نشریه تحقیقات مالی
پیاپی 68 (زمستان 1401)

Most common financial instruments used in social banks include crowdfunding, microfinance, and social business model (excluding dividends). Therefore, one of the social banks (SB) challenges is inadequate and unstable financial resources. This is an important constraint on pursuing their social missions. This study aims to examine the sustainability of financial resources in a social bank implementing Islamic financial instruments such as Ijarah -to give something on rent- and Mozara’ah contract. Mozara’ah is an agreement between the owner of the land and the farmer. The farmer cultivates the land and the produce is divided between the parties in an agreed with fixed-ratio.

The study compares the sustainability of SB cash flow based on Islamic financial instruments such as Ijarah and Mozara’ah with an SB cash flow based on debt facilities such as microfinance using dynamic system simulation. The simulation is done by Vensim software.

The results show that the process of cash flow in the SBs with Ijarah and Mozara’ah, enjoys an uptrend. Whereas, the cash flow of SBs with Microfinance reflects the fluctuating behavior. Also, results of the simulation model consisting of Ijarah and Mozara’ah show a rate of return of 13 % in the second year and 20% in the sixth year on deposits which is higher than the interest rate of social banks based on microfinance.

According to this research, the SBs using asset-based facilities would be more attractive for depositors in comparison with the SBs using debt-based facilities. So that they can overcome the limitation of access to sustainable financial resources. Therefore, they can be more successful in fulfilling their social mission.

Herd Behavior is one of the biases behavioral that affects financial markets and decision making. Herd Behavior in financial markets is the tendency of a group of investors to follow the actions of others and to ignore personal beliefs and information and following the market consensus. The purpose of this study is to investigate the relationship between Herd Behavior between crude oil prices and the stock market. In addition, the effect of variables such as upturns and downturns periods, investor tendencies and market liquidity on the relationship between the two variables will also be tested. This paper contributes to the debate on the cross-markets correlation by extending tests of herding behavior between the stock market and the crude oil market. We study herding behavior at the sector level during extreme oil market movements. Furthermore, we investigate the cross herding behavior between the two markets while including the volatility of the oil prices and investors’ sentiment. We use a modified version of the cross section absolute deviation model to infer the impact of the oil market on industry herding.

This research is an applied research in terms of purpose and a correlational research in terms of method. The research sample, after imposing the intended restrictions, included companies enlisted on the Tehran Stock Exchange, in the period from 2015 to 2020. To test the research hypotheses, a panel data model was used, and to measure the Herd Behavior extends the work of Chang & et al (2000). Then we tested the research hypotheses.

The research’s Results confirm the Herd Behavior in the Iranian stock market. The return of the crude oil market has an effect on the Herd Behavior  in the Tehran Stock Exchange. In addition, this behavioral biasess is greater during periods of crude oil market upturns than during periods of downturns. The results also showed that more liquidity in the market is associated with more Herd Behavir.

This paper examines herding behavior within Tehran Stock Exchange industries and explores the impact of oil prices on the intensity of herding. It further investigates the asymmetric effect of oil prices moves (downturns and upturns) on herding within sectors, while including both the volatility of the crude oil market and investors’ sentiment. The reason behind the inclusion of oil prices and volatility is to study the possible integration between the stock market and oil market and to infer the possible cross-herding behavior between them. There is herd behavior in the Tehran Stock Exchange and the crude oil market as one of the most important sectors of the economy and the largest source of government revenue also affects it. Therefore, the capital market and the effects it receives from the crude oil market should be considered more and more by economic decision makers.

Considering the importance and special position of the banking system in Iran's economy, it should not be overlooked that the fluctuations of macroeconomic variables affect the performance of the banking system. The biggest challenge of the banking industry is identifying and managing the risks in the banking system and reacting to economic shocks. Liquidity risk is one of the most important risks in the Iranian banking system, which exposes it to serious crises. The purpose of this research is to investigate macroeconomic shocks on liquidity risk indicators in Iranian banks admitted to the Tehran Stock Exchange (TSE) and the Iranian Over-the-Counter (OTC).

