Dynamics of Governance Structure and Stock market performance

doi:10.21203/rs.3.rs-4737506/v1

This paper has aimed to identify the intricate relationships between governance structures, organizational performance stock market. We have utilized data from 80 countries over the period from 1990 to 2021, the study employs dynamic panel data models and the Generalized Method of Moments (GMM) to address time dynamics and endogeneity issues. The findings reveal significant correlations between internal governance mechanisms, such as the Business Disclosure Index and the Shareholder Suits Index, and stock market returns. Moreover, external governance factors like control of corruption, political stability, and rule of law are found to positively impact stock market performance. The study underscores the importance of robust disclosure policies and governance standards in fostering market efficiency and investor confidence. These insights are crucial for policymakers and market regulators aiming to enhance financial market stability and economic growth.

Jel Codes: G30; E44; L22; L25

Structure

Governance

Stock markets

Performance

Stock markets are considered as promoters and facilitators of economic growth Anigbogu et al. (2014). The channel of stock markets transfers surplus savings into investments. Because of this trait, stock markets are vital for the economic growth of a country (Anigbogu et al., 2014) The stock markets reduce the cost that incurs because of mobilization of capital and facilitates investment Levine et al. (1996). As the stock prices vary, it affects the consumption plus the investment pattern which influences the performance of the economy Shen (2000). According to Levine (1997), stock markets mobilize the savers for investment. Liquidity of stock market affects economic activity of the country. Investments which circle profitable projects require capital commitment. Investors always want to invest in liquid markets. Liquid markets reduce the risk and cost of putting money in projects. The ease of investment in projects improves the allocation of capital due to liquid markets triggering the growth of economy Levine et al. (1996). The economy of the country is enhanced by increased capital at low cost with the help of stock markets Claessens et al. (2000).

Stock markets are superior to banks in liquidity, exchange of information and ease the transmission of funds for the business community. Investment climate and growth of a country are gauged by stock markets. The fundamental role of Stock markets in developing countries is to improve the circulation of savings in the form of investment Khan (2004). Developing markets are putting efforts in restructuring and modification of financial system mobilize resources and swell capital markets for effectual provision of resources Basu et al. (2013). There are several channels and routes which help the market to affect the economic growth. The expansion and growth of the financial sector helps in triggering the saving rates. The saving rate gets more stimulated as the financial intermediaries become competent to provide higher yield. The financial segment of the economy assists in allocation of resources. The mechanism of allocation of investment includes:

Large scale projects become possible, and it helps in making lending cheaper.

It helps in diversification of portfolios and this diversification helps in reducing risks.

Investment in projects becomes more liquid and channels of investment gets clear which helps in decision making.

The evaluation of investment projects becomes easier and fair.

The performance of borrowers is checked to make sure that they fulfill their commitments of investment VALERIANO & Lin. (1999)

The structure of an organization can be likened to its anatomy, serving as the foundational framework that enables the organization to operate. It is thought that this structure influences the behaviour of members within the organization. The design of a building plays a crucial role in shaping the actions of those inside it, just as the organizational structure impacts behaviour within institutions. Although the effects of an organizational structure may not be as visibly obvious as those of a building's architecture, they are believed to be widespread and significant. Dalton et al. (1980)

The concept of organizational performance or effectiveness, viewed as a variable that is dependent on an organization's structure, has been conceptualized and assessed through diverse lenses and methodologies. This multifaceted approach to understanding organizational performance stems from the recognition that the structural dimensions of an organization—such as its hierarchy, division of labor, coordination mechanisms, and decision-making processes—play a pivotal role in shaping its ability to achieve goals, adapt to changes, and sustain competitive advantage Dalton et al. (1980).

Researchers and theorists have employed various frameworks and indicators to measure organizational performance or effectiveness in relation to structure. For instance, some studies focus on quantitative metrics, like financial performance indicators (profitability, revenue growth), efficiency ratios, and market share, to evaluate the impact of organizational structure on outcomes. These metrics provide tangible evidence of how well an organization is performing financially and operationally under its current structural configuration Dalton et al. (1980). Other approaches delve into qualitative aspects, assessing the influence of structure on organizational behaviour, employee satisfaction, innovation capacity, and the ability to respond to environmental changes. These dimensions reflect the internal dynamics and health of the organization, indicating how structure facilitates or hinders the flow of information, decision-making speed and quality, and overall adaptability to external pressures Dalton et al. (1980)

In addition, some researchers adopt a more holistic perspective, integrating both quantitative and qualitative measures to capture a comprehensive picture of organizational effectiveness. This approach recognizes that the interplay between an organization's structure and its performance is complex and multifaceted, influenced by external environmental factors, industry characteristics, and internal capabilities and resources. Overall, the investigation into how organizational structure affects performance or effectiveness underscores the importance of aligning structural elements with strategic objectives, environmental demands, and the needs of organizational members. By envisioning and measuring organizational performance in various ways, scholars and practitioners can better understand the critical role structure plays in enabling or constraining an organization's success and sustainability.

