Finding the best way to optimize the portfolio after Markowitz's 1952 article has always been and will continue to be one of the concerns of activists in the investment management industry. Researchers have come up with different solutions to overcome this problem. The introduction of mathematical models and meta-heuristic models is one of the activities that has influenced portfolio optimization in recent decades. Along with the growing use of portfolios and despite its rich literature, there are still many unanswered issues and questions in this area. Also, Iranian capital markets, as emerging markets, require native research to answer these questions and issues. The purpose of this study is to provide a useful and effective tool to assist professionals and researchers in portfolio selection theory. This study, while comprehensively reviewing the literature on the subject and the developments and expansions made in the area of portfolio selection and optimization, reviews the types of problems and optimization methods.
In this paper we distinguish between operational risks depending on whether the operational risk naturally arises in the context of model risk. As the pricing model exposes itself to operational errors whenever it updates and improves its investment model and other related parameters. In this case, it is no longer optimal to implement the best model. Generally, an option is exercised in a jump-diffusion model, if the stock price either exactly hits the early exercise boundary or the price jumps into the exercise price region. However paths of the diffusion process are continuous. In this paper the impact of operational risk on the option pricing through the implementation of Mitra’s model with jump diffusion model is presented. A partial integral differential equation is derived and the impact of parameters of Merton’s model on operational risk and option value by operational value at risk measure is employed. The option values in the presence of operational risk on data set are computed and some of the results are presented.
This paper focuses on two main issues that are based on two important concepts: exponential Levy process and minimal entropy martingale measure. First, we intend to obtain risk measurement such as value-at-risk (VaR) and conditional value-at-risk (CvaR) using Monte-Carlo methodunder minimal entropy martingale measure (MEMM) for exponential Levy process. This Martingale measure is used for the exponential type of the processes such as exponential Levy process. Also, it can be said MEMM is a kind of important sampling method where the probability measure with minimal relative entropy replaces the main probability. Then we are going to obtain VaR and CVaR by Monte-Carlo simulation. For this purpose, we have to calculate option price, implied volatility and returns under MEMM and then obtain risk measurement by proposed algorithm. Finally, this model is simulated for exponential variance gamma process. Next, we intend to develop two theorems for implied volatility under minimal entropy martingale measure by examining the conditions. These theorems consider the asymptotic implied volatility for the case that time to maturity tends to zero and infinity.
The purpose of this study is to investigate the effective of investment inefficiency and cash holding on CEO turnover. This study applies logistic regression method estimator to investigate the relationship between examine the effective of investment inefficiency and cash holding on CEO turnover of 1,309 firm-year observations in Iran for the period of 2009-2019. According to positive relation between mentioned variables, the managers' opportunism increases investment inefficiency and cash holdings of the company because inappropriate managerial decisions lead to increased risk of wrong selection for investors. In the present study, the weaknesses caused by the ambiguity of investment efficiency in market performance-based statistical models are compensated and partially covered by quantifying the relationships and implementing models. The Results will aid policy makers to evaluate disclosure rules and firms to managing their information. The study is based on the corporate accounting and financial literature and examines CEO behavioral changes that can be applied to investors, managers, standardization committees, and legislators. Unlike other research, CEO turnover has also been addressed with regard to the origin and distribution of information. This study also considers the effect of information asymmetry and market constraints by considering the cash holding to transmit firm information
This research is related to the usefulness of different machine learning methods in forecasting time series on financial markets. The main issue in this field is that economic managers and scientific society are still longing for more accurate forecasting algorithms. Fulfilling this request leads to an increase in forecasting quality and, therefore, more profitability and efficiency. In this paper, while we introduce the most efficient features, we will show how valuable results could be achieved by the use of a financial time series technical variables that exist on the Tehran stock market. The suggested method benefits from regression-based machine learning algorithms with a focus on selecting the leading features to find the best technical variables of the inputs. The mentioned procedures were implemented using machine learning tools using the Python language. The dataset used in this paper was the stock information of two companies from the Tehran Stock Exchange, regarding 2008 to 2018 financial activities. Experimental results show that the selected technical features by the leading methods could find the best and most efficient values for the parameters of the algorithms. The use of those values results in forecasting with a minimum error rate for stock data.
