Independent studies paper, The Hong Kong University of Science and Technology, Dept. of Computer Science and Engineering.
ISSN/ISBN: Not available at this time. DOI: Not available at this time.
Abstract: Benford’s Law is an observation about the frequency distribution of leading digits of many real-life sets of numerical data. It states that in many datasets, most elements (around 30%) will have 1 as the first digit, around 17% will have 2 as the first digit, and so on, with a decreasing trend until 9, of which only 4.5% of elements in the dataset will have it as its first digit. In this report, we investigate the applications of Benford’s Law on financial data. More specifically, we verify that the closing prices of S&P500 stocks indeed closely follow the Benford distribution. We provide an analysis by sector and explore the Enron scandal as a case study of a dataset that deviates from the Benford distribution. This Benford model is then used as the motivation to design short sell recommendation strategies. Finally, we apply these strategies to potential stocks (Joyy Inc., eHealth Inc.) suggested by Muddy Waters Research.
Bibtex:
@misc{,
author = {Sedrick Scott Keh},
title = {Designing Shorting Strategies with Benford’s Law},
year = {2020},
url = {https://www.cse.ust.hk/~rossiter/independent_studies_projects/benford_shorting/benford_shorting.pdf},
}
Reference Type: Technical Report
Subject Area(s): Accounting, Economics