saeed bajalan

The investment decisions of managers of investment funds (especially equity investment funds) have an impression on the returns of individuals who have deposited their capital in these funds. Therefore, the issue of evaluating the performance of investment funds and their managers is imperative for investors. The research aims to investigate the effect of cross-sectional alpha dispersion on investors' evaluation of the performance of investment funds. We extract data regarding 31 equity investment funds from 2012 till 2022 and calculate the interquartile ratio of Jensen's alpha called "IQR" and "Performance-Flow Sensitivity" along with control variables. Then, the hypothesis test model was fitted using the multivariate regression analysis using the Generalized Least Squares method. Empirical findings show a negative and significant connection between the alpha dispersion of investment funds and performance-flow sensitivity. Based on the results, one credit increase in the standard deviation of alpha dispersion leads to a decrease of about 0.4% in the ratio of performance-flow sensitivity. Environments with high alpha dispersion of investment funds will targeted by unskilled managers to introduce themselves as successful and skilled managers to investors and mislead them. Therefore, when the alpha of investment funds has a higher dispersion, the type I error possibility investors will face increases. Individuals may consider an inefficient manager to be competent and skilled. We will provide some suggestions in this regard.

 In recent years, in particular after the 2008 financial crisis, factor investing received widespread attention from asset managers around the world. Due to the lack of enough research in this area in the Iranian capital market, the purpose of this study is to evaluate the performance of factor investing in the Tehran stock exchange (TSE).

In this research, the performance of five single-factor portfolios and three multi-factor portfolios were evaluated on Tehran Stock Exchange from 2014 to 2021. First, the five single-factor portfolios were made based on momentum, value, size, quality, and low volatility. Then, using the results of single-factor portfolios, three multi-factor portfolios were made based on equal weight (1/N), inverse volatility, and equal risk contribution (ERC). The results of single-factor and multi-factor portfolios were compared to overall and equal-weight indices.

The results achieved from all single-factor portfolios, except those from momentum, and all three multifactor portfolios were better than the overall index with a 95% confidence level. However, compared to the equal-weight index, the results were mixed and only the size factor and equal-weight multi-factor portfolios had better performance within a 95% confidence interval.

The results of this study indicate that, during the time frame of this study, factor investing showed better performance than the overall index. Therefore, this type of investing can be considered a practical type of investment by investors and asset managers on the Tehran Stock Exchange.

In recent years Bitcoin has introduced itself as an investment asset and has become an attractive asset for risky investors due to the significant growth of 420 times the market value since 2013. It''s also referred to as the new gold because of the similarities between bitcoin and gold. Therefore, we intend to first compare the volatility structure of bitcoin with Emami coin and gold ounces using APARCH and FIGARCH models. Secondly, using DCC model, we can evaluate the safe haven and hedge capability of Bitcoin compared to Emami coin and gold for TEDPIX and IFX indices. we are going to investigate the similarity of bitcoin with Emami coin and gold in terms of their volatility structure and the safe haven and hedge capability for investing in Iran. The results of this study show that the long memory property of bitcoin time series shocks is more than Emami coin and less than gold. Also, bitcoin volatilities have no leverage effect. The results of the second part of the research confirm that bitcoin has a safe haven capability for TEDPIX and IFX indices, but this feature is weaker than the Emami coin and gold. Bitcoin also has no hedge capability for these indices.

In this research, the effect of scale-time volatility of assets (currency, stocks and housing) on the efficiency of the banking network in the period 1399: 4-1388: 1 has been studied quarterly using the Markov switching model.

In this study, we first calculate the efficiency of the bank network using the data envelopment analysis model with bootstrap data. Then, the volatility of asset market (exchange rate, stock market index and housing price index) extracted using the wavelet conversion pattern and examines the impact of volatility of asset market on the efficiency of the country's banking network in the form of the Markov switching model and observing their effect on different levels of efficiency.

The average efficiency of the country's banking network in the study period has been about 56.1%, which indicates that efficiency has not been appropriate. The short-term volatility of the exchange rate in the state that the  efficiency of the bank network and the high regime has a negative and significant effect, but if the long-term exchange  volatility, regardless of the regime and the level of banking network  efficiency, has a negative and significant effect. The short-term volatility of the stock market index have had a positive and significant effect on the level of low banking network efficiency. But if volatility are continued in the stock market, regardless of the level and regime, the efficiency of the banking network has a negative and significant effect. The short-term  volatility in the housing market have had a positive and significant effect on the level of bank network efficiency but in the opposite side of the long-term volatility in this market and in a high level of bank network  efficiency, it can lead to significant reductions. Therefore, by stabilizing the economy (lack of large exchange rate, stock index and housing), it can be expected to improve the efficiency of the country's banking network due to its level and regime.

