Big data revolutionizes accounting and auditing, offering deep insights but also introducing challenges like data privacy and security. With data from IoT, social media, and transactions, traditional practices are evolving. Professionals must adapt to these changes, utilizing AI and machine learning for efficient data analysis and anomaly detection. Key to overcoming these challenges are enhanced analytics tools, continuous learning, and industry collaboration. By addressing these areas, the accounting and auditing fields can harness big data's potential while ensuring accuracy, transparency, and integrity in financial reporting. Keywords: Big Data, Accounting, Audit, Data Privacy, AI, Machine Learning, Transparency.
{"title":"Study of the Impact of the Big Data Era on Accounting and Auditing","authors":"Yuxiang Sun, Jingyi Li, Mengdie Lu, Zongying Guo","doi":"arxiv-2403.07180","DOIUrl":"https://doi.org/arxiv-2403.07180","url":null,"abstract":"Big data revolutionizes accounting and auditing, offering deep insights but\u0000also introducing challenges like data privacy and security. With data from IoT,\u0000social media, and transactions, traditional practices are evolving.\u0000Professionals must adapt to these changes, utilizing AI and machine learning\u0000for efficient data analysis and anomaly detection. Key to overcoming these\u0000challenges are enhanced analytics tools, continuous learning, and industry\u0000collaboration. By addressing these areas, the accounting and auditing fields\u0000can harness big data's potential while ensuring accuracy, transparency, and\u0000integrity in financial reporting. Keywords: Big Data, Accounting, Audit, Data\u0000Privacy, AI, Machine Learning, Transparency.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140116420","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}
Shareholders' expectations of stock returns and fluctuations are constantly changing due to restrictions in financial status and undesirable capital structure, which constrain managers to limit the changes in price trends in order to cover the risk instigated and infused by the unfavorable situation. The present research examines the moderating impact of information asymmetry on the relationship between capital structure adjustment and expected returns. The data from 120 companies approved in the Tehran Stock Exchange were extracted, and a hybrid data regression model was used to test the research hypotheses. Findings indicate that the capital structure adjustment speed correlates with the expected returns. Moreover, the information asymmetry positively affects the relationship between capital structure adjustment speed and expected returns.
{"title":"Capital Structure Adjustment Speed and Expected Returns: Examination of Information Asymmetry as a Moderating Role","authors":"Masoud Taherinia, Mehrdad Matin, Jamal Valipour, Kavian Abdolahi, Peyman Shouryabi, Mohammad Mahdi Barzegar","doi":"arxiv-2403.06035","DOIUrl":"https://doi.org/arxiv-2403.06035","url":null,"abstract":"Shareholders' expectations of stock returns and fluctuations are constantly\u0000changing due to restrictions in financial status and undesirable capital\u0000structure, which constrain managers to limit the changes in price trends in\u0000order to cover the risk instigated and infused by the unfavorable situation.\u0000The present research examines the moderating impact of information asymmetry on\u0000the relationship between capital structure adjustment and expected returns. The\u0000data from 120 companies approved in the Tehran Stock Exchange were extracted,\u0000and a hybrid data regression model was used to test the research hypotheses.\u0000Findings indicate that the capital structure adjustment speed correlates with\u0000the expected returns. Moreover, the information asymmetry positively affects\u0000the relationship between capital structure adjustment speed and expected\u0000returns.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"153 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140105183","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}
Empirical business cycle studies using cross-country data usually cannot achieve causal relationships while within-country studies mostly focus on the bust period. We provide the first causal investigation into the boom period of the 1999-2010 U.S. cross-metropolitan business cycle. Using a novel research design, we show that credit expansion in private-label mortgages causes a differentially stronger boom (2000-2006) and bust (2007-2010) cycle in the house-related industries in the high net-export-growth areas. Most importantly, our unique research design enables us to perform the most comprehensive tests on theories (hypotheses) regarding the business cycle. We show that the following theories (hypotheses) cannot explain the cause of the 1999-2010 U.S. business cycle: the speculative euphoria hypothesis, the real business cycle theory, the collateral-driven credit cycle theory, the business uncertainty theory, and the extrapolative expectation theory.
