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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

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
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

why we analyze data
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

analysis to action regulatory management
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

what we analyze
What We Analyze …

www.institutionalriskanalytics.com

analysis data path
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

analyst s concerns regarding data
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

modeling goes beyond financials
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

machine readable public filings
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

m2m benefit to financial analytics
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

m2m data transmission map basel ii
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

contact information
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