FINANCIAL RESEARCH
Year:2016 | Volume:18 | Issue:1 |Papers Count:14

The Purpose of this research is to evaluate the ability of mutual funds investors to forecast the performance of mutual funds and selecting the best performed funds. It should be mentioned that both individual and institutional investors have been evaluated in this study. In order to answer the question whether the investors have the ability to forecast the performance of mutual funds or not, we have gathered 40 Iranian mutual funds data that are founded and active in Tehran stock exchange market. Time period of this assessment data is 36 months, from the beginning of March 2011 to the end of March 2014. We have constructed two portfolios of new money. The first portfolio consists of all funds with a positive net cash flow. The second portfolio comprises all funds with a negative net cash flow. Next, we estimate the performance of each of the portfolios using both the Fama-French’s (1993) model and the Carhart’s (1997) model including a momentum factor. The result of using the mentioned models showed that investors are unable to select the appropriate fund, also there is not a significant difference between the ability of individual and institutional investors for selecting the best performed funds.

The main purpose of this study, is surveying the factors that affect Price Impact of block Trades in the stock market. For this reason, the sample consisted of 525 block trades have been selected randomly of accepted companies in Tehran Stock Exchange, that have block trade during the period 1390 to 1392. In this paper, total, temporary and permanent price impact is used as dependent variables, and the size of block trade, stock price volatility, trading turnover, market return, momentum and Bid-Ask Spread are used as explanatory variables. The results show that the relationship between turn over, Market return and Bid-Ask Spread with three dependent variable (total, temporary and permanent price impact) are significant. Also the relationship between size of block trade with total, permanent price impact, the relationship between Volatility of stock price with total, temporary price impact and, the relationship between momentum with temporary price impact are significant.

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.

This article investigates fluctuations in stocks prices at Tehran Stock Exchange, assuming that investors' utility stems from fluctuations in value of stocks as well as consumption. Thus, the two behavioral phenomena discussed in Prospect theory, i. e. Loss aversion and House money effect, were factored into Consumption-based Asset Pricing Model and Investor's Utility Function, which takes into account utility both from consumption and financial investments. Price equations were defined in the two economic environments based on Lucas Theory (1978). The processes of consumption and dividend are equal in the first economy but different in the second economy. Next the P/D ratio was simulated in both economies and then compared with real market data. Utilizing ANOVA and K-Means, it became clear that the mean and standard deviation in the second economy are closer to real market data than those of the first economy. It, therefore, can be concluded that the second economy provides a more accurate estimation of the P/D ratio, implying that the aforementioned behavioral phenomena do in fact exist in the market and affect investors' pricing of stocks.

This paper takes fundamental analysis research beyond the spatial and temporal bounds of previous studies. It was investigated that how detailed financial statement data enter the decisions of market makers by examining how current changes in the fundamental signals chosen can provide information on subsequent earnings changes. Using Tehran Stock Exchange’s firms data from 2003 to 2012, the role of fundamental signals for prediction of future earnings changes was tested. In addition, the influence of contextual factors including prior earnings news and macroeconomic variables (GDP growth and inflation) on the relationship between fundamental signals and future earnings changes was investigated. Results indicate that the fundamental signals are significant predictors of short-term but not long-term future earnings changes. Moreover, prior earnings news doesn’t affect earnings predictability, but this predictability is significantly higher in lower GDP and higher inflation periods. In addition to corroborating prior research with fundamental analysis, this research adds to what has previously been conducted and extends investigations with examination of the predictive link between the fundamental signals and future earnings change by introducing prior earnings news and macroeconomic conditions as contextual factors in order to look for a possible effect which has not been investigated in prior research.

In this study, provided preliminary evidence on a comprehensive program of study focusing on investors’ behavior in the Tehran Stock Exchange (TSE) in 2014. Using a survey-based methodology, 561 investors from the TSE representing individual investors, mutual fund experts, funding institutions experts, institutional investors, and stock brokers have been randomly selected to respond to a set of behavioral and economic questions recommended by the previous research in the field of behavioral finance. Factor loading from the collected data reveal that both behavioral and economic variables appear to influence investors’ decisions in the TSE. In the further to be shared new theoretical and econometric insights that is currently being amended preliminary evidence including dis-aggregating the sample by investors’ type (individual vs. institutional investors) as well as using structural equation modeling to test the statistical significance of the proposed variables and their differences across samples.

This research aims to investigate relationship between skewness and future stock return and the impact of information releases on this relationship. Then, the data of 89 companies listed in Tehran Stock Exchange are collected and analyzed for 2009 to 2013. Furthermore, the fixed effect panel data method is used to examine the hypothesis. The results show that there exists a negative and significant relationship between skewness and future stock return. Moreover, the earnings announcements have high impact on the relationship between skewness and stock return. Hence, skewness is negatively related to stock return when there are no earnings announcements, while this relationship loses its significance when there are such information releases.

The credit portfolio management and the optimal credit portfolio selection are identified as one of the most effective factors in banks’ credit risk. Two main strategies in this regard include diversification versus concentration. In this study, at first, the status of diversification of Iran’s banking sector is analyzed, then the relationship between diversification of the credit portfolio and credit risk is considered. It should be noted that in order to measure credit portfolio diversification two set of naive indexes and benchmark (distance) indexes is used. The empirical evidences have obtained through a panel data analysis in a sample of 16 Iranian banks during 2006 to 2011 time period. The findings indicate that increase in credit portfolio diversification lead to increment in bank’s credit risk and this relationship is independent of the type of bank ownership.

With regard to the Basel Committee’s emphasis on the necessity of using 10-day Value-at-Risk (VaR) internal models in order to determine minimum market risk capital requirements, and downsides of the square-root-of-time rule, our purpose is to produce more accurate forecasts of the multi-period VaR using sixteen models for three stock indices, the TEPIX, NASDAQ, and FTSE. The results, based on the sum of the loss function and efficiency criteria indicate that the bootstrapped historical simulation (BHS) model performs the best for the TEPIX. Also, at the 95% confidence level the parametric EGARCH model with the Student’s t innovation and at the 99% and 99.5% confidence levels the EGARCH model with the normal innovation clearly outperform other models in estimating the 5-day VaR for both the NASDAQ and FTSE indices. In addition, our findings indicate that the best model based on the conditional coverage test is not necessarily the most economical model in estimating the 5-day VaR.

To achieve the optimal model for capital asset pricing has always been a central issue in studies of the financial field. In this study we consider Fama and French three-factor model augmented by the Pastor and Stambaugh (2003) liquidity risk factor. Unlike most previous studies in this model, stock level beta is allowed to vary with firm-level size and book-to-market value. To verify the above mentioned model, risk-adjusted returns calculated using the model and its relationship with financial market anomalies are examined. The examined anomalies in this study are: firm size, ratio of book value to market value, stock turnover ratio and the past returns. Using individual daily and monthly returns of sample’s companies of Tehran Stock Exchange and Securities for the period 1380 to 1393, we find that all considered anomalies can be captured by this model.