Simultaneously discovering and quantifying risk types from textual risk disclosures

Management Science

Volumn 60, Issue 6, 2014, Pages 1371-1391

  Bao, Yang ^a   Datta, Anindya ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Econometric analysis; Latent Dirichlet allocation; Risk disclosures; Text analysis; Topic modeling

Indexed keywords

QUALITY CONTROL; RISK PERCEPTION; STATISTICS;

COMPREHENSIVE EVALUATION; ECONOMETRIC ANALYSIS; INFORMATIVE NESS; LATENT DIRICHLET ALLOCATION; NON-TRIVIAL TASKS; TEXT ANALYSIS; TOPIC MODELING; UNSTRUCTURED TEXTS;

RISK ASSESSMENT;

EID: 84902288534     PISSN: 00251909     EISSN: 15265501     Source Type: Journal    
DOI: 10.1287/mnsc.2014.1930     Document Type: Article

References (51)

1. Aral S, Ipeirotis P, Taylor S (2011) Content and context: Identifying the impact of qualitative information on consumer choice. Galletta DF, Liang TP, eds. 32nd Internat. Conf. Inform. Systems (AIS, Atlanta), 1-9.
2. Asuncion A, Welling M, Smyth P, Teh YW (2009) On smoothing and inference for topic models. Blimes J, Ng AY, eds. The 25th Conf. Uncertainty in Artificial Intelligence (AUAI Press, Corvallis, OR), 27-34.
3. Azzopardi L, Girolami M, Van Risjbergen K (2003) Investigating the relationship between language model perplexity and IR precision-recall measures. The 26th Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (ACM, New York), 369-370.
4. Blei DM, Jordan MI (2006) Variational inference for Dirichlet process mixtures. Bayesian Anal. 1(1):121-143.
5. Blei DM, Lafferty JD (2007) A correlated topic model of Science. Ann. Appl. Statist. 17-35.
6. Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J. Machine Learn. Res. 3:993-1022.
7. Brody S, Elhadad N (2010) An unsupervised aspect-sentiment model for online reviews. The 11th Annual Conf. North Amer. Chapter of the Assoc. Comput. Linguistics (ACL, Stroudsburg, PA), 804-812.
8. Campbell JL, Chen HC, Dhaliwal DS, Lu HM, Steele LB (2014) The information content of mandatory risk factor disclosures in corporate filings. Rev. Accounting Stud. 19(1):396-455.
9. Cecchini M, Aytug H, Koehler GJ, Pathak P (2010) Making words work: Using financial text as a predictor of financial events. Decision Support Systems 50(1):164-175.
10. Chang J, Boyd-Graber JL, Gerrish S, Wang C, Blei DM (2009) Reading tea leaves: How humans interpret topic models. Bengio Y, Schuurmans D, Lafferty JD, Williams CKI, Culotta A, eds. Adv. Neural Inform. Processing Systems (Curran Associates, New York), 288-296.
11. Chang YL, Chien JT (2009) Latent Dirichlet learning for document summarization. The 34th IEEE Intenat. Conf. Acoustics, Speech, and Signal Processing (IEEE, New York), 1689-1692.
12. Du L, Buntine W, Jin HD (2010) A segmented topic model based on the two-parameter Poisson-Dirichlet process. Machine Learn. 81(1):5-19.
13. Easley D, O'Hara M (2004) Information and the cost of capital. J. Finance 59(4):1553-1583.
14. Fama EF, French KR (1993) Common risk factors in the returns on stocks and bonds. J. Financial Econom. 33(1):3-56.
15. Feldman R, Govindaraj S, Livnat J, Segal B (2010) Management's tone change, post earnings announcement drift and accruals. Rev. Accounting Stud. 15(4):915-953.
16. Garfinkel JA (2009) Measuring investors' opinion divergence. J. Accounting Res. 47(5):1317-1348.
17. Griffiths TL, Steyvers M (2004) Finding scientific topics. Proc. Natl. Acad. Sci. USA 101 (Suppl 1) 5228-5235.
18. Grimmer J (2010) A Bayesian hierarchical topic model for political texts: Measuring expressed agendas in senate press releases. Political Anal. 18(1):1-35.
19. Grimmer J, King G (2011) General purpose computer-assisted clustering and conceptualization. Proc. Natl. Acad. Sci. USA 108(7):2643-2650.
20. Grimmer J, Stewart BM (2013) Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Anal. 21(3):267-297.
21. Huang KW, Li ZL (2011) A multilabel text classification algorithm for labeling risk factors in SEC form 10-K. ACM Trans. Management Inform. Systems 2(3):1-19.
22. Humpherys SL, Moffitt KC, Burns MB, Burgoon JK, Felix WF (2011) Identification of fraudulent financial statements using linguistic credibility analysis. Decision Support Systems 50(3):585-594.
23. Jo Y, Oh AH (2011) Aspect and sentiment unification model for online review analysis. Davison BD, Suel T, Craswell N, Liu B, eds. The 4th ACM Internat. Conf. Web Search and Data Mining (ACM, New York), 815-824.
24. Kothari SP, Li X, Short JE (2009) The effect of disclosures by management, analysts, and business press on cost of capital, return volatility, and analyst forecasts: A study using content analysis. The Accounting Rev. 84(5):1639-1670.