Besides the structural importance of organization, there is a need to govern the system that prevails in the stock markets. Governing of stock markets can be internal as well as external. The regulation by which the financial system works is called governance Hooper et al. (2009). The exercise of actions that control and direct members, citizens, communities, societies, and states are governance World Bank (1989). The monitoring and control in the banking decreases encounters between shareholders and managers Daadaa (2020). Legal, political, and supervisory systems are the basis of any institution to provide the proper functioning. Factors like system, lawful actions, political stability, control of corruption, and accountability define the quality of the institution Hooper et al. (2009). According to Low et al. (2011) the worth of governance is scaled by regulation, stability, and rule, law, regulations, and liability and corruption control. Good governance is needed for secure and developed societies, in which there is absence of violence, misconduct and law breaking that provides stability UNDP (2015). Governance is very vital for functioning and dealing between creditors and debtors Hooper et al. (2009).

Reforms that include the opening of new financial markets, trade openness, and the flow of capital across the borders, affect the county through different challenges and risks. Effective governance systems throughout the country in financial as well as non-financial sectors are very imperative to keep the country among globally competitive financial markets Low et al. (2011). Through this importance, we can inaugurate that governance framework affects and has deep consequences on the configuration of investors and firms in the financial markets

Low et al. (2011). Firms do not run in vacuity and void but are affected by the classification, coordination, and organization in which these exist Low et al. (2011).

Stock market performance appeals to investors locally as well as internationally. Factors like unproductive government, weak monitoring structure, poor laws enforcement, fraud, sleaze, and corruption are barricades in the way of foreign direct investment (FDI) Low et al. (2011). Besides governmental factors that affect the performance of the stock market, other factors that are inside the stock markets are also manipulating the latter’s performance like a board of directors, change in policies, change of management team earnings, dividends, etc Osuagwu, (2009). The politics of a country is one of the foremost characteristics and phases of country-level governance. Different political events have different types of effects on the returns of a market. If a politician enacts policies that are favorable to the market leads to an increase in the market returns and vice versa Jensen et al. (2005). The performance of the stock market is affected by political risk and both domestic and international investors are concerned about that Murtaza et al. (2015).

The research explores how the structural dynamics and governance protocols within stock markets influence their overall performance. It hypothesizes that internal governance mechanisms, such as robust regulatory frameworks, transparency, and accountability, play a crucial role in operational efficacy and financial outcomes. The study systematically analyzes the correlation between governance practices and market performance, aiming to understand how effective governance can enhance market efficiency and investor confidence. This investigation seeks to contribute to financial economics and offer actionable insights for policymakers and market regulators to improve governance structures within stock markets, ultimately impacting their stability, growth, and sustainability globally.

H ₀: the effectiveness of internal governance mechanisms within stock markets is positively correlated with the performance of these markets.

H ₁: The structural configuration of stock markets is significantly linked to their performance outcomes.

H ₂: The performance of stock markets is influenced by the quality of external governance mechanisms.

Although structure, governance, and stock market performance have significant implications on the issues of market stability and economic growth, the relationship among them has been central to the literature on finance. In this regard, many studies have intensively addressed various aspects of the dynamics of the stock market to highlight the critical role that governance protocols and structural frameworks play in determining market efficiency and investor confidence. There is always much debate on the influential role of governance structures in enhancing a stable and developed market environment. This literature review aims to find out how structural dynamics and governance practices determine the performance of the stock market through the current state of knowledge. It attempts to identify some of the main themes, gaps, and insights that have guided both academic inquiry and practical policymaking within financial markets.

Studies reveal that macroeconomic factors like interest rates, inflation, exchange rates, and foreign direct investment significantly impact stock market performance (SMP). Jahur et al. (2014) and Olugbenga et al. (2012) found predominantly negative effects of interest rates and inflation, with dual impacts from exchange rates. Institutional factors further complicate these relationships, as shown by Islam et al. (2017). Regional studies, such as those by Maku et al. (2010) and Zafar (2013), present varied effects. Commodity prices also play a role, influencing SMP differently across contexts. Overall, SMP is influenced by a complex interplay of local and global economic variables.

