arXiv - QuantFin - General Finance最新文献_第3页

Voting Participation and Engagement in Blockchain-Based Fan Tokens 基于区块链的粉丝代币的投票参与度和互动性

arXiv - QuantFin - General Finance Pub Date : 2024-04-13 DOI: arxiv-2404.08906

Lennart Ante, Aman Saggu, Benjamin Schellinger, Friedrich Wazinksi

{"title":"Voting Participation and Engagement in Blockchain-Based Fan Tokens","authors":"Lennart Ante, Aman Saggu, Benjamin Schellinger, Friedrich Wazinksi","doi":"arxiv-2404.08906","DOIUrl":"https://doi.org/arxiv-2404.08906","url":null,"abstract":"This paper investigates the potential of blockchain-based fan tokens, a class\u0000of crypto asset that grants holders access to voting on club decisions and\u0000other perks, as a mechanism for stimulating democratized decision-making and\u0000fan engagement in the sports and esports sectors. By utilizing an extensive\u0000dataset of 3,576 fan token polls, we reveal that fan tokens engage an average\u0000of 4,003 participants per poll, representing around 50% of token holders,\u0000underscoring their relative effectiveness in boosting fan engagement. The\u0000analyses identify significant determinants of fan token poll participation,\u0000including levels of voter (dis-)agreement, poll type, sports sectors,\u0000demographics, and club-level factors. This study provides valuable stakeholder\u0000insights into the current state of adoption and voting trends for fan token\u0000polls. It also suggests strategies for increasing fan engagement, thereby\u0000optimizing the utility of fan tokens in sports. Moreover, we highlight the\u0000broader applicability of fan token principles to any community, brand, or\u0000organization focused on customer engagement, suggesting a wider potential for\u0000this digital innovation.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140602016","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

The Quantum Dynamics of Cost Accounting: Investigating WIP via the Time-Independent Schrodinger Equation 成本会计的量子动力学：通过与时间无关的薛定谔方程研究 WIP

arXiv - QuantFin - General Finance Pub Date : 2024-04-11 DOI: arxiv-2405.00047

Maksym Lazirko

引用次数: 0

Social Dynamics of Consumer Response: A Unified Framework Integrating Statistical Physics and Marketing Dynamics 消费者反应的社会动力学：整合统计物理学和营销动力学的统一框架

arXiv - QuantFin - General Finance Pub Date : 2024-04-01 DOI: arxiv-2404.02175

Javier Marin

{"title":"Social Dynamics of Consumer Response: A Unified Framework Integrating Statistical Physics and Marketing Dynamics","authors":"Javier Marin","doi":"arxiv-2404.02175","DOIUrl":"https://doi.org/arxiv-2404.02175","url":null,"abstract":"Comprehending how consumers react to advertising inputs is essential for\u0000marketers aiming to optimize advertising strategies and improve campaign\u0000effectiveness. This study examines the complex nature of consumer behaviour by\u0000applying theoretical frameworks derived from physics and social psychology. We\u0000present an innovative equation that captures the relation between spending on\u0000advertising and consumer response, using concepts such as symmetries, scaling\u0000laws, and phase transitions. By validating our equation against well-known\u0000models such as the Michaelis-Menten and Hill equations, we prove its\u0000effectiveness in accurately representing the complexity of consumer response\u0000dynamics. The analysis emphasizes the importance of key model parameters, such\u0000as marketing effectiveness, response sensitivity, and behavioural sensitivity,\u0000in influencing consumer behaviour. The work explores the practical implications\u0000for advertisers and marketers, as well as discussing the limitations and future\u0000research directions. In summary, this study provides a thorough framework for\u0000comprehending and forecasting consumer reactions to advertising, which has\u0000implications for optimizing advertising strategies and allocating resources.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"81 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140601397","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

High-Dimensional Mean-Variance Spanning Tests 高维均方差跨度测试

arXiv - QuantFin - General Finance Pub Date : 2024-03-25 DOI: arxiv-2403.17127

David Ardia, Sébastien Laurent, Rosnel Sessinou

引用次数: 0

Improving Retrieval for RAG based Question Answering Models on Financial Documents 改进基于 RAG 问题解答模型的金融文档检索

