I investigate the association between rent extraction and qualitative/quantitative characteristics of 10-K filings (i.e. readability, financial statement comparability and earnings transparency), subject to existing monitoring constraints. This study focuses on one type of such rent extraction – investment inefficiency (i.e. overinvestment or underinvestment), as extant research provides evidence that it provides personal benefits to managers, often at the expense of shareholders. Managers have incentives to invest inefficiently but such behavior may be undesirable and result in negative consequences to the manager, such as turnover. Therefore, I expect that managers are likely to obfuscate information in order to make it difficult for investors to detect investment inefficiency, although monitoring over financial reporting may limit their ability to do so. I test whether monitoring over financial reporting reduces information obfuscation. Last, I study the joint effects of investment inefficiency and information obfuscation on CEO turnover and compensation. I expect that investment inefficiency is positively associated with information obfuscation but this relation is weaker for firms with effective monitoring mechanisms over financial reporting. Further, I examine how these factors affect CEO disciplining. Managers get disciplined for inefficient investment decisions. Obfuscating information makes it difficult for investors to evaluate managers’ investment decisions. Therefore, I examine whether information obfuscation prevents managers from being disciplined as a result of inefficient investment behavior. I find that investment inefficiency is positively associated with information obfuscation. Managers are more likely to obfuscate information for overinvestment type of inefficiency as opposed to underinvestment. Further, the results suggest that, while internal monitoring does not reduce information obfuscation, external monitoring constrains information obfuscation. I find that external monitoring (i.e. auditors) provide more stringent monitoring by reducing information obfuscation. I do not find support for my last prediction that information obfuscation prevents disciplining of CEOs. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (leaves 98-100).

Thesis (MMS.)--University of Waikato, 2006. / Title from PDF cover (viewed February 26, 2008) Includes bibliographical references (p. 133-144)

Thesis (Ph.D.)--RAND Graduate School, 2007. / Includes bibliographical references.

Thesis (Ph.D.)--York University, 2003. Graduate Programme in Administration. / Typescript. Includes bibliographical references (180-193). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://wwwlib.umi.com/cr/yorku/fullcit?pNQ99259

Regulators have long disagreed whether regulation would reduce hedge funds’ financial misreporting. On the one hand, critics have stated that hedge funds are unlikely to misreport because their investors are highly sophisticated financial players who can detect and deter financial misconduct. On the other hand, recent changes in the composition of hedge funds’ investors have led many to question this argument. In this paper, I test whether hedge fund regulation reduces misreporting by using a quasi-natural experiment in which a subset of hedge funds was regulated, deregulated, and then regulated again. Unique features of the setting permit me to study not only whether hedge fund regulation reduces financial misreporting—but, if so, why the regulation reduces misreporting. The results show that regulation reduces misreporting at hedge funds and that the imposition of disclosure requirements, even without other concurrent changes in regulation, can reduce hedge funds’ misreporting. The result seems surprising, because hedge funds’ investors are commonly thought to have access to far more information than is required by disclosure rules. Further inquiries suggest that disclosure requirements led funds to make changes in their internal governance, and that these changes in governance induced funds to report their financial performance more honestly and accurately.

Financial fraud rampages onwards seemingly uncontained. The annual cost of fraud in the UK is estimated to be as high as £193bn a year [1] . From a data science perspective and hitherto less explored this thesis demonstrates how the use of linguistic features to drive data mining algorithms can aid in unravelling fraud. To this end, the spotlight is turned on Financial Statement Fraud (FSF), known to be the costliest type of fraud [2]. A new corpus of 6.3 million words is composed of102 annual reports/10-K (narrative sections) from firms formally indicted for FSF juxtaposed with 306 non-fraud firms of similar size and industrial grouping. Differently from other similar studies, this thesis uniquely takes a wide angled view and extracts a range of features of different categories from the corpus. These linguistic correlates of deception are uncovered using a variety of techniques and tools. Corpus linguistics methodology is applied to extract keywords and to examine linguistic structure. N-grams are extracted to draw out collocations. Readability measurement in financial text is advanced through the extraction of new indices that probe the text at a deeper level. Cognitive and perceptual processes are also picked out. Tone, intention and liquidity are gauged using customised word lists. Linguistic ratios are derived from grammatical constructs and word categories. An attempt is also made to determine ‘what’ was said as opposed to ‘how’. Further a new module is developed to condense synonyms into concepts. Lastly frequency counts from keywords unearthed from a previous content analysis study on financial narrative are also used. These features are then used to drive machine learning based classification and clustering algorithms to determine if they aid in discriminating a fraud from a non-fraud firm. The results derived from the battery of models built typically exceed classification accuracy of 70%. The above process is amalgamated into a framework. The process outlined, driven by empirical data demonstrates in a practical way how linguistic analysis could aid in fraud detection and also constitutes a unique contribution made to deception detection studies.