نشریه تحقیقات مالی
پیاپی 66 (تابستان 1401)

Liquidity creation is one of the functions of banks based on the theory of financial intermediation. The purpose of this article is to examine the effects of oil price shocks and economic sanctions on the liquidity creation of Iranian banks, given the country's specific circumstances.

This study was conducted in a nonlinear framework using PSTR models.

Based on the achieved results of this study, the effects of oil price shocks and economic sanctions can be explained using the smooth transition threshold models. Negative oil price shocks have negative effects on the on-balance sheet liquidity creation as well as on the total liquidity creation of large banks but they have a positive effect on smaller banks. The effects of positive oil price shocks are only significant on the on-balance sheet liquidity creation of the larger banks. By examining the average oil price shocks, the results show that oil price shocks harm the liquidity creation of large banks but do not have a significant effect on smaller banks. Economic sanctions have a positive effect on the liquidity creation of large banks and do not have a significant effect on smaller banks.

Overall, the results proved that the effect of negative oil price shocks on banks' liquidity creation is more than the effect of positive oil price shocks and economic sanctions. In addition, mechanisms need to be established to offset its negative effects on banks.

The purpose of this study was to apply the relative robust approach that minimizes the maximum regret to deal with the present uncertainty in the input data of the Markowitz mean-variance portfolio optimization model by reconstructing that model as a second-order conic program. Regret is defined as the difference between the obtained solution and the optimal solution under a specified input data set. This approach uses scenarios to consider the present uncertainty in the input data. Moreover, the robust portfolio optimization model introduced by Bertsimas and Sim, which considers uncertainty as an interval, was used to be compared with the relative robust approach and the Markowitz model.

In this research, the return of 50 more active stocks of the Tehran Stock Exchange (TSE) was used to obtain the optimal portfolio using the minimax regret method based on the Markowitz Model. Then, using the out-of-sample Sharpe criterion, the results of the minimax regret method were compared with the classic methods.

Based on the research findings, the relative robust approach in the out-of-sample test on most corresponding points of the efficient frontier showed better performance in comparison with the Markowitz model. Also, the Bertsimas and sim approach delivered better performance than the Markowitz model in the out-of-sample test. The results did not prove any significant difference for out-of-sample outputs between the relative robust and Bertsimas and sim approaches.

According to the obtained results, the relative robust approach can surpass the mean-variance approach for investors with almost all levels of risk-return preferences. The approach presented in this research can provide investors with a new risk criterion that can be considered in choosing the optimal portfolio. Furthermore, the results confirmed that investors act indifferently in choosing between the relative robust solution and the solution of Bertsimas and Sim's approach. This method can be applied as a portfolio optimization approach and also different markets can be considered under this technique to have a better understanding of its capability.

Financial distress, which is defined as the uncertainty about the company's ability to meet its obligations and repay its debts, has been estimated by different models divided into three groups of fundamental models (based on accounting or financial data), structural models (based on the company's capital structure or market information) and hybrid models.  Accurately predicting financial distress is still a major point of challenge for financial researchers. Scholars acknowledge that financial distress will be experienced when it happens. Therefore, the best thing to do is to initially estimate the probability of a company's financial distress. In this regard, in the current study, first, a hybrid model was presented to investigate the ability of financial distress prediction models. Next, in order to compare the hybrid model with accounting-based models, the second version of Altman's Z model known as the Z˝ model was used. To compare the hybrid model with market-based models, Merton's model was used in three groups including healthy, distressing, and distressed companies.

In this research, by reviewing past studies, 47 variables affecting financial distress, such as accounting variables, market variables, and macroeconomic indicators were identified. Afterward, considering the frequency and successful performance of these variables in past studies, 19 variables were selected. In the next step, using the Stepwise regression test, among the 19 variables, 10 variables with probability values ​​smaller than 0.05 were chosen. Also, to determine the dependent variable, the European option pricing model (Merton's model) was used. Finally, by the use of the Multinomial logit model and identifying the relationship between the dependent and independent variables, the hybrid model for predicting financial distress was designed. In order to compare the produced hybrid model with accounting-based fundamental models, the second version of Altman's Z model known as the Z˝ model was used. To compare the hybrid model with market-based structural models, Merton's model was used. Moreover, in order to test the ability of financial distress prediction models, a sample including 100 companies listed on the Tehran Stock Exchange (TSE) or Iran FaraBourse (IFB) was selected. Then, considering the defined criteria, these companies were divided into three groups consisting of healthy, distressing, and distressed companies. Finally, the ability of the above-mentioned models in predicting financial distress was investigated.

