جستجوی مقالات مرتبط با کلیدواژه "ریسک نقدشوندگی" در نشریات گروه "مالی"

Corporate financial policies are used as tools to maximize shareholders’ wealth. Liquidity risk is one of the main systematic risks affecting the equity cost of capital. This study is to investigate the relationship between dividends and the components of systematic risk of liquidity to discover the influence on the named risk.
 
In this study, the Amihud measure of liquidity and the liquidity-adjusted capital asset pricing model were used to measure and decompose the components of liquidity risk. The decomposed measures of liquidity risk included 1) the covariance of stock illiquidity and market illiquidity, 2) the covariance of stock returns and market illiquidity, and 3) the covariance of stock illiquidity and Market returns.
 
Findings showed that, according to the theoretical expectation, the covariance of stock returns and market illiquidity and the covariance of stock illiquidity and market return increase when managers distribute more dividends. The results also indicated that contrary to the theoretical expectations, the covariance of stock illiquidity and market illiquidity increases by dividend distribution. In addition, according to the findings, dividends can be used to manage the liquidity risk of equity leading to lower costs of capital and higher shareholders’ wealth. Mismanagement of this type of risk can destroy the liquidity risk effect of dividends i.e. dividends should be used to affect the appropriate channel of dividend risk or appropriate covariance. Based on the results of this study, Under conditions of low liquidity or market recession, dividend growth can boost the stock return and enhance liquidity to some extent. However, in an illiquid market, despite its positive impact on stock returns, dividend growth cannot significantly increase the liquidity of the stocks.
 
The dividend policy of the previous year can reduce the comprehensive measure of liquidity risk for the next one and thereby reduce the expected return of investors. Since the comprehensive measure is multi-dimensional, to investigate separately the effect of dividend distribution on dimensions of liquidity risk, , , and  were investigated as components of liquidity beta. The results showed that dividends have a positive effect on the covariance of stock returns with market illiquidity ( ) and the covariance of stock illiquidity with market returns ( ), but contrary to theoretical expectations, they positively impact the covariance of stock illiquidity with market illiquidity ( ). Therefore, the channel of the effect of dividends on liquidity risk is  and . Indeed, within a market characterized by high illiquidity, companies offering generous dividends can potentially achieve higher returns. i.e., the covariance of stock returns and market illiquidity increases by dividend growth, and the risk component related to  will be decreased. During a market recession, companies that offer high dividends tend to encounter reduced illiquidity. This means that the covariance between the stock's illiquidity and the market return increases, leading to a decrease in the risk component associated with β^4. However, the results proved that in a market with high illiquidity, companies with high dividends cannot experience less illiquidity and reduce the risk dimension related to  liquidity.

The return of assets and various investment opportunities are two of the most basic principles of optimal allocation of resources in the economy. Liquidity is one of the most important factors affecting the risk and expected return of assets, and liquidity risks arise from it. The main purpose of this study is to determine the effect of different liquidity risks on the stock returns using the LCAPM model with the most optimal illiquidity measure. To test the hypotheses of research, a sample of 167 companies listed on the Tehran Stock Exchange during the years 1389 to 1398 has been selected. In order to select the optimal illiquidity measure from among six different measures, Principal Component Analysis approach is used and PSpread measure has been chosen. For estimating the model, the Generalized Method of Moments method has been used. The results of this study show that investors price liquidity risks in bearish markets in addition to the illiquidity effect, however, they do not pay much attention to these risks in the bullish markets.

The present study aimed to investigate the effect of liquidity risk on the SEO timing.. This study tries to evaluate the effect of liquidity risk on the timing of the seasoned equity offering based on Tehran Stock Exchange data during the period 2012-2018 and the use of the data panel’s regression models. In order to achieve the research goals of 71 firms listed in the Tehran Stock Exchange, which had Capital increase, they were analyzed as prototype before and after season equity offering.the results of the research show that there is a negative and significant relationship between liquidity risk and the SEO timing. Also, by examining the periods before and after the seasoned equity offering, it was found that the liquidity risk index was three years ago, one year ago and in the second year of supply, there is a significant relationship with stock pricing Therefore, the hypothesis Timing is confirmed, but the liquidity risk index has not changed significantly after the seasoned equity offering.

