Automatic Assessment of Information Disclosure Quality in Chinese Annual Reports

1. 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

2. Outline • Background • Methodology and Design • Results and Analysis • Conclusions

3. 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

4. Research Background • All about ENGLISH documents; • No research is conducted about Chinese information disclosure

5. 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);

6. Our work • General goal: • to pave the way for the study of Chinese information disclosure from text mining perspective

7. Our work • In this work: • To build automatic system to evaluate Chinese disclosure quality • To explore and mine features factorsfor better understanding and utilization of Chinese reports • More specifically: • Multi-class classification system • Readability analysis with regression

8. Methodology • Four-class classification for automatic quality evaluation

9. Methodology • Chinese Readability index

10. Methodology • Regression analysis about readability and analysts following

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

12. Results and Analysis

13. Results and Analysis

14. 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

15. 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 failquality reports is much more efficient than between goodand passquality reports.

16. Current Work

17. Current Work

18. Current Work

19. Thank you!