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Applying Deep Learning to Examine Ta...

Lin, Tony Liang Ju.

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Applying Deep Learning to Examine Tax Footnotes: A Study of Emotions and Tax Outcomes.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Applying Deep Learning to Examine Tax Footnotes: A Study of Emotions and Tax Outcomes./
Author:	Lin, Tony Liang Ju.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	84 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-02, Section: B.
Contained By:	Dissertations Abstracts International82-02B.
Subject:	Artificial intelligence. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28001712
ISBN:	9798662441682

Applying Deep Learning to Examine Tax Footnotes: A Study of Emotions and Tax Outcomes.
Lin, Tony Liang Ju.

Applying Deep Learning to Examine Tax Footnotes: A Study of Emotions and Tax Outcomes. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 84 p.

Source: Dissertations Abstracts International, Volume: 82-02, Section: B.

Thesis (Ph.D.)--Drexel University, 2020.

This item must not be sold to any third party vendors.

This study applies deep learning algorithms provided and validated by the IBM Watson to examine the relation between qualitative textual characteristics (i.e., sentiment and emotion) detected in tax footnotes and tax outcomes. Extant studies provide evidence that a firm's reported unrecognized tax benefits (UTBs) consist of tax avoidance/complexity and financial reporting incentives. I use the emotion of joy conveyed in tax footnotes to measure the comfortability of a firm's tax position, employing the IBM Watson Natural Language Understanding (NLU) service. I provide evidence and show that, unlike emotion, sentiment (i.e., a positive, negative, or neutral tone) is easier to modify. Specifically, enabling targeted sentiment and emotion features on the service, I find that targeted sentiment, not targeted emotion, associated with and conveyed by the concept of uncertain tax positions explains financial reporting incentives of UTBs. I further find that detected emotion predicts UTBs related settlements with taxing authorities. In related analyses, I also find that these qualitative textual characteristics can explain a firm's demand for auditor provided tax services (APTS). Overall, my findings provide important initial evidence on the role of NLU detected qualitative textual characteristics and their relation to tax outcomes and APTS. Nevertheless, this study may help taxing authorities develop a more concrete audit strategy.

ISBN: 9798662441682Subjects--Topical Terms:

516317
Artificial intelligence.
Subjects--Index Terms:

Auditor provided tax services

Applying Deep Learning to Examine Tax Footnotes: A Study of Emotions and Tax Outcomes.
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245 1 0 $a Applying Deep Learning to Examine Tax Footnotes: A Study of Emotions and Tax Outcomes.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 84 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-02, Section: B.
500 $a Advisor: Curatola, Anthony.
502 $a Thesis (Ph.D.)--Drexel University, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a This study applies deep learning algorithms provided and validated by the IBM Watson to examine the relation between qualitative textual characteristics (i.e., sentiment and emotion) detected in tax footnotes and tax outcomes. Extant studies provide evidence that a firm's reported unrecognized tax benefits (UTBs) consist of tax avoidance/complexity and financial reporting incentives. I use the emotion of joy conveyed in tax footnotes to measure the comfortability of a firm's tax position, employing the IBM Watson Natural Language Understanding (NLU) service. I provide evidence and show that, unlike emotion, sentiment (i.e., a positive, negative, or neutral tone) is easier to modify. Specifically, enabling targeted sentiment and emotion features on the service, I find that targeted sentiment, not targeted emotion, associated with and conveyed by the concept of uncertain tax positions explains financial reporting incentives of UTBs. I further find that detected emotion predicts UTBs related settlements with taxing authorities. In related analyses, I also find that these qualitative textual characteristics can explain a firm's demand for auditor provided tax services (APTS). Overall, my findings provide important initial evidence on the role of NLU detected qualitative textual characteristics and their relation to tax outcomes and APTS. Nevertheless, this study may help taxing authorities develop a more concrete audit strategy.
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650 4 $a Quantitative psychology. $3 2144748
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653 $a Emotion & sentiment
653 $a Tax settlements
653 $a Unrecognized tax benefits
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793 $a English
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