Today’s Discussion Linguistic feature mining of 2 contrasting corpora: Text from Financial...
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Today’s Discussion • Linguistic feature mining of 2 contrasting corpora: Text from Financial Statements Transcripts of 911 Homicide Calls Text Verbal communication transcribed to text Carefully written and edited over weeks to months Unrehearsed Formal: conforms to genre for financial Informal: includes slang
Today’s Discussion Linguistic feature mining of 2 contrasting corpora: Text from Financial Statements Transcripts of 911 Homicide Calls TextVerbal communication
Todays Discussion Linguistic feature mining of 2 contrasting
corpora: Text from Financial Statements Transcripts of 911 Homicide
Calls TextVerbal communication transcribed to text Carefully
written and edited over weeks to months Unrehearsed Formal:
conforms to genre for financial communiqus Informal: includes
slang
Slide 3
Financial Statement Fraud: Problem and Motivation Investors
look for credibility, transparency, and clarity of financial
documents to make investment decisions and to maintain confidence
in companies Managements Discussion and Analysis (MD&A) is
among the sections of 10-Ks that is read most often Auditors need
innovative ways to assess risk based on not only financial and
nonfinancial measures but also financial statement texts
Slide 4
Deception Is Strategic (Buller and Burgoon, 1996) FOOTNOTE 16.
RELATED PARTY TRANSACTIONS In 2000 and 1999, Enron entered into
transactions with limited partnerships (the Related Party) whose
general partners managing member is a senior officer of Enron. The
limited partners of the Related Party are unrelated to Enron.
Management believes that the terms of the transactions with the
Related Party were reasonable compared to those which could have
been negotiated with unrelated third partiesSubsequently, Enron
sold a portion of its interest in the partnership through
securitizations. (Enron 2000)
Slide 5
Leakage Theory Applied to Fraudulent Financial Reporting (Ekman
1969) Managers engaging in fraud cannot completely match behavior
exhibited when truthful Cues leak out unintentionally Language
usage should leave clues to deception
Slide 6
Mining Linguistic Features for Detecting Obfuscation in
Financial Reports Do MD&A sections of fraudulent 10-Ks have a
higher level of obfuscation? Based on the research in deception
detection and obfuscation, we can look for the following (among
other cues) in fraudulent MD&As: More complex words More
complex sentences More causation words More achievement words
Slide 7
Our Methodology Linguistic Extraction and Classification Tools
Linguistic Cues for Deception Classified as Deceptive Classified as
Not Deceptive 101 MD&As with fraud problems 101 MD&As with
no fraud problems
Slide 8
Example of Results Greater in Fraudulent MD&As Rate of
Three Syllable Words** Conjunctions** Causation Words** Achievement
Words* ** = p
Examples of Results Variable nameDirectionExample 1st person
plural D>TWe don't know. 3rd person plural D>TYes, they said,
they said if they heard anything they were going to my house.
NegationD>TNo nothing, he's gone. AssentD>TOkay, they're
here.
Slide 14
Truthtellers: Display more negative emotion (including
emotion-filled swearing) and anxiety than deceivers. Refer to
singular others (she or he). Use more numbers to ensure responders
find address as quickly as possible or know phone number. Use more
generic names of locations, such as apartment or garage to give
more accurate, helpful information to responders. Discussion:
Truthtellers
Slide 15
Deceivers: Distance themselves from what is said by referencing
others in the 3 rd person (they). Try to share the blame by
referring to self as plural (we) rather than as singular. Use more
negation and assent words because they are trying to subdue,
constrain, or suppress answers/affect. Tell the operator to wait or
hold on if the operator is asking them to do something, such as
CPR, that they are reluctant to do. Discussion: Deceivers