The exploration of organizational context by Pugh et al. (1969) highlights the significance of seven key elements—origins, governance, size, mission, technology, location, and interdependencies—in shaping organizational structures. Their comprehensive multivariate regression analysis of 46 organizations in the English Midlands revealed substantial correlations, notably a 0.75 correlation between the structuring of activities and size and technology, and a 0.57 correlation between line control of workflow and operational variability. These findings emphasize the profound influence of contextual factors on organizational design and operation, setting the stage for more rigorous comparative studies.

Ahmady et al. (2016) further advanced the understanding of organizational structures by emphasizing both tangible and intangible elements. Kimberly (1976) critically examined the conceptual and empirical treatment of organizational size in research, analysing 80 studies conducted between 1950 and 1974. The critique pointed out that broad definitions of size often obscured its relationship with organizational structure, leading to conceptual and empirical challenges. Kimberly proposed an alternative framework for a more precise understanding of size, suggesting that a clearer conceptualization could enhance the study of organizational structures. Pennings (1973) focused on evaluating tools for assessing structural features like centralization and formalization. By comparing institutional methods with survey methods, the study highlighted discrepancies and agreements between different measurement techniques. Pennings' research cast doubt on the reliability of certain metrics due to differing empirical scopes, suggesting a need for more standardized and accurate measurement tools.

Dalton et al. (1980) delved into the relationship between organizational structure and performance, differentiating between quantifiable and qualitative performance criteria. Their analysis highlighted the importance of distinguishing between structuring processes and established structural arrangements. The study suggested directions for future research to address gaps and imbalances, emphasizing the need to understand how organizational structure intricacies influence effectiveness and success. Ford and Slocum (1977) identified four key dimensions of organizational structure: complexity, formalization, centralization, and administrative intensity. Complexity involves differentiation across hierarchical levels, functions, and locations. Formalization pertains to the explicitness of rules and procedures, while centralization concerns the concentration of decision-making authority. Administrative intensity evaluates the ratio of administrative to production staff. These dimensions provide a comprehensive framework for analysing organizational structures.

In the realm of stock market research, Dev and Shakeel (2013) and Mukit and Arif (2014) examined the impact of stock market size, proxied by market capitalization, on economic growth. Their studies concluded that larger market capitalization positively influences stock market growth and real economic growth. However, Peprah and Borteye (2022) found a moderate negative relationship between market size and economic growth. Similarly, Osakwe et al. (2020) noted that market capitalization's impact on economic growth was significant in South Africa but not in Nigeria. Herath (2020) and Kamran et al. (2018) also supported the positive impact of stock market size on economic growth. The study by Modugu et al. (2020) on Gulf Corporation Council countries concluded that political stability, absence of violence, and rule of law enhance stock market performance. Similarly, K.B (2014) examined Nigerian stock market performance using governance indicators like control of corruption and government effectiveness, finding positive effects, while political stability dampened performance. OMODERO et al. (2018) found a positive relation between corruption and stock market performance in Nigeria.

Boadi et al. (2017) investigated the relation between equity returns and governance indicators across income levels in 23 countries from 1996 to 2014, revealing a significant association. Daouk et al. (2006) developed a capital market governance index, finding that improved governance increased market liquidity and pricing efficiency while reducing the cost of equity. Low et al. (2011) analysed stock market performance in 48 countries, concluding that most governance indicators negatively impact performance, except voice and accountability. Imran et al. (2020) studied 25 developed countries and found that better governance indicators positively affect stock market performance, with investor protection laws boosting investor confidence.

Asongu (2012) examined African countries, finding that better government institutions lead to higher market capitalization, turnover, and number of listed companies. Heikki et al. (2015) found a positive relation between political risk, democracy, and stock market performance in 49 emerging markets. Jensen et al. (2005) studied Brazilian elections and found no evidence of specific politicians affecting stock market returns, similar to Furio et al. (2012) with the Spanish stock market. Pierdzioch et al. (2004) found that conservative governments in Germany tend to increase market returns, with stock market returns also affecting government popularity. Asteriou et al. (2016) studied political instability in 18 OECD countries, finding a negative relationship with stock market returns. In Pakistan, Murtaza et al. (2015) found that political events causing governmental policy changes impacted stock market returns. Finally, Shirvastav and Kalsie (2017) concluded that higher disclosure of information improves performance.

The Fig. 1 illustrates the interrelationship between three key elements: Execution, Conduct, and Structure. Execution refers to the implementation of strategies and actions within an organization. Conduct encompasses the behaviours and practices of individuals and groups within the organization. Structure denotes the organizational design, including hierarchy, roles, and resource allocation. The arrows indicate a bidirectional influence among these elements, signifying that changes or actions in one area affect and are influenced by the others. This dynamic interaction ensures continuous feedback and adjustment, aiming for improved organizational performance and alignment with strategic goals.