Research findings indicated that in the hybrid model, the ratios of Net Working Capital to Total Assets (WCTA), Operating Cash Flow to Total Assets (OCTA), Sales to Total Assets (STA), Net Income to Total Assets (NITA), Short-term and Long-term Debts to Equity (TLTE), Price to Earnings Per Share (P/E) and Price to Sales (P/S), and the variable of Interest Rate (INT) had significant relations with company's financial distress probability. Also, a comparison of the hybrid model and conventional models revealed that in the group of financially distressed companies, respectively, the Z˝ model with 100% accuracy, Merton's model with 85% accuracy, and the hybrid model with 90% accuracy had correctly predicted the financial situation of the companies. While, in the group of financially distressing companies, the accuracy of the Z˝ model, Merton's model, and the hybrid model in predicting the financial situation of the companies, stood at 50%, 85%, and 85%, respectively. In addition, in the group of healthy companies, these models were able to correctly predict 95%, 85%, and 90% of the companies' financial situation, respectively.

According to achieved results, the Z˝ model has higher predictive power on healthy and distressed companies, compared to the hybrid and Merton models. While, the hybrid and Merton models are better at predicting the financial situation of distressing companies than the Z˝ model. Therefore, considering that the performance of the market-based model of Merton in predicting the financial situation of the companies is weaker than those of the Z˝ and that the hybrid models which are mainly formed by financial or accounting ratios, and also in regard to the findings of past studies which proved the inefficiency of the stock market in Iran, it can be concluded that it is better to use accounting variables in future research in the field of predicting financial distress.

The problem of securities optimization is a significant financial problem, and the issue of choosing the optimal stock portfolio has long occupied the minds of investment professionals. Under uncertain conditions, it is essential to determine asset allocation. The creation of a portfolio of investments is one of the most common financial challenges faced by investors. For them, building a portfolio of investments that yields the highest level of profit is imperative. Various methods have been introduced to construct a portfolio, the most famous of which is the Markowitz approach. There are numerous shortcomings with mean-variance theory due to its difficulty in estimating expected returns and covariances for different asset classes. The problem with the Markowitz variance-mean method, estimation errors, and inconsistencies led to the development of several other academics' attempts to find possible portfolio solutions that would lead to optimal asset allocation. To overcome this problem, Marcos Lopez de Prado was the first researcher to propose a hierarchical model for portfolio construction in his famous paper “Building Diversified Portfolios that outperform out-of-sample”, in 2016.

The present study is applied in terms of purpose, quantitative in terms of the implementation process, and retrospective and post-event in terms of time. This research focuses on the application of machine learning in selecting the optimal portfolio and its purpose is to find a stock portfolio optimization method that has superior performance in prototype simulation for the Tehran Stock Exchange. In this study, we use the Hierarchical Risk Parity (HRP) machine learning technique and compare the results with the minimum variance approach. The concept of HRP is based on graph theory and machine learning techniques and can be divided into three main stages including tree clustering, quasi-diagonalization, and recursive bisection. To conduct this research, the adjusted closing prices of 30 listed companies for 760 trading days from 2018 to 2020 were used. Missing values were filled by propagating the last valid observation forward

To evaluate portfolio performance, the Sharpe ratio was measured for both in-sample and out-of-sample periods. The results of in-sample and out-of-sample analyses showed that the Hierarchical Risk Parity approach performs better than the minimum variance approach.

In this study, the researchers employed a novel asset allocation method – Hierarchical Risk Parity (HRP) which has the most desirable diversification properties. HRP provides a meaningful alternative to traditional asset allocation approaches and an important risk management tool for investors. Therefore, portfolio managers should have an active approach in evaluating each method according to the conditions and situations in which they are.

The present paper aims to achieve a suitable model for predicting the behavior of major currency pairs in the Forex market based on the chaos theory and a hybrid algorithm.

This is an applied research study. The statistical population of the current study included the major currency pairs present in the Forex market having the largest trading shares (dollars, pounds, euros, and yen). The population comprised a total of 3,888 views i.e. 1,296 views for each currency pair. The trading period lasted from the beginning of January 2017 until the end of 2021. After examining the data and establishing the existence of chaos among the data, using two BDS tests and Lyapunov maximum view, three combined models were tested to achieve the best and most reliable status.

According to the results obtained from the BDS test and the maximum view of Lyapunov, there was chaos in the data of the three examined currency pairs. In addition, the chaos model with perceptron multilayer and elite non-dominant sorting genetic algorithm performed better than other models in this study. The values ​​of the Tails inequality coefficient and DM test statistics also indicated the hybrid superiority of the chaos model with perceptron multilayer and elite non-dominant genetic sorting algorithm.

The results proved the chaos model with perceptron multilayer and elite non-dominant sorting genetic algorithm to be superior to the other two hybrid models.

