فهرست مطالب

پیشرفت های حسابداری - سال نهم شماره 2 (پیاپی 73، پاییز و زمستان 1396)
  • سال نهم شماره 2 (پیاپی 73، پاییز و زمستان 1396)
  • 198 صفحه،
  • تاریخ انتشار: 1397/01/20
  • تعداد عناوین: 6
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  • شکرالله خواجوی*، مهرداد ابراهیمی صفحات 1-34

    رسوایی ها و شکست های شرکتی، اطمینان سرمایه گذاران به درست و منصفانه بودن حساب های واحدهای تجاری را مخدوش کرده است. تکنولوژی های مبتنی بر آمار و یادگیری ماشینی راهکاری اثربخش برای پیشگیری و کشف تقلب هستند؛ بنابراین در این پژوهش به بررسی این مسئله پرداخته می شود که آیا می توان از طریق شناسایی عوامل مرتبط با تقلب در صورت های مالی و با به کارگیری شیوه های داده کاوی، مدلی برای کشف تقلب در صورت های مالی شرکت های پذیرفته شده در بورس اوراق بهادار تهران ارائه کرد؟ برای پاسخ گویی به این سوال از 19 علائم خطر اشاره شده در استاندارد حسابرسی 240 به همراه شیوه های داده کاوی تحلیل مولفه های اساسی و خوشه بندی، برای تعیین شرکت های متقلب استفاده شد؛ سپس به منظور ارائه مدلی برای پیش بینی صورت های مالی متقلبانه، از 40 متغیر مالی و غیرمالی به همراه شیوه های درخت تصمیم، ماشین بردار پشتیبان و روش بوستینگ استفاده شد. یافته های پژوهش بیان گر وجود شواهدی دال بر عملکرد مناسب مدل های پیشنهادی برای پیش بینی تقلب در صورت های مالی است.