This research aims to model and present an appropriate pattern in order to forecast systemic liquidity risk of corporate stocks in capital market of Iran. For this purpose, 486 listed companies in Tehran stock exchange and OTC from 2011 to 2020 were sampled and then the companies were divided into four groups (portfolios) according to combination of indicators and types of activites of companies. Then by using types of multivariate GARCH models and comparing them, finanlly the VAR(1)-DBEKK(1,2) was selected as an optimum pattern . The results of research showed significant relationships among of liquidity shocks and volatilities with all of subsections, and consequently the main hypothesis based on “presence of systemic liquidity risk of corporate stocks in capital market of Iran” was accepted. In a way that the portfolios of company stocks with a “low level of liquidity- industry section” and “low level of liquidity- financial section” respectively had maximum and minimum liquidity shocks transmission of effects on future returns of the other portfolios, as well as the portfolio with a “high level of liquidity- financial section” had maximum volatility persistence and liquidity risk transmission to other portfolios.

The aim of the current research is to optimize the multivariate portfolio optimization algorithms under illiquid market (commodity and financial) perspective. In this regard, an optimization model for portfolio risk-return assessment with LVaR constraints is investigated using reasonable financial and operational scenarios. This approach is achieved by minimizing LVaR. The research method is descriptive and correlational. The statistical population is the companies admitted to the Tehran stock exchange, which were selected by systematic elimination sampling (screening) of 100 companies that were present in the stock exchange during the financial years of 1392-1399.The required information was extracted through the new Rah Avard software and the official website related to the Tehran Stock Exchange Organization. The unit root test of the variables was investigated using the method of Lin and Chui, and the basics of econometrics were discussed, and the variables were investigated using the vector auto-regression method (VAR) using Eviews and MATLAB statistical software. Based on the results, it can be said; Liquidity affects commodity and financial markets. Also, the effect of optimization algorithms and modeling techniques on portfolio management and risk assessment was confirmed

Considering that investing in the stock market is associated with risk, therefore, its measurement is one of the most important issues for investors. The focus of the current research is on the calculation of the value at risk of Dynamic Conditional Correlation with the Solvency Approach (DCC t-Cupola LVaR) based on the copula and also the minimization problems of the above model to choose the optimal portfolio. Therefore, the purpose of this research is to compare the performance of the Markowitz models and value-at-risk model with the Liquidity-t Cupola approach and DCC-t Cupola LVaR to optimize the portfolios in Tehran Stock Exchange. While presenting a composite model, the extracted model should be examined to select the most efficient model to optimize the investment portfolio considering the conditions of investment uncertainty and non-linearity of the correlation between asset returns. To estimate DCC-tCopula-LVaR, first, the time series of the disturbance distribution of asset returns were estimated and standardized from the ARIMA-GARCH 1,1) model. Then, the marginal distributions of assets were estimated using Student's t-copula function. In the following, DCC-tCopula-LVaR values were calculated using the parametric method. In the last step, using linear programming, the optimal combination of the portfolio and the efficient frontier of the two models were calculated at the confidence levels of 80, 85, 90, 95, and 99 percent for the above two models. The findings of this research indicate that the Markowitz model performs better as the risk level increases. As the risk level decreases and declines from 99% to 80% (risk level in a sell position), the DCC-LVaR model performs better. Also, as the value at risk increases, the Sharp value of the DCC-tCopula-LVaR model decreases compared to the Markowitz model. Numerical experiences in the presented empirical analysis, Sharpe ratio, and two loss measures mean absolute value of error (MAE), as well as the root mean square error (RMSE) show that for an arbitrary portfolio, using the DCC t-Cupola LVaR model at a low-risk level is more efficient than the Markowitz model. By reducing the increase and increasing the value at risk, the Markowitz model has better efficiency. Also, using the DCC-tCopula-LVaR model, when the value at risk-adjusted by liquidity is low, brings better performance for stock portfolio optimization compared to the Markowitz model, based on the Sharpe measurement criterion.

Eight-factor pricing model of Skocˇir and Loncˇarski (2018) is a development of Fama and French (2016). They attempt to improve the explanatory power of the model by adding three factors; default risk, liquidity and momentum, in response to some anomalies of the model. Findings show that explanatory power of the model depends highly on variable selection. Efficiency of the model has not been approved in the mentioned confidence interval. But, evidence of the new factors` power and their impact on improvement of the model shows that in combination with 5- factor model, momentum has better performance and default has the worst performance. In addition, through combination of 2 factors, intercept analysis implies that combination of liquidity and momentum leads to a better performance.

This study aims to estimate the bid-ask spread criterion based on the daily highest and lowest prices and to imply this criterion as a proxy for transaction costs. Then, using this type of transaction costs and liquidity, the consumption-based capital asset pricing model is modified. To perform experimental tests. Daily data is collected from 47 companies accepted on the Tehran Stock Exchange and for the period 2009 to 2018. This study is carried out on 20 portfolios formed based on liquidity criteria Liu (2006), DVOL, Size, and Gibbs. The results of this study show that the capital asset pricing model based on traditional consumption has a poor performance in explaining the return on cross-sectional stocks and liquidity-adjusted CCAPM can explain the bigger portion of cross-sectional return changes compared to the traditional CCAPM model. Also, the results show that the entry of trading cost variables and liquidity risk leads to improved CCAPM.

