جستجوی مقالات مرتبط با کلیدواژه « اطلاعات خصوصی » در نشریات گروه « اقتصاد »

In efficient markets, all agents are rational, and new information becomes immediately available to everyone, resulting in instantaneous price adjustments. However, in the real world, market participants differ significantly in both their access to information and their interpretation of it, leading to information asymmetry. Accurately determining the level of information asymmetry is crucial for traders to make appropriate decisions regarding asset selection, timing, risk level redefinition, and required rate of return. It is also essential for regulators to achieve designing a well-functioning market. In this paper, we estimate the level of information asymmetry using 1,260 stocks/quarters data (140 companies across 10 industries, constituting 64% of Iran's stock market value, during 1400-Q1 to 1402-Q1) by employing two prominent models, PIN and MPIN. The results indicate that, first, the probability of private information in the Iranian stock market is much higher than in other countires. Second, the highest level of information asymmetry is observed in the "Agriculture" industry, conversely, the "Petroleum Products" and "Chemical" industries exhibit the lowest MPIN values. Third, at the firm-specific level, "ZMLRD" from the Agriculture industry exhibits the highest information asymmetry, while "ARYA" from the Chemical industry shows the lowest level of information asymmetry. The findings imply that industries/companies with low market shares that produce more heterogeneous and diverse products experience higher information asymmetry. In contrast, Petro-Chimical industries/companies, with high market shares that produce relatively more homogeneous products, have less information asymmetry.

This study aims to introduce the microstructure approach to the exchange rate as the 4th generation of exchange rate models and to apply it in a simulation model to study the effects of transparency of macroeconomic data on exchange rate fluctuations. The microstructure approach to the exchange rate is developed to include decentralized and multi-layer structure of currency markets along with information complexities in this market and the role of trading mechanisms in exchange rate determination. After introducing this approach, we have developed our theoretical model to use it for simulation. In this simulation, we have studied the effects of transparency of macro data release on exchange rate fluctuation. To achieve this goal, we have used two variables of “delay in macro data release” and “error in macro data release”. Our simulation results show that an increase in macro data release delay leads to higher volatility of The exchange rate. This is because of increasing uncertainty for economic agents. In addition, an increase in macro data release delay leads to a farther delay in responsiveness of the exchange rate to movements in its macro fundamental variables. Although we have found a non-linear relationship between  the “error in macro data release” variable and exchange rate volatility, the magnitude of this effect is less than the effect of the “delay in macro data release” variable on exchange rate volatility. Based on our results, we recommend that to have lower exchange rate volatility, authorities should increase the transparency of macroeconomic data releases and especially they should lower the delay in macro data releases.

The aim of this study is to investigate the dynamics of information risk at the Tehran Stock Exchange (TSE). We estimated the daily probability of information based trade (PIN) for 22 stocks from 11 different industries of TSE over 4 years. The total average of the daily PIN for all stocks was 27% from 2013 to 2016. The lowest and the highest average of PIN estimates for individual stocks are 20.2% and 39.4%, respectively. In this research, the lowest and the highest daily PIN for individual stocks are estimated as 1.2% and 93.3%, respectively, which indicate that information risk varies substantially along time and there is a substantial need to use dynamic models to study this risk. Generally, it seems that the average and the maximum of information risk at TSE are much higher than in developed markets. Results showed that petrochemical and metal industries benefit from the lowest information risk and the highest is recorded for insurance and cement industries