Corporate structure refers to the design and arrangement of a company's hierarchy, division of labor, departments, and resource allocation aimed at achieving business goals. Effective structuring aligns organizational processes with strategic aims, enhancing operational efficiency, cost management, and strategic agility, which are valued by investors. A scalable and adaptable structure boosts investor confidence and stock market performance. Governance, on the other hand, encompasses the rules, practices, and processes that direct and control a company. It defines the rights and responsibilities within the corporation, ensuring accountability, fairness, and transparency, which can improve risk management and strategic decisions, positively impacting stock market performance.

Good corporate governance reduces the risk of mismanagement and scandals, enhancing stock prices. Conversely, poor governance can lead to calls for structural changes and governance reforms to improve stock performance. The relationship between stock market performance, corporate structure, and governance is bidirectional and cyclical. Changes in governance can lead to structural adjustments, which affect stock performance, and vice versa. This continuous feedback loop involves constant interaction between corporate performance, governance, and market behaviours, necessitating regular adjustments to maintain equilibrium and achieve long-term success. Stock market performance acts as a barometer for corporate health and economic outlook. Rising stock prices indicate investor confidence and robust company performance, while declining prices signal concerns. Effective governance structures attract investors, and immediate market reactions to changes in governance or structure provide feedback for companies. Revaluation often follows poor market performance, prompting examinations of leadership, operational efficiency, and strategic direction.

Implementation of changes based on revaluation may involve leadership changes, shifts in operational focus, or new governance policies. Market reactions to these adjustments influence corporate strategies, creating an ongoing cycle of action and reaction. Managing this feedback loop requires balancing market signals with strategic objectives, avoiding overreaction to short-term fluctuations, and considering external variables like economic conditions and regulatory changes. Efficient governance and adaptive structures foster continuous improvement, investor confidence, and long-term success.

The current study focuses on the impact of governance and structure on stock market performance. Governance, as defined by Assaduzaman et al. (2016) and Javed et al. (2006), involves the system of rules, practices, and processes by which an organization is directed and controlled. Effective governance includes transparency, accountability, participation, and rule of law, which are crucial for improving institutional performance and protecting investor rights. Good governance fosters a system where conflicts of interest are managed, and corporate activities are aligned with shareholder interests, thereby enhancing stock market performance.

Corporate governance systems vary between developed and developing countries (Azeez, 2015), and include aspects such as global, national, organizational, service, and community governance (World Bank, 1992; Smith, 2019). Effective governance leads to better institutional performance, which attracts investor confidence and promotes financial market stability (Darko et al., 2015; Imran et al., 2020). Good governance is essential for maintaining long-term stakeholder value and ensuring transparent, efficient, and responsive services (Cohen et al., 2002; Bovaird et al., 2003).

The structure of an organization, defined by Dalton et al. (1980) and Ahmady et al. (2016), includes elements like specialization, formalization, standardization, centralization, configuration, and traditionalism. The structure influences how organizational activities are coordinated and controlled, impacting overall performance. A well-structured organization ensures efficient resource allocation, clear communication, and effective decision-making processes, which are critical for achieving business goals. The relationship between governance, structure, and stock market performance is bidirectional and cyclical. Changes in governance practices can lead to structural adjustments, affecting stock market performance. Conversely, stock market performance can prompt re-evaluation of governance and structural practices. This feedback loop necessitates constant adjustments to maintain equilibrium and achieve long-term success. Effective governance and adaptive structures foster continuous improvement, investor confidence, and better risk management.

Stock markets, where shares of firms are traded, play a vital role in the efficient development of capital and resource allocation. Stock market performance, indicated by metrics like market capitalization, number of listed companies, value traded, and turnover, is influenced by several factors, including governance and organizational structure (Asongu, 2012; Zafar, 2013; UDI et al., 2018). Good governance practices enhance market performance by ensuring transparency, protecting investor rights, and fostering a stable investment environment.

In conclusion, governance and structure significantly influence stock market performance. Effective governance ensures transparency, accountability, and alignment of corporate activities with shareholder interests, while a well-structured organization promotes efficient resource allocation and decision-making. The interplay between governance, structure, and stock market performance highlights the importance of continuous assessment and adaptation to maintain stability and achieve long-term success in financial markets.

