Financial Accounting
omid tarast; farzad ghayour; parviz piri
Abstract
1. IntroductionInvestors, who are the economic pulse of the financial industry, always want to be fully and transparently aware of the currents and events occurring in business units. Benford distribution (1938) can be evaluated as one of the options for evaluating financial reports.Benford distribution ...
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1. IntroductionInvestors, who are the economic pulse of the financial industry, always want to be fully and transparently aware of the currents and events occurring in business units. Benford distribution (1938) can be evaluated as one of the options for evaluating financial reports.Benford distribution is an analysis process that compares actual results against expected results to search for abnormal transactions. This distribution, also known as the first digit law, is a type of empirical observation that states; the first digit of the digits in many numerical data sets that occur are distributed in a special and non-uniform way. In other words, Benford distribution refers to the fact that in each of the digits (first to fourth digits), the probability of the digits one to nine is not equal and the first digits will be used more than the last digits. Therefore, the probability of the first digit occurring in the first digit is higher than the other digits.The rounding of financial figures often occurs when the distribution of figures differs slightly from the rounding. Any manipulation of figures if done artificially can be considered a self-serving act, although this act may be in the interests of the business unit or a number of specific individuals. In view of the above, the purpose of this research is to investigate and determine the extent to which profit figures of Iranian stock exchange business units are trimmed in order to round them in line with the personal interests of managers. In line with the objectives of the research, the question has been raised as to whether high and low audit quality has an effect on reducing or increasing the extent to which profit figures of business units are trimmed? And whether in each of the life cycles under study, if audit quality is present or absent, trimming of profit figures takes place or not?2. Literature ReviewThe Benford distribution was first introduced by Simon Newcomb (1881), an American mathematician who was able to discover the secret of logarithmic books. At that time, academics did not pay attention to Newcomb's research, and finally, Frank Benford (1938), a physicist at General Electric, once again addressed this phenomenon to bring this issue to a conclusion. Benford examined a set of natural data (20/229 randomly selected data) such as; baseball statistics, death rates, stock market prices, atomic weights in chemical compounds, Fibonacci numbers, river lengths and lake areas, urban population and census data, books and magazines, and the like. Finally, he confirmed the distribution observed by Simon Newcomb that in the distribution of numbers, there is a tendency towards smaller numbers and the repetition of the numbers one and two is more frequent than the numbers eight and nine. After the interest of academics, this distribution was officially named and known as the Benford distribution. However, research in this area did not progress as expected until a professor of mathematics named Roger Pinkham (1961) finally achieved mathematical proof in this field with his studies. After all these theorems, the American mathematician named Hill (1995) proved for the first time and in another way theoretically, using the form of the influential numerical center limit theorem in statistics, that the first digit follows this principle (Benford distribution). Based on his studies, Hill found that if the distribution of digits occurs randomly and random samples are extracted from that distribution, then it will be closer to the logarithmic distribution (Benford's law), and it will also help in the interpretation and prediction of digital aspects.Motivations related to profit embellishment in Iranian business units may be different from those in Western countries (Pourheidari and Hemmati, 2004). In studies and investigations conducted by Hee and Gan (2014) regarding the financial reports of Japanese business units, they found by applying Benford's law to profit and income accounts that profit embellishment and embellishment will differ from industry to industry, and that profit embellishment is more noticeable than income. In a study by Lennox et al. (2018), they addressed the topic of profit embellishment, auditor adjustments, and financing of business units and examined the amount and size of auditors' profit adjustments at the time of financing. They found that in the pre-financing stage, the size of auditor adjustments that minimize the profit of the business unit will be very large.3. MethodologyThe present study can be considered as an applied study in terms of its purpose. The information extracted using the purposive sampling method. This study seeks to design a model based on empirical observations, and on the other hand, the data used in the study are quantitative. The present study seeks a general result, so this study is a deductive and inductive study in terms of the type of reasoning and conclusion. The present study is related to all business units accepted in the Tehran Stock Exchange in the time period of 2012 to 2023, in which regard it can be said that the final sample selection was made using the systematic elimination method.4. ResultsThe first main hypothesis states that the life cycle (growth, maturity and decline) has a significant effect on the utilitarian neatness of the profit figures of business units. To achieve this, considering that the hypothesis test for the first hypothesis must be carried out in three stages (growth, maturity and decline), the test was carried out for each period. The first sub-hypothesis states that: in the growth stage, there is no neatness of profit figures and the distribution of companies' profit figures follows the Benford distribution. The results of the first sub-hypothesis indicate that the significance level in the second digit of profit figures is less than (0.05). Therefore, the test hypothesis that there is no significant difference between the actual (observed) frequency and the expected frequency is rejected. The significance level for the first, third and fourth digits of profit figures is greater than (0.05), which means that there is no significant difference between the actual frequency and the expected frequency. Also, the second digit statistic indicates a higher number than the first, third, and fourth digits. Therefore, the first sub-hypothesis is accepted. 5. Discussion The results obtained based on the hypothesis test indicate that these results are different from the results of studies conducted in other parts of the world.Although the results obtained in the current study indicate a lack of order in the profit figures of units listed on the Tehran Stock Exchange from 2012 to 2014. However, it is not possible to speak with complete certainty about the lack of order in these companies, because this phenomenon can exist in different industries in different ways and to different degrees.6. ConclusionThe existence of a phenomenon called rounding in the profit figures of business units during their life cycles is not confirmed based on the Chi-square test. The results obtained indicate the absence of tidying by management in the profit figures of the circle under study. The results of the research showed that the audit quality (high and low) has no effect on the occurrence or absence of tidying and there is no tidying in the profit figures at different stages of the life cycle.