One of the issues that can be important in policy making perspective is to consider the impact of volatility of assets market in different time periods on different levels of banking network efficiency. Because they may have a different impact on different levels of bank network efficiency as well as different periods of volatility of assets market.

Theoretical corporate finance literature claims that financial policies of firms are related to the nature of their cash flows shocks; that is firms respond to transitory and permanent cash flow shocks differently. However, a limited number of surveys are conducted in the empirical corporate finance literature to verify the validation of these models.  The main objective of this article is to fill this gap.

First of all, the existence of a permanent element in the cash flows of firms will be tested. Secondly, an adjusted filter will be devised to identify short term and permanent shocks to firms' cash flows and estimate their characteristics. This filter is adjusted according to the dynamics of this article's theoretical model. Eventually, the basic axioms of the dynamic framework on which the theoretical model is based, will be analyzed and then the hypotheses will be tested among sample classes.

1- Modeling operational cash flows of companies as a combination of permanent and short-term stochastic processes can eliminate most of the problems assuming a permanent random process governing cash flows. 2- Pieces of evidence show that in firms that are operationally the same, the characteristics of permanent cash flows shock resemble. 3- While it is common to classifying stocks of companies whose sales are not affected by economic cycles as defensive industries, it seems that the separation of short-term and permanent shocks can provide a better criterion or distinguishing tool to interpreting this concept. 4- Firms that are exposed to higher cash flows volatility or experienced a greater permanent growth rate, hold a higher level of cash flows and at any time that they do external financing via equities, issue more stocks.

According to the findings, separating short term and permanent shocks presents a strong intuition to describe firms' financial policies, and most implementations of the theoretical model of this article are acceptable.

Market manipulation remains the biggest concern of investors in today’s securities market. The development of technologies and complex trading algorithms seems to facilitate stock market manipulation and make it inevitable for regulators to use Deep Learning models to prevent manipulation. In this research, a Denoising GAN-based model has been designed. The proposed model (GAN-DAE4) consists of a three-layer encoder along with a 2-dimension encoder as the discriminator and a three-layer decoder as the generator. First, using statistical methods such as sequence, skewness, and kurtosis tests and some unsupervised learning methods such as Contextual Anomaly Detection (CAD) and some visual and graphical methods, the manipulated stocks have been detected in the Tehran Stock Exchange from 2015 to 2020; then GAN-DAE4 and some supervised deep learning models have been applied to the prepared data set. The results show that GAN-DAE4 outperformed other deep learning models (with F2-measure 73.71%) such as Decision Tree (C4.5), Random Forest, Neural Network, and Logistic Regression.

This research has aimed at studying the characteristics and data generation process of TSE index daily return. Applying various tests showed that return data of TSE index follows a chaotic and clustered behavior. Furthermore, beside the condition of efficiency in this market, a novel prediction method is developed. The method introduced in this paper is formed from two consecutive neural networks; a mixture density neural network and a Long short-term memory neural network. It is worthy of note that the proposed method is associated with the inferred statistical structure from the data.  The entire model is compiled in order to predict TSE index considering various number of regimes using daily data December 2008 up to April 2021. Results from various statistical tests rejected the weak form of efficiency and manifested a chaotic behavior in TSE index return. Furthermore, the developed prediction method gained higher accuracy than the same method without considering regimes. Results from Diebold-Mariano test significantly implied the differences of the accuracy between the models with regimes and without regimes. Finally, a back test by considering transaction cost showed that the strategy based on the predicted direction of the model with regimes gains higher return than market benchmark and the model without regimes.

Since Markowitz's (1952) pioneering work on a single-period investment model, mean-variance portfolio optimization problem has become a cornerstone of investment management in both academic and industrial fields. Despite the presence of various theories and methods, the  model continues to be considered in the portfolio selection, because it doesn`t need to estimate other parameters in optimization and computes simply. The objective of this study was to investigate the performance of Model  in the portfolio.

In this paper, various models and methods have been used to select the optimal portfolios and to evaluate the performance of the portfolio. At the end of the paper, the ELECTRE multi-criteria decision-making method has been used to rank the portfolio selection models. Portfolio selection models in this paper include  model, mean-variance model, minimum-variance model and composition of the minimum-variance model and  model. In this paper, various criteria such as Sharpe ratio, Trainer ratio, Modigliani and Modigliani ratio, Sortino ratio, Information ratio have been used to measure portfolio performance.

