequency and severity of cyber incidents,\u0000understanding their economic implications is paramount. In this context, listed\u0000firms' reactions to cyber incidents are compelling to study since they (i) are\u0000a good proxy to estimate the costs borne by other organizations, (ii) have a\u0000critical position in the economy, and (iii) have their financial information\u0000publicly available. We extract listed firms' cyber incident dates and\u0000characteristics from newswire headlines. We use an event study over 2012--2022,\u0000using a three-day window around events and standard benchmarks. We find that\u0000the magnitude of abnormal returns around cyber incidents is on par with\u0000previous studies using newswire or alternative data to identify cyber\u0000incidents. Conversely, as we adjust the standard errors accounting for\u0000event-induced variance and residual cross-correlation, we find that the\u0000previously claimed significance of abnormal returns vanishes. Given these\u0000results, we run a horse race of specifications, in which we test for the\u0000marginal effects of type of cyber incidents, target firm sector, periods, and\u0000their interactions. Data breaches are the most detrimental incident type with\u0000an average loss of -1.3% or (USD -1.9 billion) over the last decade. The\u0000health sector is the most sensitive to cyber incidents, with an average loss of\u0000-5.21% (or USD -1.2 billion), and even more so when these are data breaches.\u0000Instead, we cannot show any time-varying effect of cyber incidents or a\u0000specific effect of the type of news as had previously been advocated.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139769463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jean Lee, Nicholas Stevens, Soyeon Caren Han, Minseok Song
Large Language Models (LLMs) have shown remarkable capabilities across a wide variety of Natural Language Processing (NLP) tasks and have attracted attention from multiple domains, including financial services. Despite the extensive research into general-domain LLMs, and their immense potential in finance, Financial LLM (FinLLM) research remains limited. This survey provides a comprehensive overview of FinLLMs, including their history, techniques, performance, and opportunities and challenges. Firstly, we present a chronological overview of general-domain Pre-trained Language Models (PLMs) through to current FinLLMs, including the GPT-series, selected open-source LLMs, and financial LMs. Secondly, we compare five techniques used across financial PLMs and FinLLMs, including training methods, training data, and fine-tuning methods. Thirdly, we summarize the performance evaluations of six benchmark tasks and datasets. In addition, we provide eight advanced financial NLP tasks and datasets for developing more sophisticated FinLLMs. Finally, we discuss the opportunities and the challenges facing FinLLMs, such as hallucination, privacy, and efficiency. To support AI research in finance, we compile a collection of accessible datasets and evaluation benchmarks on GitHub.
{"title":"A Survey of Large Language Models in Finance (FinLLMs)","authors":"Jean Lee, Nicholas Stevens, Soyeon Caren Han, Minseok Song","doi":"arxiv-2402.02315","DOIUrl":"https://doi.org/arxiv-2402.02315","url":null,"abstract":"Large Language Models (LLMs) have shown remarkable capabilities across a wide\u0000variety of Natural Language Processing (NLP) tasks and have attracted attention\u0000from multiple domains, including financial services. Despite the extensive\u0000research into general-domain LLMs, and their immense potential in finance,\u0000Financial LLM (FinLLM) research remains limited. This survey provides a\u0000comprehensive overview of FinLLMs, including their history, techniques,\u0000performance, and opportunities and challenges. Firstly, we present a\u0000chronological overview of general-domain Pre-trained Language Models (PLMs)\u0000through to current FinLLMs, including the GPT-series, selected open-source\u0000LLMs, and financial LMs. Secondly, we compare five techniques used across\u0000financial PLMs and FinLLMs, including training methods, training data, and\u0000fine-tuning methods. Thirdly, we summarize the performance evaluations of six\u0000benchmark tasks and datasets. In addition, we provide eight advanced financial\u0000NLP tasks and datasets for developing more sophisticated FinLLMs. Finally, we\u0000discuss the opportunities and the challenges facing FinLLMs, such as\u0000hallucination, privacy, and efficiency. To support AI research in finance, we\u0000compile a collection of accessible datasets and evaluation benchmarks on\u0000GitHub.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139769356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sahab Zandi, Kamesh Korangi, María Óskarsdóttir, Christophe Mues, Cristián Bravo
Whereas traditional credit scoring tends to employ only individual borrower- or loan-level predictors, it has been acknowledged for some time that connections between borrowers may result in default risk propagating over a network. In this paper, we present a model for credit risk assessment leveraging a dynamic multilayer network built from a Graph Neural Network and a Recurrent Neural Network, each layer reflecting a different source of network connection. We test our methodology in a behavioural credit scoring context using a dataset provided by U.S. mortgage financier Freddie Mac, in which different types of connections arise from the geographical location of the borrower and their choice of mortgage provider. The proposed model considers both types of connections and the evolution of these connections over time. We enhance the model by using a custom attention mechanism that weights the different time snapshots according to their importance. After testing multiple configurations, a model with GAT, LSTM, and the attention mechanism provides the best results. Empirical results demonstrate that, when it comes to predicting probability of default for the borrowers, our proposed model brings both better results and novel insights for the analysis of the importance of connections and timestamps, compared to traditional methods.