    کلیدواژگان: پیش بینی تقلب، تقلب در صورت های مالی، داده کاوی، بورس اوراق بهادار تهران
  • زانیار سجادی، امید پورحیدری، احمد خدامی پور صفحات 35-62
    هدف این پژوهش بررسی اثر همبستگی درون صنعتی بر مولفه هایی از محیط گزارشگری مالی است. این مولفه ها عبارت اند از کیفیت سود، دقت پیش بینی سودهای آتی که مدیریت ارائه می دهد، کیفیت افشا و نامتقارنی اطلاعاتی. نمونه آماری این پژوهش شامل 178 شرکت پذیرفته شده در بورس اوراق بهادار تهران در بازه زمانی 1384 تا 1393 بوده است. همبستگی درون صنعتی با استفاده از عامل کواریانس برای صنایع مختلف محاسبه شده و برای آزمون فرضیات از رگرسیون های چندمتغیره با رویکرد داده های ترکیبی استفاده شده است. نتایج نشان می دهد در صنایع با همبستگی درون صنعتی بیشتر، کیفیت سود پایین تر، خطای پیش بینی کمتر، کیفیت افشا پایین تر و نامتقارنی اطلاعاتی بیشتر است.
    کلیدواژگان: همبستگی درون صنعتی، کیفیت سود، دقت پیش بینی سود، کیفیت افشا، نامتقارنی اطلاعاتی
  • داریوش فروغی، هادی امیری، آزیتا ابراهیمیان صفحات 63-92
    هدف از این پژوهش، بررسی پایداری سود بر حسب سود نقدی و تعهدی و در سطح سود خاص شرکت و سود خاص صنعت است. هر چه پایداری سود بیشتر باشد، شرکت توان بیشتری برای حفظ سودهای جاری دارد و فرض می شود کیفیت سود بالاتر است. جامعه آماری این پژوهش، شامل کلیه شرکت های پذیرفته شده در بورس اوراق بهادار تهران است. به منظور دستیابی به هدف پژوهش، 113 شرکت از بین شرکت های پذیرفته شده در بورس اوراق بهادار تهران طی سال های 1384 تا 1394 برای نمونه آماری انتخاب شدند. به منظور تجزیه و تحلیل داده ها و آزمون فرضیه ها از الگوی معادلات همزمان میشکین استفاده شده است. یافته های پژوهش حاکی از آن است که پایداری سود خاص صنعت نسبت به سود خاص شرکت بیشتر است و پایدارترین جزء در بین سایر اجزا، جزء نقدی سود خاص صنعت و ناپایدارترین جزء، جزء تعهدی سود خاص شرکت است. از دیگر یافته های این پژوهش، عدم درک پایداری متفاوت اجزای سود توسط سرمایه گذاران است.
    کلیدواژگان: اقلام تعهدی، بازده سهام، پایداری سود، جریان های نقدی
  • سجاد محمدی، الله کرم صالحی صفحات 93-119
    ریسک ریزش قیمت سهام در بازار یکی از نگرانی های اصلی سرمایه گذاران است و پژوهش در این زمینه می تواند برای بازار سرمایه دارای اهمیت باشد. افزایش پدیده ریزش قیمت سهام، سبب بدبینی سرمایه گذاران در مورد سرمایه گذاری در بورس اوراق بهادار می شود؛ این مسئله درنهایت می تواند سبب شود که سرمایه گذاران منابع خود را از بورس اوراق بهادار خارج کنند. هدف این مقاله بررسی ارتباط بین توانایی های مدیریت و سرمایه گذاری کارا با سقوط قیمت سهام شرکت های پذیرفته شده در بورس اوراق بهادار تهران است. در این مطالعه با استفاده از داده های 152 شرکت موجود در بورس اوراق بهادار تهران در دوره زمانی سال های 1385 تا 1394 به بررسی این موضوع پرداخته شد. برای اندازه گیری توانایی مدیریت از مدل دمرجیان و همکاران (2013) که مبتنی بر متغیرهای حسابداری است، استفاده شده است. داده ها به روش داده های تابلویی و رگرسیون چند متغیره تجزیه وتحلیل شده اند. نتایج پژوهش حاکی از آن است که بین توانایی مدیریت و کارایی سرمایه گذاری رابطه معناداری وجود ندارد؛ درحالی که بین توانایی مدیریت و ریسک سقوط قیمت سهام رابطه مثبت و معناداری وجود دارد.
    کلیدواژگان: توانایی مدیریتی، کارایی سرمایه گذاری، ریسک کاهش قیمت سهام، بازار بورس اوراق بهادار تهران
  • سجاد نقدی، غلامحسین اسدی، علیرضا فضل زاده صفحات 121-149
    علی رغم پژوهش های فراوان صورت گرفته در خصوص قابلیت استفاده از داده های مختلف در پیش بینی شاخص های اقتصادی، شواهد اندکی در ارتباط با روابط اطلاعات حسابداری و اقتصادی، در محیط کشورهای درحال توسعه نظیر ایران و با توجه به ویژگی های آن ارائه شده است. بدین منظور داده های موردنیاز 88 شرکت بورسی در بازه زمانی 1385 تا 1395 جمع آوری شده اند. در این پژوهش از مدل های شبکه های عصبی المانی و الگوریتم پرواز پرندگان استفاده شده است. نتایج حاکی از این است که نوسانات اطلاعات حسابداری به عنوان شاخص پیش نگر نوسانات متغیرهای اقتصادی محسوب می شوند.
    کلیدواژگان: مدل پیش بینی، شاخص های اقتصادی، متغیرهای حسابداری
  • محمدرضا نیکبخت، علی اصغر دهقانی، سمانه قوهستانی صفحات 151-178
    کیفیت مدیریت به عنوان یکی از مهم ترین منابع ایجادکننده ارزش و سودآوری در آینده کسب وکار شرکت ها شناسایی شده است. نگرش و توانایی مدیران موجب شده است که آنان برای پیشبرد اهداف سازمان، راهبردهایی (استراتژی هایی) را برگزینند تا به زعم خود عملکرد سازمان را بهبود بخشند؛ اما هنگامی که مدیران، احساسات شخصی و هنجارهای اخلاقی را در تصمیم های خود درگیر سازند، منطقی بودن آنان موردتردید قرار گرفته و در اصطلاح، شکل غیرمنطقی به خود می گیرد که یکی از این رفتارهای غیرمنطقی، اطمینان بیش ازحد است؛ این نوع از رفتار می تواند بر خط مشی آتی سازمان تاثیرگذار باشد. هدف اصلی پژوهش حاضر بررسی تجربی تاثیر قابلیت های مدیران بر میزان اطمینان بیش ازحد آنان و نگرش در انتخاب راهبرد سازمان است؛ ازاین رو نمونه آماری پژوهش، شامل 93 شرکت پذیرفته شده در بورس اوراق بهادار تهران، در بازه زمانی 1383 تا 1393 است. نتایج حاصل از تجزیه وتحلیل های آماری حاکی از آن است که رابطه ای منفی و معنادار بین قابلیت های مدیران و اطمینان بیش ازحد وجود دارد؛ به سخنی دیگر، هراندازه که مقامات ارشد سازمان توانمندتر باشند، از میزان اطمینان بیش ازحد آنان کاسته خواهد شد؛ همچنین نتایج نشان دهنده رابطه معنادار بین قابلیت های مدیران و نوع نگرش آنان در انتخاب راهبرد سازمانی است.
    کلیدواژگان: قابلیت های مدیران، اطمینان بیش ازحد، راهبرد رهبری بها، راهبرد تمایز، تحلیل پوششی داده ها
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  • Shokrolah Khajavi*, Mehrdad Ebrahimi Pages 1-34
    Introduction

