Many financial crisis are related to public corporations, which are increasing. Many investors and creditors are having trouble predicting a financial crisis, especially when managing profits. Recent studies identify the factors associated with earnings management to determine the relationship between the factors and manipulated profits. In order to reduce the risk of financial crises and to help investors avoid large losses in the stock market, it is necessary to develop a model for predicting profit management. In addition, for traditional auditing technologies, it is also difficult to limit the time, human resources, costs, and the impact of abnormal behaviors on complex and large financial information. Therefore, developing a prediction model for managing profits for auditors is useful in identifying the degree of manipulation in financial statements. This paper examines the effect of corporate financial distress on unpredicted net earnings and corporate profits on accepted companies in Tehran Stock Exchange over the period 2010-2015. The models used to test the hypotheses of the research are linear regression using panel data. The results show that the coefficients of the financial distress, institutional ownership, annual sales growth, company loss, company size, the company's market share and firm fixed costs are statistically meaningful. In other words, these independent variables influence on unforeseeable profit and earnings management.

Credit risk consists of probability of non-return, which may be in the form of bankruptcy or a decrease in financial and credit situation of the lessee. The variables are extracted from the Central Bank. In this study the independent variables are measured with six factors that are called external factors. The external factors are size of leasing, ownership interest rate, foreign exchange, inflation, and Gross Domestic Product (GDP). The present study uses related observations from 31 leasing companies from 2008 to 2013 to find out the determinants of the credit risk. The combined evidences suggest that internal factors such as upfront prepayment, credit insurance contract, security deposits, time and period contract, collateral and guarantees, contract amount, as well as external factors such as interest rate, inflation, foreign exchange, Gross Domestic Product infrastructure, and credit risk are determinants in the policy-making process involving the industrial leasing. Furthermore, the empirical results indicate the size of leasing and ownership are not the significant determinants of credit risk. The results of this dissertation provide several implications for policy-makers in the leasing industry. Policymakers will be better off employing different procedures for leasing activities in the leasing industry.

The aim of this study is to establish a framework for measuring and managing credit risk for fifteen leasing companies in Iran. An analysis on the influence of internal factors on credit performance will then be performed. This will enable a leasing industry to progress towards its goals and objectives in the most direct and effective way. Credit risk consists of probability of non-return. This may be in the form of bankruptcy or a decrease in financial and credit situation of the lessee. We can assume a correlated market and credit risk. The variables are extracted from the Central Bank of Kanoon Leasing Association in Iran. Numerical analysis reveals that lessee credit risk can have a substantial impact on a lease term structure.

Selection of optimum methods which have appropriate speed and precision for planning and decision-making has always been a challenge for investors and managers. One the most important concerns for them is investment planning and optimization for acquisition of desirable wealth under controlled risk with the best return. This paper proposes a model based on Markowitz theorem by considering the aforementioned limitations in order to help effective decisionsmaking for portfolio selection. Then, the model is investigated by fuzzy logic and genetic algorithms, for the optimization of the portfolio in selected active companies listed in Tehran Stock Exchange over the period 2012-2016 and the results of the above models are discussed. The results show that the two studied models had functional differences in portfolio optimization, its tools and the possibility of supplementing each other and their selection.