Automated Trading and Yearly Reporting: The Need for RT-XBRL

Dr. Miklos A. Vasarhelyi KPMG Distinguished Professor Rutgers Business School - Newark & New Brunswick Director, Rutgers Accounting Research Center & Continuous Auditing & Reporting Lab

PhD Student Rutgers Business School - Newark & New Brunswick

Duo (Selina) Pei

Roadmap

The Current Accounting Dilemma Information Needs The RT-XBRL Schema RT-XBRL Extensions Directions for the Future

What are some problems plaguing professionals in the field?

Ball and Brown (1968) found that income numbers encompassed 50% of the information reflected in market measures. Lev (1989) showed using data over a span of 20 years, earnings explained no more than 7% of the variation in stock returns The Financial Times reports in 2009 that 73% of U.S. trading volume can be attributed to high frequency trading (Link)

Some other phenomena observed

Trump tweets about Toyota, its U.S. shares dropped close to 1%, representing $2 billion in market value. Not just Trump, computer algorithms have been created to compile tweets from famous and important people. (Los Angeles Times January 16, 2017)

Some other phenomena observed

We need a reporting schema…

- Which links more current measures, and measures of a much wider scope - Supports interoperability across the value chain - Standardizes data to facilitate analysis and usage of sophisticated tools - Provides real-time information to satisfy the current trend of information

demands

We need a reporting schema that also integrates well with current advances in auditing

Figure: Data standardization, Apps, Business Process, and continuous Usage (adapted from Dai, 2017)

We Need RT-XBRL

ADS Layer Other Standardization Schemes

Internal

External

Structuring Layer

Output System

So what kind of information may be missing?

- Intangibles (Lev and Sougiannis, 1996; Demerjian et al., 2012; Bonacchi et al., 2015)

- Private information, under-the-table discussions which resulted in the issuance of Regulation Fair Disclosure (Bushee et al., 2004; Bailey et al., 2003)

- Interim information which resulted in introduction of the 8-K and its subsequent iterations (Lerman and Livnat 2010; Carter and Soo, 1999)

- Information which are actually used by current market participants and the market players of the future (algorithms)

- ETF tracking (Avellaneda and Lee, 2010)

- Investor sentiment (Baker and Wurgler, 2006)

- Volume, order book imbalances (Brogaard et al., 2014)

So what kind of information may be missing?

- SASB metrics - Intellectual property and data privacy

- Integrity and sponsorship

- “The relationships between [an organization’s] various operating and functional units and the capitals that the organization uses or affects” in the International Integrated Reporting Council Framework (2013)

Figure: Integrated Reporting Framework with Information Flows from The International Integrated Reporting Framework

A more value relevant reporting paradigm to cater to information needs and characteristics of the future

RT-XBRL is…

- Concept of a widened corporate measurement and assurance network - Multi-sourced information extracted to a database and fitted to a data

standard - A database which supports applications that may be independent,

correlated, integrated or heterogeneous and oblique in nature - Modeled to fit a reporting structure that may be formal and prescribed by a

standard setter (e.g. FASB) or informal based on the different entity stakeholder needs

RT-XBRL is…

Live XBRL Sheet: Reporting / Monitoring

ADS Layer Other Standardization Schemes

ERP SC

Internal

External

Social Media

Market

IoT

Competitor

Weather

Geolocational Apps

News Metrics

Structuring Layer

Output System Alerts App User App 1

User App 2

RT-XBRL may…

- Have links to sources to display relevant information (e.g. momentum, share buybacks, recent patents, brand, customer satisfaction)

- Some links could reference specific line items (equipment, contingent liability, etc.)

- Present non-traditional information from exogenous data sources “in-line” with traditional financial information in a reporting format

- Require real-time displays (dashboards) connected to, for example, Bloomberg or the Wall Street Journal

- Calculate and show new bottom line similar to “Value Created” (Lev and Gu, 2016) - Show feeds to internal systems (continuous assurance, exceptions reporting) - For the future:

- Predictive analytics and other research studies could be used to determine what are the most important information sources

- Predictive algorithms could change the information categories based on certain thresholds (i.e. report only changes of more than 5% or equivalent)

Why ADS?

- Compatibility with XBRL (Outlined in ADS Base Standard 2015) - ADS paves way for XBRL Global Ledger (Dalla Via and Garbellotto, 2015)

- Standardization - Automation

- Codifying audit-relevant organizational activities - Supports audit apps which can be integrated with RT-XBRL

- Includes aspects of the value creation cycle: inventory, order to cash, and the general ledger

- Easier for auditors to assure RT-XBRL statements

Conceptualizing “RT” and “XBRL”

- Incorporate descriptive metrics, thresholds, exceptions detection - Periodicity of information gathering and dissemination should be based on

decision usefulness, cost-benefit, and user needs - On the internal end refers to real-time monitoring but not real-time reporting

- XBRL supports tagging and is machine-readable. - RT-XBRL supports cross-referencing between external and internal data sources creating an

interconnected web of information conducive to understanding both the business and the environment in which it operates

