Lennart Ante, Aman Saggu, Benjamin Schellinger, Friedrich Wazinksi
This paper investigates the potential of blockchain-based fan tokens, a class of crypto asset that grants holders access to voting on club decisions and other perks, as a mechanism for stimulating democratized decision-making and fan engagement in the sports and esports sectors. By utilizing an extensive dataset of 3,576 fan token polls, we reveal that fan tokens engage an average of 4,003 participants per poll, representing around 50% of token holders, underscoring their relative effectiveness in boosting fan engagement. The analyses identify significant determinants of fan token poll participation, including levels of voter (dis-)agreement, poll type, sports sectors, demographics, and club-level factors. This study provides valuable stakeholder insights into the current state of adoption and voting trends for fan token polls. It also suggests strategies for increasing fan engagement, thereby optimizing the utility of fan tokens in sports. Moreover, we highlight the broader applicability of fan token principles to any community, brand, or organization focused on customer engagement, suggesting a wider potential for this digital innovation.
{"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":null,"pages":null},"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}
The intersection of quantum theory and accounting presents a novel and intriguing frontier in exploring financial valuation and accounting practices. This paper applies quantum theory to cost accounting, specifically Work in Progress (WIP) valuation. WIP is conceptualized as materials in a quantum superposition state whose financial value remains uncertain until observed or measured. This work comprehensively reviews the seminal works that explored the overlap between quantum theory and accounting. The primary contribution of this work is a more nuanced understanding of the uncertainties involved, which emerges by applying quantum phenomena to model the complexities and uncertainties inherent in managerial accounting. In contrast, previous works focus more on financial accounting or general accountancy.
{"title":"The Quantum Dynamics of Cost Accounting: Investigating WIP via the Time-Independent Schrodinger Equation","authors":"Maksym Lazirko","doi":"arxiv-2405.00047","DOIUrl":"https://doi.org/arxiv-2405.00047","url":null,"abstract":"The intersection of quantum theory and accounting presents a novel and\u0000intriguing frontier in exploring financial valuation and accounting practices.\u0000This paper applies quantum theory to cost accounting, specifically Work in\u0000Progress (WIP) valuation. WIP is conceptualized as materials in a quantum\u0000superposition state whose financial value remains uncertain until observed or\u0000measured. This work comprehensively reviews the seminal works that explored the\u0000overlap between quantum theory and accounting. The primary contribution of this\u0000work is a more nuanced understanding of the uncertainties involved, which\u0000emerges by applying quantum phenomena to model the complexities and\u0000uncertainties inherent in managerial accounting. In contrast, previous works\u0000focus more on financial accounting or general accountancy.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140830275","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}
Comprehending how consumers react to advertising inputs is essential for marketers aiming to optimize advertising strategies and improve campaign effectiveness. This study examines the complex nature of consumer behaviour by applying theoretical frameworks derived from physics and social psychology. We present an innovative equation that captures the relation between spending on advertising and consumer response, using concepts such as symmetries, scaling laws, and phase transitions. By validating our equation against well-known models such as the Michaelis-Menten and Hill equations, we prove its effectiveness in accurately representing the complexity of consumer response dynamics. The analysis emphasizes the importance of key model parameters, such as marketing effectiveness, response sensitivity, and behavioural sensitivity, in influencing consumer behaviour. The work explores the practical implications for advertisers and marketers, as well as discussing the limitations and future research directions. In summary, this study provides a thorough framework for comprehending and forecasting consumer reactions to advertising, which has implications for optimizing advertising strategies and allocating resources.
{"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":null,"pages":null},"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}
We introduce a new framework for the mean-variance spanning (MVS) hypothesis testing. The procedure can be applied to any test-asset dimension and only requires stationary asset returns and the number of benchmark assets to be smaller than the number of time periods. It involves individually testing moment conditions using a robust Student-t statistic based on the batch-mean method and combining the p-values using the Cauchy combination test. Simulations demonstrate the superior performance of the test compared to state-of-the-art approaches. For the empirical application, we look at the problem of domestic versus international diversification in equities. We find that the advantages of diversification are influenced by economic conditions and exhibit cross-country variation. We also highlight that the rejection of the MVS hypothesis originates from the potential to reduce variance within the domestic global minimum-variance portfolio.
