How Do Firms Transact? Guesstimation and Validation of Financial Transaction Networks with Satisfiability

Christos Tsigkanos, Alessio Arleo, J. Sorger, S. Dustdar
{"title":"How Do Firms Transact? Guesstimation and Validation of Financial Transaction Networks with Satisfiability","authors":"Christos Tsigkanos, Alessio Arleo, J. Sorger, S. Dustdar","doi":"10.1109/IRI.2019.00017","DOIUrl":null,"url":null,"abstract":"Knowledge of monetary flow between firms can give a significant advantage both from a profit or research point of view. So-called firm-to-firm transaction networks are valuable in analyzing a market or an economy. However, such detailed and complete data is seldom available. In this work, we aim at supporting economists by reusing available financial information from different sources at different levels of detail and completeness. With our technique, experts' domain knowledge can be fused together with publicly available information to extract a representative, coherent instance of the transaction network. Supporting underspecification is important, as experts may develop partial econometric models. Our technique fills such blanks by systematically guesstimating missing information. Our approach builds upon formal foundations of satisfiability modulo theories and thus obtained transaction networks respect constraints imposed by domain knowledge and input data sources. We outline a taxonomy of general data types in the domain, and we programmatically construct formal predicates describing them. We demonstrate both guestimation of missing information of a transaction network and validation of external, expert-provided models. Finally, we investigate feasibility and performance of the advocated technique over a fragment of the Austrian economy.","PeriodicalId":295028,"journal":{"name":"2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IRI.2019.00017","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

Knowledge of monetary flow between firms can give a significant advantage both from a profit or research point of view. So-called firm-to-firm transaction networks are valuable in analyzing a market or an economy. However, such detailed and complete data is seldom available. In this work, we aim at supporting economists by reusing available financial information from different sources at different levels of detail and completeness. With our technique, experts' domain knowledge can be fused together with publicly available information to extract a representative, coherent instance of the transaction network. Supporting underspecification is important, as experts may develop partial econometric models. Our technique fills such blanks by systematically guesstimating missing information. Our approach builds upon formal foundations of satisfiability modulo theories and thus obtained transaction networks respect constraints imposed by domain knowledge and input data sources. We outline a taxonomy of general data types in the domain, and we programmatically construct formal predicates describing them. We demonstrate both guestimation of missing information of a transaction network and validation of external, expert-provided models. Finally, we investigate feasibility and performance of the advocated technique over a fragment of the Austrian economy.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
企业如何交易?具有可满足性的金融交易网络的估计与验证
无论从利润还是研究的角度来看,企业之间的资金流动知识都能带来显著的优势。所谓的企业间交易网络在分析市场或经济时很有价值。然而,如此详细和完整的数据很少得到。在这项工作中,我们的目标是通过在不同的细节和完整性水平上重用来自不同来源的可用金融信息来支持经济学家。利用我们的技术,专家的领域知识可以与公开可用的信息融合在一起,以提取具有代表性的、连贯的交易网络实例。支持不充分说明是很重要的,因为专家可能会开发部分计量经济模型。我们的技术通过系统地猜测缺失的信息来填补这些空白。我们的方法建立在可满足性模理论的正式基础之上,从而获得了尊重领域知识和输入数据源施加的约束的交易网络。我们概述了领域中一般数据类型的分类法,并以编程方式构造描述它们的正式谓词。我们展示了交易网络缺失信息的估计和外部专家提供的模型的验证。最后,我们在奥地利经济的一个片段上研究了所提倡的技术的可行性和性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Towards Interpretable Deep Extreme Multi-Label Learning Evaluating Model Predictive Performance: A Medicare Fraud Detection Case Study AI Affective Conversational Robot with Hybrid Generative-Based and Retrieval-Based Dialogue Models Machine Learning for Classification of Economic Recessions IRI 2019 International Technical Program Committee
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1