{"title":"Testing Business Cycle Theories: Evidence from the Great Recession","authors":"Bo Li","doi":"arxiv-2403.04104","DOIUrl":"https://doi.org/arxiv-2403.04104","url":null,"abstract":"Empirical business cycle studies using cross-country data usually cannot\u0000achieve causal relationships while within-country studies mostly focus on the\u0000bust period. We provide the first causal investigation into the boom period of\u0000the 1999-2010 U.S. cross-metropolitan business cycle. Using a novel research\u0000design, we show that credit expansion in private-label mortgages causes a\u0000differentially stronger boom (2000-2006) and bust (2007-2010) cycle in the\u0000house-related industries in the high net-export-growth areas. Most importantly,\u0000our unique research design enables us to perform the most comprehensive tests\u0000on theories (hypotheses) regarding the business cycle. We show that the\u0000following theories (hypotheses) cannot explain the cause of the 1999-2010 U.S.\u0000business cycle: the speculative euphoria hypothesis, the real business cycle\u0000theory, the collateral-driven credit cycle theory, the business uncertainty\u0000theory, and the extrapolative expectation theory.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140072865","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}
Hugo Schnoering, Pierre Porthaux, Michalis Vazirgiannis
Exploring transactions within the Bitcoin blockchain entails examining the transfer of bitcoins among several hundred million entities. However, it is often impractical and resource-consuming to study such a vast number of entities. Consequently, entity clustering serves as an initial step in most analytical studies. This process often employs heuristics grounded in the practices and behaviors of these entities. In this research, we delve into the examination of two widely used heuristics, alongside the introduction of four novel ones. Our contribution includes the introduction of the textit{clustering ratio}, a metric designed to quantify the reduction in the number of entities achieved by a given heuristic. The assessment of this reduction ratio plays an important role in justifying the selection of a specific heuristic for analytical purposes. Given the dynamic nature of the Bitcoin system, characterized by a continuous increase in the number of entities on the blockchain, and the evolving behaviors of these entities, we extend our study to explore the temporal evolution of the clustering ratio for each heuristic. This temporal analysis enhances our understanding of the effectiveness of these heuristics over time.
{"title":"Assessing the Efficacy of Heuristic-Based Address Clustering for Bitcoin","authors":"Hugo Schnoering, Pierre Porthaux, Michalis Vazirgiannis","doi":"arxiv-2403.00523","DOIUrl":"https://doi.org/arxiv-2403.00523","url":null,"abstract":"Exploring transactions within the Bitcoin blockchain entails examining the\u0000transfer of bitcoins among several hundred million entities. However, it is\u0000often impractical and resource-consuming to study such a vast number of\u0000entities. Consequently, entity clustering serves as an initial step in most\u0000analytical studies. This process often employs heuristics grounded in the\u0000practices and behaviors of these entities. In this research, we delve into the\u0000examination of two widely used heuristics, alongside the introduction of four\u0000novel ones. Our contribution includes the introduction of the\u0000textit{clustering ratio}, a metric designed to quantify the reduction in the\u0000number of entities achieved by a given heuristic. The assessment of this\u0000reduction ratio plays an important role in justifying the selection of a\u0000specific heuristic for analytical purposes. Given the dynamic nature of the\u0000Bitcoin system, characterized by a continuous increase in the number of\u0000entities on the blockchain, and the evolving behaviors of these entities, we\u0000extend our study to explore the temporal evolution of the clustering ratio for\u0000each heuristic. This temporal analysis enhances our understanding of the\u0000effectiveness of these heuristics over time.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140034510","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 study aims to empirically investigate the impact of managers' characteristics on their choice between in-court and out-of-court restructuring. Based on the theory of upper echelons, we tested the preferences of 342 managers of financially distressed French firms regarding restructuring decisions. The overall findings of this study provide empirical support for the upper echelons theory. Specifically, managers with a long tenure and those with a high level of education are less likely to restructure before the court and are more likely to restructure privately. The findings also indicate that managers' age and gender do not significantly affect their choice between in-court and out-of-court restructuring. This study contributes to the literature on bankruptcy and corporate restructuring by turning the focus from firm characteristics to manager characteristics to explain restructuring decisions.