{"title":"Attention-based Dynamic Multilayer Graph Neural Networks for Loan Default Prediction","authors":"Sahab Zandi, Kamesh Korangi, María Óskarsdóttir, Christophe Mues, Cristián Bravo","doi":"arxiv-2402.00299","DOIUrl":"https://doi.org/arxiv-2402.00299","url":null,"abstract":"Whereas traditional credit scoring tends to employ only individual borrower-\u0000or loan-level predictors, it has been acknowledged for some time that\u0000connections between borrowers may result in default risk propagating over a\u0000network. In this paper, we present a model for credit risk assessment\u0000leveraging a dynamic multilayer network built from a Graph Neural Network and a\u0000Recurrent Neural Network, each layer reflecting a different source of network\u0000connection. We test our methodology in a behavioural credit scoring context\u0000using a dataset provided by U.S. mortgage financier Freddie Mac, in which\u0000different types of connections arise from the geographical location of the\u0000borrower and their choice of mortgage provider. The proposed model considers\u0000both types of connections and the evolution of these connections over time. We\u0000enhance the model by using a custom attention mechanism that weights the\u0000different time snapshots according to their importance. After testing multiple\u0000configurations, a model with GAT, LSTM, and the attention mechanism provides\u0000the best results. Empirical results demonstrate that, when it comes to\u0000predicting probability of default for the borrowers, our proposed model brings\u0000both better results and novel insights for the analysis of the importance of\u0000connections and timestamps, compared to traditional methods.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139665914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The landscape of payment methods in retail is a complex and evolving area. Vendors are motivated to conduct an appropriate analysis to decide what payment methods to accept out of a vast range of options. Many factors are included in this decision process, some qualitative and some quantitative. The following research project investigates vendors' acceptance of cards and cash from various viewpoints, all chosen to represent a novel perspective, including the barriers and preferences for each and correlations with external demographic factors. We observe that lower interchange fees, limited in this instance by the regulatory framework, play a crucial role in facilitating merchants' acceptance of card payments. The regulatory constraints on interchange fees create a favorable cost structure for merchants, making card payment adoption financially feasible. However, additional factors like technological readiness and consumer preferences might also play a significant role in their decision-making process. We also note that aggregate Merchant Service Providers (MSPs) have positively impacted the payment landscape by offering more competitive fee rates, particularly beneficial for small merchants and entrepreneurs. However, associated risks, such as account freezes or abrupt terminations, pose challenges and often lack transparency. Last, the quantitative analysis of the relationship between demographic variables and acceptance of payment types is presented. This analysis combines the current landscape of payment acceptance in the UK with data from the most recent census from 2021. We show that the unemployment rates shape card and cash acceptance, age affects contactless preference, and work-from-home impacts credit card preference.
