Date of Award
2016
Document Type
Thesis
Degree Name
Bachelors
Department
Natural Sciences
First Advisor
McDonald, Patrick
Area of Concentration
Applied Mathematics
Abstract
Occasionally company's statutory earnings announcements are coupled with very large drops in a company's stock price. I address whether such drops can be foretold by prior earnings statements. Because such price behavior can be attributed to market discovery of earnings statement mismanagement, Beneish, using financial statement ratios, proposed a model to identify potential earnings manipulations by comparing these against earnings restatements in subsequent disclosures. I utilize machine learning techniques to further enhance this score. In addition, instead of earnings restatements, I target stock price drops of 15% and 25% at the next earnings announcement. I then compare the performance of the original Beneish M-Score against the new enhanced score when both are used as a trade signal. I find that this novel model enhances trading performance, with particularly strong improvements for large capitalization stocks.
Recommended Citation
Dalmasso, Jacopo, "A MACHINE LEARNING APPROACH TO THE BENEISH M-SCORE" (2016). Theses & ETDs. 5185.
https://digitalcommons.ncf.edu/theses_etds/5185