Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/23284
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: From Spin to Swindle: Identifying Falsification in Financial Text
Authors: Minhas, Saliha
Hussain, Amir
Contact Email: ahu@cs.stir.ac.uk
Keywords: Classification
Coh–Metrix
Deception
Financial statement fraud
Issue Date: Aug-2016
Publisher: Springer
Citation: Minhas SZ & Hussain A (2016) From Spin to Swindle: Identifying Falsification in Financial Text, Cognitive Computation, 8 (4), pp. 729-745.
Abstract: 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 usingn-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.
Type: Journal Article
URI: http://hdl.handle.net/1893/23284
DOI Link: http://dx.doi.org/10.1007/s12559-016-9413-9
Rights: This 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.
Affiliation: Computing Science and Mathematics
Computing Science - CSM Dept

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