    Recent scandals and corporate failures have shaken the confidence of investors as accounts which were purported to reflect a “true and fair view” of businesses have been misleading. In an era with evolutionary financial frauds, computer assisted automated fraud detection mechanisms will be more effective and efficient with specialized domain knowledge. Statistics and machine learning based technologies have been shown to be an effective way to deter and defect fraud. Therefore, this study deals with the identification of factors related to fraudulent financial statements and investigating the effectiveness of data mining techniques in detecting firms that issue fraudulent financial statements.

     Research Questions

    In order to achieve the objectives of this research, the following questions are developed:1. Can the fraudulent and non-fraudulent financial statements be detected from the text of annual corporate audit report fillings?
    2. Can a quantitative fraud detection model be developed that will provide an analytical procedure for automating detection of potential fraud?

    Methods

    Data mining is used in many domains, including finance, engineering and biomedicine. There are two categories of data mining methods: unsupervised and supervised. Identifying financial statements fraud can be regarded as a typical classification problem. Data mining proposes several classification methods derived from the fields of statistics and artificial intelligence. Three methods, which enjoy a good reputation for their classification capabilities, are employed in this research study. These methods are Decision Trees, Support Vector Machines and Boosting algorithm. In this research, nineteen red flags of fraud extracted from auditing standard No. 240 along with data mining techniques such as principal components analysis and modified K-Means clustering were used to discriminate financial statements fraud cases. We consider data mining based financial fraud detection techniques such as Decision Trees, Support Vector Machines and Boosting approach in order to identify fraud. Based upon the fraudulent financial reporting related literature, 40 predictor variables are selected as input variables. These variables represent measurement proxies for a firm’s attributes of financial leverage, profitability, asset composition, liquidity, efficiency, size, growth, overall financial position, audit firm size, audit firm tenure and auditor change. These predictor variables are collected from the financial statements of listed companies in Tehran Stock Exchange (TSE) for the period of 2003-2015.

     Results

    This research uses a quantitative approach on textual data to discriminate fraud cases using text mining techniques. The first research question was: can the fraudulent and non-fraudulent financial statements be detected from the text of annual corporate audit report fillings? Modified k-Means clustering demonstrated a good ability to discriminate fraud cases from non-fraud cases and thus it answered the first research question. the successful implementation of the Decision Trees, Support Vector Machines and Boosting algorithms also answers the second research question: can a quantitative fraud detection model be developed that will provide an analytical procedure for automating detection of potential fraud? In comparative assessment of the models’ performance, Support Vector Machines achieved the best performance to correctly classify validation sample in a 5-fold cross validation procedure. Decision trees and boosting algorithms also achieve a satisfactorily high performance.

