The Dynamic Function Market Maker (DFMM) introduced a fully automated framework for operating a multi-asset market, wherein an algorithmic accounting asset was used to connect different liquidity pools and ensure efficient rebalancing of risks, and internal accounting processes. In the DFMM design, this asset was not tradaeble; however, in this work, we explore the characteristics of this asset, if it were to be made tradeable. Named the Intermediating DFMM Asset (IDA), this asset serves as a unit of account in cross-chain finance, functioning as an intermediating asset for predictable budgeting, and efficient multichain transfers and settlements. Harnessing its robust liquidity as the key counterpart asset in DFMM, it achieves capital efficiency through the strategic repurposing of its asset base, while simultaneously mitigating risk via the dynamic optimisation of its multicollateral foundation. We outline key characteristics of the proposed asset, unique risk mitigation aspects enabled by the adopting AMM (DFMM), and control levers enabling the protocol's tactical asset and liability management toolkit to harmonise the asset's objectives with its real-world realisation, through a novel prudential market operation to incentivise productive use of a finite asset and dynamic AMM fee to ensure alignment of behaviours. The proposed design has the potential to harmonise the interests of diverse market participants, leading to synergetic reactions to informational flow, aiding IDA protocol in achieving its objectives.
{"title":"Intermediating DFMM Asset (IDA)","authors":"Arman Abgaryan, Utkarsh Sharma","doi":"arxiv-2311.05234","DOIUrl":"https://doi.org/arxiv-2311.05234","url":null,"abstract":"The Dynamic Function Market Maker (DFMM) introduced a fully automated\u0000framework for operating a multi-asset market, wherein an algorithmic accounting\u0000asset was used to connect different liquidity pools and ensure efficient\u0000rebalancing of risks, and internal accounting processes. In the DFMM design,\u0000this asset was not tradaeble; however, in this work, we explore the\u0000characteristics of this asset, if it were to be made tradeable. Named the\u0000Intermediating DFMM Asset (IDA), this asset serves as a unit of account in\u0000cross-chain finance, functioning as an intermediating asset for predictable\u0000budgeting, and efficient multichain transfers and settlements. Harnessing its\u0000robust liquidity as the key counterpart asset in DFMM, it achieves capital\u0000efficiency through the strategic repurposing of its asset base, while\u0000simultaneously mitigating risk via the dynamic optimisation of its\u0000multicollateral foundation. We outline key characteristics of the proposed\u0000asset, unique risk mitigation aspects enabled by the adopting AMM (DFMM), and\u0000control levers enabling the protocol's tactical asset and liability management\u0000toolkit to harmonise the asset's objectives with its real-world realisation,\u0000through a novel prudential market operation to incentivise productive use of a\u0000finite asset and dynamic AMM fee to ensure alignment of behaviours. The\u0000proposed design has the potential to harmonise the interests of diverse market\u0000participants, leading to synergetic reactions to informational flow, aiding IDA\u0000protocol in achieving its objectives.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522831","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}
Currently, the advantages of decentralization through blockchain technology in the financial sector are actively discussed. In this article, we investigate the decentralization in the governance of Decentralized Autonomous Organizations (DAO) using the Gini coefficient as an indicator of inequality among the token owners. This metric is analyzed in the context of Return on Investment (ROI) for companies in the decentralized finance (DeFi) sector. Our goal is to understand whether the level of "real" decentralization in blockchain-based governance affects financial efficiency, and to explore the benefits and possible limitations of such an approach. This analysis allows for a deeper understanding of the significance and impact of decentralization on the functioning and productivity of organizations in the DeFi sector, and to determine the extent to which this impact is positively or negatively reflected in their success and profitability. Additionally, the results of this analysis will provide a fuller understanding of the dynamics and potential of blockchain for organization governance.
