Compensation Disclosure: A Study via Semantic Similarity

Gaulin, Maclean; Peng, Xiaoxia

doi:10.2139/ssrn.3971286

Cited by 2 publications

(1 citation statement)

References 45 publications

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“…The authors compare their model with seven baseline models and report that their model produces the highest accuracy of document classification. Gaulin and Peng (2021) use the Doc2Vec word embedding model, which extends Word2Vec by adding document vectors to generate a vector representation for each document. The authors use these vectors as inputs to the cosine similarity model to capture semantic similarity of executive compensation disclosures across firms.…”

Section: Word Embedding Methodsmentioning

confidence: 99%

Textual Analysis in Accounting: What's Next?*

Bochkay

Brown²,

Leone³

et al. 2022

Contemporary Accting Res

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Natural language is a key form of business communication. Textual analysis is the application of natural language processing (NLP) to textual data for automated information extraction or measurement. We survey publications in top accounting journals and describe the trend and current state of textual analysis in accounting. We organize available NLP methods in a unified framework. Accounting researchers have often used textual analysis to measure disclosure sentiment, readability, and disclosure quantity; to compare disclosures to determine similarities or differences; to identify forward‐looking information; and to detect themes. For each of these tasks, we explain the conventional approach and newer approaches, which are based on machine learning, especially deep learning. We discuss how to establish the construct validity of text‐based measures and the typical decisions researchers face in implementing NLP models. Finally, we discuss opportunities for future research. We conclude that (i) textual analysis has grown as an important research method and (ii) accounting researchers should increase their knowledge and use of machine learning, especially deep learning, for textual analysis.

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Section: Word Embedding Methodsmentioning

confidence: 99%