{"title":"Cash and Card Acceptance in Retail Payments: Motivations and Factors","authors":"Samuel Vandak, Geoffrey Goodell","doi":"arxiv-2401.07682","DOIUrl":"https://doi.org/arxiv-2401.07682","url":null,"abstract":"The landscape of payment methods in retail is a complex and evolving area.\u0000Vendors are motivated to conduct an appropriate analysis to decide what payment\u0000methods to accept out of a vast range of options. Many factors are included in\u0000this decision process, some qualitative and some quantitative. The following\u0000research project investigates vendors' acceptance of cards and cash from\u0000various viewpoints, all chosen to represent a novel perspective, including the\u0000barriers and preferences for each and correlations with external demographic\u0000factors. We observe that lower interchange fees, limited in this instance by\u0000the regulatory framework, play a crucial role in facilitating merchants'\u0000acceptance of card payments. The regulatory constraints on interchange fees\u0000create a favorable cost structure for merchants, making card payment adoption\u0000financially feasible. However, additional factors like technological readiness\u0000and consumer preferences might also play a significant role in their\u0000decision-making process. We also note that aggregate Merchant Service Providers\u0000(MSPs) have positively impacted the payment landscape by offering more\u0000competitive fee rates, particularly beneficial for small merchants and\u0000entrepreneurs. However, associated risks, such as account freezes or abrupt\u0000terminations, pose challenges and often lack transparency. Last, the\u0000quantitative analysis of the relationship between demographic variables and\u0000acceptance of payment types is presented. This analysis combines the current\u0000landscape of payment acceptance in the UK with data from the most recent census\u0000from 2021. We show that the unemployment rates shape card and cash acceptance,\u0000age affects contactless preference, and work-from-home impacts credit card\u0000preference.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139483262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Large language models (LLMs) have drastically changed the possible ways to design intelligent systems, shifting the focuses from massive data acquisition and new modeling training to human alignment and strategical elicitation of the full potential of existing pre-trained models. This paradigm shift, however, is not fully realized in financial sentiment analysis (FSA), due to the discriminative nature of this task and a lack of prescriptive knowledge of how to leverage generative models in such a context. This study investigates the effectiveness of the new paradigm, i.e., using LLMs without fine-tuning for FSA. Rooted in Minsky's theory of mind and emotions, a design framework with heterogeneous LLM agents is proposed. The framework instantiates specialized agents using prior domain knowledge of the types of FSA errors and reasons on the aggregated agent discussions. Comprehensive evaluation on FSA datasets show that the framework yields better accuracies, especially when the discussions are substantial. This study contributes to the design foundations and paves new avenues for LLMs-based FSA. Implications on business and management are also discussed.
{"title":"Designing Heterogeneous LLM Agents for Financial Sentiment Analysis","authors":"Frank Xing","doi":"arxiv-2401.05799","DOIUrl":"https://doi.org/arxiv-2401.05799","url":null,"abstract":"Large language models (LLMs) have drastically changed the possible ways to\u0000design intelligent systems, shifting the focuses from massive data acquisition\u0000and new modeling training to human alignment and strategical elicitation of the\u0000full potential of existing pre-trained models. This paradigm shift, however, is\u0000not fully realized in financial sentiment analysis (FSA), due to the\u0000discriminative nature of this task and a lack of prescriptive knowledge of how\u0000to leverage generative models in such a context. This study investigates the\u0000effectiveness of the new paradigm, i.e., using LLMs without fine-tuning for\u0000FSA. Rooted in Minsky's theory of mind and emotions, a design framework with\u0000heterogeneous LLM agents is proposed. The framework instantiates specialized\u0000agents using prior domain knowledge of the types of FSA errors and reasons on\u0000the aggregated agent discussions. Comprehensive evaluation on FSA datasets show\u0000that the framework yields better accuracies, especially when the discussions\u0000are substantial. This study contributes to the design foundations and paves new\u0000avenues for LLMs-based FSA. Implications on business and management are also\u0000discussed.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139464319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Proof of Efficient Liquidity (PoEL) protocol, designed for specialised Proof of Stake (PoS) consensus-based blockchain infrastructures that incorporate intrinsic DeFi applications, aims to support sustainable liquidity bootstrapping and network security. This innovative mechanism efficiently utilises budgeted staking rewards to attract and sustain liquidity through a risk structuring engine and incentive allocation strategy, both of which are designed to maximise capital efficiency. The proposed protocol seeks to serve the dual objective of - (i) capital creation, by efficiently attracting risk capital, and maximising its operational utility for intrinsic DeFi applications, thereby asserting sustainability; and (ii) enhancing the adopting blockchain network's economic security, by augmenting their staking (PoS) mechanism with a harmonious layer seeking to attract a diversity of digital assets. Finally, in the appendix, we seek to generalise the financial incentivisation protocol to the notion of service fee credits, such that it utilises the network's auxiliary services as a means to propagate incentives to attract liquidity and facilitate the network to achieve the critical mass of usage necessary for sustained operations and growth.