     Discussion and Conclusion

    The present study contributes to auditing and accounting research by examining the suggested variables that can best discriminate cases of financial statements fraud. These results suggest that there is validity for detecting financial statement fraud using data mining techniques such as Decision Trees, Support Vector Machines and Boosting approach. Our analysis provides insight for auditors, taxation authorities, investors, the stock exchange and banking system.

    Keywords: Fraud detection, Financial statements fraud, Data mining, Tehran stock exchange
  • Zanyar Sajadi, Omid Pourhaidari, Ahmad Khodamipour Pages 35-62
    Understanding corporate information environment and factors affecting it has an important role in analyzing financial reports and setting accounting standards and laws regarding financial reporting. One of the phenomena studied in financial markets is intra-industry information transfer. Prior research on intra-industry information transfer shows that industry peers’ reports provide informative information about individual firms and documented significant stock price responses to industry peers’ announcement of earnings, (e.g. foster, 1981) management earnings forecasts, (e.g. Han, Wild and Ramesh, 1989) dividend, (Laux, Starks and Yoon, 1998) bankruptcy (Lang and Stulz, 1992) and earnings restatements. (Gleason, Jenkins and Johnson, 2008) Furthermore, the evidence in prior studies suggests that industry peers’ performance is more informative in some industries than in others. (e.g. Freeman and Tse, 1992) considering different theories suggesting effects of intra-industry information transfer phenomenon on manager’s behavior and demands for information, an important question that arises is whether financial reporting and its quality is different in industries with high intra-industry connectedness and firms operating in correlated environments.
    In this study, we examined the relationship of intra-industry connectedness with firm’s earnings quality, management forecasts accuracy, firm’s disclosure quality and information asymmetry.
    Research Questions or Hypothesis: In industries with high intra-industry connectedness industry peers’ reports are likely to provide useful information to evaluate the performance of individual firms. Under information perspective, since there is more useful information from industry peers in industries with high intra-industry connectedness, demand for voluntary disclosure is likely to be lower and it leads to less voluntary disclosure. On the other hand, herding perspective suggests two opposite effects on corporate information environment in these industries. First, firms operating in correlated environments are more likely to exhibit herding behavior (Demski and Sappington, 1984; Scharfstein and Stein, 1990). Scharfstein and Stein (1990) show that in correlated environments, managers mimic the decisions of their peers, even at the expense of ignoring their own private information. Hence, it’s likely that in industries with high intra-industry connectedness firms manage their earnings so that their earnings are closer to industry peers’ earnings. On the other hand, under herding perspective in industries with high intra-industry connectedness firm managers may have stronger incentives to herd in providing voluntary disclosures to enhance their reputation (Graham, Harvey, and Rajgopal, 2005).
    Based on the above-mentioned theories, we can expect both positive and negative effects on corporate information environment. In this study we examined the following hypotheses:H1: There is an association between intra-industry connectedness and earnings quality.
    H2: There is an association between intra-industry connectedness and management forecasts accuracy.
    H3: There is an association between intra-industry connectedness and disclosure quality.
    H4: There is an association between intra-industry connectedness and information asymmetry.
    Methods
    In this study we used panel data regression analyses for hypothesis testing. Our sample includes 178 firms listed in Iran stock markets operating in industries with at least 10 firms. We measured intra-industry connectedness as the absolute value of the covariance of sales change across all firms in the same industry for each industry. This covariance factor is calculated based on decomposition of the aggregate volatility of sales, presented and used in prior studies, (e.g. Comin and Philippon, 2005; Kim and Kwon, 2016; Pollack, 2013; Chiu, 2014). Our measure of earnings quality is based on the Dechow and Dichev (2002) model. Following Francis et al (2005) and Chiu (2014), we used the standard deviation of firm’s residuals over prior five years as a measure of earnings quality. Disclosure quality is measured based on scoring witch have been published by Tehran Stock Exchange each year and information asymmetry is measured by the bid-ask spread.
    Results
    Our results show a significant negative relation between intra-industry connectedness and earnings quality, a positive relation between intra-industry connectedness and management forecasts accuracy, a negative relation between intra-industry connectedness and disclosure quality and a negative relation between intra-industry connectedness and information asymmetry.
    Discussion and
    Conclusion