Liquidity plays a major role in financial markets, specifically in easing the burden of sharing the risks and also in improvement of efficiency of transactions. Liquidity risk can be defined as the risk of being unable to buy or sell an asset in a specific time frame without it losing value. The main goal of the present study is to to study the effects of liquidity-adjusted Capital Asset Pricing Model (LCAPM) with Bullish and Bearish of Tehran Security Exchange Market to find the better model to explain expected return of stocks. 

One of the new areas in capital asset pricing models are the "liquidity-adjusted capital asset pricing models" which help us in this regard using the factor-based modeling tools. In this study, Amihud illiquidity measure is used as the indicator of liquidity of transactions of shares and time structure breakdown has been used to determine the type of the market.

The results of the study shows that firstly, Amihud measure is in comparison one of best measures for liquidity risk estimation. Also, cumulative liquidity measure has a reverse relationship with stock returns. Meanwhile, it has been shown, for both bear and bull markets separately, that liquidity has a positive effect on stock returns.

In contrast to heterogeneous effects of liquidity risk of capital market on expected rate of return, by separating the market into bear and bull markets, the positive effects of liquidity risk on expected rate of returns can be observed. Therefore, one can conclude that effect of this type of risk on expected return is positive in the bear markets and is higher than bull markets.

Stock liquidity risk can be considered as one of the most important factors in determining the expected returns of investors.Determining a suitable measure based on the characteristics of the company in the capital market that can help define the liquidity of the stock, would lead to a proper decision of investors. In this regard, in the present study, the effect of the systemic liquidity risk of assets (in particular stocks) in the Tehran Stock Exchange was investigated during the years 1385-1395.  In this research, the d non-trading days are used as liquidity indicators of stock trades.  It was found that, first, The co-movement between individual stock liquidity and market liquidity is not significantly related to stock returns. Second . The co-movement between market returns and individual stock liquidity is positively related to stock returns. Third, The co-movement between market liquidity and individual stock returns is negetivly related to stock returns.

Financing policies made by managers can play a key role in the risk and wealth creation for stochkholders. However, identifying effective factors in managers` financing decisions is of great importance. Overconfidence is one of the most important personality traits of managers. Overconfidence is effective in risk taking. Overconfident managers overestimate future returns of business projects due to false confidence. Therefore, they might overestimate the likelihood and impact of positive shocks of future cash flows for these projects. Meanwhile, they may underestimate the negative shocks. They believe that their business entities have been undervalued in the capital market and can choose shorter maturity debt and increase shareholder wealth. They prefer to issue bonds rather than stocks. This study aims to examine the effect of a psychological factor (managerial overconfidence) on debt maturity structure. The sample consisted of selected companies listed in Tehran Stock Exchange from 2007 to 2014. Two scales which are based on managers` bias to forecast earnings and investment decisions are employed to measure the managerial overconfidence. The results show that managerial overconfidence has a positive and significant effect on debt maturity structure. Overconfident managers take shorter debt maturity structure by applying higher percentage of short-term debt. Liquidity risk related to this financing policy would not prevent them from such behavior.

Liquidity is a multidimensional concept. In this essay we studied the behavior of liquidity in the asset pricing model of Tehran Stock Exchange on the basis of multi measures related to multi dimensions of liquidity concept. The traded value measurement is used as a proxy for the value dimension, turnover ratio is used as a proxy for speed dimension and Amihud measure is used as a proxy for cost and price impact dimension. The time period is from 2011 up to 2016 and the data is calculated for 60 months. The result is that four factor model on the basis of each liquidity measure improves the style portfolios (size style, value style and liquidity style) as well as stock level. The improvement level is higher for the illiquid style portfolios. Among 201 studied stocks, the augmented model improves asset pricing model explanation of 50 stocks. Level of liquidity beta depends on the measurement used.

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.

In investor's opinion, liquidity is a critical item for a market to be chosen by investors. This paper aim is a comparison among efficiency of 5 different GARCH models for modeling and liquidity risk measurement. Due to do that, a time series of data belong stock market for a period of 1381-1390 were gathered. Then liquidity risk was modeled by some GARCH models. Moreover, an Amihood criterion was calculated in accordance with TEPIX. The results show that M-Arch is the best model among other GARCH models.