Imputation of Missing Values in the Fundamental Data: Using MICE Framework - Meghanadh, Balasubramaniam; Aravalath, Lagesh; Joshi, Bhupesh; Sathiamoorthy, Raghunathan; Kumar, Manish (Journal of quantitative economics : journal of the Indian Econometric Society, 2019)
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.