To evaluate the impact of macroeconomic shocks on the considered indicators related to liquidity risk, extensive systemic shocks were considered. In the first stage, the relationship between macroeconomic variables was estimated using the MS-VAR model to predict the trend during a specific period. In the second stage, the relationship between liquidity risk indicators (the ratio of liquid assets to total assets and the ratio of the debts to the Central Bank of Iran to total debt) with macroeconomic variables was estimated, using the Panel Data model. Finally, the effect of the probable but exceptional shocks of the macroeconomic variables, extracted in the first stage, on the considered indicators related to liquidity risk was investigated. The model presented in this research used information on the Iranian banking system from 2009 to 2019 to analyze the intended effects.

The results showed that the MSIAH(2)-VAR(1) model was selected as the optimal model. The stability of both regimes has been confirmed. The probability of being in regime I stood at 0/51, and regime II was 0/49. The shocks to macroeconomic variables, the shock to GDP growth in regimes II and I, the shock to inflation in regimes I and II, and the shock to exchange rate growth in regimes II and I, respectively, had the greatest impact on the first index of liquidity risk. For the second index of liquidity risk, respectively, the shock to the GDP growth in the first and second regimes, the shock to inflation in the second regime, the shock to the GDP growth in the second regime, and the shock to the exchange rate growth in the II and I had the most significant impact.

Estimating the ratio of cash assets to total assets, as the first indicator, and the ratio of debt to the Central Bank to total liabilities, as the second indicator, with the shock to macroeconomic variables indicated that liquidity risk indicators were strongly affected in both regimes under the crises. According to the results obtained from both of the indicators, among the considered macroeconomic variables, the shock to GDP growth in regime I had the greatest impact on both liquidity risk indicators.

The main purpose of this study is to select an appropriate model for daily prediction of the total index of the Tehran Stock Exchange (TEDPIX). In this regard, dimension reduction techniques have been used to select effective and representative features to increase the accuracy of the selected model.

Since dimensionality reduction can be performed by two different methods (feature selection and extraction), in this study, two methods were used simultaneously to select the appropriate features of the prediction model. Hence, the MID algorithm was used to select the features, and the PCA algorithm was used to extract them. In this regard, after collecting 34 financial and economic features affecting the stock market, the features were prioritized by the MID algorithm. Then, the appropriate model was selected by comparing the performance of two different neural network models called RBF and DNN, which are respectively the most important and innovative of the extant models. Then, using two types of dimensionality reduction techniques, the prediction accuracy of the selected model was examined. The appropriate method for selecting the input features of the prediction model was identified, accordingly.

Analysis of the obtained results showed that the RBF model comes with more accuracy in the daily prediction of the Tehran Exchange Dividend and Price Index. Also, by comparing the performance of the two types of dimensionality reduction techniques, it was found that compared with the PCA algorithm, the MID algorithm brings better results in selecting the input variables of the RBF model. Therefore, according to the priority of features with the MID algorithm and the pattern of changing the level of error by increasing the number of features in the RBF model, the ISF-MID algorithm was proposed to select the appropriate features of the stock index prediction model. Using this algorithm, with the minimum number of features, can end in the highest accuracy in predicting the total index of the Tehran Stock Exchange.

The proposed method can identify, prioritize and select appropriate features for the prediction model, due to the simplicity and effectiveness of its use. It can also be useful in various areas of modeling, including the capital market, foreign exchange market, etc.

Time series prediction methods based on artificial intelligence have been widely developed in recent years. Given that these data have large dimensions in the field of investment and stock price forecasting, traditional data analysis methods have low predictive power. This study examines the predictive power of a variety of models based on machine learning in the Tehran Stock Exchange.

After collecting data from 150 large companies listed on the Tehran Stock Exchange from 2012 to 2021, we want to predict the stock price trend- the movement direction of the price- and then validate each method and compare their accuracy. In these methods, we allocate part of the data to the learning section and the rest to the test section. We take these periods as training and trading sets. These methods include linear models, autocorrelation models, trees, and neural networks.