arXiv - QuantFin - General Finance Pub Date : 2024-03-23 DOI: arxiv-2404.07221

Spurthi Setty, Katherine Jijo, Eden Chung, Natan Vidra

{"title":"Improving Retrieval for RAG based Question Answering Models on Financial Documents","authors":"Spurthi Setty, Katherine Jijo, Eden Chung, Natan Vidra","doi":"arxiv-2404.07221","DOIUrl":"https://doi.org/arxiv-2404.07221","url":null,"abstract":"The effectiveness of Large Language Models (LLMs) in generating accurate\u0000responses relies heavily on the quality of input provided, particularly when\u0000employing Retrieval Augmented Generation (RAG) techniques. RAG enhances LLMs by\u0000sourcing the most relevant text chunk(s) to base queries upon. Despite the\u0000significant advancements in LLMs' response quality in recent years, users may\u0000still encounter inaccuracies or irrelevant answers; these issues often stem\u0000from suboptimal text chunk retrieval by RAG rather than the inherent\u0000capabilities of LLMs. To augment the efficacy of LLMs, it is crucial to refine\u0000the RAG process. This paper explores the existing constraints of RAG pipelines\u0000and introduces methodologies for enhancing text retrieval. It delves into\u0000strategies such as sophisticated chunking techniques, query expansion, the\u0000incorporation of metadata annotations, the application of re-ranking\u0000algorithms, and the fine-tuning of embedding algorithms. Implementing these\u0000approaches can substantially improve the retrieval quality, thereby elevating\u0000the overall performance and reliability of LLMs in processing and responding to\u0000queries.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140601589","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

Investigating Similarities Across Decentralized Financial (DeFi) Services 调查分散式金融（DeFi）服务的相似性

arXiv - QuantFin - General Finance Pub Date : 2024-03-23 DOI: arxiv-2404.00034

Junliang Luo, Stefan Kitzler, Pietro Saggese

{"title":"Investigating Similarities Across Decentralized Financial (DeFi) Services","authors":"Junliang Luo, Stefan Kitzler, Pietro Saggese","doi":"arxiv-2404.00034","DOIUrl":"https://doi.org/arxiv-2404.00034","url":null,"abstract":"We explore the adoption of graph representation learning (GRL) algorithms to\u0000investigate similarities across services offered by Decentralized Finance\u0000(DeFi) protocols. Following existing literature, we use Ethereum transaction\u0000data to identify the DeFi building blocks. These are sets of protocol-specific\u0000smart contracts that are utilized in combination within single transactions and\u0000encapsulate the logic to conduct specific financial services such as swapping\u0000or lending cryptoassets. We propose a method to categorize these blocks into\u0000clusters based on their smart contract attributes and the graph structure of\u0000their smart contract calls. We employ GRL to create embedding vectors from\u0000building blocks and agglomerative models for clustering them. To evaluate\u0000whether they are effectively grouped in clusters of similar functionalities, we\u0000associate them with eight financial functionality categories and use this\u0000information as the target label. We find that in the best-case scenario purity\u0000reaches .888. We use additional information to associate the building blocks\u0000with protocol-specific target labels, obtaining comparable purity (.864) but\u0000higher V-Measure (.571); we discuss plausible explanations for this difference.\u0000In summary, this method helps categorize existing financial products offered by\u0000DeFi protocols, and can effectively automatize the detection of similar DeFi\u0000services, especially within protocols.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"301 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140602238","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 Taxmans guide to taxation of crypto assets 加密资产税务指南

arXiv - QuantFin - General Finance Pub Date : 2024-03-22 DOI: arxiv-2403.15074

Arindam Misra

{"title":"A Taxmans guide to taxation of crypto assets","authors":"Arindam Misra","doi":"arxiv-2403.15074","DOIUrl":"https://doi.org/arxiv-2403.15074","url":null,"abstract":"The Financial system has witnessed rapid technological changes. The rise of\u0000Bitcoin and other crypto assets based on Distributed Ledger Technology mark a\u0000fundamental change in the way people transact and transmit value over a\u0000decentralized network, spread across geographies. This has created regulatory\u0000and tax policy blind spots, as governments and tax administrations take time to\u0000understand and provide policy responses to this innovative, revolutionary, and\u0000fast-paced technology. Due to the breakneck speed of innovation in blockchain\u0000technology and advent of Decentralized Finance, Decentralized Autonomous\u0000Organizations and the Metaverse, it is unlikely that the policy interventions\u0000and guidance by regulatory authorities or tax administrations would be ahead or\u0000in sync with the pace of innovation. This paper tries to explain the principles\u0000on which crypto assets function, their underlying technology and relates them\u0000to the tax issues and taxable events which arise within this ecosystem. It also\u0000provides instances of tax and regulatory policy responses already in effect in\u0000various jurisdictions, including the recent changes in reporting standards by\u0000the FATF and the OECD. This paper tries to explain the rationale behind\u0000existing laws and policies and the challenges in their implementation. It also\u0000attempts to present a ballpark estimate of tax potential of this asset class\u0000and suggests creation of global public digital infrastructure that can address\u0000issues related to pseudonymity and extra-territoriality. The paper analyses\u0000both direct and indirect taxation issues related to crypto assets and discusses\u0000more recent aspects like proof-of-stake and maximal extractable value in\u0000greater detail.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"158 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140302690","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

