Imputation of Missing Values in the Fundamental Data: Using MICE Framework
Revolutionary developments in the field of big data analytics and machine learning algorithms have transformed the business strategies of industries such as banking, financial services, asset management, and e-commerce. The most common problems these firms face while utilizing data is the presence o...
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| Published in: | Journal of quantitative economics : journal of the Indian Econometric Society Vol. 17; no. 3; pp. 459 - 475 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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New Delhi
Springer India
01.09.2019
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| ISSN: | 0971-1554, 2364-1045 |
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| Abstract | Revolutionary developments in the field of big data analytics and machine learning algorithms have transformed the business strategies of industries such as banking, financial services, asset management, and e-commerce. The most common problems these firms face while utilizing data is the presence of missing values in the dataset. The objective of this study is to impute fundamental data that is missing in financial statements. The study uses ‘Multiple Imputation by Chained Equations’ (MICE) framework by utilizing the interdependency among the variables that wholly comply with accounting rules. The proposed framework has two stages. The initial imputation is based on predictive mean matching in the first stage and resolving financial constraints in the second stage. The MICE framework allows us to incorporate accounting constraints in the imputation process. The performance tests conducted on the imputed dataset indicate that the imputed values for the 177 line items are good and in line with the expectations of subject matter experts. |
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| AbstractList | Revolutionary developments in the field of big data analytics and machine learning algorithms have transformed the business strategies of industries such as banking, financial services, asset management, and e-commerce. The most common problems these firms face while utilizing data is the presence of missing values in the dataset. The objective of this study is to impute fundamental data that is missing in financial statements. The study uses ‘Multiple Imputation by Chained Equations’ (MICE) framework by utilizing the interdependency among the variables that wholly comply with accounting rules. The proposed framework has two stages. The initial imputation is based on predictive mean matching in the first stage and resolving financial constraints in the second stage. The MICE framework allows us to incorporate accounting constraints in the imputation process. The performance tests conducted on the imputed dataset indicate that the imputed values for the 177 line items are good and in line with the expectations of subject matter experts. |
| Author | Aravalath, Lagesh Meghanadh, Balasubramaniam Joshi, Bhupesh Sathiamoorthy, Raghunathan Kumar, Manish |
| Author_xml | – sequence: 1 givenname: Balasubramaniam surname: Meghanadh fullname: Meghanadh, Balasubramaniam organization: CRISIL GR&A – sequence: 2 givenname: Lagesh surname: Aravalath fullname: Aravalath, Lagesh organization: CRISIL GR&A – sequence: 3 givenname: Bhupesh surname: Joshi fullname: Joshi, Bhupesh organization: CRISIL GR&A – sequence: 4 givenname: Raghunathan surname: Sathiamoorthy fullname: Sathiamoorthy, Raghunathan organization: CRISIL GR&A – sequence: 5 givenname: Manish surname: Kumar fullname: Kumar, Manish email: manish.kumar@crisil.com organization: CRISIL GR&A |
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| Cites_doi | 10.1201/9781439821862 10.1186/2196-0739-1-4 10.1177/096228029900800102 10.1080/01621459.1996.10476908 10.1093/aje/kwp026 10.1002/9780470904848 10.1002/9780470316696 10.1177/0962280208101273 10.1002/9781119013563 10.1080/10629360600810434 10.1080/01621459.1988.10478722 10.1093/biomet/63.3.581 |
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| References_xml | – volume: 27 start-page: 85 year: 2001 end-page: 96 ident: CR14 article-title: A multivariate technique for multiply imputing missing values using a sequence of regression models publication-title: Survey methodology – year: 1997 ident: CR18 publication-title: Analysis of incomplete multivariate data doi: 10.1201/9781439821862 – year: 1998 ident: CR9 publication-title: List-wise deletion is evil: what to do about missing data in political science – volume: 1 start-page: 1 year: 2013 end-page: 33 ident: CR1 article-title: Multiple imputation using chained equations for missing data in TIMSS: a case study publication-title: Large-scale Assessments in Education doi: 10.1186/2196-0739-1-4 – year: 2012 ident: CR6 publication-title: Missing data methods in credit risk – volume: 8 start-page: 3 year: 1999 end-page: 15 ident: CR19 article-title: Multiple imputation: a primer publication-title: Statistical Methods in Medical Research doi: 10.1177/096228029900800102 – ident: CR13 – volume: 91 start-page: 473 year: 1996 end-page: 489 ident: CR17 article-title: Multiple imputation after 18 + years publication-title: Journal of the American statistical Association doi: 10.1080/01621459.1996.10476908 – year: 2000 ident: CR10 publication-title: Imputation methods for incomplete dependent variables in finance. 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| SubjectTerms | Econometrics Economics Economics and Finance Finance Game Theory Insurance Management Original Article Social and Behav. Sciences Statistics for Business |
| Title | Imputation of Missing Values in the Fundamental Data: Using MICE Framework |
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