{"title":"Proof of Efficient Liquidity: A Staking Mechanism for Capital Efficient Liquidity","authors":"Arman Abgaryan, Utkarsh Sharma, Joshua Tobkin","doi":"arxiv-2401.04521","DOIUrl":"https://doi.org/arxiv-2401.04521","url":null,"abstract":"The Proof of Efficient Liquidity (PoEL) protocol, designed for specialised\u0000Proof of Stake (PoS) consensus-based blockchain infrastructures that\u0000incorporate intrinsic DeFi applications, aims to support sustainable liquidity\u0000bootstrapping and network security. This innovative mechanism efficiently\u0000utilises budgeted staking rewards to attract and sustain liquidity through a\u0000risk structuring engine and incentive allocation strategy, both of which are\u0000designed to maximise capital efficiency. The proposed protocol seeks to serve\u0000the dual objective of - (i) capital creation, by efficiently attracting risk\u0000capital, and maximising its operational utility for intrinsic DeFi\u0000applications, thereby asserting sustainability; and (ii) enhancing the adopting\u0000blockchain network's economic security, by augmenting their staking (PoS)\u0000mechanism with a harmonious layer seeking to attract a diversity of digital\u0000assets. Finally, in the appendix, we seek to generalise the financial\u0000incentivisation protocol to the notion of service fee credits, such that it\u0000utilises the network's auxiliary services as a means to propagate incentives to\u0000attract liquidity and facilitate the network to achieve the critical mass of\u0000usage necessary for sustained operations and growth.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139414176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article extends, in a stochastic setting, previous results in the determination of feasible exchange ratios for merging companies. A first outcome is that shareholders of the companies involved in the merging process face both an upper and a lower bounds for acceptable exchange ratios. Secondly, in order for the improved `bargaining region' to be intelligibly displayed, the diagrammatic approach developed by Kulpa is exploited.
{"title":"Displaying risk in mergers: a diagrammatic approach for exchange ratio determination","authors":"Alessandra Mainini, Enrico Moretto, Daniela Visetti","doi":"arxiv-2401.02681","DOIUrl":"https://doi.org/arxiv-2401.02681","url":null,"abstract":"This article extends, in a stochastic setting, previous results in the\u0000determination of feasible exchange ratios for merging companies. A first\u0000outcome is that shareholders of the companies involved in the merging process\u0000face both an upper and a lower bounds for acceptable exchange ratios. Secondly,\u0000in order for the improved `bargaining region' to be intelligibly displayed, the\u0000diagrammatic approach developed by Kulpa is exploited.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"83 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139414447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The prevalence of maximal extractable value (MEV) in the Ethereum ecosystem has led to a characterization of the latter as a dark forest. Studies of MEV have thus far largely been restricted to purely on-chain MEV, i.e., sandwich attacks, cyclic arbitrage, and liquidations. In this work, we shed light on the prevalence of non-atomic arbitrage on decentralized exchanges (DEXes) on the Ethereum blockchain. Importantly, non-atomic arbitrage exploits price differences between DEXes on the Ethereum blockchain as well as exchanges outside the Ethereum blockchain (i.e., centralized exchanges or DEXes on other blockchains). Thus, non-atomic arbitrage is a type of MEV that involves actions on and off the Ethereum blockchain. In our study of non-atomic arbitrage, we uncover that more than a fourth of the volume on Ethereum's biggest five DEXes from the merge until 31 October 2023 can likely be attributed to this type of MEV. We further highlight that only eleven searchers are responsible for more than 80% of the identified non-atomic arbitrage volume sitting at a staggering 137 billion US$ and draw a connection between the centralization of the block construction market and non-atomic arbitrage. Finally, we discuss the security implications of these high-value transactions that account for more than 10% of Ethereum's total block value and outline possible mitigations.
