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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Bibliographic Details
Published in:Journal of quantitative economics : journal of the Indian Econometric Society Vol. 17; no. 3; pp. 459 - 475
Main Authors: Meghanadh, Balasubramaniam, Aravalath, Lagesh, Joshi, Bhupesh, Sathiamoorthy, Raghunathan, Kumar, Manish
Format: Journal Article
Language:English
Published: New Delhi Springer India 01.09.2019
Subjects:
Econometrics
Economics
Economics and Finance
Finance
Game Theory
Insurance
Management
Original Article
Social and Behav. Sciences
Statistics for Business
C53
C32
Accounting and financial statement
C13
G20
MICE
Multiple imputation
Fundamental data
C51
ISSN:0971-1554, 2364-1045
Online Access:Get full text
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Summary: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.
ISSN:0971-1554
2364-1045
DOI:10.1007/s40953-018-0142-7