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Deep learning applications in audit decision making

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TitleInfo
Title
Deep learning applications in audit decision making
Name (type = personal)
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Sun
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Ting
NamePart (type = date)
1984-
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Ting Sun
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author
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Vasarhelyi
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Miklos A
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Miklos A Vasarhelyi
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Advisory Committee
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chair
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Kogan
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Alexander
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Alexander Kogan
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Advisory Committee
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internal member
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Brown-Liburd
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Helen
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Helen Brown-Liburd
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Advisory Committee
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internal member
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Srivastava
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Rajendra P
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Rajendra P Srivastava
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Advisory Committee
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outside member
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Rutgers University
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degree grantor
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Graduate School - Newark
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school
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theses
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2018
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2018-05
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2018
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xx
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eng
Abstract (type = abstract)
The objective of this dissertation is to investigate whether the sentiment features of business communication documents or social media information extracted by deep learning techniques deliver relevant and reliable information to auditors. The first essay investigates the incremental informativeness of sentiment features of earnings conference calls for the prediction of internal control material weaknesses (ICMW). With the help of a deep learning textual analyzer provided by IBM Watson, Alchemy Language API, this essay obtains the overall sentiment score of the text and the confidence score of the emotion “joy.” These sentiment features are then used as additional predictors along with other determinants of ICMW suggested by prior literature (i.e., Doyle, Ge, and McVay, 2007a; Ashbaugh-Skaife, Collins, and Kinney, 2007). The results indicate that these sentiment features, especially the score of joy, improve the explanatory ability and the prediction accuracy of the model. The second essay compares deep learning to the “bag of words” approach and demonstrates the effectiveness and efficiency of deep learning-based sentiment analysis for MD&A sections of 10-K filings in the context of financial misstatement prediction. The findings include (1) sentiment features provide insights for financial misstatement prediction, primarily for fraud detection; (2) the model using deep learning-based sentiment features generally performs more effectively than the model using sentiment features extracted by the “bag of words” approach. The third essay examines how the information of tweeting activities about the client company is associated with the audit fee. It examines the relationship between the audit fee of U.S. public firms in 2015 and the properties of tweets about the client firm: the sentiment of tweets, the volume of tweets, and the popularity of tweets. All tweet information is obtained using IBM Twitter Insights, a Twitter data analysis tool that provides sentiment and other enrichments relying on deep learning algorithms. It finds that for companies without going-concern audit opinions and companies with a median level of restatement risk, the audit fee is positively associated with the frequency of negative tweets, and this association is strengthened for companies receiving more retweets than those receiving less retweets.
Subject (authority = RUETD)
Topic
Management
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Title
Rutgers University Electronic Theses and Dissertations
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ETD_8944
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electronic resource
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application/pdf
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text/xml
Extent
1 online resource (xi, 160 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Subject (authority = ETD-LCSH)
Topic
Machine learning
Subject (authority = ETD-LCSH)
Topic
Auditing
Note (type = statement of responsibility)
by Ting Sun
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TitleInfo
Title
Graduate School - Newark Electronic Theses and Dissertations
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rucore10002600001
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NjNbRU
Identifier (type = doi)
doi:10.7282/T3902767
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Rights

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The author owns the copyright to this work.
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Name
FamilyName
Sun
GivenName
Ting
Role
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Type
Permission or license
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2018-04-22 22:24:26
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Ting Sun
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Rutgers University. Graduate School - Newark
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Author Agreement License
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
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Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

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2018-04-29T10:45:33
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2018-04-29T10:45:33
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