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Research and Analytics: The Revolution of Machine- to-Machine Data

Research and Analytics: The Revolution of Machine- to-Machine Data. Christopher Whalen Institutional Risk Analytics XBRL Conference, San Jose, CA January 17-19, 2006. Infrastructure Supports the Analyst.

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Research and Analytics: The Revolution of Machine- to-Machine Data

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  1. Research and Analytics:The Revolution of Machine-to-Machine Data Christopher Whalen Institutional Risk Analytics XBRL Conference, San Jose, CA January 17-19, 2006

  2. Infrastructure Supports the Analyst • Financial analysis is fed by structured data, databases and algorithms using behind-the-scenes web services tools. • But analysis is pondered using MSFT Excel, explained using Power Point, decided via memos written in Word, rendered in ADBE PDF’s, and broadcast using links to HTML web pages. • Rule: If the transmission technology becomes visible to the analyst, something’s wrong. www.institutionalriskanalytics.com

  3. Why We Analyze Data … • Optimize investment portfolios • Assign and maintain credit ratings • Assess business safety and soundness • Identify signs of weaknesses or fraud • Understand competitor strategies • Locate or value acquisition targets • Design or test regulatory rules • … and many other reasons. www.institutionalriskanalytics.com

  4. Analysis to Action: Regulatory Management • ACHIEVE POLICY GOALS: Alter behavioral norms. Prescribe corrective action, if required. • EXECUTE DILIGENCE: Conduct formal investigations to build to “preponderance of evidence” case strength. • ALLOCATE RESOURCES: Identify where maximum regulatory effectiveness can be achieved. Design strategy. • CONFIRM: Follow up unstructured content analysis to validate areas of concern, rule out false hits. • IDENTIFY: Large scale quantitative screening to identify anomalies and issues. Each layer requires specific analysis, logic and decision processes. www.institutionalriskanalytics.com

  5. What We Analyze … www.institutionalriskanalytics.com

  6. Analysis Data Path • Collection • Authenticity, accuracy, machine readability. • Library Organization • Multiple coexisting sources, public & privileged. • Preparation • Mission specific aggregation from multiple sources and pre-processing derived indicators. • Modeling • Putting data through the interpretive logic. www.institutionalriskanalytics.com

  7. Analyst’s Concerns Regarding Data • Collection • Data validation, cleanliness and accuracy. • Non-machine readable data collection technologies. • Re-keying errors. • Library Organization • Master file management of proprietary dictionaries and rules for mapping data across sources. • Data migration from multiple sources to output. • Data Preparation Issues • Incorporating non-numeric indicators collected outside financial statement sources. • Same variable, differing meanings, between subjects, over time. • Insufficient data to perform computation conditions. • Wild “out of bounds” and “legitimate outlier” data conditions. www.institutionalriskanalytics.com

  8. Modeling Goes Beyond Financials • Market prices, dividends, splits • Unstructured data & text (footnotes, other filings, press articles, research notes) • Business contracts • Investment and loan documents • Subjective opinions of varying reliability • Legal judgments and notifications • Academic theories • Statutes and regulations • More … www.institutionalriskanalytics.com

  9. Machine Readable Public Filings • Transparency – All submittal technology solutions must support a cross mapping matrix. • Tagged Text, CSV, XML, XBRL, SQL to SQL, Hand Keyed Input • Usability – All submittal solutions must support a requirement that key test point variables are always filled and mapped to a common master table. • Timeliness - The front-end of library engine performs “regulatory grade” cleanliness, compliance check. • Reality Check – Multiple technologies for structuring data will coexist for sometime. Competition will cause the “best of breed” to prevail. www.institutionalriskanalytics.com

  10. M2M Benefit to Financial Analytics • Raising the Bar: Adoption of M2M standards with content control logic for financial reporting vastly reduces input and interpretation errors in public company data. The compliance effect could be similar to the imposition of SOX. • Industrial Realignment: M2M data allows end users to bypass traditional data vendors and obtain “as filed” data from SEC, potentially loosing some of the standardization “value adds” the vendor community provides. www.institutionalriskanalytics.com

  11. M2M Data Transmission Map: Basel II Public/Private Data Inputs XBRL Analytics Output: XLS, XML, HTML XML Standardized Raw Data Repository Simple Report Analysis Processor CSV Additional Downstream Central Portfolio Analysis Engine(s) .NET ODBC Global Standardized Metrics Engine .NET ODBC www.institutionalriskanalytics.com

  12. Corporate Offices Lord, Whalen LLC dba Institutional Risk Analytics 14352 Yukon Avenue Hawthorne, California 90250 Tel. 310.676.3300 Fax. 310.943.1570 info@institutionalriskanalytics.com Inquiries Christopher Whalen Managing Director Sales and Marketing Tel. 914.827.9272 Fax. 914.206.4238 Cell. 914.645.5304 cwhalen@institutionalriskanalytics.com Contact Information www.institutionalriskanalytics.com

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