World of Sciences Journal ISSN: 2307-3071 www.engineerspress.com Year: 2013

Volume: 01

Issue: 13

Pages: 193-205

Examining the relationship between growth and value stock and liquidity in Tehran Stock Exchange Mehdi Meshki1, Mahmoud Hematfar2 and Saeed Bahrami3* 1. Assistant professor, Faculty of Management, Payam-e-Noor University, Guilan, Iran 2. Assistant Professor , Department of Accounting ,Boroujerd Branch , Islamic Azad University , Boroujerd , Iran 3. M.S in Department of Accounting ,Boroujerd Branch , Islamic Azad University , Boroujerd , Iran `ABSTRACT

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In every financial market there are various tools to be used in investment depending on market size and depth. Asset liquidity in a main issue in investment since some investors may require their financial resources. Liquidity rate is related to investors' enthusiasm to perform transactions in the Stock Exchange. The present paper examines relationship between growth and value stocks and liquidity in Tehran Stock Exchange in a 10-year period (2002-2011). It uses three transactional standards (transaction mass, transaction frequency and stock turnover rate) to determine liquidity standard. Data pertaining to the time series are gathered annually and examined by Eviews Software through panel data method. In order to achieve a better examination of relationship between the two variables "size" and "financial leverage" enter into the model as control variables. Results demonstrate a negative, significant relationship between growth stock and liquidity and lack of a significant relationship between value stock and liquidity.

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KEYWORDS:

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growth stock, value stock, liquidity, Transaction mass, Transaction

frequency, Stock turnover rate, Leverage

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Introduction and problem definition Investment of individuals and different agencies in stock exchange is one of the most common forms of investment. Investors and analyzers are willing to gain more knowledge and information for and optimal decision-making. They consider important factors for a desirable investment one of which is the liquidity of adopted investment strategy. When an investor gains financial asset he expects to be able to change securities into cash and use resulting profit in current expenditures or another investment. The more difficult is the change, the higher is the liquidity risk. Increasing liquidity risk leads investors to enhance their required efficiency in order to compensate for that risk. Moreover, it must be noted that in every financial market there are various tools to be used in investment depending on market size and depth. Besides, many investors may be need to their investment financial resources for a necessity and this indicates the important role of liquidity as a determinant factor in all investments. Investment in stocks can be divided into growth and value investments. The stock of companies having a relatively high book-tomarket value ratio is of value type and that of companies in which this ratio is low is of growth type. If growth stock is being hold in an investment basket, the action is called adopting growth strategy and if value stock is retained, the action is called adopting value strategy. Liquidity of an asset is the ability to trade that asset in the shortest time period with least costs. Although the term is relatively representative and comprehensive but many financial texts consider liquidity as an easy and neutral concept. This means that it is difficult to measure liquidity in transaction despite easiness of its understanding. One of the main functions of capital market is to provide for liquidity. In fact, while supplying liquidity secondary markets result in a reduction in capital costs through detecting prices and the ability to transfer risks. Occasionally, some financial texts use the term "marketability" or "tradability" instead of liquidity to present its concept since the bigger is the number of potential purchasers and sellers of a certain asset, the higher is its liquidity rate. The present paper aims to examine liquidity rate of adopted strategies and evaluating the relationship between growth and value stock and stock liquidity.

Review of the literature Investors' interest in transacted stock's liquidity has always been tangible. In fact, the relationship between liquidity and risk and efficiency and its influence on adopted strategies leads investors to pay specific attention to this variable. Individuals and economic agencies tend to have successful investments and respect the stock and its features to perform transactions with a full set of information. The type of stock (value or growth) is one of its main features investigated by many researchers from different countries. Noe, Fang and Tice (2009) evaluated the relationship between liquidity and company performance. The performance measurement scale tended toward emphasizing on market-to-Book value ratio. They found that companies with liquid stock have better performance. They divided companies into 10 classes based on stock liquidity and noted that by a company's movement from the weakest rank (least liquidity) toward stronger ones its performance improves. Gutierrez and Pivinsky (2007) found that investment agencies and other investors try to achieve desirable efficiency in considered periods. On average, the stocks tend toward higher ratios of market-to-book value and demand to purchase such stocks increases liquidity. Speigal and Wang (2005) suggested that stock risk is related to its liquidity and showed the importance of liquidity in transactions predicting stock efficiency (return). They also investigated liquidity as a dependent variable in the relationship between performance and liquidity and found that liquidity is WWW.ENGINEERSPRESS.COM

