Journal of Money and Economy

In the aftermath of the global financial crisis (2007-2009), policymakers in the developing countries and emerging economies have generally relied on macroprudential policies to achieve financial stability. Since the banking system's vulnerability plays an essential role in financial instability, and the banking system's stability is exposed to vulnerability, we examine macroprudential policies' effectiveness in reducing banking vulnerability and economic instability through containing credit growth. We estimated a dynamic panel for 14 Iranian banks using GMM and Arellano-Bovar / Blundell-bond two-stage estimators during 2009-2018. The results indicate that the increase in lending rates in the interbank market leads to the banking system's contraction of lending capacity. The positive and significant effect of the economic growth index indicates the banks' procyclical behavior. That financial institutions in the business cycles behave procyclical in lending. The diminishing effect of the macroprudential policy index on the bank credit expansion indicates that macroprudential authority and policy tools' application reduces the banking system's instability and vulnerability. Therefore, to reduce financial intermediation instability, the financial sector regulator can institutionalize macroprudential policies.

In this research, the relationship between accruals quality and bankruptcy of companies has been studied. According to Dechow et al.'s (1995) model, the quality of accruals was measured, and according to the Shirata model (1998), bankruptcy was examined. Operations were considered as the control variables. The research hypothesis was tested using a multivariate regression model and a combined data method. The study's statistical sample consists of 197 companies listed on the Tehran Stock Exchange from 2011 to 2019. The results showed a significant and negative relationship between the quality of accruals and bankruptcy of the companies. It means that in bankruptcy, the use of earnings management through optional accruals reduces the quality of accruals. The results indicate that size, return on assets, and audit quality all significantly impact the quality of accruals. Besides, the leverage, life, and operating cash flow have a significant and negative effect on accruals' quality. However, the ratio of market value to book value does not significantly affect the quality of accruals.

In the Iranian economy, part of the government's fiscal policies and liabilities is always financed by banks. As government debt to banks increases, the private sector's access to loans and facilities is limited. It can cause undesirable macroeconomic outcomes. This study investigates the macroeconomic effects of government debt on banks in Iran over 1972–2016 by using an SVAR model. Results show that government debt to banks does not significantly affect the aggregate demand ratio to aggregate supply and GDP per labor. Still, it significantly increases the real exchange rate and decreases the non-tradable goods' ratio to tradable goods prices. In the long-run, the real exchange rate, the ratio of non-tradable goods to tradable goods price, and the general price level changed by 34.46, 20.95, and 46.4 percent, respectively, which can be explained by the government debt to banks. Results indicate that the government policy manages the Iranian economy.

The Sierpinski triangle is a fractal that is commonly used due to some of its characteristics and features. The Forex financial market is among the places wherein this triangle's characteristics are effective in forecasting the prices and their direction changes for the selection of the proper trading strategy and risk reduction. This study presents a novel approach to the Sierpinski triangle and introduces an innovative model based on it to forecast the direction changes in currency pairs, particularly EUR/USD. The model proposed in this study is dependent on the number of data selected for forecasting. The number of data is, in fact, the area of the initial triangle and the forecasted value of the self-similar triangles formed in each stage. For the performance assessment of the proposed method within one year (03/01/2019 to 28/02/2020), daily EUR/USD closed price data was classified into three categories, namely the training (70%), testing (20%), and validation (10%). Three approaches were proposed that led to forecasting the mean direction accuracy and the best result of over 60 percent in the third approach and over 50 percent in the first and second approaches. Results reflect the satisfactory improvements in the third approach compared to the econometrics, time-series, and machine learning methods. Moreover, the optimal number of data for the model is selected such that the difference between the accuracy of the direction forecasting in the training category and testing category is above 0.6 and below 0.05.

We seek to determine the optimal amount of the insurer’s investment in all types of assets for a small and closed economy. The goal is to detect the implications and contributions the risk seeker and risk aversion insurer commonly make and the effectiveness in the investment decision. Also, finding the optimum portfolio for each is the main goal of the present study. To this end, we adopted the optimal asset-liability management (ALM) method to control the firm's risk of financial stability and growth by balancing the assets and liabilities of the firm. In the process, stochastic interest rates and inflation risks were taken into account according to the expected utility maximization framework. All assets were established and calculated by the Kalman Filter with the stochastic interest rate following the Hull-White model; an additional stochastic process models the inflation risk. To consider the stochastic process, we employed the geometric Brownian motion in the liability process to ensure a definite liability value. We chose Iran’s Social Security Organization as our sample insurer company since it has a portfolio of five types of assets and four types of liabilities, and operates in a small and closed economy. By Applying the ALM method with the stochastic control theory approach, we acquire the optimal investment strategies for insurers to minimize their risk. Our findings demonstrate the effects of model parameters, such as the degree of risk-taking on the insurer decision.

This study aims to design a model to realize real estate income tax in Tabriz city with due attention to the tax collection process. According to the related literature, the variables of "tax payment," "real estate," "tax evasion," "investment incentive," "rent and real estate speculation," and "advertisement in tax collection" are considered as key variables, affecting the conceptual model of real estate tax system. According to the dynamic systems method, the interaction and relationships between variables are shown by state-flow diagrams based on the literature and experts' opinions. These relationships are finally simulated by Vensim software. The results show that advertisement has a significant effect on the amount of taxes paid by taxpayers. Tax transparency has also reduced rents for taxpayers and ultimately reduced tax evasion and timely tax collection in the estimated budget of real estate resources and single-occupancy jobs. Scenarios show that lowering the tax rate in the trading and business market and large corporations and businesses' rental income are strongly influenced by tax revenues.