Designing a Total Interpretive Structural Modeling (TISM) for the Effectiveness Mechanism of Stock Liquidity in the Tehran Stock Exchange Companies

Document Type : Original Article

Authors

1 Ph.D Candidate, Department of Accounting, Kashan Branch, Islamic Azad University, Kashan, Iran.

2 Associate Prof., Department of Accounting, Kashan Branch, Islamic Azad University, Kashan, Iran.

Abstract

As the capital market becomes more competitive, one of the topics that has attracted the attention of many financial researchers in recent years is the liquidity of corporate stocks that because of the dynamics it can create in corporate financing, it is of strategic importance. The purpose of this research is designing a Model of Comprehensive interpretive/structural Mechanism of Effectiveness of Stock Liquidity Tehran Stock Exchange Companies. The one-year study period 2018-2019 in both qualitative analysis and quantitative analysis was conducted with the participation of two members of the panel. In the qualitative analysis section, this research identified through the combination of Delphi and the analysis of three components of the operational mechanism, the structural/governance mechanism, and the investor/mechanism of trading mechanism in the form of the effective statement on stock liquidity. And in the Comprehensive Interpretive / Structural Analysis section, with the participation of four Stock Exchange brokers, members of the panel presented a model based on a spectrum of the most influential statements to the least effective stock liquidity statements. The results show that the Delphi analysis of 25 indicators identified early in the meta-synthesis, 7 Index Remove and 2 indicators have been merged for a total of 16 statements were approved. In the quantitative section, based on a comprehensive interpretive/structural analysis, it was identified that the increase in the number of trading transactions as the component of operational mechanisms was identified as the most influential factor in stock liquidity.

Keywords


Introduction

Capital markets in the economy are of considerable importance in booming the economic activity, investment, and optimal allocation of capital. In the course of the economic cycle or the process of privatization of state-owned companies, every year numerous firms step into the capital market and venture to the initial public offering (IPO) of their stocks on the Stock Exchange. Apart from the matter of privatization, when a firm grows, it needs liquidity for development that this process, through the company's liquidity stock capability in a competitive market, accomplishes dynamism (Bateni et al., 2013). Indeed, stock liquidity capability is based upon the functions of the type of investment, risk, and return that, depending on the strategies, structures, functions of corporations and investors, cover a level from liquidity to the illiquidity of stock (Blau et al., 2018).

On one side, it is worth mentioning for investors that if they decide to sell their assets, is there a good market for it, or how long does it take to sell it and convert it into cash? To put it another way, investors quickly pay attention to the financial resources of their investment and estimate the return resulting from the investment based on mental calculations. In such cases, liquidity capability in investment decisions is of great importance. That is to say, if investors are confident that, in the case of deciding to sell the assets, they can convert their asset into cash within a short time and there is a good market for the sale of assets, they will underestimate the illiquidity risk, and this will lead to their confidence for investment in the capital market. On the other hand, because it will bring more liquidity and increases the level of dynamism in economic development, liquidity capability for firms and capital markets could be of special importance (Chang & Young, 2019).

Put differently, liquidity in the capital market is of importance like other financial markets, for the existence of more liquidity in the stock market causes the boom of initial public offerings and a reduction in the cost and risk of underwriters and market makers, and this leads to allocation of capital with higher efficiency and results in a reduction in the cost of capital for issuers. Actually, as one of the factors influencing the returns of the securities, liquidity represents the situation of the investment environment in the capital market and the economies of the countries and exhibit the potential to attract the capitals of different markets with diverse trading strategies. Hence, understanding the stock liquidity capabilities whether at the macro-level (the level of capital market) or the micro-level (trading behavior of investors) could be a reason for the formulation of strategies by firms and analysts in order to generate a more dynamism in the capital market. The important thing is that the amount of liquidity in the stock market is influenced by several factors. In a classification, perhaps we can refer to studies such as Chung et al. (2010), Kim & Verrecchia (1994), and Ascioglu et al. (2004) that considered the stock liquidity as a result of the total volume of liquidity in the community and the amount of volume of liquidity in the development of market efficiency.

Moreover, in the identification of factors affecting the stock liquidity, another group of researchers, such as Taddei (2007), Levine (1996), Wong & McAleer (2009) and Alnaif (2014), classified the liquidity of the market in terms of macro and micro dimensions which this distinction is on the basis of economic, market and industry criteria and the trading behavior criteria of the shareholders and investors. However, as it turns out, there is no certain and unified classification of stock liquidity both in terms of research and application, and this can be attributed to the ignorance of the level of research to the strategic issue of the capital market. Thus, this study strives to investigate the propositions related to the liquidity of the capital market relying on thematic analysis in similar studies analyzing the context of similar researches and to classify them in the form of the total interpretive structural modeling (TISM) from the most effective to the least effective ones so that a more coherent understanding of these factors based on practical mechanisms in the capital market is established.

Literature Review

Stock Liquidity         

Most investors (with a short-term investment horizon) prefer highly liquid stocks compared to less liquid stocks since it will bring them greater returns. Indeed, according to the definition of Amihud & Mendelson (1986), liquidity is the degree to which an asset in the market is traded without influencing its price. In fact, Grecuhina & Timofejeva (2008) define liquidity as ease in trading securities. From another perspective, Chacko et al. (2008) consider the liquidity as the gap between the fundamental value of an asset and the price that the asset is currently traded. Liquidity can be intended an essential factor in profitability, and it is considered as a tool and mechanism to exhibit the proper status of the firm's stock from the perspective of financial affordance (Hajiannejad and Danesh Sararoudi, 2019).

The liquidity of a firm in the financial literature includes two concepts; the liquidity of its real assets and the liquidity of its stocks. An asset is a cash when it can be slowly converted into cash. This definition covers both real assets and financial assets. The former concept is the liquidity of a firm's real assets that, according to it, a company is considered to be liquid if it has a high proportion of cash assets such as cash in its balance sheet. The latter concept is the stock liquidity of the company being traded. According to this concept, a firm is liquid if its stock enjoys high liquidity. The liquidity of a company's assets is determined by its real assets in the market, while its stock liquidity is can be determined in the financial markets (Mukityanto, 2015).

