nlpcc 20131 automatic assessment of information disclosure quality in chinese annual reports qiu...
DESCRIPTION
NLP&CC 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 managementTRANSCRIPT
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NLP&CC 2013 1
Automatic Assessment of Information Disclosure
Quality in Chinese Annual Reports
QIU Xinying, JIANG Shengyi, DENG KebinCISCO School of Informatics
Guangdong University of Foreign Studies
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NLP&CC 2013 2
Outline• Background • Methodology and Design• Results and Analysis• Conclusions
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NLP&CC 2013 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
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NLP&CC 2013 4
Research Background• All about ENGLISH documents; • No research is conducted about
Chinese information disclosure
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NLP&CC 2013 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);
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NLP&CC 2013 6
Our work• General goal:
– to pave the way for the study of Chinese information disclosure from text mining perspective
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NLP&CC 2013 7
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
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NLP&CC 2013 8
Methodology• Four-class classification for
automatic quality evaluation
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NLP&CC 2013 9
Methodology• Chinese Readability index
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NLP&CC 2013 10
Methodology• Regression analysis about
readability and analysts following
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NLP&CC 2013 11
Results and Analysis• 4-class quality classification:
• About 10% better than the equivalent classification of English reports with stock return for class standards
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NLP&CC 2013 12
Results and Analysis
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NLP&CC 2013 13
Results and Analysis
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NLP&CC 2013 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
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NLP&CC 2013 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 fail quality reports is much more efficient than between good and pass quality reports.
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NLP&CC 2013 16
Current Work
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NLP&CC 2013 17
Current Work
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NLP&CC 2013 18
Current Work
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NLP&CC 2013 19
Thank you!