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depend highly on strong performance of companies having high ratios of market-to-book value. Baker and Stein (2004) considered that liquidity, as an important indicator, may be related to valuing process. They found that highly liquid stocks have higher values since stocks with strong liquidity are traded in those transactions which have a value necessity. Additionally, some researchers have also examined different aspects of the relationship between some certain variables and growth and value stock in Iran. Tehrani and Khojasteh (2008) studied the relationship between capital productivity and future return of stock in Tehran Stock Exchange and investigated the influence of this relationship on two important and widely adopted strategies (growth and value). The ration of operational profit to invested capital of the company and its ratio of book to market value of the investment were employed as capital productivity index and growth and value stock determination index, respectively. The research was performed during 11 years (1997 – 2007) and sample was selected from accepted companies in Tehran Stock Exchange. Results of hypotheses testing confirmed a relationship between capital productivity and stock future return in two general and control states and the considerable effect of this relationship on improving efficiency of both growth and value strategies. Meshkiet al. (2011) examined the role and influence of growth and value stock on total return, liquid return and return resulted from capital profit and investigated the claim on a difference between returns of two above stock types in accepted companies in Tehran Stock Exchange. On the other hand, they evaluated authenticity of a hypothesis on existence of convergence in growth and value stock returns in a certain period of time. Their sample was composed of 70 companies during 2002 – 2010. Their findings indicate that although there is a positive relationship between ratio of market-to-book value and all calculated returns but growth stock has higher liquid and capital returns than value stock. But, no significant difference was observed between liquid return of the two stock types. Moreover, results suggest a convergence in liquid and capital returns of the two stocks during a certain period. This demonstrates that no stock remains as growth or value for ever.

Research hypotheses First hypothesis (H1): There is a significant relationship between growth stock and liquidity. Second hypothesis (H2): There is a significant relationship between value stock and liquidity.

Research methodology The present research is an applied one (in terms of research objectives) and is a correlation – descriptive research respecting its method and nature. The research aims to examine the relationship between growth and value stock (independent variables) and liquidity (dependent variable) in accepted companies in Tehran Stock Exchange. Linear regression model is used to investigate the relationship between variables. Research hypotheses are tested in confidence level of 95%.

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Data collection and sample selection Library method (reference to archives) was used in this study to collect required data. Research tool included financial statements, associated notes and financial reports of studied companies gathered through Rah AvardNovin Software and official website of Tehran Stock and Securities Exchange Organization. Having data classified and variables estimated by Excel, resulted information was analyzed by Eviews Software. Research population was composed of all accepted companies in Tehran Stock Exchange during a time period from early 2002 to late 2011. The sample was also selected using systematic omission considering following criteria: 1- Companies should not change their financial year during the research period (all financial years should end in March 19th). 2- Companies must not have an operation suppression of more than 6 months during the research period. 3- Investment companies, financial institutions and banks are not involved in the research because of their reporting environment's specific conditions. 4- Rights of stakeholders of sample companies must not be negative during periods under study. 5- Companies lacking transactional suppression whose stock has been purchased or sold during the research period. Based on above conditions, 86 companies were selected as samples for the research. Research model and variable measurement procedure Research conceptual model Variables being evaluated in this research are classified into two groups: main variables and control variables. Control variables include: size and financial leverage As the following figure illustrates, research main variables are composed of stock type (growth and value) as independent variables and determinants of liquidity as dependent variables.