In his studies, Xu-Shen Zhou (2003) could identify a connection between these two concepts. In his view, the theoretical relationship between these two types of liquidity is not specified at first glance. In his proposed model, he introduces information asymmetry as an interface between the two concepts so that the less liquidity of a company’s assets results in lowering the ability to transform them into other assets by the manager. The rigidity of assets leads to information asymmetry those results in high stock liquidity. For managers of companies with less liquid assets, it is difficult to convert these assets into other assets; i.e., the problem of asset replacement declines, and investment will be faced with difficulties. Therefore, if these managers do not seek to convert those assets of the company with less liquidity capability to assets with highly-liquidity capability, these assets will cause the agency costs for investors to be lower. Taking into account the previous investigations, the factors affecting the stock liquidity shall be separated in the form of the following table:

Table 1. Factors Affecting the Stock Liquidity

Factors Affecting the Stock Liquidity

Definitions

References

Institutional and Structural Factors of the Market

According to the report of the Committee on Emerging Markets of the International Organization of Securities Commissions (2007), the institutional and structural factors affecting the improvement of liquidity in the capital market include the following items:

Raising the rate of free float stocks, providing the possibility of foreign participation in the market, increasing access to market, reducing the costs of transactions, improving the trading infrastructure of market, enhancing investment products available in the market, increasing the offering of securities, establishing links with other markets, restructuring the stock markets, and establishing communication with other markets allow stock exchanges to act profitably and, for attracting the international flows of orders, compete with each other.

 

 

 

Chung et al. (2010), Kim & Verrecchia (1994), and Ascioglu et al. (2004)

 

Firm Performance Variables

The relationship between firm performance measures and stock liquidity has been studied from different views. All of these perspectives believe that by improving the performance of business units, its stock liquidity will increase. Agency and feedback theories are examples of these views. In summary, based on agency theory, managers for that to maximize their own interests try to improve the firm performance, and this improvement is taken into account by informed investors and causes an increase in the share trading. Besides, taking into account the feedback theory and regardless of agency theory, it can be concluded that companies, by providing better performance, will attract more informed investors, and this factor will lead to generating the demand and increasing the trade by investors and raising liquidity in the market.

 

 

 

 

 

Fang et al. (2009) and Banerjee et al. (2007)

Environmental Factors

Effective environmental factors, including the state of the business cycle of the economy (recession and expansion), financial cycles, financial and banking crises, structures of the country's financing system, etc. are exogenously determined and influence the stock market and their liquidity. Considering that the environmental condition of the economy, like the business cycle, has a direct impact on the status of most firms, hence, a change in the situation can affect the attractiveness of the stock market compared to other markets.

 

 

 

Taddei (2007), Levine (1996), Wong & McAleer (2009) and Alnaif (2014)

Economic Policy-making Factors

Policy factors are those in the authority of policymakers, and policymakers can influence the liquidity of the market by exerting different policies. Policies such as monetary, financial, budgetary, and currency policies can directly or indirectly impact the liquidity of the capital market and, overall, the performance of this market.

 

Aksoy & Basso (2014) and Amihud & Mendelson (2006)

Parallel Markets in the Stock Market

Variations and developments in other parallel financial markets (banks, foreign exchange markets, housing markets, etc.) change the liquidity in the markets taking into account the degree of substitution of markets. A wide range of studies in conjunction with the effect of financial markets on each other has been conducted. Changes in laws and regulations (such as changes in rates and so on) of other financial markets, if it affects the performance of markets, can be beneficial on the stock market and its liquidity. Generally, any factor influencing the risk and returns of parallel markets in the stock market can indirectly impress the attraction of liquidity in the stock market.

 

 

 

Bilson et al. (2001) and Peebles & Wilson (1996)

Therefore, relying on theoretical foundations, the research questions based on the nature of the analysis are as follows:

  1. What are the components and propositions of stock liquidity of stock exchange companies?
  2. What are the most influential propositions of stock liquidity of stock exchange companies?

Research Background

Chen et al (2019) in their research investigated Stock liquidity and corporate tax avoidance. They investigated firms with higher stock liquidity to engage less in extreme tax avoidance. The research period ranged from 1993 to 2010. The results showed that the effect of stock liquidity on tax avoidance is statistically significant and the higher the stock liquidity, the lower the tax avoidance. Chang & Young (2019) They conducted a study entitled Optimizing Stock Behavioral Portfolio in Investment with a focus on stock liquidity. In this study, which examined a combination of behavioral criteria along with the functional criteria of capital and economic markets, it was found that the most important factor in deciding on investment portfolios is the cognitive characteristics of investors based on functional analysis of capital and economic markets. Of course, the role of economic criteria is less effective. Blau et al (2018)

An analysis of the gap between the proposed price and the sell-off. In this study, qualitative analysis methods were used to identify the causes of this gap and the results were shown in both macro and micro dimensions economic and political causes as major factors in stock liquidity in the macro dimension and structural and information causes in the micro dimension affect stock liquidity. Ahmad (2016) In one study, he examined the effect of liquidity on corporate profitability. The study, which surveyed 115 companies between 2005 and 2014, found a positive and significant relationship between liquidity and profitability.

Methodology

On the basis of the methodology of social and behavioral sciences, research is separated from the perspective of three domains of the result, type of data, and aim. Accordingly, in terms of the result, this study falls into the category of development research because there is no clear coherence from the theoretical and conceptual perspectives in connection with the subject of the factors affecting the stock liquidity, and this research can generate a ground for the separation of these factors and further integration in it. As well as, in terms of the aim, this study falls into the category of descriptive/applied research with the purpose of a better understanding of greater transparency in the capital market. Ultimately, in terms of the data type, it should be mentioned that the study involves two phases of qualitative and quantitative sections. The strategy of data collection is inductive in the qualitative section and deductive in the quantitative section. To analyze the data, given the nature of the research, meta-synthesis and Delphi analysis are used in the qualitative section as well, and interpretive/structural analysis is also employed to provide a comprehensive model concerning the effective mechanisms of stock liquidity in the capital market.