Deep learning models show better performance than other models and have an accuracy of about 70 percent. Also, we show the time series of the best-performance model accuracy of portfolios of some large industries. The best-performance model of DL in this study is Recurrent Neural Networks. In addition, we show that shallow learning models have higher accuracy and most models perform better in predicting descending stock trends.

In this study, after trying to use the models very carefully, the result is that these models do not provide stunning results to investors.

Contratum strategies are hybrid strategies in which, like the reverse strategy, the portfolio ranking is based on a long-term time horizon. However, the time horizon of their maintenance is like medium-term momentum strategies. Momentarian or momentary strategies are among the combined strategies whose portfolio ranking and stock selection are based on the medium-term time horizon, just like the momentum strategy; however, unlike reverse strategy, they are kept in long term. In this research, an attempt has been made to provide an optimal model for stock selection based on momentum, reverse, and hybrid trading strategies using the GWO algorithm and dynamic panel.

Considering the fact that the purpose of the current research is to answer the questions and test the existing theories in a specific field, it can be categorized as applied research (research and development), and due to its possibility of obtaining a descriptive explanation of a phenomenon, it can be considered as the pseudotype, experiential, descriptive and post-event. In addition, due to description, inference, and problem-solving using quantitative values, it is in the scope of quantitative research. To estimate the model, the information of 175 companies in the period from 2012 to 2021 was used, as well as the software E-Views 12 and MATLAB 2021. Based on the results of 8 time periods of 3, 6, 9, 12, 24, 36, 48, and 60 months, it was analyzed according to different momentum and reverse and combined strategies in loser, winner, and loser-winner, winner-loser positions. It should be noted that two methods of the dynamic panel and gray wolves were used to estimate the strategies.

The existence of a momentum strategy was confirmed in the Tehran Stock Exchange, which ensures the hypothesis of underreaction in Iran's capital market. Investors in this market show less reaction to the change of the fundamentals affecting the stock price. Accordingly, the correction of the stock price of these companies is done slowly until it reaches its intrinsic value. Of course, factors such as the volatility limit, the base volume, and the trading node law also fuel this slowness and cause the price correction process to take place with a delay by the formation of buying and selling queues. This helps to use the momentum strategy and obtain abnormal returns in this market. As a result, it can be said that the momentum policy and, naturally, based on the research results, combined approaches cannot create abnormal profits for their investors in the capital market of Iran.

Financial market activists and investors are advised to use mixed strategies to improve their buying and selling decisions. Due to the improvement of the results of the gray wolf method compared to the simple panel method, they should use artificial intelligence methods instead of regression methods in the calculations and formation of the optimal portfolio.

Investors' personality traits play an important role in their success in the capital market. In this regard, this study attempts to investigate the moderating effects of personality traits on the relationship between the use of financial advisory services and investor trading behaviors.

The present research is practical in nature and can be considered a descriptive-exploratory and correlational study. The required data were collected through the distribution of questionnaires among 267 investors. The study was conducted in the second and third seasons of the year 2021. The gathered data was initially analyzed through Exploratory Factor Analysis (EFA), the investors' personality traits were identified using the five-factor model of personality, and then, using Confirmatory Factor Analysis (CFA), their correct measurement was ensured. The regression model was tested afterward.

According to the regression analysis results, in addition to the independent variable (financial advice service), the variables of gender, marital status, monthly income, stock market experience, risk aversion, and financial literacy affected the dependent variable (investors’ trading behavior). In contrast, age and education level did not influence the dependent variable. After this stage, the moderating effects of investors' personality traits on the relationship between the independent variable and the dependent variable were studied. The three traits of openness to experience, conscientiousness, and agreeableness strengthened the relationship, while neuroticism debilitated it. The extraversion personality trait did not affect this relationship. Finally, the instrumental variable was used to solve the problem of the endogenous explanatory variable. This was done using two-stage least squares (2SLS) regression analysis.

Personality traits are among the factors influencing the trading behaviors of investors. Therefore their role in using financial advisors is undeniable.