{"title":"Analysis of Decentralization in Governance and Financial Efficiency of Companies: Studying the Relationship in the Field of Decentralized Finance","authors":"Kirill Kolmykov","doi":"arxiv-2311.02434","DOIUrl":"https://doi.org/arxiv-2311.02434","url":null,"abstract":"Currently, the advantages of decentralization through blockchain technology\u0000in the financial sector are actively discussed. In this article, we investigate\u0000the decentralization in the governance of Decentralized Autonomous\u0000Organizations (DAO) using the Gini coefficient as an indicator of inequality\u0000among the token owners. This metric is analyzed in the context of Return on\u0000Investment (ROI) for companies in the decentralized finance (DeFi) sector. Our\u0000goal is to understand whether the level of \"real\" decentralization in\u0000blockchain-based governance affects financial efficiency, and to explore the\u0000benefits and possible limitations of such an approach. This analysis allows for\u0000a deeper understanding of the significance and impact of decentralization on\u0000the functioning and productivity of organizations in the DeFi sector, and to\u0000determine the extent to which this impact is positively or negatively reflected\u0000in their success and profitability. Additionally, the results of this analysis\u0000will provide a fuller understanding of the dynamics and potential of blockchain\u0000for organization governance.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522786","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 exploratory study examines which investing characteristics determine success in an equity market. Based on data from 403 respondents, exploratory factor analysis results in 13 factors: middle/long time horizon, qualitative analyst, open-minded/disciplined, organized, emotional stability, na"ive, growth stock, concentrated portfolio, contrarian, value stock, globalized, intrinsic value, and price-independent. Multiple linear regression of individual investors' excess return on these factors show statistically significant relationship. These results deepen our knowledge on what sort of investing characteristics are required to survive in equity markets.
{"title":"Impact of Investing Characteristics on Financial Performance of Individual Investors: An Exploratory Study","authors":"Poompak Kusawat, Nopadol Rompho","doi":"arxiv-2311.00384","DOIUrl":"https://doi.org/arxiv-2311.00384","url":null,"abstract":"This exploratory study examines which investing characteristics determine\u0000success in an equity market. Based on data from 403 respondents, exploratory\u0000factor analysis results in 13 factors: middle/long time horizon, qualitative\u0000analyst, open-minded/disciplined, organized, emotional stability, na\"ive,\u0000growth stock, concentrated portfolio, contrarian, value stock, globalized,\u0000intrinsic value, and price-independent. Multiple linear regression of\u0000individual investors' excess return on these factors show statistically\u0000significant relationship. These results deepen our knowledge on what sort of\u0000investing characteristics are required to survive in equity markets.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522697","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}
Maurizio La Rocca, Tiziana La Rocca, Francesco Fasano, Javier Sanchez-Vidal
Corruption, fraud, and unethical activities have emerged as significant obstacles to global economic, political, and social progress. Although many empirical studies have focused on country-level corruption metrics, this study is the first to utilize a substantial international dataset to assess the effects of illicit and unethical managerial practices on firm performance. Employing cross-sectional data, this research examines the influence of corruption on corporate outcomes. Our definition of corruption evaluates the degree to which managers engage in mismanagement, misconduct, or corrupt activities. The repercussions for corporate governance, especially concerning the process of appointing managers, are both crucial and strategic.
{"title":"From the Top Down: Does Corruption Affect Performance?","authors":"Maurizio La Rocca, Tiziana La Rocca, Francesco Fasano, Javier Sanchez-Vidal","doi":"arxiv-2310.20028","DOIUrl":"https://doi.org/arxiv-2310.20028","url":null,"abstract":"Corruption, fraud, and unethical activities have emerged as significant\u0000obstacles to global economic, political, and social progress. Although many\u0000empirical studies have focused on country-level corruption metrics, this study\u0000is the first to utilize a substantial international dataset to assess the\u0000effects of illicit and unethical managerial practices on firm performance.\u0000Employing cross-sectional data, this research examines the influence of\u0000corruption on corporate outcomes. Our definition of corruption evaluates the\u0000degree to which managers engage in mismanagement, misconduct, or corrupt\u0000activities. The repercussions for corporate governance, especially concerning\u0000the process of appointing managers, are both crucial and strategic.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522847","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}
Drawing inspiration from the significant impact of the ongoing Russia-Ukraine conflict and the recent COVID-19 pandemic on global financial markets, this study conducts a thorough analysis of three key crude oil futures markets: WTI, Brent, and Shanghai (SC). Employing the visibility graph (VG) methodology, we examine both static and dynamic characteristics using daily and high-frequency data. We identified a clear power-law decay in most VG degree distributions and highlighted the pronounced clustering tendencies within crude oil futures VGs. Our results also confirm an inverse correlation between clustering coefficient and node degree and further reveal that all VGs not only adhere to the small-world property but also exhibit intricate assortative mixing. Through the time-varying characteristics of VGs, we found that WTI and Brent demonstrate aligned behavior, while the SC market, with its unique trading mechanics, deviates. The 5-minute VGs' assortativity coefficient provides a deeper understanding of these markets' reactions to the pandemic and geopolitical events. Furthermore, the differential responses during the COVID-19 and Russia-Ukraine conflict underline the unique sensitivities of each market to global disruptions. Overall, this research offers profound insights into the structure, dynamics, and adaptability of these essential commodities markets in the face of worldwide challenges.