Statistical Population of the Research

Like the nature of the research, the statistical population includes two sections; so that academic experts participate in the qualitative section through the Critical appraisal Skills Programme (CASP) in the form of meta-synthesis and Delphi analysis. In the quantitative section, 20 Stock Exchange brokers and capital market analysts with a history of more than 5 years in the market participate. How to distribute the questionnaires in both sections is done based on previous coordination for participation, taking into account the nature of the research. The remarkable thing is that, in the selection of individuals, we tried to choose people who have sufficient knowledge on the subject of the research.

Findings

In this study, 33 research was approved in terms of the context. In the next step, based on the approach of Stirling (2001), the classification and separation of contexts in the form of components and propositions related to the subject of the research should be made. According to this approach, first, 31 research approved through 10 criteria of Critical appraisal Skills Programme (CASP) including the aims of the research, methodology, appropriate research design, sampling, data collection, reflexivity (research partnership relations/recognition of researcher bias), data analysis, ethical issues, findings, and value of the research are again fitted with the help of 19 members of the research panel (experts) to achieve a more coherent understanding of the nature of the research.

Critical appraisal Skills Programme (CASP) is a 10-50 point scale in which each participant gives a score of 1 to 5 to each of the 10 criteria mentioned. Number 1 is the lowest score and number 5 is the highest score. Based on the total scores given, studies obtaining a score below 30 will be removed from the continuation of the review according to the Critical appraisal Skills Programme (CASP) Guidelines (Keshavarz et al. 2017), and the research approved to enter into the stage of determining the research components and then the research indicators.

 

 

Table 2. Critical appraisal Skills Programmer (CASP) of Research Identified

Articles/Criteria of Critical appraisal Skills Programme

(CASP)

Research Objective

Methodology Rationale

Research Design

Sampling Method

Data Collection Method

Generalization of Findings

Ethical

Statistical Analysis Method

Theoretical Capability

Significance of the Study

 

 

 

Sum

Chang & Young (2019)

3

2

3

2

4

5

5

5

4

5

38

Nadauld et al. (2019)

3

3

3

3

3

4

3

4

4

3

33

Cenesizoglu & Grass (2018)

3

4

4

4

4

4

4

5

4

5

41

Blau et al. (2018)

4

5

4

4

3

4

4

3

5

4

39

Aldatmaz et al. (2018)

3

3

3

4

3

4

3

3

3

3

32

Tang &Yan (2017)

4

5

3

4

4

4

5

4

3

4

40

Lyocsa & Molnar (2017)

4

4

4

4

4

4

3

4

4

4

43

Apergis & Voliotis (2015)

3

3

3

2

2

2

4

2

5

3

29

Mukityanto (2015)

5

5

3

4

3

2

4

4

4

4

38

Norvaisiene & Stankeviciene (2014)

4

4

3

4

4

3

4

4

4

3

35

Alnaif (2014)

3

2

3

3

3

3

4

3

3

2

29

Bhattacharya et al. (2011)

3

2

3

2

4

5

5

5

4

5

38

Collver (2009)

3

3

4

3

4

3

4

3

3

4

34

Cheng (2007)

4

4

3

4

3

3

3

3

4

4

35

Gorkitiisunthorn & Jumreornvong (2006)

3

3

3

3

4

3

3

3

4

3

32

Amihud and Mendelson (2006)

3

3

3

4

3

4

3

3

3

3

32

Ascioglu et al. (2005)

4

3

4

4

4

4

4

4

3

4

39

Dey (2005)

2

2

2

2

2

3

4

3

2

2

24

Lacker & Richardson (2004)

3

4

4

3

3

4

3

3

3

4

34

Claessens et al. (2003)

3

4

4

4

4

4

4

5

4

5

41

Dennis & Strickland (2003)

3

4

3

3

3

4

3

4

4

4

35

Clarke & Shastri (2001)

4

4

4

4

3

4

3

3

3

4

32

Neal & Wheatley (1998)

5

5

3

4

3

2

4

4

4

4

38

Chavoshi Najafabadi (2019)

3

3

3

3

3

3

3

3

3

4

31

Niknafs & Yeganeh (2019)

3

3

2

2

2

2

2

2

2

2

22

Jafari Seresht et al. (2017)

3

4

5

4

5

4

4

4

5

4

42

Ebrahimi & Farnaghi (2016)

4

5

4

4

3

4

4

3

5

4

39

Zamani & Faghani Kandari (2016)

4

4

3

4

4

3

4

4

4

3

35

Bateni et al. (2013)

3

3

3

3

3

4

3

3

3

3

31

Ahmadpour & Baghban (2014)

2

3

2

1

2

2

3

2

2

2

21

Namazi et al. (2009)

3

3

3

4

3

4

3

3

3

3

32

Yahya Zadeh far & Khorramdin (2008)

2

1

1

1

1

2

2

1

1

1

13

Ahmadpour & Rasaiyan (2007)

3

2

3

3

4

4

4

3

4

4

34

Considering the explanations given and concerning the score below 30, six studies of Apergis & Voliotis (2015), Alnaif (2014), Dey (2005), Niknafs & Yeganeh (2019), Ahmadpour & Baghban (2014), and Yahyazadehfar & Khorramdin (2008) were eliminated, and other approved studies are used in the next step to determine the components of the research. At this stage, based on the model that has been designed according to the following table, all components examined in the research approved are provided in the column of the table (3), and approved studies are placed in each line. The component that gained the highest frequency based on half of the approved studies is determined as the research component.

