dc.contributor.author | Taylor, Stacey Dianne | |
dc.date.accessioned | 2019-12-10T18:44:18Z | |
dc.date.available | 2019-12-10T18:44:18Z | |
dc.date.issued | 2019-12-10T18:44:18Z | |
dc.identifier.uri | http://hdl.handle.net/10222/76757 | |
dc.description.abstract | The Management Discussion and Analysis (MD&A) is arguably the most tonal section of the reports provided to the U.S. Securities and Exchange Commission. As part of that dialogue, companies use non-standardized financial metrics known as non-GAAP measures that do not conform with Generally Accepted Accounting Principles. Our research presents a novel extractive approach using Sentiment Analysis to measure the impact that non-GAAP measures have on the common investor versus those who are financially savvy. We find that sentiment declines once the non-GAAP sentences have been extracted with a statistical significance at the p=0.01 level. Building on this, our second research question investigated if we could use a similar approach with machine learning to predict the outcome of securities class action lawsuits. We find that we are able to predict the aggregate outcome of the lawsuits with a recall of 0.9142 using the Random Forest classifier. | en_US |
dc.language.iso | en | en_US |
dc.subject | Natural Language Processing | en_US |
dc.subject | Machine Learning | en_US |
dc.subject | Finance | en_US |
dc.subject | Accounting | en_US |
dc.subject | Statistics | en_US |
dc.subject | Sentiment Analysis | en_US |
dc.title | USING NLP TO QUANTIFY THE EFFECTS OF NON-GAAP MEASURES TO PREDICT THE OUTCOME OF SECURITIES LAWSUITS | en_US |
dc.date.defence | 2019-12-03 | |
dc.contributor.department | Faculty of Computer Science | en_US |
dc.contributor.degree | Master of Electronic Commerce | en_US |
dc.contributor.external-examiner | N/A | en_US |
dc.contributor.graduate-coordinator | Dr. Kirstie Hawkey | en_US |
dc.contributor.thesis-reader | Dr. Srinivas Sampalli | en_US |
dc.contributor.thesis-reader | Dr. Vladimir Lucic | en_US |
dc.contributor.thesis-supervisor | Dr. Vlado Keselj | en_US |
dc.contributor.ethics-approval | Not Applicable | en_US |
dc.contributor.manuscripts | Not Applicable | en_US |
dc.contributor.copyright-release | Not Applicable | en_US |