Abstract: This paper highlights the empirical results of analyzing the correlation between accounting information and systematic risk. This association is analyzed among financial ratios and systematic risk by considering the financial statement of 39 companies listed on the Tehran Stock Exchange (TSE) for five years (2014-2018). Financial ratios have been categorized into four groups and to describe the special features, as representative of accounting information we selected: Return on Asset (ROA), Debt Ratio (Total Debt to Total Asset), Current Ratio (current assets to current debt), Asset Turnover (Net sales to Total assets), and Total Assets. The hypotheses were tested through simple and multiple linear regression and T-student test. The findings illustrate that there is no significant relationship between accounting information and market risk. This indicates that in the selected sample, historical accounting information does not fully reflect the price of stocks.
Abstract: The determinants of Earnings Response Coefficient (ERC), including firm size, earnings growth, and earnings persistence are studied in this research. These determinants are supposed to be moderator variables that affect ERC and Return Response Coefficient. The research sample contains 82 Iranian listed companies in Tehran Stock Exchange (TSE) from 2001 to 2012. Gathered data have been processed by EVIEWS Software. Results show a significant positive relation between firm size and ERC, and also between earnings growth and ERC; however, there is no significant relation between earnings persistence and ERC. Also, the results show that ERC will be increased by firm size and earnings growth, but there is no relation between earnings persistence and ERC.
Abstract: One of the most important roles of financial reporting is improving the firms’ investment decisions; however, there is not much supporting evidence for this claim in emerging markets like Iran. In this study, the effect of financial reporting transparency in investment efficiency of Iranian firms has been investigated. In order to do this, 336 listed companies on Tehran Stock Exchange (TSE) has been selected for time period 2012 to 2015 as research sample. For testing our main hypothesis, we classified sample firms into two groups based on their deviation from expected investment: under-investment and over-investment cases. The results indicate that there is positive significant relationship between financial transparency and investment efficiency. In the other words, transparency can mitigate both underinvestment and overinvestment situations.
Abstract: In this paper, a hybrid expert system is developed by using fuzzy genetic network programming with reinforcement learning (GNP-RL). In this system, the frame-based structure of the system uses the trading rules extracted by GNP. These rules are extracted by using technical indices of the stock prices in the training time period. For developing this system, we applied fuzzy node transition and decision making in both processing and judgment nodes of GNP-RL. Consequently, using these method not only did increase the accuracy of node transition and decision making in GNP's nodes, but also extended the GNP's binary signals to ternary trading signals. In the other words, in our proposed Fuzzy GNP-RL model, a No Trade signal is added to conventional Buy or Sell signals. Finally, the obtained rules are used in a frame-based system implemented in Kappa-PC software. This developed trading system has been used to generate trading signals for ten companies listed in Tehran Stock Exchange (TSE). The simulation results in the testing time period shows that the developed system has more favorable performance in comparison with the Buy and Hold strategy.
Abstract: Intellectual capital is one of the most valuable and
important parts of the intangible assets of enterprises especially in
knowledge-based enterprises. With respect to increasing gap between
the market value and the book value of the companies, intellectual
capital is one of the components that can be placed in this gap. This
paper uses the value added efficiency of the three components,
capital employed, human capital and structural capital, to measure the
intellectual capital efficiency of Iranian industries groups, listed in
the Tehran Stock Exchange (TSE), using a 8 years period data set
from 2005 to 2012. In order to analyze the effect of intellectual
capital on the market-to-book value ratio of the companies, the data
set was divided into 10 industries, Banking, Pharmaceutical, Metals
& Mineral Nonmetallic, Food, Computer, Building, Investments,
Chemical, Cement and Automotive, and the panel data method was
applied to estimating pooled OLS. The results exhibited that value
added of capital employed has a positive significant relation with
increasing market value in the industries, Banking, Metals & Mineral
Nonmetallic, Food, Computer, Chemical and Cement, and also,
showed that value added efficiency of structural capital has a positive
significant relation with increasing market value in the Banking,
Pharmaceutical and Computer industries groups. The results of the
value added showed a negative relation with the Banking and
Pharmaceutical industries groups and a positive relation with
computer and Automotive industries groups. Among the studied
industries, computer industry has placed the widest gap between the
market value and book value in its intellectual capital.
Abstract: Predicting earnings management is vital for the capital
market participants, financial analysts and managers. The aim of this
research is attempting to respond to this query: Is there a significant
difference between the regression model and neural networks’
models in predicting earnings management, and which one leads to a
superior prediction of it? In approaching this question, a Linear
Regression (LR) model was compared with two neural networks
including Multi-Layer Perceptron (MLP), and Generalized
Regression Neural Network (GRNN). The population of this study
includes 94 listed companies in Tehran Stock Exchange (TSE)
market from 2003 to 2011. After the results of all models were
acquired, ANOVA was exerted to test the hypotheses. In general, the
summary of statistical results showed that the precision of GRNN did
not exhibit a significant difference in comparison with MLP. In
addition, the mean square error of the MLP and GRNN showed a
significant difference with the multi variable LR model. These
findings support the notion of nonlinear behavior of the earnings
management. Therefore, it is more appropriate for capital market
participants to analyze earnings management based upon neural
networks techniques, and not to adopt linear regression models.