Table 3. Determining the Main Components of the Research

Researchers

Governance Attributes

Firm Characteristics

Behavioral Factors

Political Features

Family Ownership

Costs of Order Execution

Operational (Technical) Features

Accounting Methods

Structure of the Board

Chang & Young (2019)

-

-

P

-

P

-

-

-

P

Nadauld et al. (2019)

-

-

P

-

-

-

-

-

P

Cenesizoglu & Grass (2018)

-

-

P

-

-

-

P

-

-

Blau et al. (2018)

P

-

P

-

-

-

P

-

-

Aldatmaz et al. (2018)

-

P

-

P

P

-

P

-

-

Tang &Yan (2017)

-

-

P

-

-

-

P

-

-

Lyocsa & Molnar (2017)

-

-

P

-

P

-

P

-

-

Mukityanto (2015)

-

P

-

-

P

-

-

P

-

Norvaisiene & Stankeviciene (2014)

-

P

-

P

P

-

P

-

-

Bhattacharya et al. (2011)

P

P

-

-

-

-

P

-

P

Collver (2009)

P

-

P

P

-

-

P

-

-

Cheng (2007)

-

P

P

-

-

-

P

P

-

Gorkitiisunthorn & Jumreornvong (2006)

-

P

-

P

-

P

-

-

-

Amihud and Mendelson (2006)

-

P

P

-

-

P

-

-

-

Ascioglu et al. (2005)

-

P

-

-

P

-

-

-

P

Lacker & Richardson (2004)

-

P

P

-

-

P

-

P

-

Claessens et al. (2003)

-

P

-

P

-

-

P

-

-

Dennis & Strickland (2003)

-

P

-

-

-

-

-

P

-

Clarke & Shastri (2001)

-

P

-

P

-

P

-

-

-

Neal & Wheatley (1998)

P

-

P

P

-

-

P

-

-

Chavoshi Najafabadi (2019)

P

P

-

-

P

-

-

-

-

Jafari Seresht et al. (2017)

-

-

P

-

-

P

-

-

-

Ebrahimi & Farnaghi (2016)

P

-

P

-

-

-

-

-

-

Zamani & Faghani Kandari (2016)

-

-

P

P

-

-

-

-

-

Bateni et al. (2013)

-

-

-

-

-

-

P

-

-

Namazi et al. (2009)

P

P

-

-

P

-

-

-

-

Ahmadpour & Rasaiyan (2007)

P

-

-

-

P

-

P

-

-

Total Frequency

8

14

14

8

9

5

15

3

3

As can be observed, the three general components of firm characteristics, behavioral factors, and technical (operational) features had the greatest frequency under the conceptual and specialized titles in the approved research. It is important to note that some of the components placed in the column may conceptually have an essential role in liquidity but may have not obtained enough score as the main component due to lack of being at the macro level and provided in the form of propositions. Hence, considering the total scores of distributions gained, we attempted to determine the research propositions. Table (4) represents the research propositions in the form of a 7-item scale checklist to enter the stage of Delphi analysis.

Table 4. Determining the research propositions in the form of a 7-item scale checklist

Components

Propositions

1

2

3

4

5

6

7

Operational (Technical) Mechanisms

Raising trade volume by balancing the bid price to buy or sell stock

 

 

 

 

 

 

 

Increasing the frequency of transactions

 

 

 

 

 

 

 

Enhancing the monetary volume of stock trading

 

 

 

 

 

 

 

Decreasing the costs of wrong selection through asymmetry of information

 

 

 

 

 

 

 

Increasing the stock turnover through market makers

 

 

 

 

 

 

 

Increasing the percentage of transaction days during the year through market makers

 

 

 

 

 

 

 

Increasing the liquidity capability of real assets such as accounts receivable and inventory

 

 

 

 

 

 

 

Raising the level of earnings quality

 

 

 

 

 

 

 

Upgrading the psychological motivation of investors by increasing the return on total assets

 

 

 

 

 

 

 

Structural Governance Mechanisms

Selecting a cohesive board of directors composition

 

 

 

 

 

 

 

Reducing the influence of the high concentration of family ownership on the board of directors

 

 

 

 

 

 

 

Restructuring of pyramid ownership

 

 

 

 

 

 

 

Enhancing the positive impact of the stock split by equalizing the ratio of stock ownership held by individuals within the company

 

 

 

 

 

 

 

Choosing a high-quality auditor

 

 

 

 

 

 

 

Increasing the level of effective surveillance over management decisions

 

 

 

 

 

 

 

Consolidating the internal controls through establishing the independence of internal auditors

 

 

 

 

 

 

 

Reducing the CEO duality

 

 

 

 

 

 

 

Investors'

Trading/ Behavioral Mechanisms

Stock selection based on the investment horizon

 

 

 

 

 

 

 

Equalizing the expected return based upon capital cost

 

 

 

 

 

 

 

Employing specialized consultations for stock on the shelf

 

 

 

 

 

 

 

Unknown identity of the traders

 

 

 

 

 

 

 

Unknown nature of the order at a specified price

 

 

 

 

 

 

 

Understanding the market and industry intended

 

 

 

 

 

 

 

Having specialized knowledge for stock selection

 

 

 

 

 

 

 

Upgrading the ability to invest in converting financial assets to cash at a price similar to the price of the last transaction

 

 

 

 

 

 

 

Delphi Analysis

The Delphi analysis is a decision-making technique on the basis of expert opinion, which is done in some stages for rounds to reach the theoretical saturation point. The point where the reliability of the contexts identified has been approved. Accordingly, at this stage of the analysis, Delphi analysis is conducted with the help of two criteria of average and agreement coefficient. Table (5) indicates the Delphi analysis of the identified propositions:

Table 5. The first round of Delphi analysis for the identified propositions

Components

Propositions

 

Mean

Measure of agreement

 

