Identifying the financial fraud behavior of listed companies in a timely manner and helping investors avoid risks is a key measure to promote the healthy development of the capital market. Aiming at the lag of traditional fraud identification method, a text classification model suitable for Chinese capital market is constructed based on user generated content (UGC) on social media platform. To detect fraudulent companies,TF-IDF features, topic features and explosive news quantity features are extracted guided by systemic functional linguistics (SFL) theory. Empirical analysis is conducted by crawling the news and comments of 124 companies from Eastmoney and JRJ.com.The empirical analysis results show that the model constructed by the article can effectively extract the implicit information in unstructured data and improve the timeliness of screening fraudulent behaviors of listed companies. The topic and keyword features in text comments play an important role in distinguishing the fraud of listed companies. On the one hand, it helps individual and institutional investors avoid investment traps, on the other hand, it helps regulators detect companies with fraudulent potential in time to prevent market risks.
KEYWORDS: Data modeling, Statistical modeling, Internet, Data mining, Computer simulations, Social sciences, Machine learning, Information technology, Data processing, Stochastic processes
Currently there is a growing concern over the issue of peer-to-peer (P2P) lending. A key challenge for personal investors in P2P lending marketplaces is how to accurately identify the subject of loan funds and how to effectively evaluate the profit and risk of the subject in the context of lending success.In this paper, we use the nuclear regression model to evaluate the probability of successful lending, to provide effective frontier for investors, and to give the optimal combination of the recommended bids for the lenders under different risk preferences.Finally we verify the scheme with data from Paipai Lending, the largest P2P network lending website in China. Experimental results reveals that the scheme can effectively provide investors more investment options.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.