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From Spin to Swindle: Identifying Falsification in Financial Text

Despite legislative attempts to curtail financial statement fraud, it continues unabated. This study makes a renewed attempt to aid in detecting this misconduct using linguistic analysis with data mining on narrative sections of annual reports/10-K form. Different from the features used in similar r...

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Detalles Bibliográficos
Autores principales: Minhas, Saliha, Hussain, Amir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4981627/
https://www.ncbi.nlm.nih.gov/pubmed/27563359
http://dx.doi.org/10.1007/s12559-016-9413-9
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author Minhas, Saliha
Hussain, Amir
author_facet Minhas, Saliha
Hussain, Amir
author_sort Minhas, Saliha
collection PubMed
description Despite legislative attempts to curtail financial statement fraud, it continues unabated. This study makes a renewed attempt to aid in detecting this misconduct using linguistic analysis with data mining on narrative sections of annual reports/10-K form. Different from the features used in similar research, this paper extracts three distinct sets of features from a newly constructed corpus of narratives (408 annual reports/10-K, 6.5 million words) from fraud and non-fraud firms. Separately each of these three sets of features is put through a suite of classification algorithms, to determine classifier performance in this binary fraud/non-fraud discrimination task. From the results produced, there is a clear indication that the language deployed by management engaged in wilful falsification of firm performance is discernibly different from truth-tellers. For the first time, this new interdisciplinary research extracts features for readability at a much deeper level, attempts to draw out collocations using n-grams and measures tone using appropriate financial dictionaries. This linguistic analysis with machine learning-driven data mining approach to fraud detection could be used by auditors in assessing financial reporting of firms and early detection of possible misdemeanours.
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spelling pubmed-49816272016-08-23 From Spin to Swindle: Identifying Falsification in Financial Text Minhas, Saliha Hussain, Amir Cognit Comput Article Despite legislative attempts to curtail financial statement fraud, it continues unabated. This study makes a renewed attempt to aid in detecting this misconduct using linguistic analysis with data mining on narrative sections of annual reports/10-K form. Different from the features used in similar research, this paper extracts three distinct sets of features from a newly constructed corpus of narratives (408 annual reports/10-K, 6.5 million words) from fraud and non-fraud firms. Separately each of these three sets of features is put through a suite of classification algorithms, to determine classifier performance in this binary fraud/non-fraud discrimination task. From the results produced, there is a clear indication that the language deployed by management engaged in wilful falsification of firm performance is discernibly different from truth-tellers. For the first time, this new interdisciplinary research extracts features for readability at a much deeper level, attempts to draw out collocations using n-grams and measures tone using appropriate financial dictionaries. This linguistic analysis with machine learning-driven data mining approach to fraud detection could be used by auditors in assessing financial reporting of firms and early detection of possible misdemeanours. Springer US 2016-05-21 2016 /pmc/articles/PMC4981627/ /pubmed/27563359 http://dx.doi.org/10.1007/s12559-016-9413-9 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Article
Minhas, Saliha
Hussain, Amir
From Spin to Swindle: Identifying Falsification in Financial Text
title From Spin to Swindle: Identifying Falsification in Financial Text
title_full From Spin to Swindle: Identifying Falsification in Financial Text
title_fullStr From Spin to Swindle: Identifying Falsification in Financial Text
title_full_unstemmed From Spin to Swindle: Identifying Falsification in Financial Text
title_short From Spin to Swindle: Identifying Falsification in Financial Text
title_sort from spin to swindle: identifying falsification in financial text
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4981627/
https://www.ncbi.nlm.nih.gov/pubmed/27563359
http://dx.doi.org/10.1007/s12559-016-9413-9
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