Yang Bao
Shanghai Jiao Tong University
14 Papers
77 Citations
Yang Bao is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Computer science & Specific risk. The author has an hindex of 9, co-authored 12 publications. Previous affiliations of Yang Bao include National University of Singapore & Nanyang Technological University.
Chat about Author
Papers
Simultaneously Discovering and Quantifying Risk Types from Textual Risk Disclosures
Yang Bao,Anindya Datta +1 more
TL;DR: This paper develops a variation of the latent Dirichlet allocation topic model and its learning algorithm for simultaneously discovering and quantifying risk types from textual risk disclosures and provides support for all three competing arguments regarding whether and how risk disclosures affect the risk perceptions of investors.
427
Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach
TL;DR: In this article, the authors used machine learning techniques to facilitate the detection of accounting fraud in publicly traded U.S. stock market. But existing studies often mimic human experts and employ the nancial or non-nancial entities.
283
Towards effective online review systems in the Chinese context: A cross-cultural empirical study
TL;DR: The impact of online reviews on product sales in the Chinese context is investigated, and it is shown that directly copying the ideas of successful online review systems in the USA will deteriorate the effectiveness of the systems in China.
99
A partially supervised cross-collection topic model for cross-domain text classification
Yang Bao,Nigel Collier,Anindya Datta +2 more
- 27 Oct 2013
TL;DR: Experimental results show that the proposed partially Supervised Cross-Collection LDA topic model for cross-domain learning outperforms two standard classifiers and four state-of-the-art methods, which demonstrates the effectiveness of the proposed model.
41
•Proceedings Article
Misleading opinions provided by advisors: dishonesty or subjectivity
TL;DR: A novel probabilistic graphical trust model is proposed to separately consider these two factors, involving three types of latent variables: benevolence, integrity and competence of advisors, trust propensity of users, and subjectivity difference between users and advisors.