25. Kravet T, Muslu V (2011) Textual risk disclosures and investors' risk perceptions. Rev. Accounting Stud. 18(4):1088-1122.
26. Li F (2010a) The information content of forward-looking statements in corporate filings: A naive Bayesian machine learning approach. J. Accounting Res. 48(5):1049-1102.
27. Li F (2010b) Textual analysis of corporate disclosures: A survey of the literature. J. Accounting Literature 29:143-165.
28. Lin CH, He YL, Everson R (2011) Sentence subjectivity detection with weakly-supervised learning. The 5th Internat. Joint Conf. Natural Language Processing (ACL, Stroudsburg, PA), 1153-1161.
29. Linsmeier TJ, Thornton DB, Venkatachalam M, Welker M (2002) The effect of mandated market risk disclosures on trading volume sensitivity to interest rate, exchange rate, and commodity price movements. Accounting Rev. 77(2):343-377.
30. Lu B, Ott M, Cardie C, Tsou BK (2011) Multi-aspect sentiment analysis with topic models. Spiliopoulou M, Wang H, Cook DJ, Pei J, Wang W, Zaïane OR, Wu X, eds. The 11th IEEE Internat. Conf. Data Mining Workshops (IEEE, New York), 81-88.
31. Mei QZ, Shen XH, Zhai CX (2007) Automatic labeling of multinomial topic models. Berkhin P, Caruana R, Wu X, eds. The 13th ACM SIGKDD Internat. Conf. Knowledge Discovery and Data Mining (ACM, New York), 490-499.
32. Mirakur Y (2011) Risk disclosure in SEC corporate filings. Working paper, University of Pennsylvania, Philadelphia. Accessed October 1, 2013, http://repository.upenn.edu/wharton-research-scholars/85.
33. O'Connor B, Bamman D, Smith NA (2011) Computational text analysis for social science: Model assumptions and complexity. Proc. 2nd Workshop Comput. Soc. Sci. Accessed March 22, 2014, http://people.cs.umass.edu/wallach/workshops/ nips2011css/papers/OConnor.pdf.
34. Quinn KM, Monroe BL, Colaresi M, Crespin MH, Radev DR (2010) How to analyze political attention with minimal assumptions and costs. Amer. J. Political Sci. 54(1):209-228.
35. Raftery AE (1995) Bayesian model selection in social research. Sociol. Methodology 25:111-164.
36. Rajgopal S (1999) Early evidence on the informativeness of the SEC's market risk disclosures: The case of commodity price risk exposure of oil and gas producers. Accounting Rev. 74(3):251-280.
37. Rogers J, Van Buskirk A, Zechman S (2011) Disclosure tone and shareholder litigation. The Accounting Rev. 86(6):2155-2183.
38. Rousseeuw PJ (1987) Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20:53-65.
39. Schrand CM, Elliott JA (1998) Risk and financial reporting: A summary of the discussion at the 1997 AAA/FASB conference. Accounting Horizons 12:271-282.
40. SEC (2005) Securites and Exchange Commission final rule, release no. 33-8591 (FR-75). http://www.sec.gov/rules/final/33-8591.pdf.
41. Shalen CT (1993) Volume, volatility, and the dispersion of beliefs. Rev. Financial Stud. 6(2):405-434.
42. Sim J, Wright C (2005) The kappa statistic in reliability studies: Use, interpretation, and sample size requirements. Physical Therapy 85(3):257-268.
43. Tausczik YR, Pennebaker JW (2010) The psychological meaning of words: LIWC and computerized text analysis methods. J. Language Soc. Psych. 29(1):24-54.
44. Teh YW, Kurihara K, Welling M (2008) Collapsed variational inference for HDP. Adv. Neural Inform. Processing Systems 20(20):1481-1488.
45. Teh YW, Newman D, Welling M (2007) A collapsed variational Bayesian inference algorithm for latent Dirichlet allocation. Adv. Neural Inform. Processing Systems 19:1353-1360.
46. Titov I, McDonald R (2008) Modeling online reviews with multi-grain topic models. The 17th ACM Internat. Conf. World Wide Web (ACM, New York), 111-120.
47. Von Luxburg U, Williamson RC, Guyon I (2012) Clustering: Science or art? J. Machine Learn. Res. 27:65-80.
48. Wallach HM, Mimno D, McCallum A (2009) Rethinking lda: Why priors matter. Adv. Neural Inform. Processing Systems 22:1973-1981.
49. Wallach HM, Jensen ST, Dicker LH, Heller KA (2010) An alternative prior process for nonparametric Bayesian clustering. J. Machine Learn. Res. 9:892-899.
50. Wang DD, Zhu SH, Li T, Gong YH (2009) Multi-document summarization using sentence-based topic models. The 4th Internat. Joint Conf. Natural Language Processing (ACL, Stroudsburg, PA), 297-300.
51. Zhai K, Boyd-Graber J, Asadi N, Alkhouja ML (2012) Mr. LDA: A flexible large scale topic modeling package using variational inference in MapReduce. Mille A, Gandon FL, Misselis J, Rabinovich M, Staab S, eds. The 21st ACM Internat. Conf. World Wide Web (ACM, New York), 879-888.