Relatively, the performance of the  model was better in terms of Sharpe ratio and Modigliani and Modigliani ratio, the performance of the mean-variance model in terms of Trainer ratio, and the performance of the composition of the minimum-variance model and  model in terms of Sortino ratio and Information ratio.

Finally, the ELECTRE multi-criteria decision-making method has been used to rank the portfolio selection models. The results indicate that  model and minimum-variance model is superior to other models.

During the last decade, studies on the factors affecting stock market returns have reached a peak with the advances of financial economics in the field of statistics and mathematics, and modeling is of great importance in this regard. Accordingly, this study seeks to present a new approach to modeling the nonlinear relationship between financial variables and stock returns. This paper employs Bayesian Markov chain Monte Carlo (MCMC) sampling methods for updating the estimates and quantile threshold regression with heteroscedasticity. To study and model this approach, we used returns of the Tehran Stock Exchange, Coin Price, Oil, and Gold Price from 2011 to 2019. The results show that these variables have different effects under low and upper quantile levels. Also, the impact of the financial variables on the stock returns is different under higher and lower threshold amount for each quantile levels. Based on the result, we can say that stock returns have a nonlinear relationship with other markets in the bullish and bearish market.

The issue of portfolio selection has always been considered as one of the key issues in the field of investment. To select optimal portfolios, various models and methods have been represented since the initial presentation of the Markowitz approach. However, finding the most efficient model in portfolio selection has always been the subject of concern. Introducing a new model, called “the composition model of minimum-variance and N/1”, this paper aims to examine the efficiency of three different models of portfolio optimization. For this purpose, the performance of the composition model is compared with the sole minimum-variance model and the sole N/1 model. To evaluate the performance of the portfolios, some criteria such as Sharpe ratio, Trainer ratio, Modigliani and Modigliani ratio, Sortino ratio, and Information ratio have been applied. Finally, the TOPSIS multi-criteria decision-making method for ranking the research models has been used. The results indicate the superiority of the composition model over the two models applied solely.

The study of dynamics and relations between markets has been one of the research subjects. This paper use state space in vector autoregressive moving average model (VARMA) to investigate the effect of gold and crude oil’s price uncertainty on stock returns of the bank. In space-state equation system, the state variable is estimated by the Kalman filter and the specified parameters of the model by the maximum likelihood method. The results showed that gold and crude oil’s price uncertainty has a negative and significant effect on stock returns of the bank and the gold price uncertainty has a major effect on the stock returns of the bank. And furthermore, crude oil’s price uncertainty has a positive and significant effect on gold price uncertainty. In this research, daily OPEC crude oil prices, gold price (Bahar Azadi Coin- Old design) and banks stock index during the period 1390 to 1396-Shahrivar were used.

In this research, we use a pair trading strategy to make a profit in an emerging market. This is a statistical arbitrage strategy used for similar assets with dissimilar valuations. In the present study, smooth transition heteroskedastic models are used with the second-order logistic function for producing thresholds as trading entry and exit signals. For generating upper and lower bounds, we apply the rolling window approach and one-step-ahead quantile forecasting. Markov chain Monte Carlo sampling method is used for optimizing the parameters. Also, passive strategy in the out-of-sample period is used to compare the profits. The population consists of 36 daily stock returns in Tehran Stock Exchange. Then, we select ten pairs from these stocks and use Minimum Square Distance method, and five pairs from one industrial sector. Finally, we see strategy1 and 2 have positive returns in the out-of-sample period, and they produce higher returns than passive strategy.

This paper analyses whether joint probability distribution function of losses due to different exposures covered under the same policy could be modeled in an appropriate manner via mixture distribution proposed and copula concept.
Special type of distribution which is a mixture of Generalized Hyperbolic Skew t distribution and Extreme Value theory (EVT) has been used for modeling marginal distributions of claims and copula function has been considered as a means of modeling dependency structure among claims. Most important copula including; Gaussian, t, Frank, Gumbel and Clayton was tested from goodness of fit point of view.
The data used in this study are the amount of property damage and bodily injury covered under automobile liability insurance.
Results reveal that joint probability distribution of claims could be effectively modeled by Clayton copula and proposed mixture distribution.

In this study we focus on developing a stock trend prediction model based on a modified version of support vector machine, named volume weighted support vector machine, along with a hybrid feature selection method named FSSFS method. In order to evaluate the prediction accuracy of this model we compare the VW-SVM classifier with plain support vector machine along with three commonly used feature selection methods including Information gain, Symmetrical uncertainty and correlation-based feature selection, via paired t-test. As the model input, we use several technical indicators and statistical measures, calculated for 10 stocks. The results show that the VW-SVM, combined with the hybrid feature selection method, significantly outperforms plain SVM model to the problem of stock trend prediction. In addition our experimental result show that VW-SVM combined with F-SSFS has the highest level of accuracies and generalization performance in comparison with the other three feature selection methods. With these results, we claim that VW-SVM combined with F-SSFS can serve as a promising addition to the existing stock trend prediction.