Abstract: This study aims to investigate empirically the valuerelevance
of accounting information to domestic investors in Tehran
stock exchange from 1999 to 2006. During the present research
impacts of two factors, including positive vs. negative earnings and
the firm size are considered as well. The authors used earnings per
share and annual change of earnings per share as the income
statement indices, and book value of equity per share as the balance
sheet index. Return and Price models through regression analysis are
deployed in order to test the research hypothesis. Results depicted
that accounting information is value-relevance to domestic investors
in Tehran Stock Exchange according to both studied models.
However, income statement information has more value-relevance
than the balance sheet information. Furthermore, positive vs. negative
earnings and firm size seems to have significant impact on valuerelevance
of accounting information.
Abstract: The aim of this study was to examine and identify the
type of Iranian financial market in terms of being symmetrical or
asymmetrical and to measure relationship between type of market
and the market's indices. In this study, daily information on the
market-s Share Price Index, Industrial Index and Top Fifty Most
Active Companies during the years 1999-2010 has been used. In
addition, to determine type of the financial market, rate of return on
Security is taken into account. In this research, by using logistic
regression analysis methods, relationship of the market type with the
above mentioned indices have been examined. The results showed
that the type of the financial market has a positive significant
association with market share price index and Industrial Index. Index
of Top Fifty Most Active Companies is significantly associated with
type of financial market, however this relationship is inverse.
Abstract: The role of corporate governance is to reduce the
divergence of interests between shareholders and managers. The role
of corporate governance is more useful when managers have an
incentive to deviate from shareholders- interests. One example of
management-s deviation from shareholders- interests is the
management of earnings through the use of accounting accruals. This
paper examines the association between corporate governance
internal mechanisms ownership concentration, board independence,
the existence of CEO-Chairman duality and earnings management.
Firm size and leverage are control variables. The population used in
this study comprises firms listed on the Tehran Stock Exchange
(TSE) between 2004 and 2008, the sample comprises 196 firms.
Panel Data method is employed as technique to estimate the model.
We find that there is negative significant association between
ownership concentration and board independence manage earnings
with earnings management, there is negative significant association
between the existence of CEO-Chairman duality and earnings
management. This study also found a positive significant association
between control variable (firm size and leverage) and earnings
management.
Abstract: In this research, the researchers have managed to
design a model to investigate the current trend of stock price of the
"IRAN KHODRO corporation" at Tehran Stock Exchange by
utilizing an Adaptive Neuro - Fuzzy Inference system. For the Longterm
Period, a Neuro-Fuzzy with two Triangular membership
functions and four independent Variables including trade volume,
Dividend Per Share (DPS), Price to Earning Ratio (P/E), and also
closing Price and Stock Price fluctuation as an dependent variable are
selected as an optimal model. For the short-term Period, a neureo –
fuzzy model with two triangular membership functions for the first
quarter of a year, two trapezoidal membership functions for the
Second quarter of a year, two Gaussian combination membership
functions for the third quarter of a year and two trapezoidal
membership functions for the fourth quarter of a year were selected
as an optimal model for the stock price forecasting. In addition, three
independent variables including trade volume, price to earning ratio,
closing Stock Price and a dependent variable of stock price
fluctuation were selected as an optimal model. The findings of the
research demonstrate that the trend of stock price could be forecasted
with the lower level of error.
Abstract: Recent evidences on liquidity and valuation of securities in the capital markets clearly show the importance of stock market liquidity and valuation of firms. In this paper, relationship between transparency, liquidity, and valuation is studied by using data obtained from 70 companies listed in Tehran Stock Exchange during2003-2012. In this study, discriminatory earnings management, as a sign of lack of transparency and Tobin's Q, was used as the criteria of valuation. The results indicate that there is a significant and reversed relationship between earnings management and liquidity. On the other hand, there is a relationship between liquidity and transparency.The results also indicate a significant relationship between transparency and valuation. Transparency has an indirect effect on firm valuation alone or through the liquidity channel. Although the effect of transparency on the value of a firm was reduced by adding the variable of liquidity, the cumulative effect of transparency and liquidity increased.
Abstract: The aim of the article is extending and developing
econometrics and network structure based methods which are able to
distinguish price manipulation in Tehran stock exchange. The
principal goal of the present study is to offer model for
approximating price manipulation in Tehran stock exchange. In order
to do so by applying separation method a sample consisting of 397
companies accepted at Tehran stock exchange were selected and
information related to their price and volume of trades during years
2001 until 2009 were collected and then through performing runs
test, skewness test and duration correlative test the selected
companies were divided into 2 sets of manipulated and non
manipulated companies. In the next stage by investigating
cumulative return process and volume of trades in manipulated
companies, the date of starting price manipulation was specified and
in this way the logit model, artificial neural network, multiple
discriminant analysis and by using information related to size of
company, clarity of information, ratio of P/E and liquidity of stock
one year prior price manipulation; a model for forecasting price
manipulation of stocks of companies present in Tehran stock
exchange were designed. At the end the power of forecasting models
were studied by using data of test set. Whereas the power of
forecasting logit model for test set was 92.1%, for artificial neural
network was 94.1% and multi audit analysis model was 90.2%;
therefore all of the 3 aforesaid models has high power to forecast
price manipulation and there is no considerable difference among
forecasting power of these 3 models.