Testing for Strategic Manipulation of Economic and Financial Data

Benford's Law ◽  
2015 ◽  
Author(s):  
Charles C. Moul ◽  
John V. C. Nye
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Strategic Behavior ◽  
Financial Data ◽  
Benford’S Law ◽  
Data Series ◽  
The Past ◽  
Strategic Manipulation ◽  
Benford's Law ◽  
The U.S

This chapter surveys applications of Benford's law within economics, such as its use in investigating the validity of macroeconomic data series. It argues that, given the strong interest in strategic behavior in economics, it makes sense to use Benford's law to investigate possible anomalies that suggest manipulation or other interference, especially when incentives increase for such tampering. The chapter then considers how a first-digit analysis informs Value at Risk (VAR) data from the U.S. financial sector over the past ten years. It finds that Benford's law fits precrisis data very well but is rejected for postcrisis data. Opportunities and incentives for such misreporting are then discussed.

scholarly journals THE LINK BETWEEN EARNINGS MANAGEMENT AND DIGITAL PATTERN / O elo entre gerenciamento de resultado e padrão digital

2014 ◽  
Vol 14 (1) ◽  
pp. 351
Author(s):  
Jennifer Martínez Ferrero ◽  
Beatriz Cuadrado Ballesteros ◽  
Marco Antonio Figueiredo Milani Filho
Keyword(s):  
Earnings Management ◽  
Financial Reporting ◽  
Reporting Quality ◽  
Poor Quality ◽  
Financial Data ◽  
Benford’S Law ◽  
Similar Distribution ◽  
Data Set ◽  
Benford's Law ◽  
High Degree

<p>According to Dechow and Dichev (2002) and Lin and Wu (2014), a high degree of earnings management (EM) is associated with a poor quality of information. In this sense, it is possible to assume that the financial data of companies that manage earnings can present different patterns from those with low degree of EM. The aim of this exploratory study is to test whether a financial data set (operating expenses) of companies with high degree of EM presents bias. For this analysis, we used the model of Kothari and the modified model of Jones (“Dechow model” hereafter) to estimate the degree of EM, and we used the logarithmic distribution of data predicted by the Benford’s Law to detect abnormal patterns of digits in number sets. The sample was composed of 845 international listed non-financial companies for the year 2010. To analyze the discrepancies between the actual and expected frequencies of the significant-digit, two statistics were calculated: Z-test and Pearson’s chi-square test. The results show that, with a confidence level of 90%, the companies with a high degree of EM according to the Kothari model presented similar distribution to that one predicted by the Benford’s Law, suggesting that, in a preliminary analysis, their financial data are free from bias. On the other hand, the data set of the organizations that manage earnings according to the Dechow model presented abnormal patterns. The Benford´s Law has been implemented to successfully detect manipulated data. These results offer insights into the interactions between EM and patterns of financial data, and stimulate new comparative studies about the accuracy of models to estimate EM.</p><p>Keywords:<strong> </strong>Earnings management (EM). Financial Reporting Quality (FRQ). Benford’s Law.</p>

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.

A REMARK CONCERNING VALUE-AT-RISK

2010 ◽  
Vol 13 (04) ◽  
pp. 507-515
Author(s):  
S. Y. NOVAK
Keyword(s):  
At Risk ◽  
Normal Distribution ◽  
Value At Risk ◽  
Financial Risk ◽  
Daily Basis ◽  
Square Root ◽  
Rates Of Return ◽  
The Past ◽  
Light Tail ◽  
Significant Underestimation

Over the past two decades Value-at-Risk (VaR) became arguably the most popular measure of financial risk. Major banks calculate VaR on daily basis in order to determine the amount of capital a bank needs to offset the market risk. Banks use calculation methods of their choice, and many estimations are based on the assumption that portfolio rates of return have normal distribution. The important question is whether the chosen method of VaR calculation is accurate. As the light-tail property of the normal distribution can cause significant underestimation of VaR, the Basel Committee suggested to calculate the amount of capital needed by multiplying the bank's internal estimate of VaR by the factor 3. It's also common to use the so-called "square root of time" rule when evaluating VaR over a longer time horizon. This article aims to refine Stahl's argument behind the "factor 3" rule and say a word of caution concerning the "square root of time" rule.

scholarly journals Are COVID-19 Data Reliable? A Quantitative Analysis of Pandemic Data from 182 Countries

COVID ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 137-152
Author(s):  
Noah Farhadi ◽  
Hooshang Lahooti
Keyword(s):  
Reliable Data ◽  
Benford’S Law ◽  
Global Health Security ◽  
Quality Of Data ◽  
Health Security ◽  
The Past ◽  
Six Nations ◽  
Benford's Law ◽  
The Uk