In this study, a prediction model based on support vector machines (SVM) improved by introducing a volume weighted penalty function to the model was introduced to increase the accuracy of forecasting short term trends on the stock market to develop the optimal trading strategy. Along with VW-SVM classifier, a hybrid feature selection method was used that consisted of F-score as the filter part and supported Sequential forward selection as the wrapper part, to select the optimal feature subset. In order to verify the capability of the proposed model in successfully predicting short term trends, a trading strategy was developed. The model input included several technical indicators and statistical measures that were calculated for chosen 10 stocks from Tehran Stock Exchange. The results show that the VW-SVM, combined with the hybrid feature selection method, significantly increases the profitability of the proposed strategy compared to rival strategies, in terms of both overall rate of return and the maximum draw down during trading period.

This paper examines whether combining Generalized Hyperbolic Skew-t distribution, recently introduced in the field of insurance, and Extreme Value Theory (EVT) could result in a modeling of loss function that could model central value as well as extreme value in appropriate manner.
The data used in this study are the amount of property damage and bodily injury covered under automobile liability insurance.
In order to calibrate Generalized Hyperbolic Skew-t distribution, Expectation Maximization (EM) algorithm has been used. For modeling extreme value based on Peak over Threshold approach, the Maximum Likelihood Estimation (MLE) has been applied.
Results reveal that proposed combined distribution could model the losses caused by this type of insurance in a satisfactory manner.

One of the most important issues in financial distress prediction is the selection of predicting variables. The present research in contrast to previous works provides a new approach to feature selection using the classification of financial ratios based on financial semantics and a combination of statistical methods with meta-heuristic algorithms.
To this end, 34 financial ratios based on the audited financial statements are collected for distressed manufacturing companies according to paragraph 141 of Iran Trade Law and healthy companies listed in Tehran stock exchange (TSE) sampled randomly between 2001 and 2011 for one and two years prior to distress. Then, using t-test and genetic algorithm respectively, best ratios from the primary feature set are selected and support vector machine is applied to predict financial distress. The experimental results showed that the proposed HARC method outperforms logistic regression and Altman model significantly for one and two years prior to distress in predicting financial distress.

This paper investigates factors which can discriminate between financial successful companies and unsuccessful companies. Data from all listed companies in TSE for period of 1380-1383 was gathered. Companies which their data had missing value for this period were removed from the sample.Financial success was measured through using two performance appraisal criteria i.e., the Sharp ratio and Jensen's alpha multiplier. 9 different company specific characteristics which have a significant relationship with company’s financial performance according to researches have been done in other countries were used as potential indictors of company financial success. To test the results of research, financial performance of companies was predicted for 1384. In order to quantify and make comparable forecasted performance with actual performance binary logic function was used.The results show that large companies which have a good profitability and no problem with working capital management have better financial performance than rest. In addition results indicate the relatively high power of identified factors in forecasting financial performance of companies.

This paper examines the calendar anomalies in daily return of the Tehran stock market. ARCH and GARCH models are employed to capture the wide range of different calendar anomalies exist in the literature. This study finds the evidence of strong Esfand and Mehr effects in the stock return. In addition, the results show that the stock market return has decreased with the lapses of time. After identifying and removing the calendar effects from daily return, BDS statistic is used to test the presence of any remaining non-linearity in the residuals before employing the GARCH models. The BDS test shows that there is a high probability of the dependency between residuals in spite of removing calendar anomalies. The results confirm that both the ARCH and GARCH models have considerable success in modeling dependencies. Finally, the importance of calendar effects in return forecasting is tested. The conclusion is that the inclusion of calendar effects improves the forecast accuracy. However, simple regression which includes calendar effects has better performance than the GARCH (1, 1) models.

Inflation and its causes are one of the main issues in economics. One of the theories that try to interpret this phenomenon is Quantity Theory of Money. According to this theory, there is pure linear relationship between the amount of liquidity and general level of prices. This article tests the accuracy of this theory in Iran. In addition, the government of Iran has taken the policy of price stabilizing for several years. This article also investigates the effect of this policy on inflation rate. The results show that although the relationship between liquidity and inflation in Iran is not linear, changes in liquidity have a direct effect on inflation. In addition, resultsindicate that there is statistically significant positive relationship betweeninflation uncertainty and inflation rate. So government could control inflationuncertainty through this policy, which in turn lead to decrease in inflation rate.