FINANCIAL ACCOUNTING
Year:2017 | Volume:9 | Issue:33|Papers Count:9

Working capital management is one of the strategies to achieve the optimal liquidity for the company. This study tests the effect of accounting variables and characteristics of peer firms (companies in the same industry) on working capital management strategy. The statistic sample consists of listed companies in Tehran Stock Exchange during the years 2009 to 2015. In order to test hypothesis, hierarchical Bayes model, Gibbs sampling and Monte Carlo simulations - Markov chain is used. The results show that the sector (industry) type and peer firms' stock returns average have a significant relationship with working capital management.

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 most of the reported cases management have defrauded the company and covered it up by manipulating the financial statements of the company to reflect what management wanted the public to see. 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 and selection of financial and non-financial variables related to fraudulent financial statements and investigates the effectiveness of Data Mining techniques in detecting firms that issue fraudulent financial statements. To achieve this aim 40 financial and non-financial variables derived from financial statements of listed companies in Tehran Stock Exchange (TSE). We consider Data Mining based financial fraud detection techniques such as Artificial Neural Networks (ANN), Bayesian Networks (BN) and Random Forest (RF) in order to identify fraud. The results suggest that Data Mining techniques have a good ability to detect financial statements fraud. The three models are compared in terms of their performance. The results of Wilcoxon signed-rank test indicates that The Random Forest algorithm and Bayesian Networks outperforms the other model. Artificial Neural Networks also achieve good performance.

This study aims to investigate the impact of corporate social responsibility on the relationship between the disclosure of the cost of research and development (R & D) and stock returns of listed companies in Tehran Stock Exchange. Information needed for the study has been collected through financial statements and reports of boards of the 88 companies for the period 2009-2015. For the measurement of disclosure of the cost of research and development (R & D) and social responsibility, we utilized content analysis of board reports. Results of statistical analysis show that the disclosure of the cost of research and development is associated with stock returns. Furthermore, the findings indicated that social responsibility strengthens the relationship between disclosure of the cost of research and development (R & D) and stock returns.

According to audit standards, internal controls affect the auditor’s reliance on collected auditing evidence and the process of determining the type, nature, and timing of engagement and the range of audit methods. This research aims at exploring the effects of internal controls quality on audit report lag on the firms enlisted in Tehran Stock Exchange. To this end, the research data for the period after the enactment of internal controls instructions, during the years from 2012 to 2015 were analyzed. The results indicate a significant, positive relationship between internal control weaknesses and audit report lag. In other words, not weakness in internal controls is associated with punctual audit report. The research findings provide empirical evidence regarding the common viewpoint in audit standards, based on which internal controls affect the process of determining the timing of audit engagement.

One of the challenges face corporate managers in the world is how working capital and growth opportunities influences capital structure of their companies. Show this problem for all company managers and other decision-makers in finance will be effective. The main purpose of this research is the assessment of the effect working capital and the growth opportunity on capital structure of accepted companies in Tehran Stock Exchange. In this research, the number of 107 companies in Tehran Stock Exchange has been examined during the period of 2009 until 2015. In order to test these hypotheses, we used Multiple-regression model for combined data. The results of research suggested that there was significant correlation between the financial leverage and sales growth rate, dividend growth rate, structural health ratio, debtor-sales ratio, liquidity ratio. In addition, no significant correlation between financial leverage and net income growth rate, inventory-sales ratio, cash-sales ratio, creditor-sales ratio, current asset-sales ratio.

Although corporate information environment plays an important role in transparency and accountability, no comprehensive index has yet been devised for it. In spite of the various methods proposed to compute information environment, this study managed to introduce a comprehensive index for measuring information environment on mathematical foundations. To test and examine the reliability of the model, the required data were collected from a sample of 149 firms listed in Tehran Stock Exchange during the years 2009-2016 using panel data technique. To do so, a questionnaire aiming to elicit the experts’ opinions on the weight and importance of the measures proposed to compute information environment was prepared and distributed among them, and then the weight and importance of the measures were determined using one of the multiple-criteria decision making methods (Shannon's entropy). Moreover, confirmatory factor analysis was employed to examine the association between the latent variable (information environment) and the measurement items. The findings confirm the good fitness of the proposed model.