نشریه چشم انداز مدیریت مالی
پیاپی 38 (تابستان 1401)

The low (high) abnormal returns of stocks with a high (low) left tail risk is a financial anomaly studied in empirical capital asset pricing research. This anomaly is caused by undesirable and unexpected events that incur severe losses for investors, and this loss has the characteristic of continuity. Since the prediction of left-tail risk can help formulate an appropriate trading strategy, this study aims to predict the left-tail risk through past left tail risk information via portfolio analysis and Fama and Macbeth's (1973) regression. To this end, the data of 307 companies of Tehran Stock Exchange and Iran Fara Bourse from 2005 to 2020 were used. The results revealed the ability to predict the left tail risk by past risk information in the research sample. Further exploration by additional portfolio analysis suggested that the future left-tail risk prediction power by past information left-tail risk is greater among stocks with small size characteristics and high unsystematic volatility, but only a small portion of the market is devoted to stocks with these characteristics.

Nowadays, financial derivatives play an important role in the development of financial markets and risk management. Financial derivatives markets are not only a tool for risk management but also a secondary market to attract small capital for implementing large projects. Countries with extremely volatile financial markets need some novel financial risk management tools. In this paper, the spread option is used as a tool for investment and risk management. First, we obtain a model by using stochastic differential equations and partial differential equations. Then the model derived from a stochastic differential equation with a jump term is transformed into a risk-free integral partial differential equation which represents the spread option price on the Libor interest rates since the model does not have a closed-form solution or analytical solution, the solution is estimated at discrete points by using the alternating direction implicit (ADI) method. The stability of the method is also proved. In the next step, the pricing model is implemented in MATLAB software and the results are illustrated. Finally, it is concluded that the ADI method is an efficient and appropriate method that solves the problems in pricing models caused by jumps.

If the central bank and the relevant bank risk committee do not monitor and control the amount of liquidity created by a bank, it risks bankruptcy and ultimately all the banks in the country. Therefore, this study examines the impact of liquidity creation on the profitability and financial stability of banks. In terms of purpose and method, this research is applied post-event. A multivariate regression model and data panel approach were used to analyze the information of 15 banks listed on the Tehran Stock Exchange over the period 2013-2020. Berger and Bauman's (2009) method was used to calculate the liquidity creation index and the profitability and financial stability of banks were measured by the return on assets ratio (ROA) and Z-Score, respectively, as well as by control variables at the bank level (size, deposits, facilities, and non-interest income) and industry level (concentration index of banks). According to the results, banks are more profitable and stable when liquidity is created.

The purpose of this research is to investigate the information of circulation capital management information for predicting financial helplessness based on artificial neural networks and particle cumulative optimization algorithms. The statistical population of the research consists of 120 companies listed in Tehran Stock Exchange during the years 2008-2019. In order to achieve the goals of the research, first, by studying previous studies in the field of financial distress, 28 variables affecting financial distress and then, using the leading logistic regression model, the estimated model and 5 variables were selected. Then, in order to verify the information of the information management information in circulation, comparing the research model with attention and regardless of the circulation of circulation management based on the combination of artificial neural networks and the optimization of the cumulative particle movement. The results of the two models based on the combination of artificial neural networks and the optimization algorithm of cumulative particle movement showed that the development of the research model reduced the error of neural network training with the cumulative particle movement algorithm to 0.0641. Also, with the development of the research model, the subcutaneous level of Rock increases to 6,248 and, consequently, the research model is added to 70.5%. This result shows the effectiveness of the entry of capital management in the research model.

Due to the rapid advancement of technology and computer technologies, a more accurate model of this phenomenon can be drawn based on previous experiences and used in the form of a decision support system. Relying on the generalizability of artificial neural network models, this approach has been used to model the dynamics of the financial crisis phenomenon. Variables of economic status, GDP, export value index, import value index, time position and geographical location of each country during the financial crisis as inputs of the artificial neural network model and the optimal combination of policies to deal with the financial crisis as Model output is defined. In order to show the capability of the proposed model, how to design and implement the proposed system in the event of a Covid-19 virus outbreak crisis in Iran was explored. The results indicate that using the proposed model as a support for policymakers and decision-makers in the field of financial management can help solve semi-structured problems and improve decision-making efficiency and pay more attention to its effectiveness. According to the results of the present study, the adoption of expansionary monetary and fiscal policies and the provision of support packages as basic solutions to reduce the effects of the financial crisis caused by the corona epidemic in Iran is recommended.

Although a positive mean-variance relation is a cornerstone of traditional finance theory, empirical evidence supporting it is controversial and mixed. According to behavioral finance theory, the mixed risk-return tradeoffs attributes to investor sentiment in the financial market. In this paper, we investigated the effect of individual investor sentiment on the mean-variance relationship in 103 Tehran Stock Exchange firms using the BSI index. Meanwhile, we examined the relationship between small and large companies, high and low-priced firms, and growth and value stocks. The conditional volatility of stocks was calculated with GARCH models, and the research hypotheses were examined using a panel data method. The results show that the risk-return relationship in the total sample, growth stocks, and high-priced entities are less affected by sentiments, but sentiments strengthen the positive mean-variance relation in value stocks, low capitalization, and low-priced firms. However, sentiment does weaken the positive relation in high-capitalization firms. According to the research results, in constructing portfolios based on variance, investors should consider not only the sentiment of investors but also the features of the share in terms of value and growth, the market value of the company, and the stock price of the companies.

Currently, Iran is facing a devaluation of the currency, and irrational behaviors regarding the purchase of currency have created many problems for the country. The present study was conducted to investigate the behavior of buyers and sellers of foreign exchange with an interpretive structural approach. The statistical population of this study consists of 12 senior managers who have lived experience in economic and foreign exchange sector. In the qualitative part of this study, in-depth semi-structured interviews were used to collect information. The obtained data were analyzed in the coding process using continuous comparison method. In this study, first, open, axial and selective coding was performed. In the quantitative part, the method of structural interpretive modeling has been used. The structural-interpretive model of the study was presented at seven levels, with the highest level of components of the seller's ability to accept currency, knowledge-based companies, sanctions, and information costs; In the second level, the buyer's expected profit, the degree of attachment to the place and the emergence of bias, in the third level, currency exchange, the effects of contract structures, reduction of capital control and exchange, in the fourth level, seller costs, arbitrage, in the fifth level, legal and policy restrictions Related to government interventions, at the sixth level, exchange rate fluctuations and at the seventh level, exchange rate flexibility, interactions between the parties, and risk reduction policies were identified. Based on MICMAC analysis, the identified components were classified into affective or outcome, risk, target, regulatory and independent variables.