INVESTMENT KNOWLEDGE
Year:2012 | Volume:1 | Issue:2|Papers Count:11

This study investigates the effect of institutional investors on stock price crash risk reduction in companies listed in Tehran Stock Exchange. Crash of stock price is a phenomenon where stock price will suffer an unexpected severe negative adjustment (Chen, J., H. Hong, et al, 2001). One of the most important methods for manipulating accounting information is expediting the identification of good news versus postponing bad news in profits (Callen, J. L. and X. Fang, 2011).But always there will be a final level for accumulating bad news in the company, and with achieving the final level, this bad news will be published, which is that the company’s stock price crash.In this study, the numbers of stock prices crash of Hutton’s model (2009) is used to measure stock prices crash for 56 companies listed in Tehran Stock Exchange from period of 1380 to 1389. In the present study strong evidences are provided that the existence of institutional investors will significantly reduce the probability of the stock price crash probability.

This paper propounds to examine the day of the week effect on the returns of daily stock price entire index, in Tehran Stock Exchange market during 1383 to 1388 and 1389. Various approaches have been presented for investigation about calendar effects on stock returns. We apply "Least Mean Square (LMS) Algorithm Regression". In fact, Least Mean Square (LMS) Algorithm Regression avoids the classification of dummy variables to values of one and zero, as we do in the traditional statistical and econometric methodology. The paper concludes that during 1383 to 1388 will lead to a positive effect on the returns on Sunday and in the course of 1389, there is no efficiency significant.

This research has been done with the aim of identification of effective factors which influence credit risk and designing model for estimating credit Rating of the companies which have borrowed from a commercial Bank in the one-year period by using Data Envelopment Analysis and neural network model and comparison of these two models. For this purpose the necessary sample data on financial and non-financial information of 146 companies (as random simple) was selected. In this research, 27 explanatory variables (include financial and non-financial variables) were obtained, by application of factor analysis and Delphi method for examination. Finally 8 variables which had significant effect on credit risk were selected and entered to DEA model. Efficiency of companies was calculated with these variables. Also variables as well as the input vector three-layer perceptron neural network models were added to the model. finally data was processes with logistic regression. Results from data envelopment analysis model and Neural network and Logistic regression in comparisons to the actual results obtained from neural network models to predict credit risk legal customers and credit rating suggest that neural network is more efficient than data envelopment analysis and logistic regression.

This article examines the forecast performance of ARFIMA and ARIMA models using data on daily stock price index of Tehran in period 25/11/2001 to 30/11/2011. To estimate the d parameter and other parameters, the NLS method in the software package Oxmetric / pcgive was used. After comparing the results of research models, ARFIMA models based on AIC, the model was found superior in modeling TEPIX. Also we use naive methods for estimating the prediction. Comparing the accuracy of the prediction models by criteria such as MAPFE and RMSFE and confidence intervals of the real values, we can deduce that the first Performance difference between the predicted long-term memory ARFIMA model is very minor compared to the ARIMA model And Secondly, inefficient ARFIMA model in Tehran capital market forecast is quite evident.

This paper examines the institutional investors effect on Company Value Based profit management perspective. The paper result show that companies group in growth will significantly to profit management.

One of the main criteria for deciding on stock exchange market is stock return. Stock return includes information content and most actual and potential investors in financial analysis and forecasts use it. Many studies have been conducted about the relationship between risk and return. The three-factor model of Fama and French is one of the most important models. In this paper we consider Fama and French three-factor model augmented by the Pastor and Stambaugh (2003) market liquidity risk factor. Then it is compared with three-factor model of Fama and French. Unlike most previous studies, in this model, stock level beta is allowed to vary with firm-level size and book-to-market ratio. We use monthly time series data for sample companies of Tehran stock exchange market for the period 1380 to 1389, and evaluate them by using Eviews software by panel method. The results show the effects of excess market return, firm size and book-to-market ratio is significant and the effect of market liquidity factor is insignificant. Also the market beta is only a function of size variable. Finally we find that market liquidity factor of Pastor and Stambaugh and the time variation in the market beta increase the explanatory power of Fama and French three-factor model.

The mutual funds industry has recently grown drastically and is now considered as an alternative channel investment in financial markets. Many of these funds are relatively risky in that for higher returns they aim to select growth companies which are expected to post large stock price increases. There are many traditional indices to assess the relationship between returns and risks in financial economics. The main drawback of these indices is being one-dimensional. This paper is to introduce and apply a new multi-criteria method based upon multi-directional inefficiency analysis to assess the relationship between return and risk and to measure risk excesses of a sample of Iranian mutual funds in the year 2010. The findings of this study show that the sampled founds can acquire the same returns by merely accepting 66 and 64 per cent of the received unsystematic and systematic risks, respectively.

The main purpose of this article is to analyze the effects of the capital structure on the cumulative abnormal returns to reach such a goal we select information of 44 firms in the years 1376-1386. Multiple regressions (stepwise method) were used to test the hypothesis and their meaningfulness was reached by using t and F statistics. On the other hand, the Durbin-Watson test was applied to examine the autocorrelation of the model. The results showed that: Leverage doesn’t have a meaningful impact on the cumulative abnormal returns; Systematic risk, P/E ratio, BM ratio and the size of the firm would significantly affect the cumulative abnormal returns.