نشریه تحقیقات مالی
پیاپی 57 (بهار 1399)

Itradayn patterns of trading volume, size, return, and volatility in the Tehran Stock Exchange (TSE) have been investigated using high frequency data from 2008 to 2015.

First, the daily patterns of the four variables namely, trade volume, trade value, price volatility range, and returns in stocks have been investigated using high-frequency data. Then, the role of insider information in formation of the given patterns has been examined.

Our results indicate that trading value and volume follow a J-Shaped pattern , whereas absolute return exhibits an L-Shaped behavior . Our findings are consistent with the existing studies which document an increase in trading value and volume as well as absolute return. However, unlike the existing literature, we do not find a U-Shaped pattern in returns, and no statistically significant difference in returns is found throughout the trading hours. To examine the behavior of informed traders, we examine midsize trades and, consistent with the predictions of Barclay and Warner’s (1993) stealth-trading hypothesis, we find that they have higher price impact compared to other trade sizes. However, our findings do not support the intraday stealth trading pattern, as insiders prefer to trade in low and medium trade size to avoid revealing their information. This may be due to the low liquidity of the TSE.

The findings of this study may show policymakers and planners in the Iranian capital market that halts in the stock market can directly affect traders and increase their transaction risks. Meanwhile, traders who are planning to buy or sell stock in large volumes can use the patterns provided in this paper to understand when the price effect is at its lowest point, and this can help them execute their transactions more efficiently.

After the financial crisis in 2008, market practitioners and financial researchers began to attach more importance to risk measurement and modeling. Expected shortfall is recognized risk measures in financial literature.

By the estimation of expected shortfall as a coherent risk measure, and by use of conditional extreme value theory and combining new volatility measures, this research attempts to introduce a new model for risk measurement. Intraday data has been used in this research in order to estimate mentioned risk measures.

The results show that in comparison with alternative models, such as GARCH conditional peak over threshold models, multifractal conditional peak over threshold models, which utilize intraday data, perform better in risk estimation. In addition, the use of extreme value theory brings about more favorable results in risk estimation. In this research, we use a new back-testing models in order to back-test expected shortfall.

The use of the normal distribution function for the disruption components to estimate the expected drop has not been successful, and has led to an estimate of the low risk category. The use of Student's t-distribution in estimating risk measures has been acceptable, although in some cases it has led to an estimate of high risk. Considering extreme value theory of value in the above models has in most cases led to improved model performance. This means that it has moderately adjusted the estimates of the upper hand and the estimates of the.

Ensuring fair and equitable treatment of shareholders can pave the way for an efficient, fair and transparent market. The purpose of this study is to examine the adequacy and implementation of the regulations related to fair and equitable transactions between shareholders and issuers.

In this study, principle 17 of IOSCO Objectives and Principles of Securities Regulationon Fair and Equitable Treatment of Shareholders are reviewed, and the analysis showed that the Iran-US regulatory framework is in line with the standards of this principle. In this process, the weaknesses of the existing regulations appear. In the following, with a quantitative approach, using the questionnaire tool and designing the hypothesis test, the extent of implementation of the existing rules and regulations has been investigated.

The three key themes highlighted in the IOSCO document are: fundamental rights of shareholders, reporting of transactions affecting the control and reporting of inside information holders. The most important structural weakness of the regulations in this field, is neglecting the transactions affecting the control of the company. While US regulations have precise requirements for tracking and reporting such transactions. In general, the extent of disclosure in US is more than Iran.

In implementing the existing laws and regulations, in the area of ​​fundamental rights of shareholders, except for the payment of dividend, implementation of laws and regulations was approved in other sectors. However, there were serious problems in the reporting of insiders, and none of these hypotheses were confirmed with the relevant sub-assumptions.

Investors are always trying to predict stock prices in order to earn returns proportional to risk. One way to predict price is using candlestick charting which is common among analysts mainly due to its simplicity. So in this research we examine its profitability in different conditions.

For this purpose, daily stock price data of companies listed in Tehran Stock Exchange during 15 years from October 2003 to October 2018is used to calculate returns and winning rates of investment based on thirteen different candlestick charts in two horizon times of one day and ten days in different conditions of uptrends and downtrends for different turnover values.