The Impact of Effective Corporate Governance on the Relationship between Tax Gap and Future Profit Changes in Iranian Economy
The Iranian economy in recent years is due to the development of economic sanctions, a sharp decline in the price of oil and the deficit resulting from revenue - dependent on oil revenues and the trend towards tax revenues. While comparing the volume of the Iranian economy with the amount of tax income indicates the existence of a relatively significant tax gap. The tax gap is the difference between the collected taxes and the tax required by the law. The purpose of this study is to answer the question whether corporate governance is effective and strong on the relation between tax gap and future earnings changes? The statistical population of the research in Tehran Stock Exchange (TSE) firms and statistical sample consists of 120 companies in the period of 2007-2017. In order to test the hypotheses, multivariate regression using mixed data - data approach has been used. The results indicate that there is a significant inverse relationship between the tax gap and future earnings changes. It can be argued that increasing the difference between earnings accounting earnings can be associated with decreasing interest in the next year and less stability. On the other hand, significant positive relation between corporate governance is efficient and strong with future earnings changes. because corporate governance will ultimately lead to more sustainable future gains due to the decline of discretionary accruals in discretionary accruals. It is also reinforced by the effect of the tax gap on future earnings changes in firms that have efficient corporate governance, and this effect is only seen for a year later. And is not effective for the second and third years.
Internal Control Quality Assessment based on the Characteristics of the Entity and Auditor and their Expected Goals in the Firm's Listed in Tehran Stock Exchange
According to the domestic studies conducted on in the field of internal controls, the gap of providing models for identifying weak internal controls is felt completely. The present study is aimed at providing a model for assessing the quality of internal controls based on the characteristics of the economic unit, the characteristics of auditor as well as their expected goals in the Firm's listed in Tehran Stock Exchange. This model is designed according to quantitative criteria. To achieve the research’s goal, 86 Firm's from all Firm's accepted in Tehran Stock Exchange for the period 2012-2017 were selected and the research hypothesis was tested using the combined data approach. Given the research hypothesis that points out that the internal assessment model based on the characteristics of the economic units, the characteristics of auditor and their expected objectives may more accurately assess the quality of the internal controls, according to the significance level of less than 0.05, the independent variables indicate a significant relationship between the internal controls assessment model based on the characteristics of the economic units, the characteristics of auditor and their expected objectives as well as the weaknesses of the internal control of the Firm. Furthermore, the estimated coefficient of the control variables of the research indicates a significant relationship between these variables and the weaknesses of the internal controls.
Determining the effect of auditors' skeptical personality traits with considering the characteristics of organizational behaviour on job Audit Durability
The problem of desertion the effective and efficient staff and specialist staff is one of serious problem has been created for the organizations managers that seek to maintain, improve and utilize their human resources. Researchers believe that staff desertion will have a negative impact on the organization and not only reduce the organizational performance, but also increase the direct and indirect costs of staff desertion, such as transferring capabilities to competitors, increasing costs of hiring and training new staff, etc. The purpose of this study is to determine the effect of auditors' skeptical personality traits with considering the characteristics of organizational behaviour on job durability. For this purpose, the relationship between interpersonal trust, locus of control and presumptive doubt and neutral of professional skepticism and three characteristics of organizational behaviour, organizational commitment, perceived organizational support and organizational citizenship behaviour on job durability are assessed. The statistical society consists of all auditors of audit institutions that are the member of the Association of Certified Public Accountants who are working and not the organization's partners. The structural equation modeling has been used for analysis. The findings of the study show that auditors' skeptical personality traits did not directly affect their job durability.
The Role of Social Interest Rate Risk Management in the Relationship between Sustainability Performance and Investment Efficiency
Sustainability is a wide concept that contains other concepts such as social responsibility and has been investigated with concepts such as competition sustainability, reporting sustainability, and social sustainability. The present study aims to investigate the role of social interest rate risk management (SIRRM) in the relationship between sustainability performance and investment efficiency. In terms of purpose, this study is an applied one, and form the methodological point of view is a descriptive correlational study. Accordingly, the required data has been collected from 79 listed firms on Tehran Stock Exchange during 2013-2017. In this research, social risk management includes SIRRM, and sustainability performance includes sustainability of reporting, competition, and ownership. The results indicate that SIRRM reinforces the relationship between competition sustainability, reporting sustainability, and ownership sustainability with investment efficiency.
In recent years, authors have focused on modeling and forecasting volatility in financial series it is crucial for the characterization of markets, portfolio optimization and asset valuation. One of the most used methods to forecast market volatility is the linear regression. Nonetheless, the errors in prediction using this approach are often quite high. Hence, continued research is conducted to improve forecasting models employing a variety of techniques. In this paper, we extend the field of expert systems, forecasting, and model by applying an Artificial Neural Network. ANN model is applied to forecast market volatility. The results show an overall improvement in forecasting using the neural network as compared to linear regression method.