Regression analysis of our research focuses on three primary variables: structure, governance, and stock market performance. The relationship among these variables is empirically examined through the subsequent regression. Below is the mathematical representation of this model:

SMP = α ₀ + α₁ IGov_it + α₂ STR_it + α₃ GE_it + α₄ X_it +€_it………… 4.1

SMP= (Internal Governance, Structure of Stock market, Governing Economy related variables, Macroeconomic variables)

Econometric representation of the model is as follows:

SMP = f (IG, GE, STR, X)

and

SMP = f (BDI, SMV, Mcap. Fdi, Trad, GDP, Bm, CCR, GEF, PSV, RQ, Rl, Va) …… 4.2

SMP = f (SSI, SMV, Mcap. Fdi, Trad, GDP, Bm, CCR, GEF, PSV, RQ, Rl, Va) …… 4.3

SMP = f (Business extent of Disclosure index, Ease of shareholder suits index, Stock market volatility, Market capitalization % of GDP, Inflation, Trade, Gross Domestic Product, Broad Money, Control of corruption, Government effectiveness, Political stability, Regulatory quality, Rule of Law, voice and accountability)

So the model can be written as follows:

SMP = α ₀ + α₁ bdi_it + α₂ smv_it + α₃ mcap_it + α₄ fdi_it + α₅ trad_it + α₆ GDP_it + α₇ Bm_it + α₈ Ccr_it+

α ₉ Gef_it + α₁₀ Psv_it + α₁₁ Rq_it + α₁₂ Rl_it + α₁₃ va_it + €_it ……….4.4

SMP = α ₀ + α₁ ssi_it + α₂ smv_it + α₃ mcap_it + α₄ fdi_it + α₅ trad_it + α₆ GDP_it + α₇ Bm_it + α₈ Ccr_it+

α ₉ Gef_it + α₁₀ Psv_it + α₁₁ Rq_it + α₁₂ Rl_it + α₁₃ va_it + €_it ……….4.5

Where, the subscript i denotes the ith country (i = 1, …, 80) and the subscript t denotes that tth year (t = 1, ….., 31).

Our study focuses on the structure, governance, and performance of 80 stock markets from 1990 to 2021. Governance variables are divided into internal and external categories. Internal governance is proxied by the business extent of disclosure index and ease of shareholder suits index. External governance includes Voice and Accountability, Political Stability, Regulatory Quality, Rule of Law, Control of Corruption, and Government Effectiveness. Stock market performance is measured by stock market capitalization to GDP (%), total value traded to GDP (%), and year-on-year stock market return (%). Countries included in analysis are shown in Annex A(b).

In our study, the structure of the stock market is represented by market capitalization as a percentage of GDP, with data sourced from the World Development Indicators (WDI) for the period from 1990 to 2021. To gather data on external governance, we sourced information from the World Development Indicators (WDI) for the period from 1990 to 2021. The WDI data includes variables that quantify and understand external governance, such as the effectiveness of legal frameworks, the efficiency of regulatory bodies, and the degree of transparency and accountability in public institutions. These variables provide insights into the external governance environment influencing stock markets.

The internal governance of the stock market is assessed through specific proxy variables, including the Business Extent of Disclosure Index and the Ease of Shareholder Suits Index. Data is taken from WDI from 1990–2021. These variables capture the essence of internal governance practices such as corporate governance codes, internal control mechanisms, board structure and functions, shareholder rights, executive compensation policies, and audit procedures. By examining these factors, the study aims to understand how internal governance mechanisms contribute to the stock market's efficiency, integrity, and stability. This analysis is crucial for identifying areas for improvement and understanding their impact on market performance.

In this study, the performance of the stock market is the dependent variable, with data sourced from the World Development Indicators (WDI) for the years 1990 to 2021. This extensive period allows for the analysis of trends and changes in stock market performance. The specific measure used is the year-on-year percentage return of the stock market, which represents the annual percentage change in market value. This metric captures the dynamic nature of the stock market, reflecting both short-term fluctuations and long-term trends.

In our research we carefully select control variables crucial for assessing the complex relationship between corporate structure, governance mechanisms, and their combined impact on stock market performance. To ensure the robustness of our findings, we incorporate Gross Domestic Product (GDP), Foreign Direct Investment (FDI), Broad Money (M3), and Trade as our control variables. The data is taken from WDI from 1990 to 2021.

In our research, structure and governance are the independent variables, while stock market performance is the dependent variable in our econometric model. The dataset spans from 1990 to 2021, excluding trends and changes outside these years. Therefore, our analysis may not capture long-term historical trends or recent developments post-2021. The dataset includes 80 countries based on data availability and completeness. This limitation restricts the scope of our analysis, potentially affecting the applicability and representativeness of our findings for countries not included in the study. These constraints should be considered when interpreting the research outcomes.

The Pearson correlation coefficient measures the strength and direction of the linear relationship between two variables, ranging from − 1 to + 1. A value close to + 1 indicates a strong positive relationship, close to -1 indicates a strong negative relationship, and around 0 indicates no linear relationship. It's important to note that this coefficient only assesses linear correlations and does not account for nonlinear relationships. The table #1 is given in Annex (a).