Detecting and Triaging Spoofing using Temporal Convolutional Networks 利用时态卷积网络检测和处理欺骗行为

arXiv - QuantFin - General Finance Pub Date : 2024-03-20 DOI: arxiv-2403.13429

Kaushalya Kularatnam, Tania Stathaki

{"title":"Detecting and Triaging Spoofing using Temporal Convolutional Networks","authors":"Kaushalya Kularatnam, Tania Stathaki","doi":"arxiv-2403.13429","DOIUrl":"https://doi.org/arxiv-2403.13429","url":null,"abstract":"As algorithmic trading and electronic markets continue to transform the\u0000landscape of financial markets, detecting and deterring rogue agents to\u0000maintain a fair and efficient marketplace is crucial. The explosion of large\u0000datasets and the continually changing tricks of the trade make it difficult to\u0000adapt to new market conditions and detect bad actors. To that end, we propose a\u0000framework that can be adapted easily to various problems in the space of\u0000detecting market manipulation. Our approach entails initially employing a\u0000labelling algorithm which we use to create a training set to learn a weakly\u0000supervised model to identify potentially suspicious sequences of order book\u0000states. The main goal here is to learn a representation of the order book that\u0000can be used to easily compare future events. Subsequently, we posit the\u0000incorporation of expert assessment to scrutinize specific flagged order book\u0000states. In the event of an expert's unavailability, recourse is taken to the\u0000application of a more complex algorithm on the identified suspicious order book\u0000states. We then conduct a similarity search between any new representation of\u0000the order book against the expert labelled representations to rank the results\u0000of the weak learner. We show some preliminary results that are promising to\u0000explore further in this direction","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140199752","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

Effect of Leaders Voice on Financial Market: An Empirical Deep Learning Expedition on NASDAQ, NSE, and Beyond 领导者声音对金融市场的影响：纳斯达克、NSE 及其他市场的深度学习实证考察

arXiv - QuantFin - General Finance Pub Date : 2024-03-18 DOI: arxiv-2403.12161

Arijit Das, Tanmoy Nandi, Prasanta Saha, Suman Das, Saronyo Mukherjee, Sudip Kumar Naskar, Diganta Saha

{"title":"Effect of Leaders Voice on Financial Market: An Empirical Deep Learning Expedition on NASDAQ, NSE, and Beyond","authors":"Arijit Das, Tanmoy Nandi, Prasanta Saha, Suman Das, Saronyo Mukherjee, Sudip Kumar Naskar, Diganta Saha","doi":"arxiv-2403.12161","DOIUrl":"https://doi.org/arxiv-2403.12161","url":null,"abstract":"Financial market like the price of stock, share, gold, oil, mutual funds are\u0000affected by the news and posts on social media. In this work deep learning\u0000based models are proposed to predict the trend of financial market based on NLP\u0000analysis of the twitter handles of leaders of different fields. There are many\u0000models available to predict financial market based on only the historical data\u0000of the financial component but combining historical data with news and posts of\u0000the social media like Twitter is the main objective of the present work.\u0000Substantial improvement is shown in the result. The main features of the\u0000present work are: a) proposing completely generalized algorithm which is able\u0000to generate models for any twitter handle and any financial component, b)\u0000predicting the time window for a tweets effect on a stock price c) analyzing\u0000the effect of multiple twitter handles for predicting the trend. A detailed\u0000survey is done to find out the latest work in recent years in the similar\u0000field, find the research gap, and collect the required data for analysis and\u0000prediction. State-of-the-art algorithm is proposed and complete implementation\u0000with environment is given. An insightful trend of the result improvement\u0000considering the NLP analysis of twitter data on financial market components is\u0000shown. The Indian and USA financial markets are explored in the present work\u0000where as other markets can be taken in future. The socio-economic impact of the\u0000present work is discussed in conclusion.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140166222","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

Household Leverage Cycle Around the Great Recession 大衰退前后的家庭杠杆周期

arXiv - QuantFin - General Finance Pub Date : 2024-03-15 DOI: arxiv-2407.01539

Bo Li

引用次数: 0