Enhancing Substantive Analytical Procedures with Third-Party Generated Information from Social Media

1. Enhancing Substantive Analytical Procedures with Third-Party Generated Information from Social Media Presented by: Andrea M. Rozario Ph.D. Candidate

2. Introduction (1) “Investors, and others, are accessing and analyzing massive amounts of information from sources, like social media, unimaginable just a few years ago. This new data may be empowering investors to make smarter investment decisions” Kara Stein – SEC Commissioner 2015

3. PCAOB AS 2110 Introduction (2) • Internal Financial • Internal Nonfinancial Less reliable Develop account expectation • External Financial • External Nonfinancial More reliable but not timely! Analytical Procedures Compare account expectation to actual Determine whether difference is significant

4. Introduction (3) • Internal Financial • Internal Nonfinancial Less reliable Develop account expectation • External Financial • External Nonfinancial More reliable but not timely! Analytical Procedures Compare account expectation to actual • External Nonfinancial Information from Social Media Determine whether difference is significant PCAOB AS 2110

5. Objectives • Do Twitter proxies of consumer interest and consumer satisfaction enhance substantive analytical procedures for the revenue account? • Investigate whether information generated by third-parties on social media can • Improve the prediction performance of substantive analytical procedures • Improve the error detection performance of substantive analytical procedures

6. Motivation • Social media postings contain incremental information about firms’ stock market prices, and sales performance (e.g. Bollen, Mao, Zheng 2011; Tang 2017) • Inspection findings indicate that accounting firms fail to develop precise expectations (PCAOB 2007; PCAOB 2016a) • Social media consumer postings about firms’ products and brands could be used as a source of audit evidence

7. Findings • Auditors can benefit from incorporating social media information in continuous substantive analytical models • Model with lagged sales, TCI, and GDP outperforms other models Contribution • Contributes to the auditing literature by investigating the relevance of social media information that is generated by third parties to auditing

8. Research Questions • RQ 1A: For the revenue account, do traditional substantive analytical models that contain Twitter-based information produce more accurate predictions than traditional substantive analytical models that do not incorporate it? • RQ 1B: For the revenue account, do continuous substantive analytical models that contain Twitter-based information produce more accurate predictions than continuous substantive analytical models that do not incorporate it? • RQ 2A: For the revenue account, are traditional substantive analytical models that contain Twitter-based information better at detecting errors than traditional substantive analytical models that do not incorporate it? • RQ 2B: For the revenue account, are continuous substantive analytical models that contain Twitter-based information better at detecting errors than continuous substantive analytical models that do not incorporate it?

9. Research Design (1) • Sample – 24 B2C industries • Likefolio, https://home.likefolio.com/, and Compustat • Quarterly financial information is interpolated into monthly observations and matched with Twitter data

10. Research Design (2) • Twitter Measures • Likefolio, https://home.likefolio.com/, provided customer interest and satisfaction for products and brands • Mapping of brands and products to the company • Customer Interest to Buy TCI: total count of tweets related to the firm’s product or brand past/future interest to buy TCS: ratio of positive tweets to total (positive and negative) tweets • Customer Sentiment

11. Research Design (3) Traditional SAPs Continuous SAPs • Models (1) (5) (6) (2) (7) (8) (3) (9) (10) (4) (11) (12)

12. Results (1) • Prediction Performance – 24 industries

13. Results (2) • Error Detection Performance – 24 industries

14. Results (3) • Error Detection Performance Cost Ratio – 24 industries

15. Conclusion • Used third-party generated Tweets of firms’ products and brands to provide insights into the usefulness of this information in enhancing substantive analytical procedures • Continuous substantive analytical procedures with Twitter information can benefit auditors, especially models with TCI • Limitations?

16. Hmm… What is the next disruptive technology? How to apply it to auditing? Would it be appealing to the regulators? My professors? Introduction (1) “Investors, and others, are accessing and analyzing massive amounts of information from sources, like social media, unimaginable just a few years ago. This new data may be empowering investors to make smarter investment decisions” Kara Stein – SEC Commissioner 2015

17. Thank you!