1. NLP&CC 20131 Automatic Assessment of Information Disclosure Quality in Chinese Annual Reports QIU Xinying, JIANG Shengyi, DENG Kebin CISCO School of Informatics Guangdong University of Foreign Studies QIU Xinying, JIANG Shengyi, DENG Kebin CISCO School of Informatics Guangdong University of Foreign Studies

2. NLP&CC 20132 Outline Background Methodology and Design Results and Analysis Conclusions Background Methodology and Design Results and Analysis Conclusions

3. NLP&CC 20133 Research Background Corporate information disclosure: –Annual reports; Quarterly reports –Earnings forecast; press release –Financial news Why study them? –Forecast of companies’ performance –Investment decisions –Regulations and management Corporate information disclosure: –Annual reports; Quarterly reports –Earnings forecast; press release –Financial news Why study them? –Forecast of companies’ performance –Investment decisions –Regulations and management

4. NLP&CC 20134 Research Background All about ENGLISH documents; No research is conducted about Chinese information disclosure All about ENGLISH documents; No research is conducted about Chinese information disclosure

5. NLP&CC 20135 Research Background Research perspectives: –Document level Build predictive models with disclosure documents for stock return forecasts –Tsai et al. (ECIR ‘13); Lin et al. (ACM TOMIS ‘11); Balakrishnan et al. (EJOR ‘10); Kogan et al. (NAACL ‘09) –Feature level Risk; Tone; Readability; Forward looking statement –Feldman et al. (RAS ‘10); Lehavy et al. (TAR ‘11); Li (JAE ‘08); Li (JAR ‘10); Research perspectives: –Document level Build predictive models with disclosure documents for stock return forecasts –Tsai et al. (ECIR ‘13); Lin et al. (ACM TOMIS ‘11); Balakrishnan et al. (EJOR ‘10); Kogan et al. (NAACL ‘09) –Feature level Risk; Tone; Readability; Forward looking statement –Feldman et al. (RAS ‘10); Lehavy et al. (TAR ‘11); Li (JAE ‘08); Li (JAR ‘10);

6. NLP&CC 20136 Our work General goal: –to pave the way for the study of Chinese information disclosure from text mining perspective General goal: –to pave the way for the study of Chinese information disclosure from text mining perspective

7. NLP&CC 20137 Our work In this work: –To build automatic system to evaluate Chinese disclosure quality –To explore and mine features factors for better understanding and utilization of Chinese reports More specifically: –Multi-class classification system –Readability analysis with regression In this work: –To build automatic system to evaluate Chinese disclosure quality –To explore and mine features factors for better understanding and utilization of Chinese reports More specifically: –Multi-class classification system –Readability analysis with regression

8. NLP&CC 20138 Methodology Four-class classification for automatic quality evaluation

9. NLP&CC 20139 Methodology Chinese Readability index

10. NLP&CC 201310 Methodology Regression analysis about readability and analysts following

11. NLP&CC 201311 Results and Analysis 4-class quality classification: About 10% better than the equivalent classification of English reports with stock return for class standards 4-class quality classification: About 10% better than the equivalent classification of English reports with stock return for class standards

12. NLP&CC 201312 Results and Analysis

13. NLP&CC 201313 Results and Analysis

14. NLP&CC 201314 Results and Analysis Analysts effort in following annual reports is negatively associated with the level of difficulty in reading the reports. In other words, easier to read annual reports attract more attention from analysts in their evaluation. Results different from counterpart analysis with English reports Analysts effort in following annual reports is negatively associated with the level of difficulty in reading the reports. In other words, easier to read annual reports attract more attention from analysts in their evaluation. Results different from counterpart analysis with English reports

15. NLP&CC 201315 Conclusions Our model for overall four-class classification achieves better performance to the extent of classification accuracy than the counterpart research on English reports. Distinguishing between excellent versus fail quality reports is much more efficient than between good and pass quality reports. Our model for overall four-class classification achieves better performance to the extent of classification accuracy than the counterpart research on English reports. Distinguishing between excellent versus fail quality reports is much more efficient than between good and pass quality reports.

16. NLP&CC 201316 Current Work

17. NLP&CC 201317 Current Work

18. NLP&CC 201318 Current Work

19. NLP&CC 201319 Thank you!