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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2016
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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. |
format | Online Article Text |
id | pubmed-4981627 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
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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