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|Title:||A multilabel text classification algorithm for labeling risk factors in sec form 10-K||Authors:||Huang, K.-W.
|Issue Date:||2011||Citation:||Huang, K.-W.,Li, Z. (2011). A multilabel text classification algorithm for labeling risk factors in sec form 10-K. ACM Transactions on Management Information Systems 2 (3). ScholarBank@NUS Repository. https://doi.org/10.1145/2019618.2019624||Abstract:||This study develops, implements, and evaluates a multilabel text classification algorithm called the multilabel categorical K-nearest neighbor (ML-CKNN). The proposed algorithm is designed to automatically identify 25 types of risk factors with specific meanings reported in Section 1A of SEC form 10-K. The idea of ML-CKNN is to compute a categorical similarity score for each label by the K-nearest neighbors in that category. ML-CKNN is tailored to achieve the goal of extracting risk factors from 10Ks. The proposed algorithm can perfectly classify 74.94% of risk factors and 98.75% of labels. Moreover, ML-CKNN is empirically shown to outperform ML-KNN and other multilabel algorithms. The extracted risk factors could be valuable to empirical studies in accounting or finance. © 2011 ACM.||Source Title:||ACM Transactions on Management Information Systems||URI:||http://scholarbank.nus.edu.sg/handle/10635/42467||ISSN:||2158656X||DOI:||10.1145/2019618.2019624|
|Appears in Collections:||Staff Publications|
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