نشریه تحقیقات مالی
پیاپی 69 (بهار 1402)

In this study, we investigated the existence of the bank lending channel (BLC) as one of the monetary transmission mechanisms and the effect of banking characteristics on this channel in the Iranian economy.

The Factor-Augmented Vector Autoregressive (FAVAR) model, introduced by Bernanke, Boivin, and Eliasz (2005) was used. This research studied 61 macroeconomic variables from 2004Q2 to 2020Q1 and 24 banking variables from 2009Q1 to 2020Q1.

The investigation delivered two main results. First, by considering the growth of M2 as a proxy of monetary policy, the monetary policy proved to have a significant effect on bank lending and the BLC in the Iranian economy. When identifying monetary policy shock, the response of real lending growth was found positive and significant in the quarter when the shock was identified and also in the following three quarters. Substituting nominal lending growth for real lending growth, lending growth had a more significant response to the monetary policy shock. Also, by considering the monetary base as a proxy of monetary policy, lending growth had a less significant response to the monetary policy shock. Second, we found that bank characteristics don’t have a significant effect on the BLC. We investigated the effect of bank characteristics on the BLC at both aggregated and disaggregated lending. The results of the analysis of aggregated lending response showed that by including the bank factors, compared with the case where there are only economic factors in the model, the aggregated lending response doesn’t change significantly after considering the bank factors. Therefore, the bank characteristics do not significantly impact the response of the aggregated lending growth to the monetary policy shock. The results of the analysis of disaggregated lending response showed that except for Parsian and Pasargad banks, whose lending response to monetary policy shock is positive and insignificant, other banks give positive and significant responses to monetary policy shock. Overall, the bank characteristics have a more significant effect on the BLC in the disaggregated lending case.

According to the achieved results, the BLC can be considered an active channel in the Iranian economy, by which the real economy can be affected. Also, the bank characteristics don’t have a significant effect on the BLC. Therefore, considering the strength of the BLC in the Iranian economy, the very close relationship between the BLC and monetary policy variable (M2), and regarding the insignificant effect of bank characteristics on the BLC, monetary policy-maker should take into account the BLC when setting monetary policy.

Stock market manipulation has gained significant attention in recent years. The fact that markets can be manipulated has great implications for designing trading rules and market efficiency. Market manipulation has been developing since the beginning. Thoughtful and accurate manipulation techniques adopted by manipulators may not easily be detected. This research examines stock price manipulation in Tehran Stock Exchange and addresses the identification and detection of stock price manipulation by mathematical algorithm (spoof trading). This study examines transaction-based manipulation in which manipulators place bid and ask orders to control the stock prices. More specifically, this paper studies manipulated stocks by looking at market microstructure (intraday transactions) and aims to introduce an algorithm for detecting suspicious trades so that regulators can detect them beforehand. The ultimate goal of this paper is to help the “Securities and Exchange Organization” (SEO) of Iran to reform market regulations in order to prevent the occurrence of suspicious trades in the market.
This research relies on price data placed by investors in the market. Transaction data consists of two levels; the first level includes executed bid-ask orders available to the public, such as open, close, high, and low prices as well as trading volumes in a specific period. The second level includes the first layer plus all buying and selling orders whether they are executed or not. The latter can only be observed by the regulator and is not available to the public. This paper focuses on the second level in which characteristics and patterns of manipulated stocks are examined by utilizing the intraday transactions of 50 manipulated companies from 2013 to 2016. Panel data analysis and F-limer, Hausman Test, Heteroskedasticity Test, Cointegration Test, Variance Inflation Factors Test, as well as econometric tests relating to price manipulation including stationary, autocorrelation, kurtosis, skewness, run and duration dependence test, were applied to the considered data. Finally, an artificial neural network was used to test the effectiveness of the designed algorithm. The obtained results were illustrated in a confusion matrix.
The results of the mentioned econometric tests were consistent with the results of the designed algorithm. The study’s hypotheses were accepted.
The results of the designed algorithm indicated that the efficiency of the utilized algorithm for detecting suspicious trades is equal to 90.4%, which is an excellent level of performance for accepting an algorithm. Additionally, this research shows that the price and volume of buy and sell orders in the first two lines of the ticker screen are effective in detecting stock price manipulation.

Pairs trading strategies have been around since the mid-1980s and have gained widespread acceptance in recent years. A pairs trading strategy is one of the forms of statistical arbitrage done to make a profit. It bases on the return related to the average spread between pairs of financial securities. The purpose of this research is to investigate the application of the Kalman filter to estimate the dynamic hedge ratio in the pairs trading strategy. The main question of this article is whether individual investors can make a profit by using this trading strategy or not.