Operational (Technical) Mechanisms

Raising trade volume by balancing the bid price to buy or sell stock

4

0.50

Merge

Increasing the frequency of transactions

5

0.7

Enhancing the monetary volume of stock trading

5

0.72

Confirm

Decreasing the costs of wrong selection through asymmetry of information

4

0.40

Removed

Increasing the stock turnover through market makers

3.50

0.33

Removed

Increasing the percentage of transaction days during the year through market makers

5.10

0.75

Confirm

Increasing the liquidity capability of real assets such as accounts receivable and inventory

5.10

0.75

Confirm

Raising the level of earnings quality

5.20

0.80

Confirm

Upgrading the psychological motivation of investors by increasing the return on total assets

3.5

0.35

Removed

Structural Governance Mechanisms

Selecting a cohesive board of directors composition

 

0.90

Confirm

Reducing the influence of the high concentration of family ownership on the board of directors

5.20

0.80

Confirm

Restructuring of pyramid ownership

3

0.25

Removed

Enhancing the positive impact of the stock split by equalizing the ratio of stock ownership held by individuals within the company

5.10

0.75

Confirm

Choosing a high-quality auditor

4

0.40

Removed

Increasing the level of effective surveillance over management decisions

3

0.25

Removed

Consolidating the internal controls through establishing the independence of internal auditors

5.20

0.80

Confirm

Reducing the CEO duality

5.20

0.80

Confirm

Investors'

Trading/ Behavioral Mechanisms

Stock selection based on the investment horizon

5

0.7

Confirm

Equalizing the expected return based upon capital cost

5

0.72

Confirm

Employing specialized consultations for stock on the shelf

2.5

0.20

Removed

Unknown identity of the traders

5.30

0.85

Confirm

Unknown nature of the order at a specified price

6

0.80

Confirm

Understanding the market and industry intended

5.20

0.80

Confirm

Having specialized knowledge for stock selection

5.20

0.80

Confirm

Upgrading the ability to invest in converting financial assets to cash at a price similar to the price of the last transaction

4

0.40

Removed

As seen in the above table, the two criteria of agreement coefficient and average and determine the removal or approval of the index in question. In this connection it should be stated, taking into account a 7-item scale, the indicators obtained an average 5 and higher than 5 and the indicators gained an agreement coefficient of higher than the desired level of 0.50 are approved. Accordingly, concerning the results of Table (), based on the two criteria of average and agreement coefficient, it was found that the following 8 indices were eliminated:

  1. Decreasing the costs of wrong selection through asymmetry of information
  2. Increasing the stock turnover through market makers
  3. Upgrading the psychological motivation of investors by increasing the return on total assets
  4. Restructuring the pyramid ownership
  5. Choosing a high-quality auditor
  6. Increasing the level of effective surveillance over management decisions
  7. Employing specialized consultations for stock on the shelf
  8. Upgrading the ability to invest in converting financial assets to cash at a price similar to the price of the last transaction

Furthermore, concerning the results gained from two indicators of raising the trade volume by balancing the bid price to buy or sell stock and enhancing the monetary volume of stock trading were merged since they have been merged at the discretion of the researchers considering boundary scores they earned and given their close concepts with each other. However, to achieve empirical adequacy, we remove the deleted indicators from the checklist again. According to the arrangements accomplished, the score checklists will be distributed among the members of the panel (experts). In this section, it is attempted that empirical adequacy to be attained.

Table 6. The second round of Delphi analysis

Components

Propositions

 

Mean

Measure of agreement

 

Operational (Technical) Mechanisms

Raising trade volume by balancing the bid price to buy or sell stock

5.20

0.80

Confirm

Enhancing the monetary volume of stock trading

5.20

0.80

Confirm

Increasing the percentage of transaction days during the year through market makers

5.30

0.85

Confirm

Increasing the liquidity capability of real assets such as accounts receivable and inventory

6

0.90

Confirm

Raising the level of earnings quality

5.30

0.85

Confirm

Structural Governance Mechanisms

Selecting a cohesive board of directors composition

6

0.90

Confirm

Reducing the influence of the high concentration of family ownership on the board of directors

5.20

0.80

Confirm

Enhancing the positive impact of the stock split by equalizing the ratio of stock ownership held by individuals within the company

5.30

0.85

Confirm

Consolidating the internal controls through establishing the independence of internal auditors

5.20

0.80

Confirm

Reducing the CEO duality

5.30

0.85

Confirm

Investors'

Trading/ Behavioral Mechanisms

Stock selection based on the investment horizon

6

0.90

Confirm

Equalizing the expected return based upon capital cost

6

0.90

Confirm

Employing specialized consultations for stock on the shelf

5.25

0.85

Removed

Unknown identity of the traders

5.20

0.80

Confirm

Unknown nature of the order at a specified price

5.30

0.85

Confirm

Understanding the market and industry intended

5.25

0.85

Confirm

Having specialized knowledge for stock selection

6

0.90

Confirm

According to the results gained, it was found that all indicators were approved, and empirical adequacy was generated. Hence, considering the results obtained, 16 approved indicators to perform the analysis in the quantitative section shall be examined in the form of Total Interpretive Structural Modeling (TISM).

