Understanding Corporate Financial Information in the EU

1. Intellectual Output 2 „Fromaccu“ (Sorry, noaudio! – however on someslides I addedcommentsbolowtheslides)

2. Output 3.1a • 3.1 Data availability of digital corporate reports and financial information • a) Disclosure of corporate financial information in the EU • What different kinds of financial information are available in the EU in the context of auditing? (Which parts do these reports have and what information do they contain?) • audited or not (small companies) • for all companies or only for listed companies • periodic reporting like annual and quarterly reports; event-driven reporting like ad-hoc disclosures

3. Output 3.1b • b) Data gathering of corporate financial information in the EU • What are the information platforms (websites) where corporate financial information can be found in EU member states? • e.g. Companies House, Unternehmensregister etc. • Differences between information platforms? • anonymous data access or login required • free or costly • only single download or bulk downloads allowed • data formats provided • language(s) of website

4. Case study • Group work for students: • Case study downloading financial reports • students use the information platform ###xyz • students search for the annual report of company ###abc • students download and store the file

5. Example: https://datacvr.virk.dk/data/ possible help: https://translate.google.com

6. Which file formats are currently used? unstructured semi-structured structured RDF XBRL • Which file formats are currently used? • students get to know features and very basic technical characteristics of formats (e.g. plaint text, pdf, html, xml, xhtml, xbrl) • benefits of a structured data format (like xbrl or RDF)compared to an unstructured format like e.g. pdf

7. European Electronic Access Point • Which changes will take place by the “European Electronic Access Point” required by Article 21a of the amended Transparency Directive? • just one single access point for all data about listed companies • independent from where in the EU they are listed • Article 21a: European electronic access point • 1. A web portal serving as a European electronic access point (“the access point”) shall be established by 1 January 2018. ESMA shall develop and operate the access point. • 2. The system of interconnection of officially appointed mechanisms shall be composed of: • — the mechanisms referred to in Article 21(2), • — the portal serving as the European electronic access point. • 3. Member States shall ensure access to their central storage mechanisms via the access point.

8. Reporting forlistedcompanies in the EU • How will the data format for listed companies look like? • The European Securities and Markets Authority (ESMA) has set out the “European Single Electronic Format – ESEF” which issuers in the EU must use to report their company information. • It concludes that XHTML together with iXBRL is the most suitable technology for Consolidated Accounts under IFRS.

9. ESEF Standard (1) • All annual financial reports shall be prepared in XHTML which can be opened with standard web browsers and can be prepared and displayed as intended by the issuer; • Where the annual financial report contains IFRS consolidated financial statements, these shall be labelled with XBRL ‘tags’ which make the labelled disclosures structured and machine-readable. This allows for instance the analysis of large amounts of financial information without extensive and burdensome manual processing. Furthermore, as XBRL taxonomies can contain labels in several languages, users can compare numeric information in the financial statements across issuers even though the issuers prepare their financial statements in different languages. In addition to that, the machine-readable XBRL information can be easily transformed to other formats such as SQL or Excel thus avoiding onerous manual rekeying;

10. ESEF Standard (2) • The XBRL ‘tags’ shall be embedded in the XHTML document using the Inline XBRL technology which allows to encapsulate the XBRL tags in the XHTML document within a single document set; • Structured electronic reporting using XBRL requires the existence of a taxonomy, which is a given hierarchical structure used to classify financial information. The IFRS Taxonomy, issued by the IFRS Foundation has been specifically developed to mark-up IFRS financial statements with XBRL tags. Therefore, the core taxonomy to be used is an extension of the IFRS Taxonomy. • The IFRS consolidated primary financial statements (income statement, balance sheet, etc.) shall be marked up in detail, • whereas the notes to these financial statements need to be marked up by applying mark-ups for whole sections of the notes (block tagging).

11. Case Study (# End of 3.1b) • Case study reading XHTML/iXBRL reports in the web browser • given: examples for financial report in XHTML/iXBRL data format • students open the files in a web browser (XHTML) • can easily be read by humans • Demo: If e.g. the “Graffiti iXBRL Accounts Highlighter“ for Firefox is used: • pushing a button highlights the figures and narratives that are marked with iXBRL-tags and can be processed by a computer • students explore how the tags (in source code) look like

13. Output 3.2a • 3.2 Use of digital corporate information for the auditing process • a) Use of financial ratios in auditing • Relevance of financial ratios for planning the audit ? Distinguishing between areas of material risk and areas of less importance . • ISA 300, Planning an Audit of Financial Statements • ISA 320, Materiality in Planning and Performing an Audit • Using analytical procedures as substantive tests. How can analytical procedures be applied? • Different types of tests in audits (test of controls, substantive analytical procedures, substantive test of transactions) • ISA 520, Analytical Procedures

