درخت حوزه‌های تخصصی

One of the main causes of firms’ ineffectiveness is the absence or insufficiency of appropriate investment methods. This deficiency could also be attributed to an unfortunate selecting of an inappropriate investment methods which may ultimately endanger the firms’ prospect of survival. According to the firm life cycle theory, various firms demonstrate diverse behavior when provided with an investment opportunity. These responses are largely in accordance with the stage of the life cycle in which the firm resides in at that moment. In this research, the selection of the investment method appropriate for a firm has been studied following the premises of the life cycle theory. The target populations of this study were companies admitted to Tehran Stock Exchange. Systematic removal method was adopted to recruit a sample of 118 firms. The study period was 8 years (2011-2018).  Findings suggest that firms choose to invest in operational properties when they are at the stage of growth, maturity and decline. In other words, the capital under the companies’ authority and control were employed for the firms’ mainstream activities. However, such a link was not found at the introduction stage of their life cycle. This relation has been illustrated in various industries.

The Stock Exchange is a private sector savings and liquidity fund to fund long-term investment projects. The indicators of this market are influenced by several factors, one of which are economic variables. Banks are also one of the most important investors in the financial market. In this study, the effect of exchange rate, bank interest rate, liquidity and inflation rate on stock price index using time series data of 1978-1988 and using equation system model of four equations of the stock price index, inflation rate, profit rate Deposits, and facility interest rates were estimated using the three-stage least squares method. The results showed that the effect of the exchange rate, deposit interest rate and positive liquidity volume and the effect of inflation rate on stock price index was negative. Other equations have been estimated because of the relationship between the bank interest rate and inflation rate, and the results show that both deposit interest rate and facility interest rate have a positive relationship with the inflation rate

Tax avoidance is making use of legal loop holes to display an individual's financial situation as if it were lower than what it is in order to decrease the amount of income tax owed. Behavioral economics and taxation literature indicate that psychological factors can provide further insight on accountants' financial decisions. The literature claim that tax compliance can be influenced by an individual’s personality and beliefs. Therefore, in this research, the effects of psychological variables including Machiavellianism, emotional manipulation and moral foundations are examined on tax avoidance in accounting and finance professions. The aim of this study is to investigate the role of Machiavellianism and emotional manipulation as two negative attributes of human beings and moral foundations in tax avoidance in listed and unlisted firms. For this purpose, a sample consisting of 500 accountants and financial managers of listed and unlisted companies of Tehran stock exchange was selected. This study is an applied and descriptive survey. The hypotheses of the research have been analyzed by structural equation modeling using Lisrel software. The evidence of this study show that Machiavellianism has a positive and moral foundations have a negative effect on tax avoidance. But, this study doesn’t confirm any significant relation between emotional manipulation and tax avoidance. This paper also states that social and psychological variables  would  explain the tax avoidance phenomenon.

Businesses are increasingly interested in how big data, artificial intelligence, machine learning, and predictive analytics can be used to increase revenue, lower costs, and improve their business processes. In this paper, we describe how we have developed a data-driven machine learning method to optimize the collection process for a debt collection agency. Precisely speaking, we create a framework for the data-driven scheduling of outbound calls made by debt collectors. These phone calls are used to persuade debtors to settle their debt, or to negotiate payment arrangements in case debtors are willing, but unable to repay. We determine daily which debtors should be called to maximize the amount of delinquent debt recovered in the long term, under the constraint that only a limited number of phone calls can be made each day. Our approach is to formulate a Markov decision process and, given its intractability, approximate the value function based on historical data through the use of state-of-the-art machine learning techniques. Precisely, we predict the likelihood with which a debtor in a particular state is going to settle its debt and use this as a proxy for the value function. Based on this value function approximation, we compute for each debtor the marginal value of making a call. This leads to a particularly straightforward optimization procedure, namely, we prioritize the debtors that have the highest marginal value per phone call. We believe that our optimized policy substantially outperforms the current scheduling policy that has been used in business practice for many years. Most importantly, our policy collects more debt in less time, whilst using substantially fewer resources leading to a large increase in the amount of debt collected per phone call.

In this study, the effect of socioeconomic variables on provincial bread demand using the QUAIDS model has been investigated. In this model, socioeconomic variables such as age, gender and marital status of the head of household, the education level of the head of household and spouse’s head of household, household size, and occupation status of the head household have been used. The virtual variable of the target law of subsidies is also used to study the effects of this policy. To this end, the quadratic almost ideal demand system was applied. This model has been estimated using the consolidated data and Iterated Linear Least Squares Estimator and the information of more than 165,000 urban households in Iran for the years 1386-1394 and for different provinces. The results of the research show that during the studied years, bread is an essential good for all provinces except in Isfahan, Chaharmahal o Bakhtyari, and Kohgilouye. The compensated and uncompensated price elasticities of bread for all provinces are negative and less than one, so the bread is an inelastic good for all provinces. Also, the coefficients related to household size, gender, and marital status of the head of household and the target law of subsidies are positive and significant.

It is generally believed that macroeconomic and financial performance in oil exporting countries is interlinked to oil price movements. Regarding that assumption, the present study aims to examine the impact of oil price movements on bank nonperforming loans (NPLs) ,as a criterion for evaluation of bank credit risk, by applying the Generalized Method of Moments (GMM) on data from 18 Iranian banks data over period 2006–2017. The result of the estimated model indicates that there is a significant relation between fluctuations of oil price and bank nonperforming loans; accordingly, any decrease in the price of oil will result in an increase in bank nonperforming loans. Also, in order to have comprehensive assessment, economic and bank specific control variables were used in the model. Findings show that the NPLs ratio increases as economic growth decreases and exchange rate and real interest rates rise. Among bank specific factors, equity ratio as a criterion for efficiency and loan growth has a negative effect on NPLs, but by raising bank industry concentration, credit risk and financial stability can be threatened. Thus, the reliance of oil rich economies on oil incomes leads to the linkage of oil prices, and macroeconomic and financial performance. Therefore, the result of this study will be useful in adapting and diversifying macroeconomic policies in the face of drastic changes in oil prices and mitigating its adverse effects.

Stock price prediction is one of the crucial concepts in finance area. Machine learning can provide the opportunity for traders and investors to predict stock prices more accurately. In this paper, Closing Price is dependent variable and First Price, Last Price, Opening Price, Today’s High, Today’s Low, Volume, Total Index of Tehran Stock Exchange, Brent Index, WTI Index and Exchange Rate are independent variables. Seven different machine learning algorithms are implemented to predict stock prices. Those include Bayesian Linear, Boosted Tree, Decision Forest, Neural Network, Support Vector, and Ensemble Regression. The sample of the study is fifteen oil and gas companies active in the Tehran Stock Exchange. For each stock the data from the September 23, 2017 to September 23, 2019 gathered. Each algorithm provided two metrics for performance: Root Mean Square Error and Mean Absolute Error. By comparing the aforementioned metrics, the Bayesian Linear Regression had the best performance to predict stock price in the oil and gas industry in the Tehran Stock Exchange.