Total Interpretive Structural Modeling (TISM) 

This analysis is an advanced hierarchical analysis, which is conducted based upon the qualitative propositions of the qualitative section in the form of matrix structure scales by the participants of the quantitative section. The Total Interpretive Structural Modeling (TISM) is a comprehensive analysis method that, in addition to examining the vertical and horizontal relationship, explores the diagonal relationship between the propositions. Accordingly, the approved propositions must be initially coded:

Table 7. Determining the abbreviated codes for matrix analysis

Propositions

L

Raising trade volume by balancing the bid price to buy or sell stock

L1

Enhancing the monetary volume of stock trading

L2

Increasing the percentage of transaction days during the year through market makers

L3

Increasing the liquidity capability of real assets such as accounts receivable and inventory

L4

Raising the level of earnings quality

L5

Selecting a cohesive board of directors composition

L6

Reducing the influence of the high concentration of family ownership on the board of directors

L7

Enhancing the positive impact of the stock split by equalizing the ratio of stock ownership held by individuals within the company

L8

Consolidating the internal controls through establishing the independence of internal auditors

L9

Reducing the CEO duality

L10

Stock selection based on the investment horizon

L11

Equalizing the expected return based upon capital cost

L12

Employing specialized consultations for stock on the shelf

L13

Unknown identity of the traders

L14

Unknown nature of the order at a specified price

L15

Understanding the market and industry intended

L16

Having specialized knowledge for stock selection

L1

Following the formation of the reachability matrix, the indirect relations between propositions, i.e., the advantages of Total Interpretive Structural Modeling (TISM) over Interpretive Structural Modeling (ISM), are used to investigate other dimensions. Otherwise stated, any pairwise comparison should be thoroughly interpreted by answering the interpretive question expressed in the previous step to evolve ISM into TISM. For pairwise comparisons, the ith proposition is compared pairwise with all elements, from (i + 1) the element to nth element. For each relation, the answer is either “Y” or “N.” If the answer is yes, i.e., “Y,” the reason is stated. Otherwise, if the answer is no, i.e., “N,” the pair of variables considered by the participants should be commented on.

Table 8. Pair comparison between propositions based on matrix form

No

 

 

 󠇯  Raising trade volume by balancing the bid price to buy or sell stock

1

 

Yes ☒   No ☐

2

 

Yes ☐   No ☒

3

 

Yes ☐   No ☒

4

 

Yes ☐   No ☒

5

 

Yes ☒   No ☐

6

 

Yes ☐   No ☒

7

 

Yes ☐   No ☒

8

 

Yes ☒   No ☐

9

 

Yes ☐   No ☒

10

 

Yes ☐   No ☒

11

 

Yes ☐   No ☒

12

 

Yes ☐   No ☒

13

 

Yes ☐   No ☒

14

 

Yes ☐   No ☒

15

 

Yes ☐   No ☒

16

 

Yes ☐   No ☒

17

 

Yes ☐   No ☒

18

 

Yes ☐   No ☒

19

 

Yes ☒   No ☐

20

 

Yes ☐   No ☒

21

 

Yes ☐   No ☒

22

 

Yes ☐   No ☒

23

 

Yes ☐   No ☒

24

 

Yes ☐   No ☒

25

 

Yes ☐   No ☒

26

 

Yes ☐   No ☒

27

 

Yes ☐   No ☒

28

 

Yes ☐   No ☒

29

 

Yes ☐   No ☒

30

 

Yes ☐   No ☒

Now, the SSIM must be formed based on polewise and pairwise comparisons. For pairwise comparisons, the ith proposition is compared pairwise with all elements, from (i + 1)th element to nth element. For each relation, the answer is either “Y” or “N.” If the answer is yes, i.e., “Y,” the reason is stated. In this case, the interpretive logic of paired relations is indicated in the basic scientific-logical interpretive form. In this step, the relationships are entered as a reachability matrix as “1” or “0” demonstrated in Table 9. According to this table, “1” is assigned to cells with “Y” and 0 to cells with “N.” This matrix is ​​obtained by transforming an SSIM into a binary matrix of 0 and 1.

Table 9. Reachability Matrix (RM)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1

0

0

0

0

0

0

0

0

1

0

1

0

1

0

1

 

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

 

1

0

0

1

0

1

0

0

0

0

0

1

0

1

0

0

 

1

0

0

0

1

1

1

1

1

1

0

1

1

1

0

1

 

0

0

0

0

0

0

0

0

0

0

0

1

0

0

0

0

 

1

0

1

0

0

0

0

1

0

0

1

1

0

0

0

0

 

0

0

0

0

0

0

0

1

0

1

0

1

0

1

0

1

 

1

0

0

0

0

0

1

1

1

0

0

1

1

0

0

0

 

1

0

0

0

0

0

0

1

0

0

0

1

0

0

0

0

 

1

0

1

1

0

0

1

0

0

0

1

1

0

0

0

0

 

1

0

1

0

0

1

0

1

0

0

0

1

0

0

0

0

 

1

0

0

1

1

0

0

1

0

0

0

1

1

0

0

1

 

1

0

0

1

0

1

0

1

0

0

0

1

0

1

0

0

 

1

0

1

0

0

1

0

1

0

0

1

1

0

0

0

0

 

1

1

0

0

0

1

0

0

0

0

0

1

0

1

0

0

 

1

0

0

0

0

0

0

0

0

0

0

1

0

0

0

0

 

Then, at this stage, the formation of scores is done based on the interaction of the compared indicators to generate a self-interaction reachability matrix.

Table 10. Reachability matrix in terms of the transitivity of the relationship between the propositions

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

9

1

0

*1

*1

0

*1

0

*1

0

1

0

1

0

1

0

1

 

16

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

 

6

1

0

0

1

0

1

0

*1

0

0

0

1

0

1

0

0

 

14

1

0

*1

*1

1

1

1

1

1

1

*1

1

1

1

0

1

 

1

0

0

0

0

0

0

0

0

0

0

0

1

0

0

0

0

 

6

1

0

1

0

0

*1

0

1

0

0

1

1

0

0

0

0

 

9

1

0

*1

1

0

*1

0

1

0

1

0

1

0

1

0

1

 

12

1

0

0

*1

0

*1

1

1

1

0

*1

1

1

*1

0

*1

 

 

3

1

0

0

0

0

0

0

1

0

0

0

1

0

0

0

0

 

8

1

0

1

1

0

*1

1

*1

0

0

1

1

0

0

0

0

 

5

1

0

1

0

0

1

0

1

0

0

0

1

0

0

0

0

 

10

1

0

0

1

1

*1

0

1

0

0

*1

 

1

*1

0

1

 

7

1

0

0

1

0

1

0

1

0

0

*1

1

0

1

0

0

 

6

1

0

1

0

0

1

0

1

0

0

1

1

0

0

0

0

 

8

1

1

*1

*1

0

1

0

0

0

0

*1

1

0

1

0

0

 

2

1

0

0

0

0

0

0

0

0

0

0

1

0

0

0

0

 

 

15

2

9

9

3

13

4

13

3

5

9

16

4

9

1

6

 

 

As observed in the above table, the conceptual symbols assigned based on the mode index became scores 0, 1, and 1 * concerning the definition of conceptual relationships to numbers according to the previous table.