14. Case Study • Group work for students: • Case study about audit planning and analytical procedures • given: digital financial reports • students use the files and extract financial data • students calculate ratios from financial data • students apply the results for planning the audit (including the application of analytical procedures) • students present results

16. Output 3.2b • b) Use of summarized or highlighted narratives in auditing • Internal documents like contracts, emails or minutes of meetings • The texts of these documents might give hints for high audit risks (error or fraud), might contain monetary figures for accounting or might be relevant for the accounting treatment of a specific issue (e.g. type of financial instrument, type of lease arrangement). • Financial statements • Financial statements also include huge amounts of text. There are different approaches for extracting information content from texts. One common way is to summarize the language tendencies of a report using a method called “sentiment mining”. Another method would be to draw attention to specific parts of the report by highlighting “red flags”

17. Case study • Group work for students: • Case study about processing a large number of contracts • given: contracts in digital form (pdf) about renting apartments • students define search patterns (using “regular expressions”) to extract key contract data: e.g. Tentant, Rent, Start date, End date, Deposit • students export the results into Excel • students analyze the data: • compare the sum of the deposits for all relevant contracts with the liability shown in the balance sheet (of the landlord) • use the sum of the rents per building to compare them with the cash flow projections for the building (used to calculate the “value in use”) • students present results

18. Example: First lines of a contract

19. Code andresults

20. Fraud risk • Examples of popular companies that committed fraud in the past. • real world cases which are examples for different types of fraud. • What circumstances might facilitate the existence of fraud? • ISA 240, The Auditor's Responsibilities Relating to Fraud in an Audit of Financial Statements; • “fraud triangle” to explain conditions where fraud becomes very likely [Henselmann / Hofmann, pp. 276-283]

21. Howtoearlyidentifyfraudrisks? • Indicators to early identify fraud [Henselmann / Hofmann, pp. 268-275, pp. 285-307]. • “Red flags” show circumstances under which insolvency / fraud etc. is more likely. • Texts might be investigated whether they contain certain words or combinations of words that indicate “red flags” for insolvency or fraud.

22. Case study • Group work for students: • Case study searching for “red flags” as risk indicators • given: digital financial reports * • students suggest phrases / word lists for “red flags” • students use these word lists for analyzing the text and identify areas of risk • students present the solution • * ALTERNATIVE: internal email communication • in this case the analysis has to be somewhat different – in the best case we would need a network analysis of the communication per email – text analysis would only be a second step – personally I think that I could construct an example where we assume that some network analysis has already been done (so that no extra software is needed) • https://www.technologyreview.com/s/515801/the-immortal-life-of-the-enron-e-mails/

24. Insolvencyrisk • Examples of popular companies that went insolvent in the past. • ### • How must accounting change if the going concern assumption is no longer valid? • ### • What might be early warning indicators for insolvency? • ISA 570, Going Concern • ISA 315, Identifying and Assessing the Risks of Material Misstatement through Understanding the Entity and Its Environment • Are there phrases in financial reports that give an indication for insolvency risk? • heavy use of pro forma items e.g. “special items, core earnings” • typical reaction in a beginning crisis: specific information  boilerplate information

25. Output 3.2c • c) Assessing consistency between accounting figures and reporting narratives • Directive 2013/34/EU Art. 34 (1) a) i) requires auditors to express an opinion on whether the management report is consistent with the financial statements for the same financial year. • Assessing the consistency between quantitative and qualitative information can be a difficult task. Scores that summarize important language tendencies in a quantitative measure can help in this discussion. • An example of different language poles may be:positive ("pleasing") vs. negative ("stagnant") words  “sentiment” • Case study: Narratives of a company in trouble

26. Output 3.3a • 3.3 Use of software to unveil hidden information • a) Application of sentiment mining • Group work for students: • given: digital financial reports • students do a sentiment analysis with the computer • count how many positive and negative words occur in a text • calculate a ratio or score of the sentiment • compare sentiment scores between years  changes? • compare sentiment scores with financial data (profits, ROE) • look at possibleearningsmanagementbymanagementjudgement: Which growth rates and which discount rates were used in every year for calculating the value in use ofbrandsorgoodwill? • present results

28. b) Journal Entry Testing Overview ### to becompleted ###

29. Final discussion • Moderated group discussion (by teacher): • What are the advantages and the limits of computer assisted audits? • What features should powerful software have?