    The negative relation between intra-industry connectedness and earnings quality can be explained from herding perspective. In industries with high intra-industry connectedness, managers have intensives to mimic other managers and bring their earnings closer to those of other firms by earnings management. This herding explanation can be backed up by results of Kedia et al (2015) study which shows firms are more likely to begin managing earnings after the public announcement of a restatement by another firm in their industry. This herding behavior can happen about management earnings forecast too; when a manager mimics others in declaring goals and earning forecast, he would try to achieve that earnings by earnings management if it’s not possible to achieve them normally. The tendency to bring earnings closer to management forecasts explains positive relation between intra-industry connectedness and management forecasts accuracy. The negative relation between intra-industry connectedness and disclosure quality can be explained by information perspective. In these industries providing useful information from industry peers can reduce demand for disclosure by individual firms and lead to lower disclosure quality. Overall, results of first three hypotheses can explain the negative relation between intra-industry connectedness and information asymmetry. It would not be surprising to find out the tendency to earnings managements and low-quality disclosure in industries with high intra-industry connectedness lead to low information asymmetry.
    Findings of our research can be used as a warning to investors, accounting standard setting board and lawmakers to be careful and take in account the tendency of managers to mimic others in industries with high intra-industry connectedness.
    Keywords: Intra-industry connectedness, Earnings quality, Management forecasts accuracy, Disclosure quality, Information asymmetry.
  • Dariush Forughi, Hadi Amiri, Azita Ebrahimian Pages 63-92
    Introduction
    Economic theory suggests that firm performance determined by industry fundamentals is relatively long lasting. On the other hand, performance that deviates from industry norms tends to dissipate more quickly. Investors tend to fixate on reported earnings, however, not fully recognizing differences in the persistence of its components. As a result, we expect the market to underreact to the higher persistence of the industry-wide component of earnings.
    Research Hypothesis: The aim of the study is to investigate the persistence of components of industry and firm earnings.
    H1. The industry-wide component of earnings is more persistent than the firm-specific component.
    H2. Investors fail to fully appreciate difference persistence of industry-wide component of earnings and firm-specific earnings.
    H3. Investors underreact to the persistence of industry-wide earning.
    H4. Investors overreact to the persistence of firm-specific earning.
    H5. Industry wide cash flows is the most persistent component of earnings.
    H6. Firm-specific accruals is the least persistent component of earnings.
    H7. Investors underreact to industry-wide cash flows component.
    H8. Investors overreact to firm-specific accruals component.
    Method
    To achieve the purpose of this study, a sample consisting of 113 companies listed in Tehran Stock Exchange during the year 2005 to 2015 were selected. In order to test the hypothesis, model of Mishkin approach was applied. The Mishkin test was conducted using non-linear least square estimation. The cross-equation restrictions were tested using a Wald-based test with robust Wald statistics.
    Results
    The results of research indicate that industry-wide component of earnings is more persistent than the firm-specific component and investors fail to fully appreciate difference persistence of industry-wide component of earnings and firm-specific earnings; also they underreact to the persistence of industry-wide earning and overreact to the persistence of firm-specific earning. The results show that industry wide cash flows is the most persistent component of earnings and firm-specific accruals is the least persistent component of earnings. Research data show investors underreact to industry-wide cash flows component and they overreact firm-specific accruals component.
    Conclusion
    We find that industry-wide earnings are significantly more persistent than firm-specific earnings. However, investors fail to fully distinguish this differential persistence as stock prices place similar weights on these two earnings components in forecasting one-year-ahead earnings. In other words, the market underreacts to the persistence of industry-wide earnings and overreacts to the persistence of firm-specific earnings. Finally,we find that industry-wide cash flow is the most persistent component of earnings while firm-specific accruals is the least persistent. Results indicates that investors underreact to industry-wide cash flows component while they overreact to firm-specific accruals component.
    Keywords: Accruals, Cash flows, Earning persistence, Stock returns
  • Sajad Mohamadi, Allahkaram Sallehi Pages 93-119
    Introduction