Then, to determine the relationships between the variables, the output set, the input set, and the commonalities must be initially identified. The score for determining the level and priority of the variables of the reachability set and the antecedent set for each variable are determined. The reachability set of each variable includes variables that we can reach them through this variable, and the antecedent set includes variables based on them this variable can be reached.

 Next, the commonalities of the reachability set and the antecedent set of all the factors are determined and, in the case of an equality of reachability set with its commonality set, the factor (factors) is considered as the top level. The level refers to the designed layers of the final model. In order to obtain other levels, the previous levels should be removed from the matrix, and the process repeated. After determining the levels, the resulting matrix is sorted in order of levels again that the new matrix is called a cone matrix. Put differently, after determining the output elements, the input elements, and the commonalities, the index which has the same output elements and commonalities is determined as the first level and the least effective internal outcome of the stock liquidity. After determining this level, i.e. the least effective level of internal consequences, we will remove the index and examine the same parameters of the input elements and commonalities, and choose it as the next level. This operation continues as long as the components constituting all levels of the system to be identified.

 

 

Table 11. The output and input set elements and commonalities of propositions

Abbreviation

Reachability set

Antecedent set

Intersection set

Level

L1

1.3.5.7.9.11.13.14.16

1.2.4.7.8.12

1.7

 

L2

1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16

2

2

 

L3

3.5.9.11.13.16

1.2.3.4.7.8.12.13.15

3.16

 

L4

1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16

2.4.8.12

4.8.12

 

L5

5

1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16

5

 

L6

5.6.9.11.14.16

2.4.6.8.10.12.13.14.15

6.14

 

L7

1.3.5.6.7.8.9.10.11.13.16

1.2.4.7.8

1.7

 

L8

1.3.4.5.6.7.8.9.10.11.13.16

2.4.8

4.8

 

L9

5.6.16

1.2.3.4.6.7.8.9.10.11.12.13.14

9

 

L10

5.6.9.11.14.16

2.4.8.10

10

 

L11

5.9.11.14.16

1.2.3.4.6.7.8.9.10.11.12.13.14.15

11.14

 

L12

1.2.3.4.5.6.9.11.12.13.16

2.4.12

4.12

 

L13

3.5.6.9.11.13.16

1.2.3.4.7.8.9.10.12.13.15

3.13

 

L14

5.6.9.11.14.16

1.2.4.6.7.10.11.14.15

6.11.14

 

L15

3.5.6.11.13.14.15.16

2.15

15

 

L16

5.16

1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16

16

 

As the results demonstrate,  i.e. the proposition of raising the level of earnings quality is recognized as the least effective criterion for stock liquidity and  i.e. the proposition of increasing the frequency of transactions as the most effective criterion for stock liquidity in Tehran Stock Exchange companies, respectively.

On the basis of Figure (2) and analysis of the cone matrix, it became clear that the total number of stock liquidity propositions in the capital market includes 8 levels ranging from the most effective to the least effective propositions. Accordingly, the most effective proposition concerning the stock liquidity of capital market companies is the increase in the frequency of transactions (L2) as a statement of the component of operational (technical) mechanisms. Moreover, based on the analysis, it was found that the least effective proposition concerning the stock liquidity is the enhancement in earnings quality (L5) as a statement of the component of operational (technical) mechanisms. Besides, indirect relationships between the propositions are indicated with a dashed line. The most effective indirect propositions in stock liquidity include enhancing the positive impact of the stock split by equalizing the ratio of stock ownership held by individuals within the company (L8) as a proposition of structural/governance mechanisms, enhancing the monetary volume of stock trading through balancing the bid price to buy or sell stock (L1) as a proposition of operational (technical) mechanisms, reducing the influence of the high concentration of family ownership on the board of directors (L7) as a proposition of structural/governance mechanisms, reducing the CEO duality (L10) as a proposition of structural/governance mechanisms, understanding the market and industry intended (L15) as a proposition of investors' trading/behavioral mechanisms. As is known, in this section, structural/governance mechanisms have the greatest effect on the mediating role in stock liquidity.

Increasing the frequency of transactions

Enhancing the positive impact of the stock split

Increasing the liquidity capability

Equalizing the expected return based upon capital cost

Reducing the influence of ownership concentration

Enhancing the monetary volume of stock trading

Understanding the market and industry intended

Reducing the CEO duality

Unknown identity of the traders

Increasing the percentage of transaction days

Selecting a cohesive board of directors composition

Unknown nature of the order at a specified price

Stock selection based on the investment horizon

Consolidating the internal controls

Having specialized knowledge for stock selection

 

Raising the level of earnings quality

Balancing the bid price to buy or sell stock leads to an investment horizon

Enhancing the stock split causes an increase in the monetary volume of transactions

 

Understanding the market as a reason for having specialized knowledge

Reducing the influence of ownership concentration as a reason for the cohesion of board of directors

 

Reducing the duality as a reason for greater coherence of internal controls

1

2

3

4

5

6

7

8


Figure 2. The hierarchical levels of the most effective propositions for stock liquidity

Conclusion

With regard to the importance of stock liquidity factors in the capital market, in this study, it was attempted that, by combining qualitative and quantitative methods, while identifying propositions related to stock liquidity, the most effective of these propositions to be identified based upon the total interpretive/structural modeling (TISM). The results in the meta-synthesis and Delphi analysis section revealed that out of 33 external and internal studies examined, 25 propositions in the form of three components of operational (technical) mechanisms, structural/governance mechanisms, and investors' trading/behavioral mechanisms were identified. During the two stages of Delphi analysis, 7 propositions were removed, and 2 propositions were merged. Finally, in the second stage of Delphi analysis, a total of 16 propositions achieved the empirical adequacy, which was approved based on the participation of members of the panel (experts).