{"title":"Manager Characteristics and SMEs' Restructuring Decisions: In-Court vs. Out-of-Court Restructuring","authors":"Rachid AchbahUL2 UFR SEG","doi":"arxiv-2402.18135","DOIUrl":"https://doi.org/arxiv-2402.18135","url":null,"abstract":"This study aims to empirically investigate the impact of managers'\u0000characteristics on their choice between in-court and out-of-court\u0000restructuring. Based on the theory of upper echelons, we tested the preferences\u0000of 342 managers of financially distressed French firms regarding restructuring\u0000decisions. The overall findings of this study provide empirical support for the\u0000upper echelons theory. Specifically, managers with a long tenure and those with\u0000a high level of education are less likely to restructure before the court and\u0000are more likely to restructure privately. The findings also indicate that\u0000managers' age and gender do not significantly affect their choice between\u0000in-court and out-of-court restructuring. This study contributes to the\u0000literature on bankruptcy and corporate restructuring by turning the focus from\u0000firm characteristics to manager characteristics to explain restructuring\u0000decisions.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140005964","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}
Deepeka Garg, Benjamin Patrick Evans, Leo Ardon, Annapoorani Lakshmi Narayanan, Jared Vann, Udari Madhushani, Makada Henry-Nickie, Sumitra Ganesh
Mortgages account for the largest portion of household debt in the United States, totaling around $12 trillion nationwide. In times of financial hardship, alleviating mortgage burdens is essential for supporting affected households. The mortgage servicing industry plays a vital role in offering this assistance, yet there has been limited research modelling the complex relationship between households and servicers. To bridge this gap, we developed an agent-based model that explores household behavior and the effectiveness of relief measures during financial distress. Our model represents households as adaptive learning agents with realistic financial attributes. These households experience exogenous income shocks, which may influence their ability to make mortgage payments. Mortgage servicers provide relief options to these households, who then choose the most suitable relief based on their unique financial circumstances and individual preferences. We analyze the impact of various external shocks and the success of different mortgage relief strategies on specific borrower subgroups. Through this analysis, we show that our model can not only replicate real-world mortgage studies but also act as a tool for conducting a broad range of what-if scenario analyses. Our approach offers fine-grained insights that can inform the development of more effective and inclusive mortgage relief solutions.
{"title":"A Heterogeneous Agent Model of Mortgage Servicing: An Income-based Relief Analysis","authors":"Deepeka Garg, Benjamin Patrick Evans, Leo Ardon, Annapoorani Lakshmi Narayanan, Jared Vann, Udari Madhushani, Makada Henry-Nickie, Sumitra Ganesh","doi":"arxiv-2402.17932","DOIUrl":"https://doi.org/arxiv-2402.17932","url":null,"abstract":"Mortgages account for the largest portion of household debt in the United\u0000States, totaling around $12 trillion nationwide. In times of financial\u0000hardship, alleviating mortgage burdens is essential for supporting affected\u0000households. The mortgage servicing industry plays a vital role in offering this\u0000assistance, yet there has been limited research modelling the complex\u0000relationship between households and servicers. To bridge this gap, we developed\u0000an agent-based model that explores household behavior and the effectiveness of\u0000relief measures during financial distress. Our model represents households as adaptive learning agents with realistic\u0000financial attributes. These households experience exogenous income shocks,\u0000which may influence their ability to make mortgage payments. Mortgage servicers\u0000provide relief options to these households, who then choose the most suitable\u0000relief based on their unique financial circumstances and individual\u0000preferences. We analyze the impact of various external shocks and the success\u0000of different mortgage relief strategies on specific borrower subgroups. Through this analysis, we show that our model can not only replicate\u0000real-world mortgage studies but also act as a tool for conducting a broad range\u0000of what-if scenario analyses. Our approach offers fine-grained insights that\u0000can inform the development of more effective and inclusive mortgage relief\u0000solutions.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140005623","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}
