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XBRL and Data Analysis David vun Kannon Director Deloitte & Touche, LLP slide 1.

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Presentation on theme: "XBRL and Data Analysis David vun Kannon Director Deloitte & Touche, LLP slide 1."— Presentation transcript:

1 XBRL and Data Analysis David vun Kannon Director Deloitte & Touche, LLP slide 1

2 Analyzing XBRL Data What differentiates XBRL? The fundamental kinds of analysis Pre-processing XBRL slide 2

3 Depth – Fine grained base taxonomy – Extension taxonomies – Still need several more years of time series data Scope – All filers Variety – 10-K, -Q, Form SD, FDIC, other regulators, extensions What differentiates XBRL? slide 3

4 Conceptual consistency – Example – ‘risk free rate’ Reporting consistency – Same data point reported multiple times across filings – Can change due to restatement, slipstreaming accounting change Time series – Not enough data yet – Impeded by element switching, renaming in taxonomies Unit analysis – Dimension analysis Instance document data Fundamental types of analysis slide 4

5 Ratio analysis – Example – Altman Z-score Peer group analysis – Industry benchmarking Accounting policy change analysis – Example – lease accounting Text mining – “Unexpected” – Footnote specific searching Instance document data Fundamental types of analysis slide 5

6 Period-to-period consistency – Example – element naming Usage ratios – Primary items – Dimensions Disclosure structure – ‘height’ of extensions – Label content Taxonomy data Fundamental types of analysis slide 6

7 Recreating CompuStat – But with traceability Pivoting data – Coalescing similar concepts – Using presentation and calculation when lacking definition clues Denormalizing the data – Classic tradeoff of operational vs analytic database design – Textual facts separated and indexed Pre-processing XBRL slide 7

8 Why financial analysis at all? Asset class allocation is critical to portfolio returns – Stocks outperform all other asset classes over periods longer than 5 years Ibbotson SBBI data – Broader stock indexes outperform managers The broader the better At the cost of volatility But closer than 5 years, you need analysis

9 XBRL improvements over the current state Using XBRL data does not change the audit methodology or the tools we use today. It enables the tools to be more targeted and powerful. Better metrics Industry specific metrics Better benchmarking choices Improved data quality As tools become more targeted and powerful, the audit benefits by Fewer false positives Better risk identification Easier to explain to the client Better insight into the client

10 Value Network: XBRL to the Audit Better Audit Support Better client insight Easier to explain Better risk identification Fewer false positives Improved data quality Better benchmarking Industry metrics Better metrics TransparentTimelyComprehensiveDetailed XBRL is more… Tools provide…

11 Detail Drives Better Metrics Earnings EBIT EBITDA Detail and Insight

12 Opportunities for more detail Trade receivables, net of allowances Other receivables Sales Inventories, net of allowances Total operating expenses SG&A expenses Depreciation & Amortization expenses Total Assets Current Assets Net Fixed Assets Depreciation Expense Income Tax Provision Income Before Extraordinary Items Long-Term Return on Pension Plan Assets Total Liabilities Current Liabilities Long-Term Debt Capitalized Leases Deferred Taxes and Investment Tax Credits Minority Interest Cash From Operating Activities Common Shares Outstanding EPS Diluted Before Extraordinary Items and Disc Ops Total Outstanding Shares Retained Earnings Capital Expenditures

13 Industry specific metrics provide better insight Altman Z-score (bankruptcy predictor) Large Industrial companies Z’-score (bankruptcy predictor) Private companies Z”-score (bankruptcy predictor) Service and foreign companies Changing components and component weights XBRL data is more detailed and industry specific, enabling new industry specific metrics in tools.