The research findings show that regardless of the trend or trading turnover, the sell candlestick, when holding for one day, and the buy candlestick, when holding for ten days, have the highest returns (more than transaction costs). By examining candlesticks in uptrends and downtrends their performance will improve. Buy candlestick in uptrends for companies with medium turnover value has the best return when holding for ten days if we consider the trading turnover value. The results of the research can also be considered as a recommendation for adopting a short-term outlook in the bearish periods.

Based on the findings, investors can use the mentioned patterns to gain returns in the Tehran Stock Exchange based on their preferred time horizon, as well as ascending and descending conditions among the companies with different trading turnover value.

The commodities especially oil, wheat and iron have key role in economy because of they are the main components of many common goods in human lives. An increases or a decrease in the commodity prices affects the economies all over the world. Despite the importance of commodity prices, only few studies have emphasized their impact on stock prices. This study contributes to the empirical literature about the relationships between stock and commodity markets. Given that the Tehran Stock Exchange is a commodity-based market, the purpose of this study is to investigate the effect of world commodity price on Tehran stock returns.

In this study, the monthly data of stock market during the period of 2009–2019 were used by applying Markov switching model with time-varying transition probabilities (MS-TVTP).

Based on the results, model MSIH (2)-AR (1) has been chosen as the optimal model. In the estimated model, the first regime determines the lower stock return and the second regime determines the higher stock return, and transmission probabilities in two models represent the persistence of first regime in the stockmarket of Iran. In addition, results show that one percent increase in commodity price will lead to 0.343 percent increase in stock return, but in the higher Stock return regime, lead to a 1.133 percent increase in stock returns. In this regard, the inequality of the two coefficients in the two regimes has confirmed by the Wald test. Also, expected duration in lower stock return regime is about 12 months and in higherstock regime is about 6 months.

This study illustrates the asymmetric effect of commodity priceon stockreturn in various regimes in Iran. It indicates that lower stock return regime is more stationary. Therefore, this study proposes to use the commodity price index as a warning indicator of a change in the stock return regime for investors.

The main objective of this paper is to identify the factors affecting the business cycle in Iran through analyzing financial market indicators including, money supply, loans and deposits of banks (from the money market), and the stock price index (from the capital market).

First, using Hodrick Prescott filter, we extract the business cycles. Then, Markov Switching model estimates the optimal interrupt, and then revealed facts regarding the business cycle, including the momentum indicators, their relative variability, and their stability throughout the cycles between the variables of the financial market are compared. We used the Johansson coincidence test to recognize co integration. Finally, the model estimation is performed using Vector Error Correction Model (VECM).

The evaluated indices, regarding various facts about business cycles, show that money supply and loans are the two variables that can result in the mentioned cycles. The Johansen test and the Wald test respectively confirm the relation between the variables in both the long-term and short-term. Meanwhile, the variance analysis table shows that money supply and loans, each respectively cause, 13% and 9% of business cycles' fluctuations.

The results show that the stock market does not affect business cycles and its fluctuations. Meanwhile, the estimated error correction term in the model for the money market and business cycle variables is -0.55. This shows that every year 55% of imbalances present in the aforementioned relations are corrected in the following year. Hence, the equilibrium quickly moves towards a long-term equilibrium.

The purpose of this research is to explain the price of share using performance variables, management system and audit committee, as well as forecasting the price of share to help decision makers and investors.

For this purpose, the information about 208 listed companies in Tehran Stock Exchange, during 2010-2017, have been extracted and analyzed using the linear and nonlinear method of Gaussian process.

Preliminary research results show that performance criteria are more capable of explaining stock prices compared to governance criteria. The results also indicate the high power of machine learning methods for predicting the price of companies' stocks. The latter is especially true for the nonlinear Gaussian process, which has also proven to perform better than the CART or the lasso algorithms.

Since corporate governance and the audit committee are two governing bodies of the stock exchange in Iran, and the stock market participants are not aware of how they operate, it seems that the performance information content is better for investors than governance measurements. Among the non-financial criteria, ownership concentration is the only factor that is able to explain the price of share of the company, and it can be argued that major shareholders have the motivation and ability to supervise the manager and increase the company's performance.