THE IRANIAN ACCOUNTING AND AUDITING REVIEW
Year:2007 | Volume:14 | Issue:47|Papers Count:14

In the finance literature, capital assets pricing model (CAPM) relates systematic risk of a financial asset to its expected return. One of the main concerns of investors and financial managers is estimation of beta for calculation of the expected return. In general, time series data is used for the estimation of beta. Therefore, financial managers should decide on the period of return calculation as well as the method of estimation. In this paper we used five estimation methods of beta in Tehran Stock Exchange. The methods are generalized least squares (GLS), maximum likelihood (ML), generalized method of moments (GMM), nonparametric regression (NP) and least absolute deviation (LAD). Results show that residual sum of squares (RSS) for nonparametric method is less than the others. Monthly data is helpful for estimation of the beta. Estimated values of beta from different methods are different. Therefore, classification of companies to high/low risk class may affected by method of estimation. However, this problem is not important in rating assets based on their risk, because of same effect of estimation bias on all of assets. In addition, we used daily and monthly data for the examination return period on value of estimated beta. Results of daily and monthly reruns are substantially different such that some companies are high risk based on daily data and low risk based on monthly return. Also, we used Black and Sharp versions of the CAPM model. Results of these two models are not so different. All in all, beta should be used with more caution and it is better one use more than one estimation method in practice.

Nowadays companies increasingly derive revenue from the creation and sustenance of long-term relationships with their customers. In such an environment, marketing serves the purpose of maximizing customer lifetime value (CLV) and customer equity, which is the sum of the lifetime values of the company’s customers. A frequently-encountered difficulty for companies wishing to measure customer profitability is that management accounting and reporting systems have tended to reflect product profitability rather than customer profitability. But in spite of these difficulties, Companies looking for methods to know how calculate their customers's CLV. In this paper, we used K-Mean clustering approach to determine customers's CLV and segment them based on recency, frequency and monetary (RFM) measures. We also used Discriminant analysis to approve clustering results. Data required applying this method gathered from one branch of an Iranian private bank which is established newly. Finally, in terms of this segmentation, we proposed customer retention strategies for treating with the bank customers.

An accepted financial axiom is that the role of managers is to maximize the long terme wealth of shareholders by the efficient allocation of resources. In order to operationalize this objective, shareholder wealth is traditionally a proxy for either standard accounting procedures or financial ratios like as P/E ratio- which is broadly used by investors and analysts. Managers. shareholders and other interest parties then use this financial information to assess and examine their performance. Unfortunately one can not just rely on accounting based measures to explain changes in shareholder wealth. A recent innovation in the field of internal performance measurement is the concept of Cash Value Added which is directly linked to the creation of shareholder value over time. This model only measures cash flow and is focused on the profitability of investment. Pooled time- series data on 85 industrial companies of Tehran Stock Exchange over the period 1999 (1378)- 2003 (1382) is employed to examine the information content of Cash Value Added and P/E ratio. Relative information content tests reveals Returns to be more closely associated with CVA than P/E ratio. Incremental information content tests suggest that both of CVA and P/E ratio have explanatory power to each other.

When planning the audit, auditors will typically examine the financial statements for unexpected fluctuations in account balances and ratios that indicate where additional audit work may be warranted. After observing any unexpected fluctuations and asking management for explanations, Auditor decides how to revise the audit plan and document in the audit work papers justifications for the plan. The purpose of this study is to determine how the anticipation of the audit review process and the degree to which evidence supporting of refuting management’s explanations influence the justifications of audit planning decisions. In this study, we examined how two aspects of the audit environment- The audit review process and the type of evidence available- influence the way auditors Justify their audit planning decisions. Overall, our results indicate that these two factors do influence auditor’s justification. Auditors who expected a subsequent review documented more total justifications than auditors who did not expect a review. Our result also show the auditors’ justifications, are affected by the type of available evidence. Auditors receiving inconsistent evidence documented more Justification in their planning memos than those who received corroborating evidence or no evidence.

The Capital Asset Pricing Model (CAPM) argues that only systematic risk should be priced in the market; Specific or idiosyncratic risk does not deserve a risk premium. However recent empirical studies have raised serious challenges to this belief. It appears that  b as a measure of systematic risk, has little power in explaining cross- sectional risk and return relationships over long periods of time, while other variables such as Firm Size and Book to market ratio, appear to be more useful risk proxies. This pattern is named anomalies. Anomalies are empirical results that seem to be inconsistent with maintained theories of asset- pricing behavior. They indicate either market inefficiency or inadequacies in the underlying asset- pricing model. Sales growth rate and z- score are the best indicators in measuring profitability and balance sheet strength. in this paper, we use z- score and sales growth to distinguish this anomalies, exist in Tehran stock exchange with applying the time-series regressions. According to result of statistical examinations, they don’t have as strong explanatory power in explaining Tehran stock exchange.

This research considers three general objectives. 1) Identifying income smoother companies. 2) Study of relationship between income smoothing and type of industry (corel or peripheral) and size of company (sale). 3) Study of relationship between artificial income smoothing and size of companies (total sale) and type of industry (corel or peripheral). We have used two categories of information for studying the above-mentioned objectives. The first , our library studies , and the second are as secondary information in the form of derivated financial information from financial statements of accepted companies in Tehran’s stock exchange for 78- 83. The results revealed that reported income smoothing and size of company (sale) are correlated having reliability coefficient at 90% and also there is a weak probability that income smoothing is related to type of industry. And between artificial income smoothing and size of company (sale) are correlated having reliability coefficient at 90%. Moreover, there is no significant relationship between artificial income smoothing and type of industry (corel or peripheral.

This study investigates the relationship between PE and macroeconomic variables. In this paper, 21 macroeconomic variables are selected with decomposition to nominal and real variables. Also for detrending, trend variable is entered in model as an independent variable and multiple regression and factorial analysis are used for surveying research hypothesis. The multiple regression analysis result indicates that there is strict correlation between selected variables. However, it is confirmed the relationship between the independent variables and dependent variable (PE) by using factorial analysis and major component method.