Financial institutions and businesses face an ongoing challenge from fraudulent transactions, prompting the need for effective detection methods. Detecting credit card fraud is crucial for identifying and preventing unauthorized transactions.Timely detection of fraud enables investigators to take swift actions to mitigate further losses. However, the investigation process is often time-consuming, limiting the number of alerts that can be thoroughly examined each day. Therefore, the primary objective of a fraud detection model is to provide accurate alerts while minimizing false alarms and missed fraud cases. In this paper, we introduce a state-of-the-art hybrid ensemble (ENS) dependable Machine learning (ML) model that intelligently combines multiple algorithms with proper weighted optimization using Grid search, including Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), and Multilayer Perceptron (MLP), to enhance fraud identification. To address the data imbalance issue, we employ the Instant Hardness Threshold (IHT) technique in conjunction with Logistic Regression (LR), surpassing conventional approaches. Our experiments are conducted on a publicly available credit card dataset comprising 284,807 transactions. The proposed model achieves impressive accuracy rates of 99.66%, 99.73%, 98.56%, and 99.79%, and a perfect 100% for the DT, RF, KNN, MLP and ENS models, respectively. The hybrid ensemble model outperforms existing works, establishing a new benchmark for detecting fraudulent transactions in high-frequency scenarios. The results highlight the effectiveness and reliability of our approach, demonstrating superior performance metrics and showcasing its exceptional potential for real-world fraud detection applications.
{"title":"Securing Transactions: A Hybrid Dependable Ensemble Machine Learning Model using IHT-LR and Grid Search","authors":"Md. Alamin Talukder, Rakib Hossen, Md Ashraf Uddin, Mohammed Nasir Uddin, Uzzal Kumar Acharjee","doi":"arxiv-2402.14389","DOIUrl":"https://doi.org/arxiv-2402.14389","url":null,"abstract":"Financial institutions and businesses face an ongoing challenge from\u0000fraudulent transactions, prompting the need for effective detection methods.\u0000Detecting credit card fraud is crucial for identifying and preventing\u0000unauthorized transactions.Timely detection of fraud enables investigators to\u0000take swift actions to mitigate further losses. However, the investigation\u0000process is often time-consuming, limiting the number of alerts that can be\u0000thoroughly examined each day. Therefore, the primary objective of a fraud\u0000detection model is to provide accurate alerts while minimizing false alarms and\u0000missed fraud cases. In this paper, we introduce a state-of-the-art hybrid\u0000ensemble (ENS) dependable Machine learning (ML) model that intelligently\u0000combines multiple algorithms with proper weighted optimization using Grid\u0000search, including Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor\u0000(KNN), and Multilayer Perceptron (MLP), to enhance fraud identification. To\u0000address the data imbalance issue, we employ the Instant Hardness Threshold\u0000(IHT) technique in conjunction with Logistic Regression (LR), surpassing\u0000conventional approaches. Our experiments are conducted on a publicly available\u0000credit card dataset comprising 284,807 transactions. The proposed model\u0000achieves impressive accuracy rates of 99.66%, 99.73%, 98.56%, and 99.79%, and a\u0000perfect 100% for the DT, RF, KNN, MLP and ENS models, respectively. The hybrid\u0000ensemble model outperforms existing works, establishing a new benchmark for\u0000detecting fraudulent transactions in high-frequency scenarios. The results\u0000highlight the effectiveness and reliability of our approach, demonstrating\u0000superior performance metrics and showcasing its exceptional potential for\u0000real-world fraud detection applications.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139949019","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}
Joerg Osterrieder, Stephen Chan, Jeffrey Chu, Yuanyuan Zhang, Branka Hadji Misheva, Codruta Mare
Blockchain technology, a foundational distributed ledger system, enables secure and transparent multi-party transactions. Despite its advantages, blockchain networks are susceptible to anomalies and frauds, posing significant risks to their integrity and security. This paper offers a detailed examination of blockchain's key definitions and properties, alongside a thorough analysis of the various anomalies and frauds that undermine these networks. It describes an array of detection and prevention strategies, encompassing statistical and machine learning methods, game-theoretic solutions, digital forensics, reputation-based systems, and comprehensive risk assessment techniques. Through case studies, we explore practical applications of anomaly and fraud detection in blockchain networks, extracting valuable insights and implications for both current practice and future research. Moreover, we spotlight emerging trends and challenges within the field, proposing directions for future investigation and technological development. Aimed at both practitioners and researchers, this paper seeks to provide a technical, in-depth overview of anomaly and fraud detection within blockchain networks, marking a significant step forward in the search for enhanced network security and reliability.