我们为均值-方差跨度（MVS）假设检验引入了一个新框架。该程序可应用于任何测试资产维度，且只要求资产回报率稳定，基准资产数量少于时间段数量。它包括使用基于批量均值法的稳健 Student-t 统计量单独测试时刻条件，并使用考奇组合检验合并 p 值。在实证应用方面，我们研究了国内与国际股票分散投资的问题。我们发现，分散投资的优势受到经济条件的影响，并呈现出跨国差异。我们还强调，MVS 假设的拒绝源于国内全球最小方差投资组合中减少方差的潜力。
{"title":"High-Dimensional Mean-Variance Spanning Tests","authors":"David Ardia, Sébastien Laurent, Rosnel Sessinou","doi":"arxiv-2403.17127","DOIUrl":"https://doi.org/arxiv-2403.17127","url":null,"abstract":"We introduce a new framework for the mean-variance spanning (MVS) hypothesis\u0000testing. The procedure can be applied to any test-asset dimension and only\u0000requires stationary asset returns and the number of benchmark assets to be\u0000smaller than the number of time periods. It involves individually testing\u0000moment conditions using a robust Student-t statistic based on the batch-mean\u0000method and combining the p-values using the Cauchy combination test.\u0000Simulations demonstrate the superior performance of the test compared to\u0000state-of-the-art approaches. For the empirical application, we look at the\u0000problem of domestic versus international diversification in equities. We find\u0000that the advantages of diversification are influenced by economic conditions\u0000and exhibit cross-country variation. We also highlight that the rejection of\u0000the MVS hypothesis originates from the potential to reduce variance within the\u0000domestic global minimum-variance portfolio.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140312966","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}
Spurthi Setty, Katherine Jijo, Eden Chung, Natan Vidra
The effectiveness of Large Language Models (LLMs) in generating accurate responses relies heavily on the quality of input provided, particularly when employing Retrieval Augmented Generation (RAG) techniques. RAG enhances LLMs by sourcing the most relevant text chunk(s) to base queries upon. Despite the significant advancements in LLMs' response quality in recent years, users may still encounter inaccuracies or irrelevant answers; these issues often stem from suboptimal text chunk retrieval by RAG rather than the inherent capabilities of LLMs. To augment the efficacy of LLMs, it is crucial to refine the RAG process. This paper explores the existing constraints of RAG pipelines and introduces methodologies for enhancing text retrieval. It delves into strategies such as sophisticated chunking techniques, query expansion, the incorporation of metadata annotations, the application of re-ranking algorithms, and the fine-tuning of embedding algorithms. Implementing these approaches can substantially improve the retrieval quality, thereby elevating the overall performance and reliability of LLMs in processing and responding to queries.
{"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":null,"pages":null},"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}
We explore the adoption of graph representation learning (GRL) algorithms to investigate similarities across services offered by Decentralized Finance (DeFi) protocols. Following existing literature, we use Ethereum transaction data to identify the DeFi building blocks. These are sets of protocol-specific smart contracts that are utilized in combination within single transactions and encapsulate the logic to conduct specific financial services such as swapping or lending cryptoassets. We propose a method to categorize these blocks into clusters based on their smart contract attributes and the graph structure of their smart contract calls. We employ GRL to create embedding vectors from building blocks and agglomerative models for clustering them. To evaluate whether they are effectively grouped in clusters of similar functionalities, we associate them with eight financial functionality categories and use this information as the target label. We find that in the best-case scenario purity reaches .888. We use additional information to associate the building blocks with protocol-specific target labels, obtaining comparable purity (.864) but higher V-Measure (.571); we discuss plausible explanations for this difference. In summary, this method helps categorize existing financial products offered by DeFi protocols, and can effectively automatize the detection of similar DeFi services, especially within protocols.
{"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":null,"pages":null},"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}
The Financial system has witnessed rapid technological changes. The rise of Bitcoin and other crypto assets based on Distributed Ledger Technology mark a fundamental change in the way people transact and transmit value over a decentralized network, spread across geographies. This has created regulatory and tax policy blind spots, as governments and tax administrations take time to understand and provide policy responses to this innovative, revolutionary, and fast-paced technology. Due to the breakneck speed of innovation in blockchain technology and advent of Decentralized Finance, Decentralized Autonomous Organizations and the Metaverse, it is unlikely that the policy interventions and guidance by regulatory authorities or tax administrations would be ahead or in sync with the pace of innovation. This paper tries to explain the principles on which crypto assets function, their underlying technology and relates them to the tax issues and taxable events which arise within this ecosystem. It also provides instances of tax and regulatory policy responses already in effect in various jurisdictions, including the recent changes in reporting standards by the FATF and the OECD. This paper tries to explain the rationale behind existing laws and policies and the challenges in their implementation. It also attempts to present a ballpark estimate of tax potential of this asset class and suggests creation of global public digital infrastructure that can address issues related to pseudonymity and extra-territoriality. The paper analyses both direct and indirect taxation issues related to crypto assets and discusses more recent aspects like proof-of-stake and maximal extractable value in greater detail.