{"title":"Visibility graph analysis of crude oil futures markets: Insights from the COVID-19 pandemic and Russia-Ukraine conflict","authors":"Ying-Hui Shao, Yan-Hong Yang","doi":"arxiv-2310.18903","DOIUrl":"https://doi.org/arxiv-2310.18903","url":null,"abstract":"Drawing inspiration from the significant impact of the ongoing Russia-Ukraine\u0000conflict and the recent COVID-19 pandemic on global financial markets, this\u0000study conducts a thorough analysis of three key crude oil futures markets: WTI,\u0000Brent, and Shanghai (SC). Employing the visibility graph (VG) methodology, we\u0000examine both static and dynamic characteristics using daily and high-frequency\u0000data. We identified a clear power-law decay in most VG degree distributions and\u0000highlighted the pronounced clustering tendencies within crude oil futures VGs.\u0000Our results also confirm an inverse correlation between clustering coefficient\u0000and node degree and further reveal that all VGs not only adhere to the\u0000small-world property but also exhibit intricate assortative mixing. Through the\u0000time-varying characteristics of VGs, we found that WTI and Brent demonstrate\u0000aligned behavior, while the SC market, with its unique trading mechanics,\u0000deviates. The 5-minute VGs' assortativity coefficient provides a deeper\u0000understanding of these markets' reactions to the pandemic and geopolitical\u0000events. Furthermore, the differential responses during the COVID-19 and\u0000Russia-Ukraine conflict underline the unique sensitivities of each market to\u0000global disruptions. Overall, this research offers profound insights into the\u0000structure, dynamics, and adaptability of these essential commodities markets in\u0000the face of worldwide challenges.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522841","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 investigates the influence of monetary policy and monetary policy uncertainties on Bitcoin returns, utilizing monthly data of BTC, and MPU from July 2010 to August 2023, and employing the Markov Switching Means VAR (MSM-VAR) method. The findings reveal that Bitcoin returns can be categorized into two distinct regimes: 1) regime 1 with low volatility, and 2) regime 2 with high volatility. In both regimes, an increase in MPU leads to a decline in Bitcoin returns: -0.028 in regime 1 and -0.44 in regime 2. This indicates that monetary policy uncertainty exerts a negative influence on Bitcoin returns during both downturns and upswings. Furthermore, the study explores Bitcoin's sensitivity to Federal Open Market Committee (FOMC) decisions.
{"title":"Examining the Effect of Monetary Policy and Monetary Policy Uncertainty on Cryptocurrencies Market","authors":"Mohammadreza Mahmoudi","doi":"arxiv-2311.10739","DOIUrl":"https://doi.org/arxiv-2311.10739","url":null,"abstract":"This study investigates the influence of monetary policy and monetary policy\u0000uncertainties on Bitcoin returns, utilizing monthly data of BTC, and MPU from\u0000July 2010 to August 2023, and employing the Markov Switching Means VAR\u0000(MSM-VAR) method. The findings reveal that Bitcoin returns can be categorized\u0000into two distinct regimes: 1) regime 1 with low volatility, and 2) regime 2\u0000with high volatility. In both regimes, an increase in MPU leads to a decline in\u0000Bitcoin returns: -0.028 in regime 1 and -0.44 in regime 2. This indicates that\u0000monetary policy uncertainty exerts a negative influence on Bitcoin returns\u0000during both downturns and upswings. Furthermore, the study explores Bitcoin's\u0000sensitivity to Federal Open Market Committee (FOMC) decisions.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522846","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}
A political party can be considered as a company whose value depends on the voters support i.e. on the percentage of population supporting the party. Dynamics of the support is thus as a stochastic process with a deterministic growth rate perturbed by a white noise modeled through the Wiener process. This is in an analogy with the option modeling where the stock price behaves similarly as the voters' support. While in the option theory we have the question of fair price of an option, the question that we ask here is what is a reasonable level of support that the coalition of a major party (safely above the election threshold) and a minor party (under or around the election threshold) should achieve in order the minor party to get one more representative. We shall elaborate some of the conclusions in the case of recent elections in Montenegro (June, 2023) which are particularly interesting due to lots of political subjects entering the race.