Then, in the total interpretive/structural modeling (TISM) section, through the participation of 20 stock exchange brokers and capital market analysts via matrix questionnaires, a total of 16 propositions approved as stock liquidity measures were prioritized at 8 levels from the least effective to the most effective propositions related to stock liquidity. According to the results in this section, it was found that increasing the frequency of transactions (L2) as a proposition of the operational (technical) mechanisms of firms is the most effective factor in stock liquidity in a competitive market. Indeed, increasing trading volume is a driving force for the dynamism of investment in a country's capital market and economy since the fees and interests of all organizations in the capital market are determined based on the tariffs caused by the trading volume, and they will greatly benefit from the volume of transactions rather than benefitting from the increase or decrease in the stock price.

In fact, an increase in trading volume on the charts in charts in the specified time interval could exhibit a level of demand to purchasing the stock that, by controlling the supply level, we can expect stock liquidity in the future. Although a sharp increase in trading volume can be due to negative news, concentration on increasing liquidity level as an operational strategy could bring stability to purchase and sell stocks and generate positive psychological motivations in the capital market. Moreover, it was found that three propositions of enhancing the positive impact of the stock split by equalizing the ratio of stock ownership held by individuals within the company (L8) as a proposition of structural/governance mechanisms, increasing the liquidity capability of real assets such as accounts receivable and inventory (L4) as a proposition of operational (technical) mechanisms, and equalizing the expected return based upon capital cost (L12) as a proposition of investors' trading mechanisms were at the next effective levels, respectively.

In actual fact, enhancing the positive impact of the stock split by equalizing the ratio of stock ownership held by individuals within the company is a strategy that increases the number of shares available to the retailer investors or outsiders by reducing the high proportion of stocks held by specific individuals such as family ownership. By adjusting firms' ownership structures and strategic flexibility to protect the interests of shareholders and investors, this can put the stock liquidity capabilities in line with the positive psychological burden on the market and lead to greater dynamism in stock liquidity. On the other hand, increasing the liquidity capability of real assets such as accounts receivable and inventory will cause the more expected level of stock liquidity in the capital market to be predictable.

Factually, liquid assets such as accounts receivable and inventory and their equivalents can be easily assessed, and very low information asymmetry is taken into account to them. While less liquid assets, including investments and growth opportunities, can be hardly assessed, and the possibility of insider trading about them will be less welcomed in the capital market, which occurs as the result of more information asymmetry. Equalizing the expected return based upon capital cost as another effective proposition in this level refers to the alignment of expected returns based on the firms' capital structure. That is to say, according to investment plans and projects available, companies can generate a level of expectation of their functions on investment returns in the future for investors in terms of financing to prevent the creation of more yields than expectations of the capital market. The existence of these investment facts could be due to the flow of information and the asymmetry of information disclosed that will contribute to better understand the capital market by investors.

Overall, based on the results gained, the most important and effective factors of liquidity in the capital market are the technical (operational) mechanisms, then the investors' trading/behavioral mechanisms, and finally the structural/governance mechanisms. That is, to generate dynamism in the capital market, the operational nature of firms in the supply and sale of stocks must be planned and targeted based on an increase in trading volume and liquidity capabilities of assets so that, by providing timely supply to the capital market, it brings to the most attraction of liquidity for the development of its investment plans and projects and will create an advantage in the competitive market level compared to other companies. Furthermore, through the development of interaction-oriented programs with shareholders and investors, while increasing their mental satisfaction, firms can help raise the level of awareness and proportionality of future return expectations with the companies' real performance and lead to increasing the success in the competitive market. This necessitates the selection of a cohesive board and a reduction in the influence of ownership concentration to build greater confidence and trust in the firm performance in the capital market. The presence of a dynamic board of directors and the lack of CEO duality can cause investors to look at the firms' performance with a more positive prospect and be more aware of the disclosure of information. In accordance with the results obtained, it is proposed:

While contributing to enhancing the firms' stock liquidity level based on increasing the trading volume of companies' stocks, considering the components of the capital market structure at the firms' operational level can however cause understanding the market to balance supply with the company's demand for stocks and lead to greater dynamism in the company's stock exchanges. In other words, the amount of money generated by balancing the bid price to buy and sell stocks leads to an increase in the level of stock liquidity and to be traded as a reliable cash asset.

Besides, it is suggested that the Stock Exchange and other regulatory bodies, by providing information such as liquidity rating and percentage of trading days and the level of the firms' capital structure, help shareholders and investors better understand the market and industry intended and promote the level of expertise to select the appropriate portfolio for investment so that the level of decision-making is adopted based on the functional realities of industry and companies operating in the capital market, and the presence of stock liquidity bubbles in the capital market to be prevented until the risk of investments is decreased, and the return caused by it to be more balanced and logical by surrounding influential factors. Furthermore, it is recommended that corporate governance to develop their stock liquidity capabilities, by choosing a cohesive and integrated board of directors while increasing the level of effectiveness in internal controls as the front line of flow of information feedback to stakeholders, can contribute to greater dynamism in the company's strategies, such as stock splits based on the firms' performance requirements, and increase the level of competitive advantage in the firms' investments. Eventually, it should be stated that political mechanisms and partisan decisions were not explored in this study because control on it was impossible, and the role of these factors in the form of independent research based on the analysis desired in this research can be taken into account in further investigations.

Funding: This research received no external funding.

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