Global catastrophic risks are classified into 62 categories, 34 of which have been recorded in Iran. Such risks are major threats to the Iranian economy. Using the capacity of the alternative risk transfer instruments, as one of the recommended ex-ante risk management strategies, creates financial protection in the macro frameworks of the domestic catastrophe risk management via incorporating the financial instruments of the Iranian capital market as well as the Islamic financial market. Transferring such catastrophic risks requires appropriate financial instruments. The purpose of this study is to provide, explain and design the domestic catastrophic risk management framework by alternative risk transfer instruments.

The present study has been conducted based on a systematic literature review method. For this purpose, the related literature was systematically reviewed over a period of eight months. Firstly, after specifying the subject of study, the research question was developed. Then, the research protocol was designed and an appropriate mental model was designed accordingly. Subsequently, inclusion and exclusion criteria were determined. The researchers next began the comprehensive and systematic research through which 112 papers were examined. After classifying, screening, and measuring their quality, 73 studies were selected for the final in-depth analysis. In the next step, through the purposive and snowball sampling method, 18 experts were interviewed. The Delphi method was used to obtain the opinion of experts until data saturation was achieved. Their viewpoint was embedded in the final proposed framework. In the last step, the obtained data were classified, codified (using the open coding method through MAXQDA 2020   software) synthesized, and finally summarized. The trustworthiness and authenticity of the current research was measured according to the four-stage model of Lincoln and Guba.

In general, all the factors that affect the catastrophic risk management framework can be classified into four main categories of “necessity, implementation of infrastructure, risk measurement, and designing appropriate financial instruments”. Each category includes several subcategories and codes. The proposed framework in the present study is made up of 4 categories, 10 sub-categories, and 415. Also, 37 risks were collected and classified out of 62 catastrophic risks to obtain the “Iran Disaster Risks Diversity Table”. Finally, the appropriate financial instruments for risk transferring based on the capacity of the domestic capital market, i.e. “Insurance Sukuk Vekala” and “Cat Takaful (CT) Sukuk” (for the international Islamic Capital Market), were designed and proposed by this research.

Finally, a schematic model was proposed to establish Iran’s catastrophic risk management framework through alternative risk transfer instruments, according to the diversity of detected factors. As a multi-dimensional concept, the framework should be considered in the implementation process.

The primary purpose of this study is to quantify uncertainty and consequently develop an indicator of financial uncertainty for the Tehran Stock Exchange (TSE). Since paying attention to uncertainty is of importance in the development of economics and management, it is worth acquiring a deep and accurate understanding of it. Financial market participants are always faced with uncertainties about the future; therefore, getting familiar with the concept and understanding it is necessary for all market participants.

In the present study, conditional fluctuations were filtered from the series of efficiency fluctuations in order to obtain a more appropriate criterion for expressing uncertainty. According to recent studies, the Fama-French five-factor model can explain 69 to 93% of cross-sectional changes in expected returns. Accordingly, in this study, the estimation of this model was initially compared with two methods of ordinary least squares and state space by the filter-Kalman algorithm to obtain a suitable model for filtering conditional fluctuations. In this study, state space by  Kalman filter algorithm and ordinary least squares method for selecting the appropriate model for filtering conditional fluctuations from the series of efficiency fluctuations was compared in the first step to quantify the uncertainty from the fitting of the 5-factor model of Fama and French in two ways.

Information criteria along with Debold and Mariano approaches for comparing the fit of the 5-factor model of Fama and French with two methods of state space by Kalman filter algorithm and ordinary least squares were used. The results indicated the superiority of state space estimation by Kalman filter algorithm. After selecting the appropriate model, the conditional performance fluctuations of each portfolio were filtered from the series of yield fluctuations to obtain a more appropriate measure of uncertainty. In this regard, the returns of 18 portfolios were used based on the classifications of variables; size factor, book value to market value factor, profitability factor, and investment factor.

Finally, the extracted uncertainty components were averaged monthly to obtain our index. This index clearly showed the conditions under uncertainty that occurred continuously in the past.Practicability: To prevent the reduction of investment and the outflow of capital, as well as the loss of shareholders, government, and governmental institutions, including the stock exchange can use this index in times of uncertainty by increasing the use of financial instruments, proper pricing of securities, as well as other methods that control uncertainty to reduce investment. Researchers who study the relationship between economic and financial variables with uncertainty are advised instead of using different criteria to express uncertainty, they should use the approach used in this research, which shows uncertainty better,in order to reach better results.Limitations: The index in this study was obtained based on the observance of the data frequency on a monthly basis, so this index is weak in explaining the uncertainties that have a short-term impact on the market. Since in creating the index, the average components of uncertainty were used and they showed the general uncertainty of the market, comparing the results with the uncertainty of companies individually may provide different results.