The selection of estimation techniques is guided by the hypothesis, objectives and research questions of the study. Given the need to address time dynamics and endogeneity issues, we employ dynamic panel data models, which incorporate lagged values of dependent and independent variables. This approach helps control for trends, unobserved heterogeneity, and endogeneity. Panel data allows us to capture variability across countries and over time, reducing estimation bias associated with country-specific effects.

Generalized Method of Moments (GMM)

To tackle endogeneity, we use the Generalized Method of Moments (GMM), as suggested by Hansen (1982). GMM is particularly suitable for large panels with fewer time periods, providing consistent and reliable estimates. The Sargan test is applied to check for the over-identification of instruments in the model. Dynamic models, by including lagged dependent variables, address issues of autocorrelation and heterogeneity. Pooled OLS and fixed effect models often result in biased and inconsistent estimates in dynamic settings, making GMM a preferable choice.

We use two proxies for internal governance within stock markets: the Business Disclosure Index and the Shareholder Suits Index. The study begins with ordinary least squares (OLS) estimations, using simple models that do not include the dynamic effect of the dependent variable. Subsequently, dynamic models incorporating lagged dependent variables are estimated. The following equations represent our models:

Smr _it = α + β₁ bdi_it + β₂ smv_it + β₃ mcap_it + β₄ fdi_it + β₅ trad_it + β₆ GDP_it + β₇ Bm_it + β₈ ccr_it + β₉ gef_it + β₁₀ psv_it + β₁₁ rq_it + β₁₂ rl_it + β₁₃ va_it +€_it……….. 6.1

Smr _it = α + β₁ ssi_it + β₂ smv_it + β₃ mcap_it + β₄ fdi_it + β₅ trad_it + β₆ GDP_it + β₇ Bm_it + β₈ ccr_it + β₉ gef_it + β₁₀ psv_it + β₁₁ rq_it + β₁₂ rl_it + β₁₃ va_it +€_it……….. 6.2

Where, the subscript i denotes the ith country (i = 1, …, 80) and the subscript t denotes that tth year (t = 1, ….., 31).

In next part, we have added dynamic effect within the model so the model is:

Smr _it = α + β₁ smr_it−1 + β₂ bdi_it + β₃ smv_it + β₄ mcap_it + β₅ fdi_it + β₆ trad_it + β₇GDP_it + β₈ Bm_it + β₉ ccr_it + β₁₀ gef_it + β₁₁ psv_it + β₁₂ rq_it + β₁₃ rl_it + β₁₄ va_it +€_it……….. 6.3

Smr _it = α + β₁ smr_it−1 + β₂ ssi_it + β₃ smv_it + β₄ mcap_it + β₅ fdi_it + β₆ trad_it + β₇GDP_it + β₈ Bm_it + β₉ ccr_it + β₁₀ gef_it + β₁₁ psv_it + β₁₂ rq_it + β₁₃ rl_it + β₁₄ va_it +€_it……….. 6.4

Where, smr _t−1 is first lag of smr.

7.1 Business Disclosure Index

The section discusses the application of Ordinary Least Squares (OLS) regression models to understand the relationships between various factors and stock market returns, with a particular focus on models both with and without lagged dependent variables. The data is structured according to panels study using STATA, including variables related to stock market structure, governance within stock markets, external governance, and control variables. The results are shown in table 2.

Initially, OLS was applied to the model (Eq. 6.1) and the results without a lagged dependent variable showed an R-squared value of 0.2230. When a lagged dependent variable was included, the R-squared value increased to 0.4425, indicating a substantial improvement in the model's explanatory power. The coefficient of the lagged dependent variable (Smr_t-1) was 0.5011832 (p-value 0.000), showing a strong and significant effect of past values on the current stock market return.

Significant findings include the positive impact of the Business Disclosure Index (BDI) on stock market returns. Without the lagged dependent variable, a unit change in BDI was associated with a 0.6585753 increase in stock market return (p-value 0.052). With the lagged dependent variable, the effect decreased to 0.3870693 and became statistically insignificant (p-value 0.181). Other variables, such as Bm, Trad, GDP, and FDI, showed weak or negligible relationships with the dependent variable in both models.

Stock market volatility (Smv) consistently had a significant and positive relationship with stock market returns in both regressions. Market capitalization (Mcap) showed a marginally non-significant positive effect. For external governance factors, control of corruption (ccr) had a significant positive relationship with stock market returns without the lagged dependent variable, while government effectiveness (gef) had an insignificant positive effect. Political stability (psv) and other governance factors like regulatory quality (rq) and rule of law (rl) showed insignificant effects.

