Using Benford’s Law to Assess Anomalies in Accounting Numbers of Local Public Companies

2016 ◽  
Vol 14 (2) ◽  
pp. 123-153
Author(s):  
Dong-wuk Kim ◽  
Keyword(s):  
Benford’S Law ◽  
Public Companies ◽  
Accounting Numbers ◽  
Benford's Law

Benford’s Law and Stock Market—The Implications for Investors: The Evidence from India Nifty Fifty

2018 ◽  
Vol 7 (2) ◽  
pp. 103-121
Author(s):  
M. Jayasree ◽  
C. S. Pavana Jyothi ◽  
P. Ramya
Keyword(s):  
Stock Returns ◽  
Stock Prices ◽  
Moving Average ◽  
Benford’S Law ◽  
Business Research ◽  
Volume Number ◽  
Accounting Numbers ◽  
Benford's Law ◽  
Money Flow ◽  
Exhaustive Study

Benford’s law which is also known as first digit law states that data follow a certain frequency. This law was applied to accounting by Nigrini (2012, Benford’s Law: Applications for forensic accounting, auditing, and fraud detection [Vol. 586], John Wiley & Sons) and later on, an exhaustive study was carried out by Amiram, Bozanic, and Rouen (2015, Review of Accounting Studies, 20(4), 1540–1593) to explore the applicability of the law to detect accounting frauds which was proven to be working. The literature has substantial evidence on relationship between accounting numbers and stock returns. The application of Benford’s law to stock trade and returns was explored and it was found that stock trade that included volume, number of trades, and turnover confirmed the distribution but stock returns did not conform the distribution (Jayasree, 2017, Jindal Journal of Business Research, 6(2), 172–186). In this context, the present study attempts to understand its implications to investors by examining the three-day moving average of stock prices and volatility volume by using Chainkin money flow during announcement and post-announcement period of observation. The study also examines whether stocks conforming the distribution and stocks not conforming the distribution are significantly different in buying and selling.

Benford's Law: Analyzing a Decade of Financial Data

2013 ◽  
Vol 10 (1) ◽  
pp. 1-39 ◽  
Author(s):  
Fatima A. Alali ◽  
Silvia Romero
Keyword(s):  
Discretionary Accruals ◽  
Financial Data ◽  
Financial Accounting ◽  
Benford’S Law ◽  
Public Companies ◽  
Accounting Data ◽  
Large Sample ◽  
Big 4 ◽  
Benford's Law

ABSTRACT This study uses a decade of financial accounting data to examine if and how they depart from Benford's Law. Using a large sample of U.S. public companies, we conduct an analysis of the first-two digits of data items generally used in research to measure total accruals and discretionary accruals and where fraud, restatements, and enforcement actions are revealed. We break down a decade of data into six subperiods; pre-SOX Period (2001), SOX 1 Period (2002–2003), SOX 2 Period (2004–2006), SOX 3 Period (2007), Crisis 1 Period (2008), and Crisis 2 Period (2009–2010). We find different indicators of manipulation during the periods studied, as well as differences between small and big companies and companies audited by Big 4 and non-Big 4 firms.

scholarly journals Using Benford’s Law to detect data error and fraud: An examination of companies listed on the Johannesburg Stock Exchange

2014 ◽  
Vol 9 (3) ◽  
pp. 341-354 ◽  
Author(s):  
A Saville
Keyword(s):  
Stock Exchange ◽  
Financial Analysts ◽  
Benford’S Law ◽  
Accounting Data ◽  
Johannesburg Stock Exchange ◽  
Accounting Numbers ◽  
Benford's Law ◽  
Data Error ◽  
The Law ◽  
Private Investors

Accounting numbers generally obey a mathematical law called Benford’s Law, and this outcome is so unexpected that manipulators of information generally fail to observe the law. Armed with this knowledge, it becomes possible to detect the occurrence of accounting data that are presented fraudulently. However, the law also allows for the possibility of detecting instances where data are presented containing errors. Given this backdrop, this paper uses data drawn from companies listed on the Johannesburg Stock Exchange to test the hypothesis that Benford’s Law can be used to identify false or fraudulent reporting of accounting data. The results support the argument that Benford’s Law can be used effectively to detect accounting error and fraud. Accordingly, the findings are of particular relevance to auditors, shareholders, financial analysts, investment managers, private investors and other users of publicly reported accounting data, such as the revenue services

Stock Trading and Stock Returns: Understanding the Distributional Properties of the Numbers—The Evidence from India Nifty Fifty

2017 ◽  
Vol 6 (2) ◽  
pp. 171-185 ◽  
Author(s):  
M. Jayasree
Keyword(s):  
Stock Returns ◽  
Strong Relationship ◽  
Benford’S Law ◽  
Trading Activity ◽  
Stock Trading ◽  
Frequency Pattern ◽  
Distributional Properties ◽  
Accounting Numbers ◽  
Benford's Law ◽  
Daily Stock Returns

Benford’s law which studied the distributional properties of numbers observed that data patterns follow a certain frequency. The application of the Benford law to accounting numbers was tested by Dan Amiram, Zahn Bozanic, and Ethan Roven (2015), and was proven that accounting numbers follow the same frequency. There are several theories that advocated a strong relationship between accounting numbers and stock returns. Taking this as a base, the study aims to investigate whether Benford’s law, which was proven to be working for accounting numbers, would also work for stock trading and stock returns. The study uses data from National Stock exchange of Nifty Fifty stocks. Initially, data of daily stock returns and daily stock trade for five years from 2012 to 2016 are observed for the theoretical distribution. Later, the daily stock returns and daily trading activity for the results announcement months of April and May covering the five years were observed. It was examined whether data of stock returns and trading activity followed the distribution of Prob ( d) = log10 (1+ (1/ d)), for d = 1, 2, 3 ….9. Later the frequency pattern of stock returns and trading activity is tested by KS statistic to conclude whether data followed the same frequency as Benford’s law. The Kernel density estimates were also used to confirm the results.

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