    Stock price crash risk in the market is a key concern of investors, and research in this field is important for the capital markets. Increasing the phenomenon of stock price crash will result in investor’s pessimism about investing in the stock market, and this could finally force the investors to withdraw their investment. On the other hand, investment decisions are widely dependent on information asymmetry and agency problems. Therefore, the management ability which is a qualitative component that captures the intrinsic talent of managers by varying circumstances, can increase investment efficiency by reducing information asymmetry and agency costs, and thus provide the optimal use of resources, accountability, transparency, fairness and rights of all stakeholders companies.
    Hypothesis: The aim of this study is to investigate the relationship between efficient investment and management ability with stock price crash of listed companies on the Tehran Stock Exchange.
    Methods
    Data of 152 listed companies on the Tehran Stock Exchange during the period of 2006 to 2015 is investigated. To measure the stock price crash risk from the regression model Hutton et al. (2009) and the investment efficiency from the extended version of Biddle et al. (2009) model and the management ability, Demerjian et al.(2013) model, based on accounting variables is used. Data are analyzed by using panel data approach and multiple regressions model.
    Results
    The results show that there is not a significant relationship between management ability and the investment efficiency; there is a significant and positive relationship between management ability and stock price crash risk. One of the reasons that led to such results in the Iran capital market may refer to the inability to analyze managers from the current and future conditions of the company and industry, and to utilize their intelligence and talent to achieve personal returns, raising information asymmetry and agency costs between stokholders, creditors and managers, lack of voluntary disclosure of information in accounting reportings, ups and downs of the capital market in recent years and the existence of a dominant government structure over Iran's economic system
    Conclusion
    There are two views regarding the impact of management ability on investment efficiency: An optimistic view, which states that a competent manager can use economic assets in order to increase shareholder's wealth in an appropriate time; and based on this view, there is a significant positive relationship between the ability of managers and investment efficiency. The pessimistic view argues that, in addition to the agency theory and conflict of interest between managers and owners, managers may use their talents in order to achieve their personal interests; or may act with high confidence and overestimate the return on investments because of their high intelligence, so that this fact will also cause losses. Our research results support pessimistic view. Therefore, the stock exchange should provide a mechanism to prevent opportunistic management practices for hoarding information and creating price bubbles and investor's uncertainty about such information.
    Keywords: Management ability, Investment efficiency, Stock price crash risk, Tehran Stock Exchange.
  • Sajad Nagdi, Gollamhossain Assadi, Alireza Fazllzadeh Pages 121-149
    Importance of macroeconomic variables is clear to everyone and announcements of them are seen and carefully scrutinized by different groups of users; however, initial estimates and economic forecasting of macro variable is raised as a serious challenge in economic planning. In this context, little or no evidence has been provided for exploring the relationship between accounting and economics (Macro Accounting) in developing countries like Iran. The idea of macro accounting was based on the idea that accounting variables such as aggregate accounting earnings convey information about Macroeconomics. This paper presents the use of fundamental accounting variables as the best leading indicators of macroeconomics variables.
    Research Questions: The main questions of this paper are as follows:Can the combination of Elman neural network and particle swarm optimization improve models prediction in comparison to others?
    How can fundamental accounting variables improve the predictive power of the model?
    Methods
    In this study we rely on predictive power of various models including Elman neural networks and particle swarm optimizationn. An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems. There are many types of artificial neural networks such as Elman Networks. Elman Networks are a form of recurrent neural networks which have connections from their hidden layer back to a special copy layer. Particle swarm optimization (PSO) is a population based stochastic optimization technique developed by Eberhart and Kennedy in 1995, inspired by social behavior of bird flocking or fish schooling. PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). We construct portfolio based on 88 largest firms in Tehran Stock Exchange. The sample period covers 20 semi annuals from 1385 to 1395. For this purpose, fundamental accounting variables (including net income, gross income, inventory, accounts receivable, administrative, general and sales expense, capital expenditures, debt and tax costs) have been chosen and their explanatory power in predicting macro.
    Results