The results underscore the importance of including lagged dependent variables in OLS models, as past values significantly affect current outcomes. However, this also introduces potential issues such as autocorrelation and multicollinearity, which require careful interpretation and additional diagnostics to confirm the model's robustness. References to previous studies highlight the relevance and limitations of OLS models. For instance, Godfrey (2011) emphasizes testing for error autocorrelation, while Min et al. (2021) suggest spatial regression analysis might be superior in certain contexts. The increase in R-squared value when including a lagged dependent variable underscores the substantial impact of past values, but also the necessity for rigorous validation to ensure reliable and accurate results.

The analysis utilizes Pooled OLS, Fixed Effect models, and One-Step Difference GMM to predict stock market returns, emphasizing the role of a lagged dependent variable (shareholder suits index) to enhance explanatory power.

Pooled OLS Regression

We applied Pooled OLS on Eq. 6.3. Pooled OLS regression analyses panel data by pooling cross-sectional units over time into a single regression equation. When including the Business Disclosure Index (BDI) for internal governance, the pooled regression results show a positive coefficient (0.335509) with a significant p-value (0.290). This indicates that higher business disclosure levels are associated with better stock market performance. The Business Disclosure Index, encompassing financial reports, risk factors, governance practices, and feasibility reports, enhances investor decision-making by increasing transparency and reducing information asymmetry. Such transparency is crucial for attracting investors and reducing stock price volatility, aligning with the consensus that effective disclosure practices can enhance market efficiency and firm valuation.

Fixed Effect Model

We applied Fixed effect model on Eq. 6.3. Fixed effect regression results also indicate a significant positive relationship between BDI and stock market returns. This method controls for unobserved heterogeneity by considering only within-entity variations, thus highlighting the importance of business disclosure in improving stock market performance. Additionally, various studies, such as those by Khanifah et al. (2020) and Modugu & Dempere (2020), have explored the impact of governance factors on stock market returns, emphasizing the positive effects of political stability, rule of law, and control of corruption, while regulatory quality and voice and accountability often show negative relationships.

External Governance Factors

The study includes external governance variables such as control of corruption, government effectiveness, political stability, rule of law, regulatory quality, and voice and accountability. These factors significantly influence stock market returns. Improved control of corruption and political stability enhance investor confidence and market performance, while regulatory quality and voice and accountability may negatively impact market flexibility. Government effectiveness and rule of law generally have positive but sometimes insignificant effects. This complex relationship underscores the need for a nuanced understanding of governance factors in market dynamics.

Market Structure and Performance

The relationship between market structure, indicated by market capitalization (% of GDP), and stock market performance is not straightforward. While some studies suggest a positive effect of market share on financial performance, others indicate an inverse U-shaped relationship between stock market structure and performance. The fixed effect regression suggests a significant positive relationship between market cap relative to GDP and stock market returns, reflecting a potentially robust market. However, the effect of trade and Federal Direct Investment (FDI) on stock market returns remains non-significant, highlighting the multifaceted nature of these relationships.

Lagged Dependent Variable and One-Step Difference GMM

The inclusion of a lagged dependent variable in the model Eq. 6.3, significantly enhances explanatory power. The pooled regression and fixed effect models show significant positive coefficients (0.4734945 and 0.3882565, respectively), suggesting that past stock market returns have strong predictive value for future returns, indicating a momentum effect. The one-step difference GMM method, which also shows a significant positive relationship (0.482358), is crucial for addressing endogeneity issues in panel data by using lagged values as instruments. This method provides more reliable estimates, especially in smaller samples or with weak instruments, offering robust standard errors and simplicity. The results are shown in table 3.

The study highlights the importance of including lagged dependent variables to capture the dynamic nature of stock market returns. The significant increase in R-squared value from 0.2097 to 0.4172 when including the lagged dependent variable underscores the substantial impact of past values on current returns. However, it is essential to address potential issues such as autocorrelation and multicollinearity to ensure robust and reliable results.

The positive relationship between BDI and stock market performance emphasizes the need for transparency and effective disclosure practices. Similarly, external governance factors play a crucial role in shaping stock market returns, with control of corruption, political stability, and rule of law positively influencing market performance. The nuanced effects of market structure and other variables like GDP and trade highlight the complexity of these relationships.

The inclusion of a lagged dependent variable significantly improves the explanatory power of regression models predicting stock market returns. The Pooled OLS, Fixed Effect, and One-Step Difference GMM models each provide unique insights, with the GMM method addressing endogeneity concerns effectively. The findings underscore the importance of governance factors and transparency in enhancing market performance. However, the complex and multifaceted nature of these relationships necessitates careful interpretation and further research to ensure robust conclusions and practical implications for investors and policymakers.