Accounting and various aspects of finance
Zahra Yousefzadeh; Gholamreza Mansourfar; Farzad Ghayour
Abstract
Today, with rapid and sustained changes in business markets, a growing number of companies have turned diversification into new product segments or global markets by shifting their business to increase the importance of long-term financial viability and sustainability. Moreover, increasing the variety ...
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Today, with rapid and sustained changes in business markets, a growing number of companies have turned diversification into new product segments or global markets by shifting their business to increase the importance of long-term financial viability and sustainability. Moreover, increasing the variety of products, covering the uncertain demand of customers, managing inventories and timely action have been important issues in manufacturing companies. Accordingly, the main purpose of this study is to investigate the impact of diversification strategy on inventory performance, which is one of the topics of operations management by considering the classification of the diversity into related, unrelated and international. The statistical population studied includes all companies listed in the Tehran Stock Exchange during the years 2009-2018. Sampling has been done by screening method, and the number of companies in the final sample has reached 120 companies. The hypotheses have been tested by the estimated generalized least squares method. The results show that related and international diversification have positive and significant effects on inventory performance. The findings also indicate that unrelated diversification has an adverse effect on inventory performance, but this relationship is not statistically significant. Based on the acquired results, an increase in related and international variety of products, relying on higher safety stock, has led to an increase in sales. In addition, the insignificancy in the effect of unrelated diversification on inventory performance can be attributed to production costs and marketing programs of manufactured products.
Gholamreza Mansourfar; Farzad Ghayour; Shabnam Khaleghparast Athari
Abstract
The purpose of study is to investigate comparative ability of accountinginformation to predict indices volatility of companies listed in Tehran StockExchange using intelligent methods including Support Vector Machine,Artificial Neural Network and classic Logistic Regression model. Sample ofstudy includes ...
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The purpose of study is to investigate comparative ability of accountinginformation to predict indices volatility of companies listed in Tehran StockExchange using intelligent methods including Support Vector Machine,Artificial Neural Network and classic Logistic Regression model. Sample ofstudy includes 91 companies listed in Tehran Stock Exchange that have beenclassified in 9 industrious groups during time period of 2003-3013.Considering 11 corporate financial variables, study results show that despitepredicting ability of around 60% by Support Vector Machine and ArtificialNeural Network, there is significant difference between actual and predictedresults. Classic Logistic Regression model also can explain only 4%industries’ indices volatility using selected 11 corporate financial variables.Finally, although intelligent methods are superior to classic methods,accounting information solely are not well-explainer variables for predictingindustry index volatility and variety of variables such as financial, political,economical are effective in predicting industry index volatility.
Abstract
The purpose of the study is to investigate comparative ability of accounting information to predict indices volatility of companies listed in Tehran Stock Exchange using intelligent methods including Support Vector Machine, Artificial Neural Network and classic Logistic Regression model. Sample of study ...
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The purpose of the study is to investigate comparative ability of accounting information to predict indices volatility of companies listed in Tehran Stock Exchange using intelligent methods including Support Vector Machine, Artificial Neural Network and classic Logistic Regression model. Sample of study includes 91 companies listed in Tehran Stock Exchange which have been classified in 9 industry group during time period of 2003-3013. Considering 11 corporate financial variables, study results show that despite predicting ability of around 60% by Support Vector Machine and Artificial Neural Network, there is significant difference between actual and predicted results. Also, classic Logistic Regression model can explain only 4% industries’ indices volatility using selected 11 corporate financial variables. Finally, although intelligent methods are superior to classic methods, accounting information solely aren’t well-explainer variables for predicting industry index volatility and variety of variables such as financial, political, economical … are effective in predicting industry index volatility.