    Taking into consideration more alternative measures for accounting can decrease model's errors. As mentioned in previous subsection, using fundamental accounting variables would enable producing more reliable and accurate results. Our findings suggest that fluctuations in accounting information including net income, gross income, inventory, account receivables, general and sales expense and capital expenditure are a leading indicator of macroeconomic variables. Results show that fundamental accounting variables have predictive power in predicting GDP growth and unemployment rate for the next one and two quarters respectively. Also the empirical results from combination of artificial intelligence models show that optimization of Elman artificial network with particle swarm optimization improves effectiveness of model in comparison to Elman artificial network.
    Discussion and
    Conclusion
    This study distinguishes itself from previous papers with the introduction of key variables that have not been studied previously in macro accounting subject such as fundamental accounting variables. Prior studies mostly address accounting earnings in general neglecting predictive power of fundamental accounting variables. The main consequences of this study are effective links between accounting and economic information that must be included in the economic and financial decisions. So we recommend studies in application of accounting numbers in modeling macro. Overall the consequences of this paper introduce a new idea that the informativeness of accounting variables is not only in the micro level, but also in macro economy level.
    Keywords: Economic forecasting, Macro variable, Macro Accounting
  • Mohamadreza Nikbakht, Aliazgar Dehgani, Samaneh Gohestani Pages 151-178
    Importance of macroeconomic variables is clear to everyone and announcements of them are seen and carefully scrutinized by different groups of users; however, initial estimates and economic forecasting of macro variable is raised as a serious challenge in economic planning. In this context, little or no evidence has been provided for exploring the relationship between accounting and economics (Macro Accounting) in developing countries like Iran. The idea of macro accounting was based on the idea that accounting variables such as aggregate accounting earnings convey information about Macroeconomics. This paper presents the use of fundamental accounting variables as the best leading indicators of macroeconomics variables.
    Research Questions: The main questions of this paper are as follows: Can the combination of Elman neural network and particle swarm optimization improve models prediction in comparison to others?
    How can fundamental accounting variables improve the predictive power of the model?
    Methods
    In this study we rely on predictive power of various models including Elman neural networks and particle swarm optimizationn. An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems. There are many types of artificial neural networks such as Elman Networks. Elman Networks are a form of recurrent neural networks which have connections from their hidden layer back to a special copy layer. Particle swarm optimization (PSO) is a population based stochastic optimization technique developed by Eberhart and Kennedy in 1995, inspired by social behavior of bird flocking or fish schooling. PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). We construct portfolio based on 88 largest firms in Tehran Stock Exchange. The sample period covers 20 semi annuals from 1385 to 1395. For this purpose, fundamental accounting variables (including net income, gross income, inventory, accounts receivable, administrative, general and sales expense, capital expenditures, debt and tax costs) have been chosen and their explanatory power in predicting macro.
    Results
    Taking into consideration more alternative measures for accounting can decrease model's errors. As mentioned in previous subsection, using fundamental accounting variables would enable producing more reliable and accurate results. Our findings suggest that fluctuations in accounting information including net income, gross income, inventory, account receivables, general and sales expense and capital expenditure are a leading indicator of macroeconomic variables. Results show that fundamental accounting variables have predictive power in predicting GDP growth and unemployment rate for the next one and two quarters respectively. Also the empirical results from combination of artificial intelligence models show that optimization of Elman artificial network with particle swarm optimization improves effectiveness of model in comparison to Elman artificial network.
    Discussion and
    Conclusion
    This study distinguishes itself from previous papers with the introduction of key variables that have not been studied previously in macro accounting subject such as fundamental accounting variables. Prior studies mostly address accounting earnings in general neglecting predictive power of fundamental accounting variables. The main consequences of this study are effective links between accounting and economic information that must be included in the economic and financial decisions. So we recommend studies in application of accounting numbers in modeling macro. Overall the consequences of this paper introduce a new idea that the informativeness of accounting variables is not only in the micro level, but also in macro economy level.
    Keywords: Economic forecasting, Macro variable, Macro Accounting