When it comes to COVID-19, access to reliable data is vital. It is crucial for the scientific community to use data reported by independent territories worldwide. This study evaluates the reliability of the pandemic data disclosed by 182 countries worldwide. We collected and assessed conformity of COVID-19 daily infections, deaths, tests, and vaccinations with Benford’s law since the beginning of the coronavirus pandemic. It is commonly accepted that the frequency of leading digits of the pandemic data shall conform to Benford’s law. Our analysis of Benfordness elicits that most countries partially distributed reliable data over the past eighteen months. Notably, the UK, Australia, Spain, Israel, and Germany, followed by 22 different nations, provided the most reliable COVID-19 data within the same period. In contrast, twenty-six nations, including Tajikistan, Belarus, Bangladesh, and Myanmar, published less reliable data on the coronavirus spread. In this context, over 31% of countries worldwide seem to have improved reliability. Our measurement of Benfordness moderately correlates with Johns Hopkin’s Global Health Security Index, suggesting that the quality of data may depend on national healthcare policies and systems. We conclude that economically or politically distressed societies have declined in conformity to the law over time. Our results are particularly relevant for policymakers worldwide.

scholarly journals A New Portfolio Rebalancing Model with Transaction Costs

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Meihua Wang ◽  
Cheng Li ◽  
Honggang Xue ◽  
Fengmin Xu
Keyword(s):  
At Risk ◽  
Transaction Costs ◽  
Cost Function ◽  
Transaction Cost ◽  
Value At Risk ◽  
Financial Data ◽  
Conditional Value At Risk ◽  
Expected Return ◽  
Portfolio Rebalancing ◽  
Proposed Model

A portfolio rebalancing model with self-finance strategy and consideration of V-shaped transaction cost is presented in this paper. Our main contribution is that a new constraint is introduced to confirm that the rebalance necessity of the existing portfolio needs to be adjusted. The constraint is constructed by considering both the transaction amount and transaction cost without any additional supply to the investment amount. The V-shaped transaction cost function is used to calculate the transaction cost of the portfolio, and conditional value at risk (CVaR) is used to measure the risk of the portfolios. Computational tests on practical financial data show that the proposed model is effective and the rebalanced portfolio increases the expected return of the portfolio and reduces the CVaR risk of the portfolio.

scholarly journals Do the Financial Statements of Listed Companies on the Ljubljana Stock Exchange Pass the Benford’s Law Test?

2018 ◽  
Vol 12 (1) ◽  
pp. 54
Author(s):  
Natasa Omerzu ◽  
Iztok Kolar
Keyword(s):  
Stock Exchange ◽  
Financial Statements ◽  
Listed Companies ◽  
Financial Data ◽  
Theoretical Distribution ◽  
Benford’S Law ◽  
Digital Analysis ◽  
Accounting Data ◽  
Benford's Law ◽  
Long Time

Currently, we need to think about the risks in using the financial statements. Abroad, for a long time, in the detection of irregularities in the financial statements, Benford&#39;s law test has been used, which is a very simple, objective and efficient digital analysis that can help identify controversial areas. Since, in Slovenia, its use is still unknown and in practice, and it is rarely used, we checked whether the financial statements of Slovenian companies listed on the Ljubljana Stock Exchange pass the Benford&rsquo;s law test. Our study is original, as no one has ever tested the company&#39;s financial statements on the Ljubljana Stock Exchange with this test. We found that the tested data very well matched the theoretical distribution according to Benford&#39;s law. If the deviation of the analysed data from the theoretical distribution is very large, this does not mean that this is a possible fraud in the used financial data. Benford&#39;s law helps us identify the controversial areas that require our attention and the decision on how to proceed with the audit or possible investigation of accounting data.

Can Vote Countsʼ Digits and Benfordʼs Law Diagnose Elections?

Benford's Law ◽  
2015 ◽  
Author(s):  
Walter R. Mebane,
Keyword(s):  
United States ◽  
Political Parties ◽  
Strategic Behavior ◽  
The United States ◽  
Benford’S Law ◽  
Conditional Mean ◽  
Benford's Law ◽  
Election Fraud

This chapter illustrates how the conditional mean of precinct vote counts' second digits can respond to strategic behavior by voters in response to the presence of a coalition among political parties. The digits in vote counts can help diagnose both the strategies voters use in elections and nonstrategic special mobilizations affecting votes for some candidates. The digits can also sometimes help diagnose some kinds of election fraud. The claim that deviations in vote counts' second digits from the distribution implied by Benford's law is an indicator for election fraud, generally fails for precinct vote counts. This chapter shows that such tests routinely fail in data from elections in the United States, Germany, Canada and Mexico, countries where it is usually thought that there is negligible fraud.

AN EMPIRICAL INVESTIGATION OF THE FORWARD INTEREST RATE TERM STRUCTURE

2000 ◽  
Vol 03 (04) ◽  
pp. 703-729 ◽  
Author(s):  
ANDREW MATACZ ◽  
JEAN-PHILIPPE BOUCHAUD
Keyword(s):  
Term Structure ◽  
Value At Risk ◽  
Previous Investigation ◽  
Empirical Investigation ◽  
Forward Rate ◽  
The Past ◽  
A Value ◽  
Interest Rate Term Structure ◽  
Spot Volatility ◽  
The U.S

In this paper we study empirically the Forward Rate Curve (FRC) of 5 different currencies. We confirm and extend the findings of a previous investigation of the U.S. FRC. In particular, the average FRC follows a square-root law, with a prefactor related to the spot volatility, suggesting a Value-at-Risk-like pricing. We find a striking correlation between the instantaneous FRC and the past spot trend over a certain time horizon, in agreement with the idea of an extrapolated trend effect. We present a model which can be adequately calibrated to account for these effects.

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