{"title":"Pre-electoral coalition agreement from the Black-Scholes point of view","authors":"Darko Mitrovic","doi":"arxiv-2310.16424","DOIUrl":"https://doi.org/arxiv-2310.16424","url":null,"abstract":"A political party can be considered as a company whose value depends on the\u0000voters support i.e. on the percentage of population supporting the party.\u0000Dynamics of the support is thus as a stochastic process with a deterministic\u0000growth rate perturbed by a white noise modeled through the Wiener process. This\u0000is in an analogy with the option modeling where the stock price behaves\u0000similarly as the voters' support. While in the option theory we have the\u0000question of fair price of an option, the question that we ask here is what is a\u0000reasonable level of support that the coalition of a major party (safely above\u0000the election threshold) and a minor party (under or around the election\u0000threshold) should achieve in order the minor party to get one more\u0000representative. We shall elaborate some of the conclusions in the case of\u0000recent elections in Montenegro (June, 2023) which are particularly interesting\u0000due to lots of political subjects entering the race.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522787","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}
In the wake of relentless digital transformation, data-driven solutions are emerging as powerful tools to address multifarious industrial tasks such as forecasting, anomaly detection, planning, and even complex decision-making. Although data-centric R&D has been pivotal in harnessing these solutions, it often comes with significant costs in terms of human, computational, and time resources. This paper delves into the potential of large language models (LLMs) to expedite the evolution cycle of data-centric R&D. Assessing the foundational elements of data-centric R&D, including heterogeneous task-related data, multi-facet domain knowledge, and diverse computing-functional tools, we explore how well LLMs can understand domain-specific requirements, generate professional ideas, utilize domain-specific tools to conduct experiments, interpret results, and incorporate knowledge from past endeavors to tackle new challenges. We take quantitative investment research as a typical example of industrial data-centric R&D scenario and verified our proposed framework upon our full-stack open-sourced quantitative research platform Qlib and obtained promising results which shed light on our vision of automatic evolving of industrial data-centric R&D cycle.
{"title":"Leveraging Large Language Model for Automatic Evolving of Industrial Data-Centric R&D Cycle","authors":"Xu Yang, Xiao Yang, Weiqing Liu, Jinhui Li, Peng Yu, Zeqi Ye, Jiang Bian","doi":"arxiv-2310.11249","DOIUrl":"https://doi.org/arxiv-2310.11249","url":null,"abstract":"In the wake of relentless digital transformation, data-driven solutions are\u0000emerging as powerful tools to address multifarious industrial tasks such as\u0000forecasting, anomaly detection, planning, and even complex decision-making.\u0000Although data-centric R&D has been pivotal in harnessing these solutions, it\u0000often comes with significant costs in terms of human, computational, and time\u0000resources. This paper delves into the potential of large language models (LLMs)\u0000to expedite the evolution cycle of data-centric R&D. Assessing the foundational\u0000elements of data-centric R&D, including heterogeneous task-related data,\u0000multi-facet domain knowledge, and diverse computing-functional tools, we\u0000explore how well LLMs can understand domain-specific requirements, generate\u0000professional ideas, utilize domain-specific tools to conduct experiments,\u0000interpret results, and incorporate knowledge from past endeavors to tackle new\u0000challenges. We take quantitative investment research as a typical example of\u0000industrial data-centric R&D scenario and verified our proposed framework upon\u0000our full-stack open-sourced quantitative research platform Qlib and obtained\u0000promising results which shed light on our vision of automatic evolving of\u0000industrial data-centric R&D cycle.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522835","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}
Bank crisis is challenging to define but can be manifested through bank contagion. This study presents a comprehensive framework grounded in nonlinear time series analysis to identify potential early warning signals (EWS) for impending phase transitions in bank systems, with the goal of anticipating severe bank crisis. In contrast to traditional analyses of exposure networks using low-frequency data, we argue that studying the dynamic relationships among bank stocks using high-frequency data offers a more insightful perspective on changes in the banking system. We construct multiple recurrence networks (MRNs) based on multidimensional returns of listed banks' stocks in China, aiming to monitor the nonlinear dynamics of the system through the corresponding indicators and topological structures. Empirical findings indicate that key indicators of MRNs, specifically the average mutual information, provide valuable insights into periods of extreme volatility of bank system. This paper contributes to the ongoing discourse on early warning signals for bank instability, highlighting the applicability of predicting systemic risks in the context of banking networks.
