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Predicting population-level socio-economic characteristics using Call Detail Records (CDRs) in Sri Lanka
Prior work has shown that mobile network big data can be used as a high-frequency alternative data source to derive proxy measures that have strong predictive capacity to estimate census and poverty data in developing countries. Given that the ...
Feature Selection Methods For Understanding Business Competitor Relationships
- Rahul Gupta,
- Jay Pujara,
- Craig A. Knoblock,
- Shushyam M. Sharanappa,
- Bharat Pulavarti,
- Gerard Hoberg,
- Gordon Phillips
Understanding competition between businesses is essential for assessing the likely success of new ventures or products, for making decisions before investing capital in new businesses, and understanding the impacts of regulatory policy. One important ...
An Ontology of Ownership and Control Relations for Bank Holding Companies
We consider the challenges and benefits of ontologies for information management for regulatory reporting from bank holding companies (BHCs). Many BHCs, especially the largest and most complex firms, have multiple federal supervisors who oversee a ...
Learning Financial Networks using Quantile Granger Causality
In the post-crisis era, financial regulators and policymakers require data-driven tools to quantify systemic risk and to identify systemically important firms. We propose a statistical method that measures connectivity in the financial sector using time ...
Community Detection in Financial Entities: An Extended Abstract
In this work we explore relationships between financial entities for the purpose of community detection. We use the MFIBlocks algorithm to perform the task via subspace clustering and present some initial results over the FEIII 2018 challenge dataset.
Analysis of year-over-year changes in Risk Factors Disclosure in 10-K filings
Risk Factor Disclosures -- Item 1A -- in 10-K forms filed with SEC is one of the important sections since it contains a company's yearly risk updates, and thus helps investors decide whether to invest in a company or not. It is crucial to read this ...
Financial Entity Identification and Information Integration (FEIII) 2018 Challenge: The Report of the Organizing Committee
- Louiqa Raschid,
- Douglas Burdick,
- John Grant,
- Joe Langsam,
- Jay Pujara,
- Elizabeth Roman,
- Ian Soboroff,
- Mohammed Zaki,
- Elena Zotkina
This report presents the goals and outcomes of the 2018 Financial Entity Identification and Information Integration (FEIII) Challenge. We describe the challenge task and the training dataset. The report summarizes the process, outcomes and plans for the ...
Defining and Capturing the Competitor Relationship across Financial Datasets
The 2018 FEIII Data Challenge aims to enhance a given knowledge graph by validating and enriching the set of competitor edges in the graph using multiple datasets. Upon an investigation of the data, we find that some of the competitor edges given as ...
PREFER: PREdiction Model for Financial Entity Relation
The Financial Entity Identification and Information Integration (FEIII) is a competition for the understanding relationships between financial entities. To predict competitor relation between two entities, there are three challenges - 1) relevant ...
Predicting competitor links in company networks
The scored task at FEIII Challenge 2018 proposed the identification of competitor relationships in a network of companies from the financial and IT sectors. This article describe our BBVA Data & Analytics submission to the challenge and our experiments ...
Using supervised learning techniques for entity relationships
Given different financial data resources, it is very challenging to relate entities across the various resources since each resource has its own way of describing the entities and relationships. We work on identifying such relationships using context ...
Index Terms
- Proceedings of the Fourth International Workshop on Data Science for Macro-Modeling with Financial and Economic Datasets