The second step was related to the pairs trading strategy, introducing the trading rules and determining the thresholds for buying, selling, and exiting the transaction. Any significant deviation from the average in the spread between two securities was a signal to trade and make a profit. We sold or bought the spread when the spread deviated as much as the positive or negative side of the two standard deviations from the average, and when the spread converges within the positive or the negative halfway point of the standard deviation, we exited the trade to make a profit. Of course, as with all trading strategies, the more you use them, the less likely you will make a profit. In this research, the co-integration method was used to select the pair of stocks, and based on the space-state approach and using the Kalman filter algorithm, the dynamic hedge ratio was estimated to provide trading signals. Trading signals were used to develop a pairs trading strategy between 26 companies in the automobile industry on the Tehran Stock Exchange during the period 2016 to 2020.

According to the co-integration results, out of the 26 stocks we selected for analysis, only 16 stocks had a co-integration relationship. According to the Kalman filter method, the CAGR was 0.11 and the Sharp Ratio was 3.06.

The results obtained from the Sharp ratio and Compound annual growth rate (CAGR) showed that the pairs trading strategy in the period under review was profitable in the automobile industry. The results also proved the superiority of the Kalman filter method over the co-integration method for pairs trading.

In recent years, the increasing speed of information transfer and market connectedness has caused markets to converge and affect each other. The spillover effect occurs when the return or volatility of one market cause fluctuation in another market or other markets. Nowadays every shock or volatility from one market affects others. Properly identifying the effects of spillover on financial markets is crucial for managing and controlling fluctuations. This paper aims to measure and analyze the nexus and spillover effects among the stock market, currency market, gold market, and commodity market.
In this study, the researchers selected some variables like the stock market, currency market, gold market, and commodity market and for better analyses of these variables, they used daily returns of the stock index, currency (dollar), gold, and commodity for 12 years, starting from January 1, 2009, to December 31, 2020. Next, they applied the VARMA-BEKK-AGARCH Model to measure and estimate the spillover effects. The model is very strong and robust for measuring and examining the return and volatility spillovers between markets.
Results achieved from the estimation model showed unidirectional return spillovers from the gold, dollar, and commodity to the stock index, unidirectional shock spillover from commodity to the stock index, and unidirectional volatility spillover from the stock index to currency. Also, the leverage effect of shock was proved as bidirectional between currency and gold and unidirectionally from stock to currency.
According to the obtained results and considering the inverse relationship between stocks and gold, creating a portfolio consisting of stocks and gold seems useful to reduce the effects of risk spillover and helps investors to reduce their investment risk. Regarding the confirmation of shocks from stocks to the foreign exchange market, to control the foreign exchange market, it is recommended to manage the stock market and prevent severe fluctuations. Depending on the established flexibility and interaction among markets, shock and volatility spillover may have three outcomes. First, when markets are flexible and with a good and coherent relationship, the hitting negative shocks transmit among them making no damage to the markets. Second, when markets are inflexible but the relationship among them is weak, the negative shocks would damage one but would have not any effects on other markets. Third, when markets are inflexible but the relationship among them is good and coherent, negative shocks hitting one market not only damages that market but also harm those connected to it. Therefore, to diminish and modify the effects of shock and volatility spillover on the markets, markets need to be improved and enhanced from the two established aspects of flexibility and relationship among them.

The competitive business environment is growing, accordingly, companies are forced to compete with various national and international factors and expand their activities through their new investments. To make these investments, they need financial resources. The importance of accessing useful information for decision-making is important, as it is considered an investor right. Credit rating is useful information for investment decision-making. This study examines the relationship between credit rating and the cost of capital. Its purpose is to examine whether an increase in credit rating leads to an increase in the cost of equity and a decrease in debt costs or not. Because of the narrow background of ranking institutions in Iran and the reluctance of the companies toward the ranking system, a general ranking model is commonly used to determine the rank of the investigated companies.
This study examines a sample of 142 companies listed on the Tehran stock exchange from 2014 to 2020. The companies were selected through a systematic elimination process. The credit rating for each company was measured using the Emerging Market Credit Scoring Model. This rating was then adjusted in three steps: (1) considering the company's vulnerability to exchange rate fluctuations, (2) assessing the degree of credit rating of the industry, and (3) evaluating the company's competitiveness within the industry. A dummy variable was used to indicate an increase or decrease in credit rating from the previous year, where an increase takes the value of one and a decrease takes the value of zero. The cost of debt was calculated by dividing the interest expense in year t+1 by the average debt during years t and t+1, while the cost of equity was calculated by dividing net profits by the equity market value in year t. We used multiple regression with the Generalized Least Squares (GLS) method.
According to the obtained results, there is a positive relationship between credit ratings and equity costs, and any increase in credit ratings can reduce the costs of debts.
Based on the achieved results, there appears to be a positive correlation between credit ratings and the cost of equity. Furthermore, any increase in credit ratings has the potential to decrease the cost of debt. As a result, an increase in credit rating may lead to an increase in profits, because debt costs decrease and equity costs are affected. This subsequently influences investment decisions. Additionally, the risk of a company can have an impact on its expected rate of return. These findings align with Chen, Chen, Chang, & Yang (2013) as well as Kissgen and Esrahan (2010). It is worth noting that the Emerging Market Model has been validated by comparing its rankings to those announced by the rating agencies. This model takes into account both quantitative and qualitative factors. Therefore, we suggest that rating agencies consider utilizing these results as a guideline for ranking purposes.

