Research and Analytics:
The Revolution of Machineto-Machine Data
Christopher Whalen
Institutional Risk Analytics
XBRL Conference, San Jose, CA
January 17-19, 2006
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.
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Why We Analyze Data …
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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.
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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.
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What We Analyze …
Measurements
Discontinuities
Statistics
Raw data is
Metrics are
Data and metrics
reformulated to examined to look are examined in
maximize the
for change, its
the context of
illustration of the magnitude, and relevant peers to
question being
whether
reveal the
asked.
anomalies are
degree of
Manipulations
accelerating or
deviation, good
from simple to
stabilizing.
or bad.
intricate.
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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.
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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
• 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 …
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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.
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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
Public/Private
Data
Inputs
Analytics
Output:
XLS, XML,
HTML
XBRL
XML
Standardized
Raw Data
Repository
CSV
Simple
Report
Analysis
Processor
.NET
ODBC
Global
Standardized
Metrics Engine
.NET
ODBC
www.institutionalriskanalytics.com
Additional
Downstream
Central
Portfolio
Analysis
Engine(s)
Contact Information
Corporate Offices
Inquiries
Lord, Whalen LLC
dba Institutional Risk Analytics
14352 Yukon Avenue
Hawthorne, California 90250
Tel. 310.676.3300
Fax. 310.943.1570
info@institutionalriskanalytics.com
Christopher Whalen
Managing Director
Sales and Marketing
Tel. 914.827.9272
Fax. 914.206.4238
Cell. 914.645.5304
cwhalen@institutionalriskanalytics.com
www.institutionalriskanalytics.com