14 Classical financial ratio analysis Altman Z-score – Bankruptcy predictor – Weighted sum of 5 ratios Z‘ score estimated for private firms – T 1 = (Current Assets − Current Liabilities) / Total Assets – T 2 = Retained Earnings / Total Assets – T 3 = Earnings Before Interest and Taxes / Total Assets – T 4 = Book Value of Equity / Total Liabilities – T 5 = Sales/ Total Assets Z' Score Bankruptcy Model: – Z' = 0.717T 1 + 0.847T 2 + 3.107T 3 + 0.420T 4 + 0.998T 5 Zones of Discrimination: – Z' > 2.9 -“Safe” Zone – 1.23 < Z' < 2. 9 -“Grey” Zone – Z' < 1.23 -“Distress” Zone

15 Concepts used Balance Sheet – Current Assets – Total Assets – Current Liabilities – Total Liabilities – Retained Earnings – Common Stock, Value Income Statement – Sales – EBIT

16 XBRL data prep Capture XBRL files via EDGAR RSS Load into SQL database (XBRL US) Database schema maps to XBRL model – Not optimal for analysis!

17 From EntityDateConceptValue IBM201xAssets7 IBM201xLiabilities4 IBM201xEquity3 MSFT201xAssets9 MSFT201xLiabilities3 MSFT201xEquity6 Even this hides the complexity of joining 5 tables for each fact!

18 To EntityDateAssetsLiabilitiesEquity IBM201x743 MSFT201x936

19 Vast improvement Can calculate ratios easily in SQL SELECT Easily understood by most analysts, programmers

20 Fact table changes Separate base, numeric facts – These are the facts used in ratios today Subsets – Instant in context Balance sheet items – Period in context Income statement items Annual period Quarterly period

21 Important next steps What is the most recent version of a fact? – Same fact is reported across multiple filings Coalesce uses of different concepts – Revenue vs Oil Revenue vs …

22 XBRL US Data Analysis Committee Prioritized element list – Most commonly used elements in ratio analysis Sales Assets Element maps – Open and extensible Period alignment

23 XBRL Database All public companies file XBRL with the SEC Phased in on 10-K, 10-Q filings since 2009 – 35,000 documents Growing 30,000 documents per year – Over 50,000,000 individual facts Filings use FASB tags and extensions

24 Analytics Actionable decision support Audit – Risk assessment – AUP Advisory – Pursuit target selection – Peer benchmarking Controller’s Dashboard

25 Pursuit selection XBRL data provides detailed data – Not available in aggregated data – Text searchable at note and disclosure level Examples – Impact of capitalization of leases – Pension obligations – “Variable consideration”

26 Benchmarking Peer group selection Peer group refinement Analytic reporting – Ratio analysis – Tag comparison – Disclosure comparison Application to Form SD extractive industries

27 Extractive Industries Payments to Foreign Governments (EIP2G) Affects Oil, Natural Gas, and Mining Companies – Estimated 1,100 companies affected Affected registrants must report on payments, fees, bonuses, etc. – A high degree of granularity in the data requested – Data to be filed in XBRL format – No accompanying HTML document Filed on Form SD Significant differences to existing XBRL usage by the SEC – Data is ‘filed’ not ‘furnished’ – a higher degree of liability in case of error – XBRL only, no underlying HTML document The data will be consumed by analytics applications, not reviewed by human analysts directly. Shows tighter integration of XBRL into core regulatory processes within the SEC

28 Opportunities for Deloitte GRC – design and implementation of reporting systems – Connection to core financial systems – Pre-filing analytics for management – Connection to journal entry testing to ensure compliance, 100% coverage – AUP on filed XBRL Oil & Gas, Mining Industry – Creation of industry level extension taxonomies for clients – Education and training on XBRL

29 Risk Assessment Going concern Fraud Stress testing/sensitivity analysis Robocop – accounting quality model – Accruals analysis

30 Current Ratio as a Scatter Plot (with regression line) 30

31 Benefits of scatter plot presentation More robust Can handle 0 values without ‘divide by 0’ problem Extreme values for one company do not distort presentation of other companies Can handle negative values Can add regression line Represents the average value of the ratio for the peer group Help identify outliers in context 31

