arXiv - QuantFin - General Finance最新文献_第5页

Token vs Equity for Startup Financing 代币与股权对初创企业融资的影响

arXiv - QuantFin - General Finance Pub Date : 2024-02-07 DOI: arxiv-2402.04662

Guangye Cao

引用次数: 0

Prioritizing Investments in Cybersecurity: Empirical Evidence from an Event Study on the Determinants of Cyberattack Costs 确定网络安全投资的优先次序：网络攻击成本决定因素事件研究的经验证据

arXiv - QuantFin - General Finance Pub Date : 2024-02-07 DOI: arxiv-2402.04773

Daniel Celeny, Loïc Maréchal, Evgueni Rousselot, Alain Mermoud, Mathias Humbert

{"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":null,"pages":null},"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}

引用次数: 0

A Survey of Large Language Models in Finance (FinLLMs) 金融领域大型语言模型（FinLLMs）概览

arXiv - QuantFin - General Finance Pub Date : 2024-02-04 DOI: arxiv-2402.02315

Jean Lee, Nicholas Stevens, Soyeon Caren Han, Minseok Song

{"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":null,"pages":null},"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}

引用次数: 0

Attention-based Dynamic Multilayer Graph Neural Networks for Loan Default Prediction 基于注意力的动态多层图神经网络用于贷款违约预测

arXiv - QuantFin - General Finance Pub Date : 2024-02-01 DOI: arxiv-2402.00299

Sahab Zandi, Kamesh Korangi, María Óskarsdóttir, Christophe Mues, Cristián Bravo

{"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":null,"pages":null},"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}

引用次数: 0

Cash and Card Acceptance in Retail Payments: Motivations and Factors 零售支付中的现金和银行卡接受度：动机和因素

arXiv - QuantFin - General Finance Pub Date : 2024-01-15 DOI: arxiv-2401.07682

Samuel Vandak, Geoffrey Goodell

{"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":null,"pages":null},"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}

引用次数: 0

Designing Heterogeneous LLM Agents for Financial Sentiment Analysis 为金融情感分析设计异构 LLM 代理

arXiv - QuantFin - General Finance Pub Date : 2024-01-11 DOI: arxiv-2401.05799

Frank Xing

{"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":null,"pages":null},"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}

引用次数: 0

Proof of Efficient Liquidity: A Staking Mechanism for Capital Efficient Liquidity 高效流动性的证明：资本高效流动性的押注机制

arXiv - QuantFin - General Finance Pub Date : 2024-01-09 DOI: arxiv-2401.04521

Arman Abgaryan, Utkarsh Sharma, Joshua Tobkin

{"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":null,"pages":null},"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}

引用次数: 0

Displaying risk in mergers: a diagrammatic approach for exchange ratio determination 显示兼并中的风险：确定交换比率的图解法

arXiv - QuantFin - General Finance Pub Date : 2024-01-05 DOI: arxiv-2401.02681

Alessandra Mainini, Enrico Moretto, Daniela Visetti

引用次数: 0

Non-Atomic Arbitrage in Decentralized Finance 分散金融中的非原子套利

arXiv - QuantFin - General Finance Pub Date : 2024-01-03 DOI: arxiv-2401.01622

Lioba Heimbach, Vabuk Pahari, Eric Schertenleib

{"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":null,"pages":null},"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}

引用次数: 0

Synthetic Data Applications in Finance 合成数据在金融领域的应用

arXiv - QuantFin - General Finance Pub Date : 2023-12-29 DOI: arxiv-2401.00081

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

引用次数: 0