Investors' behavioral patterns are among the important and new conceptsfocused on by many academic researchers and analysts of behavioral finance in recent years. It emphasizes that the investors' decisions and their destructive behavior could be affected by other investors' attitudes, feelings, emotions, and decisions. The investors' destructive behavior affects managers' decision-making because they would change the financial policies of the companies to meet the investors' expectations. Therefore, the current study is aimed at investigating the impact of investors' destructive behavior on managers' myopia in companies listed on the Tehran Stock Exchange. For this purpose, the effects of three destructive behaviors of investors, including myopic, emotional, and herd behaviors on the managers' myopia were investigated.
The required data were collected from the financial information of 140 companies admitted to the Tehran Stock Exchange in 10 years (i.e., their financial statement information from the Iranian calendar year of 1390 to 1399). To test the hypotheses of the research, logistic regression models were used by applying the pooled data method.
The findings from testing research hypotheses indicate that in Tehran Stock Exchange, investors' myopic, emotional, and collective behaviors have a positive and significant impact on the managers' myopia. It can be implied that with the increase in the investors' destructive behavior (investors' myopic, emotional, and herd behaviors), the managers' myopia is also intensified.
The results of the research demonstrate that the investors' short-sightedness and their looking for short-term profits, put pressure on managers and consequently the managers' myopia increases. Also, the investors' emotional and herd behavior put pressure on managers to reduce costs that have long-term benefits and profitability to increase the short-term ones. Therefore, the behavioral tendencies of the company managers would be influenced by those of the investors and in response to their destructive behaviors. It means that, instead of focusing on long-term goals and plans for the company, managers focus on short-term goals and make short-term decisions to earn short-term profit. Managers who make myopic decisions to increase the current earnings in a short period to meet their investors' expectations, do not pay attention to the future interests and it would have negative effects on the financial structure and the performance of the company. It is because reducing the marketing, research, and development costs in the current competitive market can lead to a decrease in the competitiveness of the companies in the long term.

Considering that investing in the stock market is associated with risk, therefore, its measurement is one of the most important issues for investors. The focus of the current research is on the calculation of the value at risk of Dynamic Conditional Correlation with the Solvency Approach (DCC t-Cupola LVaR) based on the copula and also the minimization problems of the above model to choose the optimal portfolio. Therefore, the purpose of this research is to compare the performance of the Markowitz models and value-at-risk model with the Liquidity-t Cupola approach and DCC-t Cupola LVaR to optimize the portfolios in Tehran Stock Exchange. While presenting a composite model, the extracted model should be examined to select the most efficient model to optimize the investment portfolio considering the conditions of investment uncertainty and non-linearity of the correlation between asset returns. To estimate DCC-tCopula-LVaR, first, the time series of the disturbance distribution of asset returns were estimated and standardized from the ARIMA-GARCH 1,1) model. Then, the marginal distributions of assets were estimated using Student's t-copula function. In the following, DCC-tCopula-LVaR values were calculated using the parametric method. In the last step, using linear programming, the optimal combination of the portfolio and the efficient frontier of the two models were calculated at the confidence levels of 80, 85, 90, 95, and 99 percent for the above two models. The findings of this research indicate that the Markowitz model performs better as the risk level increases. As the risk level decreases and declines from 99% to 80% (risk level in a sell position), the DCC-LVaR model performs better. Also, as the value at risk increases, the Sharp value of the DCC-tCopula-LVaR model decreases compared to the Markowitz model. Numerical experiences in the presented empirical analysis, Sharpe ratio, and two loss measures mean absolute value of error (MAE), as well as the root mean square error (RMSE) show that for an arbitrary portfolio, using the DCC t-Cupola LVaR model at a low-risk level is more efficient than the Markowitz model. By reducing the increase and increasing the value at risk, the Markowitz model has better efficiency. Also, using the DCC-tCopula-LVaR model, when the value at risk-adjusted by liquidity is low, brings better performance for stock portfolio optimization compared to the Markowitz model, based on the Sharpe measurement criterion.