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Figure 1: Research conceptual model

Leverage

Size

Transaction mass Transaction frequency

Growth stock Liquidity

Value stock Stock turnover

Research statistical model The following statistical model was used in this research to test hypotheses: Lit= β0 + β1MTBit + β2SIZEit + β3LEVit + Vit Lit: is the liquidity rate of stock i in time period of t. In this paper, three different measures, namely transaction mass, transaction frequency and stock turnover rate, were used to determine liquidity rate. These measures were solely calculated as side models for hypotheses testing and were considered as dependent variables in this paper. Transaction mass: this measure was used for both hypotheses in the first model. It is the number of traded stock in a certain period of time (the time period is usually considered annually). Transaction frequency: this measure was used for both hypotheses in the second model. It is the annual frequency of trading a certain stock. Stock turnover rate: this measure is used for both hypotheses in the third model. This is calculates on an annual basis using the following equation: SHTO = number of transacted stock / number of issued stock

(1-1)

MTBit: Market-to-book value ratio of stock i in period t. This measure is to identify whether a company's stock is of growth of value type and is an independent variable in this research. In order to test research hypotheses at the beginning of every certain year all companies are ordered based on M/B value ration (from lowest ratios to highest ones). So, they are divided into three groups: The first 30% is called growth stock (G) with the highest M/B value ratio, the next 40% is called neutral stock (N) as an average and the last 30% is called value stock (V) with the lowest M/B value ratio. The groups are reviewed at WWW.ENGINEERSPRESS.COM

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the beginning of every year and companies may move to another group considering changes in their M/B value ratio. SIZEit: it is the size of company i during period t. It is considered as a control variable and is obtained by natural logarithm calculation of value of company's assets. LEVit: this is the financial leverage of company i during period t. It is considered as a control variable in this research and is obtained by estimating debt –to-asset ratio of the company. β0: is the Y-intercept β1: independent coefficient of MTBit in the statistical model β2: coefficient of SIZEit in the statistical model β3: coefficient of LEVit in the statistical model Vit: error sentence

Hypotheses testing Correlation coefficients and modified determinant coefficient were used to test research hypotheses and to describe the relationship between research variables. To recognize the use of regression model and generalizablity of results (throughout the population) it is necessary to test significance of correlation coefficients a goal for which t test was used in this paper.

Validity test of research variables Before estimating the model it is necessary to test variables' validity. A variable is valid when its mean, variance and self-correlation coefficient remain unchanged and stable during the time. Overall, the time origin of a variables changes, but not its mean, variance and covariance, it is considered as a valid variable. Otherwise it is not. In this paper ADF Fischer test was used to examine variable validity.

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Table 1: Results of validity test for research variables Variable Growth stock Financial leverage Stock turnover Transaction mass Transaction frequency Company size Value stock First difference D (growth stock)

Statistic 57.3270 230.886 429.023 301.579 253.091 228.974 119.107 Statistic 183.309

Probability 0.1676 0.0018 0.0000 0.0000 0.0001 0.0024 0.0007 Probability 0.0000

As can be seen, the value of all dependent and independent variables (except growth stock) in the same root test is smaller than 0.05 and this demonstrates validity of these variables. Growth stock variable was investigates in the "first difference" test where it was proven to be valid. This means that variables' mean, variance and covariance have been unchanged during the time (between different years). Therefore, making use of theses variables does not lead to a quasi-regression in the model.

Limer's F test and Hausman's test Having examined al variables' validity now it is time to determine estimation method. Data are of mixed type in this research. Hence Limer's F test was used to identify estimation method (panel or integrated data). Finally, the estimation method was recognized to be of panel data. This method is performed through two models of "random effects" and "fixed effects". Here, Hausman's test was used to choose one of these models.

Table 2: Limer's F test and Hausman's test Hypothesis

H1

H2

Model

Limer's F statistic

Probability

First Second Third First Second Third

3/227539 6/508105 2/453013 3/299566 6/554495 2/388270

0/0000 0/0000 0/0000 0/0000 0/0000 0/0000

Hausman's test statistic 7/514626 19/024565 10/630359 4/552368 13/105253 9/338856

Hausman's test probability 0/0572 0/0003 0/0139 0/2077 0/0044 0/0251

Estimation method

Model

Panel data Panel data Panel data Panel data Panel data Panel data

Fixed effects Fixed effects Fixed effects Random effects Fixed effects Fixed effects

As can be observed, the probability of Limer's F test in all models in smaller than 10% and this indicates that estimation method for all of the models is panel data method. In estimating model type, probability of all models (except the first of H2) is smaller tan 10% and this suggests the use of fixed effects model. Of course, random effects model must be used for the exceptional model (model 1 of H 2).

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Results of co-linearity between descriptive variables The use of correlation coefficients of descriptive variables is a simple measure to identify co-linearity. If the coefficients are relatively big, then co-linearity is almost strong and small coefficient indicates weak co-linearity.