{"title":"Enhancing Security in Blockchain Networks: Anomalies, Frauds, and Advanced Detection Techniques","authors":"Joerg Osterrieder, Stephen Chan, Jeffrey Chu, Yuanyuan Zhang, Branka Hadji Misheva, Codruta Mare","doi":"arxiv-2402.11231","DOIUrl":"https://doi.org/arxiv-2402.11231","url":null,"abstract":"Blockchain technology, a foundational distributed ledger system, enables\u0000secure and transparent multi-party transactions. Despite its advantages,\u0000blockchain networks are susceptible to anomalies and frauds, posing significant\u0000risks to their integrity and security. This paper offers a detailed examination\u0000of blockchain's key definitions and properties, alongside a thorough analysis\u0000of the various anomalies and frauds that undermine these networks. It describes\u0000an array of detection and prevention strategies, encompassing statistical and\u0000machine learning methods, game-theoretic solutions, digital forensics,\u0000reputation-based systems, and comprehensive risk assessment techniques. Through\u0000case studies, we explore practical applications of anomaly and fraud detection\u0000in blockchain networks, extracting valuable insights and implications for both\u0000current practice and future research. Moreover, we spotlight emerging trends\u0000and challenges within the field, proposing directions for future investigation\u0000and technological development. Aimed at both practitioners and researchers,\u0000this paper seeks to provide a technical, in-depth overview of anomaly and fraud\u0000detection within blockchain networks, marking a significant step forward in the\u0000search for enhanced network security and reliability.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139909774","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 evolution of cryptocurrency and decentralized finance (DeFi) marks a significant shift in the financial landscape, making it more accessible, inclusive, and participative for various societal groups. However, this transition from traditional financial institutions to DeFi demands a meticulous policy framework that strikes a balance between innovation and safeguarding consumer interests, security, and regulatory compliance. In this script we explore the imperative need for regulatory frameworks overseeing cryptocurrencies and DeFi, aiming to leverage their potential for inclusive economic advancement. It underscores the prevalent challenges within conventional financial systems, juxtaposing them with the transformative potential offered by these emergent financial paradigms. By highlighting the role of robust regulations, we examine their capacity to ensure user security, fortify market resilience, and spur innovative strides. We aim to proffer viable strategies for formulating regulatory structures that harmonize the twin objectives of fostering innovation and upholding fairness within financial ecosystems.