{"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":null,"pages":null},"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}
As algorithmic trading and electronic markets continue to transform the landscape of financial markets, detecting and deterring rogue agents to maintain a fair and efficient marketplace is crucial. The explosion of large datasets and the continually changing tricks of the trade make it difficult to adapt to new market conditions and detect bad actors. To that end, we propose a framework that can be adapted easily to various problems in the space of detecting market manipulation. Our approach entails initially employing a labelling algorithm which we use to create a training set to learn a weakly supervised model to identify potentially suspicious sequences of order book states. The main goal here is to learn a representation of the order book that can be used to easily compare future events. Subsequently, we posit the incorporation of expert assessment to scrutinize specific flagged order book states. In the event of an expert's unavailability, recourse is taken to the application of a more complex algorithm on the identified suspicious order book states. We then conduct a similarity search between any new representation of the order book against the expert labelled representations to rank the results of the weak learner. We show some preliminary results that are promising to explore further in this direction
{"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":null,"pages":null},"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}
Financial market like the price of stock, share, gold, oil, mutual funds are affected by the news and posts on social media. In this work deep learning based models are proposed to predict the trend of financial market based on NLP analysis of the twitter handles of leaders of different fields. There are many models available to predict financial market based on only the historical data of the financial component but combining historical data with news and posts of the social media like Twitter is the main objective of the present work. Substantial improvement is shown in the result. The main features of the present work are: a) proposing completely generalized algorithm which is able to generate models for any twitter handle and any financial component, b) predicting the time window for a tweets effect on a stock price c) analyzing the effect of multiple twitter handles for predicting the trend. A detailed survey is done to find out the latest work in recent years in the similar field, find the research gap, and collect the required data for analysis and prediction. State-of-the-art algorithm is proposed and complete implementation with environment is given. An insightful trend of the result improvement considering the NLP analysis of twitter data on financial market components is shown. The Indian and USA financial markets are explored in the present work where as other markets can be taken in future. The socio-economic impact of the present work is discussed in conclusion.
{"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":null,"pages":null},"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}
This paper provides the first causal evidence that credit supply expansion caused the 1999-2010 U.S. business cycle mainly through the channel of household leverage (debt-to-income ratio). Specifically, induced by net export growth, credit expansion in private-label mortgages, rather than government-sponsored enterprise mortgages, causes a much stronger boom and bust cycle in household leverage in the high net-export-growth areas. In addition, such a stronger household leverage cycle creates a stronger boom and bust cycle in the local economy, including housing prices, residential construction investment, and house-related employment. Thus, our results are consistent with the credit-driven household demand channel (Mian and Sufi, 2018). Further, we show multiple pieces of evidence against the corporate channel, which is emphasized by other business cycle theories (hypotheses).
{"title":"Household Leverage Cycle Around the Great Recession","authors":"Bo Li","doi":"arxiv-2407.01539","DOIUrl":"https://doi.org/arxiv-2407.01539","url":null,"abstract":"This paper provides the first causal evidence that credit supply expansion\u0000caused the 1999-2010 U.S. business cycle mainly through the channel of\u0000household leverage (debt-to-income ratio). Specifically, induced by net export\u0000growth, credit expansion in private-label mortgages, rather than\u0000government-sponsored enterprise mortgages, causes a much stronger boom and bust\u0000cycle in household leverage in the high net-export-growth areas. In addition,\u0000such a stronger household leverage cycle creates a stronger boom and bust cycle\u0000in the local economy, including housing prices, residential construction\u0000investment, and house-related employment. Thus, our results are consistent with\u0000the credit-driven household demand channel (Mian and Sufi, 2018). Further, we\u0000show multiple pieces of evidence against the corporate channel, which is\u0000emphasized by other business cycle theories (hypotheses).","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141531205","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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