Table 3: results of co-linearity Hypothesis

H1

Model

Variable

Size

leverage

First

Growth stock

0/001998

-0/001499

Company size

1/0000

0/078307

Growth stock

0/078307

0/001998

Company size

1/0000

-0/001449

Growth stock

0/001998

-0/001499

Company size

1/0000

0/078307

Value stock

0/004377

0/078307

Company size

1/0000

-0/053136

Value stock

-0/053136

0/004377

Company size

1/0000

0/078307

Value stock

-0/053136

0/004377

Company size

1/0000

0/078307

Second

Third

First

H2

Second

Third

Tables show that correlation coefficients of independent variables are smaller than 0.5 indicating negligibility of colinearity.

Descriptive statistics of research variables Descriptive statistics include a set of methods used to collect, summarize, classify and describe numerical facts. Indeed, the statistics describe research data and provides a general pattern of them to facilitate their use. In sum, making proper use of descriptive statistics enables characterization of a set of information. Central parameters and distribution (dispersal) are used for this sake. One of the main functions of these measures is to show principal characteristics of a data set in the form of figures and hence help better understand a test's results besides facilitating comparison of these results with those of other tests and observations. Descriptive statistic of research variables is presented in Table 4. WWW.ENGINEERSPRESS.COM

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Table 4: Descriptive statistic of research variables Turnover

Transaction mass

Transaction frequency

Growth stock

Value stock

Size

leverage

Mean

14.20849

49562826

5637.536

0.291860

0.300000

5.704666

0.643081

Median

6.665521

4449319

831.5000

0.000000

0.000000

5.617348

0.670000

Maximum

291.9884

4.45E+09

171434.0

1.000000

1.000000

8.520003

0.980000

Minimum

0.000115

60.00000

2.000000

0.000000

0.000000

4.434505

0.060000

Standard deviation

22.59523

2.15E+08

16618.49

0.454883

0.458524

0.584091

0.159520

Skewness

4.899300

12.23591

5.340130

0.915667

0.872872

1.190010

0.808789

Kurtosis

42.22584

217.0149

37.29715

1.838446

1.761905

5.414037

3.653003

Hypotheses Analysis Analysis of H1: First hypothesis (H1): There is a significant relationship between growth stock and liquidity. Three separate models are defined and estimated based on every dependent variable in order to examine the first hypothesis. Moreover, every model was fitted in a regression model containing all dependent and independent variables. Every model is analyzed independently and finally an overall conclusion is drawn for the first hypothesis. First model: In this model the dependent variable "transaction mass" was examined with the independent variable (growth stock) and control variables (size and leverage). Estimation results show that probability of t test for growth stock variable is smaller than 0.05. Hence, the estimated coefficient of this variable is statistically significant. This means that transaction mass is an important determinant of liquidity (transaction mass). The negative and significant relationship between growth stock and liquidity is shown in the first model. The determination coefficient represents explanatory capacity of independent variables by the dependent variable (27% of changes of the dependent variable). The calculated F statistic is smaller 0.05 indicating the statistical significance of the whole model. Second model: In this model the dependent variable "transaction frequency" was examined with the independent variable (growth stock) and control variables (size and leverage). Estimation results show that probability of t test for ratio of growth stock to liquidity (transaction frequency) is smaller than 0.05. Hence, the estimated coefficient of this variable is statistically significant. This means that transaction frequency is an important determinant of liquidity. The negative

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and significant relationship between growth stock and liquidity is shown in the second model. The determination coefficient represents explanatory capacity of independent variable capable of explaining 40% of changes in the dependent variable. The calculated F statistic is smaller 0.05 indicating the statistical significance of the whole model. Third model: In this model the dependent variable "stock turnover rate" was examined with the independent variable (growth stock) and control variables (size and leverage). Estimation results show that probability of t test for ration of growth stock to liquidity (stock turnover rate) is smaller than 0.05. Hence, the estimated coefficient of this variable is statistically significant. This means that the dependent variable is an important determinant of liquidity. The negative and significant relationship between growth stock and liquidity is shown in the third model. The determination coefficient represents explanatory capacity of independent variable capable of explaining 1.7% of changes in the dependent variable. The calculated F statistic is smaller 0.05 indicating the statistical significance of the whole model.