32 Year over Year change Most Trial Balances contain at least 2 years of data, enabling year-over-year comparisons 32 Change vs Peers

33 Improving Analytic Quality Still get a lot of NULLs in data queries Need to create synonym lists – Focused effort on elements used in ratios – Look at base and extension taxonomies Use XBRL US Consistency Suite – Create list of data changes to apply if desired

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41 Looking Ahead XBRL and non-XBRL data SEC AAERS judgments Management estimates Risk of shareholder lawsuit Analysis of control diagrams

42 AAERS Judgments SEC documents (100s per year) Findings of fraud or errors in accounting – Company/ management responsible – Size of problem ($) – What was the problem – Date(s) of occurrence – Date of detection – Date of resolution – Resolution amount

43 Management Estimates All estimates are an automatic ROMM How good is the management at making estimates? Compare estimates with actual values reported later Related to management tenure? Input into audit risk analysis

44 Shareholder Lawsuit Audit requirement to check for ‘going concern’ and fraud, but not risk of lawsuit The auditor always gets included in lawsuits Reasons for lawsuit – Fraud and Bankruptcy – Restatement of accounts – Forced merger – Distressed sale of assets

45 About Deloitte Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee, and its network of member firms, each of which is a legally separate and independent entity. Please see www.deloitte.com/about for a detailed description of the legal structure of Deloitte Touche Tohmatsu Limited and its member firms. Please see www.deloitte.com/us/about for a detailed description of the legal structure of Deloitte LLP and its subsidiaries. Copyright © 2010 Deloitte Development LLC. All rights reserved. Member of Deloitte Touche Tohmatsu Limitedwww.deloitte.com/about www.deloitte.com/us/about

46 use xbrldb -- create a subset of the fact table -- in a base context (no dimensions) -- numeric facts only (no text, dates) drop table TempDB..base_numeric_13xx; select f.* into TempDB..base_numeric_13xx from fact f join element e on e.element_id = f.element_id join context c on c.context_id = f.context_id join accession a on a.accession_id = f.accession_id where c.specifies_dimensions = 0 and e.is_numeric = 1 and a.standard_industrial_classification in ( 1311, 1381, 1382, 1389) ;

47 drop table TempDB..base_numeric_13xx_instant; select f.* into TempDB..base_numeric_13xx_instant from TempDB..base_numeric_13xx f join context c on c.context_id = f.context_id where c.period_instant is not NULL ; drop table TempDB..base_numeric_13xx_period; select f.* into TempDB..base_numeric_13xx_period from TempDB..base_numeric_13xx f join context c on c.context_id = f.context_id where c.period_instant is NULL ;

48 drop table TempDB..context_base_numeric_13xx_period; select distinct c.*, datediff(d, c.period_start, c.period_end) as diff into TempDB..context_base_numeric_13xx_period from TempDB..base_numeric_13xx_period f join context c on c.context_id = f.context_id ; select c.diff, COUNT(*) from TempDB..context_base_numeric_13xx_period c group by c.diff order by c.diff ;

49 drop table TempDB..context_base_numeric_13xx_period_quarterly; select f.* into TempDB..base_numeric_13xx_period_quarterly from TempDB..base_numeric_13xx_period f join TempDB..context_base_numeric_13xx_period c on c.context_id = f.context_id where c.diff >= 89 and c.diff <= 92 ; drop table TempDB..context_base_numeric_13xx_period_annual; select f.* into TempDB..base_numeric_13xx_period_annual from TempDB..base_numeric_13xx_period f join TempDB..context_base_numeric_13xx_period c on c.context_id = f.context_id where c.diff >= 364 and c.diff <= 367 ;

50 select f.element_id, c.period_instant, c.entity_identifier, count(*) as count from TempDB..base_numeric_13xx_instant f join context c on c.context_id = f.context_id group by f.element_id, c.period_instant, c.entity_identifier having count(*) > 1 ;


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