{"title":"Unveiling Early Warning Signals of Systemic Risks in Banks: A Recurrence Network-Based Approach","authors":"Shijia Song, Handong Li","doi":"arxiv-2310.10283","DOIUrl":"https://doi.org/arxiv-2310.10283","url":null,"abstract":"Bank crisis is challenging to define but can be manifested through bank\u0000contagion. This study presents a comprehensive framework grounded in nonlinear\u0000time series analysis to identify potential early warning signals (EWS) for\u0000impending phase transitions in bank systems, with the goal of anticipating\u0000severe bank crisis. In contrast to traditional analyses of exposure networks\u0000using low-frequency data, we argue that studying the dynamic relationships\u0000among bank stocks using high-frequency data offers a more insightful\u0000perspective on changes in the banking system. We construct multiple recurrence\u0000networks (MRNs) based on multidimensional returns of listed banks' stocks in\u0000China, aiming to monitor the nonlinear dynamics of the system through the\u0000corresponding indicators and topological structures. Empirical findings\u0000indicate that key indicators of MRNs, specifically the average mutual\u0000information, provide valuable insights into periods of extreme volatility of\u0000bank system. This paper contributes to the ongoing discourse on early warning\u0000signals for bank instability, highlighting the applicability of predicting\u0000systemic risks in the context of banking networks.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522837","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}
In the rapidly evolving field of crypto-assets, white papers are essential documents for investor guidance, and are now subject to unprecedented content requirements under the EU's Markets in Crypto-Assets Regulation (MiCAR). Natural Language Processing can serve as a powerful tool for both analyzing these documents and assisting in regulatory compliance. This paper delivers two contributions to the topic. First, we survey existing applications of textual analysis to unregulated crypto-asset white papers, uncovering a research gap that could be bridged with interdisciplinary collaboration. We then conduct an analysis of the changes introduced by MiCAR, highlighting the opportunities and challenges of integrating NLP within the new regulatory framework. The findings set the stage for further research, with the potential to benefit regulators, crypto-asset issuers, and investors.
{"title":"NLP for Crypto-Asset Regulation: A Roadmap","authors":"Carolina Camassa","doi":"arxiv-2310.10333","DOIUrl":"https://doi.org/arxiv-2310.10333","url":null,"abstract":"In the rapidly evolving field of crypto-assets, white papers are essential\u0000documents for investor guidance, and are now subject to unprecedented content\u0000requirements under the EU's Markets in Crypto-Assets Regulation (MiCAR).\u0000Natural Language Processing can serve as a powerful tool for both analyzing\u0000these documents and assisting in regulatory compliance. This paper delivers two\u0000contributions to the topic. First, we survey existing applications of textual\u0000analysis to unregulated crypto-asset white papers, uncovering a research gap\u0000that could be bridged with interdisciplinary collaboration. We then conduct an\u0000analysis of the changes introduced by MiCAR, highlighting the opportunities and\u0000challenges of integrating NLP within the new regulatory framework. The findings\u0000set the stage for further research, with the potential to benefit regulators,\u0000crypto-asset issuers, and investors.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522695","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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