{"title":"Non-Atomic Arbitrage in Decentralized Finance","authors":"Lioba Heimbach, Vabuk Pahari, Eric Schertenleib","doi":"arxiv-2401.01622","DOIUrl":"https://doi.org/arxiv-2401.01622","url":null,"abstract":"The prevalence of maximal extractable value (MEV) in the Ethereum ecosystem\u0000has led to a characterization of the latter as a dark forest. Studies of MEV\u0000have thus far largely been restricted to purely on-chain MEV, i.e., sandwich\u0000attacks, cyclic arbitrage, and liquidations. In this work, we shed light on the\u0000prevalence of non-atomic arbitrage on decentralized exchanges (DEXes) on the\u0000Ethereum blockchain. Importantly, non-atomic arbitrage exploits price\u0000differences between DEXes on the Ethereum blockchain as well as exchanges\u0000outside the Ethereum blockchain (i.e., centralized exchanges or DEXes on other\u0000blockchains). Thus, non-atomic arbitrage is a type of MEV that involves actions\u0000on and off the Ethereum blockchain. In our study of non-atomic arbitrage, we uncover that more than a fourth of\u0000the volume on Ethereum's biggest five DEXes from the merge until 31 October\u00002023 can likely be attributed to this type of MEV. We further highlight that\u0000only eleven searchers are responsible for more than 80% of the identified\u0000non-atomic arbitrage volume sitting at a staggering 137 billion US$ and draw a\u0000connection between the centralization of the block construction market and\u0000non-atomic arbitrage. Finally, we discuss the security implications of these\u0000high-value transactions that account for more than 10% of Ethereum's total\u0000block value and outline possible mitigations.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"132 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139095454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Synthetic data has made tremendous strides in various commercial settings including finance, healthcare, and virtual reality. We present a broad overview of prototypical applications of synthetic data in the financial sector and in particular provide richer details for a few select ones. These cover a wide variety of data modalities including tabular, time-series, event-series, and unstructured arising from both markets and retail financial applications. Since finance is a highly regulated industry, synthetic data is a potential approach for dealing with issues related to privacy, fairness, and explainability. Various metrics are utilized in evaluating the quality and effectiveness of our approaches in these applications. We conclude with open directions in synthetic data in the context of the financial domain.
{"title":"Synthetic Data Applications in Finance","authors":"Vamsi K. Potluru, Daniel Borrajo, Andrea Coletta, Niccolò Dalmasso, Yousef El-Laham, Elizabeth Fons, Mohsen Ghassemi, Sriram Gopalakrishnan, Vikesh Gosai, Eleonora Kreačić, Ganapathy Mani, Saheed Obitayo, Deepak Paramanand, Natraj Raman, Mikhail Solonin, Srijan Sood, Svitlana Vyetrenko, Haibei Zhu, Manuela Veloso, Tucker Balch","doi":"arxiv-2401.00081","DOIUrl":"https://doi.org/arxiv-2401.00081","url":null,"abstract":"Synthetic data has made tremendous strides in various commercial settings\u0000including finance, healthcare, and virtual reality. We present a broad overview\u0000of prototypical applications of synthetic data in the financial sector and in\u0000particular provide richer details for a few select ones. These cover a wide\u0000variety of data modalities including tabular, time-series, event-series, and\u0000unstructured arising from both markets and retail financial applications. Since\u0000finance is a highly regulated industry, synthetic data is a potential approach\u0000for dealing with issues related to privacy, fairness, and explainability.\u0000Various metrics are utilized in evaluating the quality and effectiveness of our\u0000approaches in these applications. We conclude with open directions in synthetic\u0000data in the context of the financial domain.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"141 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139079947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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