Overall conclusion of the first hypothesis The three proposed models and performed analyses indicate that the coefficient of growth stock is negative and significant in regression pattern of models 1 and 2 and positive and significant in model 3. But, since the determination coefficient (representing the relationship between independent and dependent variables) is very small in model 3 (1.7%), first and second models are considered as the measure to determine type of relationship between two variables (growth stock and liquidity) in generalizing results of the first hypothesis. This relationship is negative and reverse in both models and hence it can, generally, be concluded that there is a negative (reverse) relationship between growth stock and liquidity. Analysis of H2: Second hypothesis (H2): There is a significant relationship between value stock and liquidity. Three separate models are defined and estimated based on every dependent variable in order to examine the second hypothesis. Moreover, every model was fitted in a regression model containing all dependent and independent variables. Every model is analyzed independently and finally an overall conclusion is drawn for the first hypothesis. First model In this model the dependent variable "transaction mass" was examined with the independent variable (value stock) and control variables (size and leverage). Estimation results show that probability of t statistic pertaining to value stock is greater than 0.05, hence, indicating lack of a significant relationship with liquidity. The estimated probability of F statistic is smaller than 0.05 indicating the statistical significance of the whole model. Second model In this model the dependent variable "transaction frequency" was examined with the independent variable (value stock) and control variables (size and leverage). WWW.ENGINEERSPRESS.COM

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Estimation results show that probability of t statistic pertaining to value stock is greater than 0.05, hence, indicating lack of a significant relationship with liquidity. The estimated probability of F statistic is smaller than 0.05 indicating the statistical significance of the whole model. Third model In this model the dependent variable "stock turnover rate" was examined with the independent variable (value stock) and control variables (size and leverage). Estimation results show that probability of t statistic pertaining to value stock is greater than 0.05, hence, indicating lack of a significant relationship with liquidity. The estimated probability of F statistic is smaller than 0.05 indicating the statistical significance of the whole model.

Overall conclusion of the second hypothesis According to the three proposed models it can be concluded that, in general, there is no significant relationship between value stock and liquidity and the second hypothesis is rejected since the independent variable (value stock) is insignificant in models 1, 2 and 3 (models pertaining to liquidity measures).

Table 5: Results of statistical analyses of research hypotheses testing Hypothesis

H1

Model

Independent variable

Dependent variable

1

Growth stock Growth stock Growth stock

Transaction mass Transaction frequency Stock Turnover rate Transaction mass Transaction frequency Stock Turnover rate

2 3

1

Value stock

2

Value stock

H2 3

Value stock

"t" statistic

Probability of "t"

"F" statistic

Probability of "f"

Determination coefficient

-2/241947

0/0252

104/7149

0/0000

0/268467

result

Confirm Confirm -2/155786

0/0314

188/9992

0/0000

0/398453

2/560327

0/0106

5/187020

0/001484

0/017854

Confirm

Reject -0/234234

0/8149

7/478015

0/0000

0/460487

-1/012161

0/3117

187/0025

0/0000

0/395910

-0/262130

0/7933

102/4691

0/0000

0/264230

Reject Reject

Research general conclusion Having tested every single hypothesis and drawing a conclusion upon them some general results are obtained. The main objective of this paper was to examine the effect of growth and value stock on liquidity in Tehran Stock Exchange. Results showed that if there is a reverse (negative) relationship between growth stock and liquidity, then the more a stock has incremental (growth) characteristics, its liquidity WWW.ENGINEERSPRESS.COM

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declines. But here no significant relationship was found between the two variables.

Suggestions 1.

According to results of the first hypothesis an increase in incremental characteristics of stocks results in a decline in their liquidity (in Iranian markets). Therefore, managers have to consider the rate of incremental (growth) or value characteristics of their company's stock to be able to establish a desirable status of liquidity. One of the actions they can accomplish to maintains the desirable level of liquidity is to increase company's book value in order to reduce its stock growth.

2.

Based on the significant relationship between growth stock and liquidity the Stock and Securities Exchange Organization can consider disclosure of more information on companies' growth during financial periods. Hence users of financial statements are ensured to achieve sufficient data.

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