{"title":"Regulating Cryptocurrency and Decentralized Finance for an Inclusive Economy","authors":"Amrutha Muralidhar, Muralidhar Lakkanna","doi":"arxiv-2407.01532","DOIUrl":"https://doi.org/arxiv-2407.01532","url":null,"abstract":"The evolution of cryptocurrency and decentralized finance (DeFi) marks a\u0000significant shift in the financial landscape, making it more accessible,\u0000inclusive, and participative for various societal groups. However, this\u0000transition from traditional financial institutions to DeFi demands a meticulous\u0000policy framework that strikes a balance between innovation and safeguarding\u0000consumer interests, security, and regulatory compliance. In this script we\u0000explore the imperative need for regulatory frameworks overseeing\u0000cryptocurrencies and DeFi, aiming to leverage their potential for inclusive\u0000economic advancement. It underscores the prevalent challenges within\u0000conventional financial systems, juxtaposing them with the transformative\u0000potential offered by these emergent financial paradigms. By highlighting the\u0000role of robust regulations, we examine their capacity to ensure user security,\u0000fortify market resilience, and spur innovative strides. We aim to proffer\u0000viable strategies for formulating regulatory structures that harmonize the twin\u0000objectives of fostering innovation and upholding fairness within financial\u0000ecosystems.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"227 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141517608","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 review considers the Universities Superannuation Scheme (USS) valuations from 2014 to 2023. USS is a 70-80 billion GBP Defined Benefit pension scheme with over 500,000 members who are employed (or have been employed) at around 70 UK universities. Disputes over USS have led to a decade of industrial action. New results are presented showing the high dependence of USS pension contributions on the return from UK government bonds (the gilt yield). The two conditions of the USS-specific 'self-sufficiency' (SfS) definition are examined. USS data are presented along with new analysis. It is shown that the second SfS condition of 'maintaining a high funding ratio' dominates USS modelling to amplify gilt yield dependence, inflating the SfS liabilities beyond the regulatory requirements, and leading to excessive prudence. The Red, Amber and Green status of USS metrics 'Actual' and 'Target' Reliance are also examined. It is shown that Target Reliance tethers the cost of future pensions to the SfS definition and that Actual Reliance can simultaneously be Green and Red. Implications for regulatory intervention are considered. An aim of this review is to support evidence-based decision making and consensus building.
本次审查考虑了 2014 年至 2023 年的大学退休金计划（USS）估值。USS 是一项规模达 700-800 亿英镑的福利确定型养老金计划，拥有 50 多万名成员，他们受雇于（或曾经受雇于）英国约 70 所大学。有关 USS 的争议导致了长达十年的工业行动。新结果显示 USS 养老金缴款高度依赖于英国政府债券的收益（金边债券收益率）。研究了 USS 特定的 "自给自足"（SfS）定义的两个条件。在提供 USS 数据的同时，还进行了新的分析。结果表明，"维持较高的资金比率 "这第二个 SfS 条件在 USS 模型中占主导地位，放大了对金边债券收益率的依赖性，使 SfS 负债膨胀，超出了监管要求，导致过度审慎。此外，还对 USS 指标 "实际 "和 "目标 "依赖度的红色、黄色和绿色状态进行了研究。结果表明，"目标依赖度 "将未来养老金的成本与 SfS 的定义挂钩，而 "实际依赖度 "可以同时为绿色和红色。本文还考虑了监管干预的影响。本研究的目的之一是支持以证据为基础的决策和建立共识。
{"title":"The UK Universities Superannuation Scheme valuations 2014-2023: gilt yield dependence, self-sufficiency and metrics","authors":"Jackie Grant","doi":"arxiv-2403.08811","DOIUrl":"https://doi.org/arxiv-2403.08811","url":null,"abstract":"This review considers the Universities Superannuation Scheme (USS) valuations\u0000from 2014 to 2023. USS is a 70-80 billion GBP Defined Benefit pension scheme\u0000with over 500,000 members who are employed (or have been employed) at around 70\u0000UK universities. Disputes over USS have led to a decade of industrial action.\u0000New results are presented showing the high dependence of USS pension\u0000contributions on the return from UK government bonds (the gilt yield). The two\u0000conditions of the USS-specific 'self-sufficiency' (SfS) definition are\u0000examined. USS data are presented along with new analysis. It is shown that the\u0000second SfS condition of 'maintaining a high funding ratio' dominates USS\u0000modelling to amplify gilt yield dependence, inflating the SfS liabilities\u0000beyond the regulatory requirements, and leading to excessive prudence. The Red,\u0000Amber and Green status of USS metrics 'Actual' and 'Target' Reliance are also\u0000examined. It is shown that Target Reliance tethers the cost of future pensions\u0000to the SfS definition and that Actual Reliance can simultaneously be Green and\u0000Red. Implications for regulatory intervention are considered. An aim of this\u0000review is to support evidence-based decision making and consensus building.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"241 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140150977","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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