Simultaneously Discovering and Quantifying Risk Types from Textual Risk Disclosures
By Yang Bao, Anindya Datta. Management Sciences, 2014
402 citations recorded by the source corpus.
Subfields: Artificial Intelligence, General Social Sciences, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/9eeeeea3882570753068e9ca2050c0f14d7da7ee
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Richard F.J. Haans
- Timothy R. Hannigan
- Sarah Kaplan
- P. Devereaux Jennings
- Ingrid E. Fisher
- Jeong‐Bon Kim
- Tzu-Ting Chiu
- Margaret R. Garnsey
In General Social Sciences
- Richard F.J. Haans
- Timothy R. Hannigan
- Sarah Kaplan
- P. Devereaux Jennings
- Ingrid E. Fisher
- Jeong‐Bon Kim
- Tzu-Ting Chiu
- Margaret R. Garnsey
In Management Science and Operations Research
- Richard F.J. Haans
- Timothy R. Hannigan
- Sarah Kaplan
- P. Devereaux Jennings
- Ingrid E. Fisher
- Jeong‐Bon Kim
- Tzu-Ting Chiu
- Margaret R. Garnsey
Citations in the corpus
- Customers’ Risk Factor Disclosures and Suppliers’ Investment Efficiency (2019, methodology): “The difference 21 Bao and Datta (2014) employ the latent Dirichlet allocation topic model and its learning algorithm to quantify and classify the risk factors disclosed in Item 1A into 30 risk types.”
- Topic Modeling in Management Research: Rendering New Theory from Textual Data (2019, methodology): “For example, Bao and Datta (2014) applied LDA to induce risk types from corporate 10-”
- Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research (2016, methodology): “Bao and Datta (2014) employed an LDA unsupervised topic model, the variational expectation maximization algorithm and the CKNN classification algorithm with similar results.”