The RT-XBRL approach to monitoring and reporting exceptions

- ADS tagging makes receipts and accounting entries easy to verify - “Filter-like” operations can single out anomalies quicker

- Ex. match purchase order to the invoice received, view all dates, individuals involved, product amount, type of product, purchase order and invoice IDs, and discounts given

- Identify with global and local events as well as upstream and downstream activities which may correlate with the exception

Linking continuous reporting to continuous assurance

MOD – PERF = DELTA IF | DELTA > STDELTA THEN START ALERT

Dependent on the Pulse of Application

(Vasarhelyi et al., 2010)

An ecosystem of corporate measurement and assurance

Diagram: Audit Ecosystem (modified from Kozlovski, 2017)

Directions for the future: categorization of information types

- Facts and statistics that have significance in explaining trading behavior but may be difficult to collect

- Investor sentiment (Baker and Wurgler, 2006)

- Location (Balakrishnan et al., 1990)

- Weather (Yoon et al., 2017)

- Value should be substantiated through further research or predictive analytics and modeling.

- Social media (Luo et al., 2013)

- Managerial expertise - Attempt made through network theory (Larcker et al., 2013)

Appendices: References

Avellaneda, M., & Lee, J. H. (2010). Statistical arbitrage in the US equities market. Quantitative Finance, 10(7), 761-782. Bailey, W., Li, H., Mao, C. X., & Zhong, R. (2003). Regulation fair disclosure and earnings information: Market, analyst, and corporate responses. The Journal of Finance, 58(6), 2487-2514. Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross‐section of stock returns. The Journal of Finance, 61(4), 1645-1680. Balakrishnan, R., Harris, T. S., & Sen, P. K. (1990). The predictive ability of geographic segment disclosures. Journal of Accounting Research, 305- 325. Ball, R., & Brown, P. (1968). An empirical evaluation of accounting income numbers. Journal of Accounting Research, 159-178.

Appendices: References

Bonacchi, M., Kolev, K., & Lev, B. (2015). Customer franchise—A hidden, yet crucial, asset. Contemporary Accounting Research, 32(3), 1024-1049. Brogaard, J., Hendershott, T., & Riordan, R. (2014). High-frequency trading and price discovery. The Review of Financial Studies, 27(8), 2267-2306. Bushee, B. J., Matsumoto, D. A., & Miller, G. S. (2004). Managerial and investor responses to disclosure regulation: The case of Reg FD and conference calls. The Accounting Review, 79(3), 617-643. Carter, M. E., & Soo, B. S. (1999). The relevance of Form 8-K reports. Journal of Accounting Research, 37(1), 119-132. Dai, J. (2017) Three Essays on Audit Technology: Audit 4.0, Blockchain and Audit Apps, PhD Dissertation, Rutgers Business School, Newark, NJ, 07102

Appendices: References

Dalla Via, N., & Garbellotto, G. (2015). Internal Reporting with XBRL Global Ledger. Strategic Finance, 97(4), 46. Demerjian, P., Lev, B., & McVay, S. (2012). Quantifying managerial ability: A new measure and validity tests. Management Science, 58(7), 1229-1248. International Integrated Reporting Committee. (2013). The International Integrated Reporting Framework (Publication). Retrieved August 29, 2017, from International Integrated Reporting Committee website: https://integratedreporting.org/wp-content/uploads/2013/12/13-12-08-THE- INTERNATIONAL-IR-FRAMEWORK-2-1.pdf Larcker, D. F., So, E. C., & Wang, C. C. (2013). Boardroom centrality and firm performance. Journal of Accounting and Economics, 55(2), 225-250.

Appendices: References

Lerman, A., & Livnat, J. (2010). The new Form 8-K disclosures. Review of Accounting Studies, 15(4), 752-778. Lev, B. (1989). On the usefulness of earnings and earnings research: Lessons and directions from two decades of empirical research. Journal of Accounting Research, 153-192. Lev, B., & Gu, F. (2016). The end of accounting and the path forward for investors and managers. John Wiley & Sons. Lev, B., & Sougiannis, T. (1996). The capitalization, amortization, and value- relevance of R&D. Journal of Accounting and Economics, 21(1), 107-138. Luo, X., Zhang, J., & Duan, W. (2013). Social media and firm equity value. Information Systems Research, 24(1), 146-163.

Appendices: References

Sustainability Accounting Standards Board. (2017, February). SASB Conceptual Framework (Publication). Retrieved August 29, 2017, from Sustainability Accounting Standards Board website: http://library.sasb.org/wp-content/uploads/2017/02/SASB-Conceptual- Framework.pdf?hsCtaTracking=3b37ee09-f32f-4cb3-ae50- 89c55a6eebb4%7C302f7abc-aaba-42a7-ab7d-8a4e22f43900 Yoon, K., Kogan, A., & Vasarhelyi, M. A. (2017). Weather Records as Big Data Audit Evidence. Working Paper.