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Proceedings of the 3rd International Workshop on Data Science for Macro--Modeling with Financial and Economic Datasets最新文献

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Understanding Relations using Concepts and Semantics 使用概念和语义理解关系
Proceedings of the 3rd International Workshop on Data Science for Macro--Modeling with Financial and Economic Datasets Pub Date : 2017-05-14 DOI: 10.1145/3077240.3077250
Jouyon Park, Hyunsouk Cho, Seung-won Hwang
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引用次数: 4
Thomson Reuters' Solution for Triple Ranking in the FEIII 2017 Challenge 汤森路透在FEIII 2017挑战赛中获得三重排名的解决方案
Proceedings of the 3rd International Workshop on Data Science for Macro--Modeling with Financial and Economic Datasets Pub Date : 2017-05-14 DOI: 10.1145/3077240.3077253
E. Roman, B. Ulicny, Yi Du, Srijith Poduval, A. Ko
{"title":"Thomson Reuters' Solution for Triple Ranking in the FEIII 2017 Challenge","authors":"E. Roman, B. Ulicny, Yi Du, Srijith Poduval, A. Ko","doi":"10.1145/3077240.3077253","DOIUrl":"https://doi.org/10.1145/3077240.3077253","url":null,"abstract":"In this paper we describe our approach to the triple ranking task of the FEIII 2017 challenge. Our method leveraged different machine learning classifiers in an ensemble as well as Thomson Reuters knowledge bases and information services to bring in external world knowledge of mentioned entities and extract information from the contextual sentences. Internal evaluation of our method was done by computing the Normalized Discounted Cumulative Gain (NDCG) as tracked by the challenge and classification accuracy. The official FEIII Challenge evaluation showed our system performed highly in single ranking of all triples, placing in 2nd or 3rd place out of 17 participants for 4 of 6 scoring variants; the system also performed above average in per role ranking for 4 of 6 average role NDCG scoring variants.","PeriodicalId":326424,"journal":{"name":"Proceedings of the 3rd International Workshop on Data Science for Macro--Modeling with Financial and Economic Datasets","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130590993","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
Tensor Factors to Monitor the Co-Movement of Equity Prices 张量因子监测股票价格的共同运动
Proceedings of the 3rd International Workshop on Data Science for Macro--Modeling with Financial and Economic Datasets Pub Date : 2017-05-14 DOI: 10.1145/3077240.3077242
L. Raschid, J. Langsam, Tharindu Pieris, Anushka Bandara
{"title":"Tensor Factors to Monitor the Co-Movement of Equity Prices","authors":"L. Raschid, J. Langsam, Tharindu Pieris, Anushka Bandara","doi":"10.1145/3077240.3077242","DOIUrl":"https://doi.org/10.1145/3077240.3077242","url":null,"abstract":"We identify a set of features that are related to extremes of price changes of individual equities. Our hypothesis is that these extreme features may be used to isolate co-movements of prices for groups of equities, reflecting systematic risk. The equities are classified within industry sectors and we create a three mode tensor to represent the dataset; the dimensions of the three mode tensor correspond to the equity, the industry sector and the day on which the feature occurred. We use a method for non-negative tensor factorization (NOTF) to identify factors or communities that are composed of multiple equities, and / or industry sectors. Our preliminary results indicate that our NOTF approach has the potential to identify such communities of price related features that may experience co-movement across industry sectors and temporal intervals.","PeriodicalId":326424,"journal":{"name":"Proceedings of the 3rd International Workshop on Data Science for Macro--Modeling with Financial and Economic Datasets","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114375981","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: 1
Balance Sheet Driven Probability Factorization for Inferring Bank Holdings: Extended Abstract 资产负债表驱动的概率分解法推断银行持股:扩展摘要
Proceedings of the 3rd International Workshop on Data Science for Macro--Modeling with Financial and Economic Datasets Pub Date : 2017-05-14 DOI: 10.1145/3077240.3077243
Shawn Mankad, Celso Brunetti, J. Harris
{"title":"Balance Sheet Driven Probability Factorization for Inferring Bank Holdings: Extended Abstract","authors":"Shawn Mankad, Celso Brunetti, J. Harris","doi":"10.1145/3077240.3077243","DOIUrl":"https://doi.org/10.1145/3077240.3077243","url":null,"abstract":" Assistant Professor of Operations, Technology and Information Management, Samuel Curtis Johnson Graduate School of Management, Cornell University, 2015 – Present o Graduate Field Member in Statistics, 2017 – Present  Assistant Professor of Business Analytics, Robert H. Smith School of Business, University of Maryland, 2013 – 2015 o Affiliate Faculty of Applied Mathematics and Scientific Computation, University of Maryland, 2014 – 2015  Federal Contractor, the U.S. Commodity Futures Trading Commission, 2009 – 2013  Dissertation Intern, Federal Reserve Board of Governors, Summer 2012","PeriodicalId":326424,"journal":{"name":"Proceedings of the 3rd International Workshop on Data Science for Macro--Modeling with Financial and Economic Datasets","volume":"213 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124188821","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: 1
Predicting Role Relevance with Minimal Domain Expertise in a Financial Domain 用最小的领域专业知识预测金融领域的角色相关性
Proceedings of the 3rd International Workshop on Data Science for Macro--Modeling with Financial and Economic Datasets Pub Date : 2017-04-19 DOI: 10.1145/3077240.3077249
M. Kejriwal
{"title":"Predicting Role Relevance with Minimal Domain Expertise in a Financial Domain","authors":"M. Kejriwal","doi":"10.1145/3077240.3077249","DOIUrl":"https://doi.org/10.1145/3077240.3077249","url":null,"abstract":"Word embeddings have made enormous inroads in recent years in a wide variety of text mining applications. In this paper, we explore a word embedding-based architecture for predicting the relevance of a role between two financial entities within the context of natural language sentences. In this extended abstract, we propose a pooled approach that uses a collection of sentences to train word embeddings using the skip-gram word2vec architecture. We use the word embeddings to obtain context vectors that are assigned one or more labels based on manual annotations. We train a machine learning classifier using the labeled context vectors, and use the trained classifier to predict contextual role relevance on test data. Our approach serves as a good minimal-expertise baseline for the task as it is simple and intuitive, uses open-source modules, requires little feature crafting effort and performs well across roles.","PeriodicalId":326424,"journal":{"name":"Proceedings of the 3rd International Workshop on Data Science for Macro--Modeling with Financial and Economic Datasets","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116719722","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: 2
Proceedings of the 3rd International Workshop on Data Science for Macro--Modeling with Financial and Economic Datasets 第三届宏观数据科学国际研讨会论文集——用金融和经济数据集建模
Proceedings of the 3rd International Workshop on Data Science for Macro--Modeling with Financial and Economic Datasets Pub Date : 1900-01-01 DOI: 10.1145/3077240
{"title":"Proceedings of the 3rd International Workshop on Data Science for Macro--Modeling with Financial and Economic Datasets","authors":"","doi":"10.1145/3077240","DOIUrl":"https://doi.org/10.1145/3077240","url":null,"abstract":"","PeriodicalId":326424,"journal":{"name":"Proceedings of the 3rd International Workshop on Data Science for Macro--Modeling with Financial and Economic Datasets","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116451991","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
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