Table# 2 Author’s self-calculation (OLS with lagged dependent variable, OLS without lagged dependent variable)

Division of variables

Independent variables

Smr(OLS)

Smr(OLS) with lagged dependent variable

Lagged Dependent variable

Smr_t−1

-

0.5011832

(0.000) ***

Internal Governance

Bdi

0.6585753

(0.052) **

0.3870693

(0.181)

Size

Mcap

0.0252053

(0.250)

0.0302958

(0.104)

Control variables

Bm

0.0021816

(0.914)

0.0067859

(0.692)

Trad

0.0106941

(0.567)

0.0048402

(0.760)

Smv

0.8232857

(0.000) ***

0.3961754

(0.000) ***

GDP

-1.18e-12

(0.050) **

-6.90e-13

(0.180)

Fdi

-6.87e-12

(0.587)

-9.52e-13

(0.929)

External Governance

Ccr

0.4536734

(0.000) ***

.2735994

(0.003) ***

Gef

0.006589

(0.952)

.0170321

(0.854)

Psv

0.0763157

(0.122)

0.0754632

(0.854)

Rq

-0.0746267

(0.416)

− .0851

(0.278)

Va

-0.694388

(0.000) ***

− .4589411

(0.000) ***

Rl

-0.0030059

(0.947)

.0385624

(0.322)

No. of Observation 757 752

R squared 0.2230 0.4425

*, ** and *** denote significance at the 1,5, or 10 percent level respectively.

Table# 3 Author’s self-calculation ( Pooled OLS, Fixed Effect model & one step difference GMM)

Division of variables

Independent variables

Smr (pooled OLS)

Smr (Fixed effect model)

Smr (one step Difference GMM)

Lagged dependent variable

Smr_t−1

0.4734945

(0.000) ***

0.3882565

(0.000) ***

0.482358

(0.000) ***

Internal Governance

Bdi

0.335509

(0.290)

-0.5211664

(0.529)

1.877884

(0.883)

Size

Mcap

0.003629

(0.833)

0.0871511

(0.126)

0.1090132

(0.242)

Control variables

Bm

0.0067332

(0.664)

-0.0541507

(0.532)

0.2187428

(0.454)

Trad

0.0051276

(0.700)

-0.0494894

(0.247)

-0.5287685

(0.513)

Smv

0.3097193

(0.002) ***

0.5992612

(0.003) ***

0.5758001

(0.044) **

GDP

-4.08e-13

(0.477)

-3.02e-12

(0.004) ***

-0.491e-12

(0.050) **

Fdi

-7.66e-12

(0.303

2.64e-12

(0.790)

3.62e-10

(0.055) **

External Governance

Ccr

0.2195196

(0.042) **

0.2499753

(0.268)

0.4641771

(0.284)

Gef

-0.0012067

(0.990)

0.0997592

(0.726)

-0.1322472

(0.752)

Psv

0.0662885

(0.110)

-0.0927165

(0.438)

1.070658

(0.295)

Rq

-0.0534739

(0.559)

-0.3962999

(0.010) *

-0.5092442

(0.080) **

Rl

0.0117883

(0.785)

-0.1269324

(0.472)

-0.5957851

(0.461)

Va

-0.3694435

(0.004) ***

-0.3783092

(0.098) *

-0.7932876

(0.095) *

No. of Observation 752 752 702

*, ** and *** denote significance at the 1,5, or 10 percent level respectively.

Arellano Bond Test of Autocorrelation

The test for dynamic panel data models examined autocorrelation in the residuals, with the null hypothesis being no autocorrelation. The AR(2) test yielded a z-value of 1.29 and a p-value of 0.198. Since the p-value exceeds the conventional threshold of 0.05, we fail to reject the null hypothesis, indicating no significant autocorrelation in the residuals. This suggests the model is appropriate for analysing the relationship between structure, governance, and stock market performance, enhancing the credibility and reliability of the results.

Additionally, the Hansen test of overidentification restrictions was applied to validate the instrumental variables. The test returned a p-value of 0.971, suggesting the instruments are valid, as the high p-value indicates no reason to reject the null hypothesis. However, the unusually high p-value raises concerns about potential computational or methodological issues, warranting careful interpretation. Ensuring the validity of instruments is crucial for drawing credible inferences in the research on stock market performance. The results are shown in table 4.

Sargan Test

The Sargan test of over-identifying restrictions evaluates the validity of instruments in models estimated through IV techniques or GMM. It tests the null hypothesis that the instruments are valid, meaning they are uncorrelated with the error term and correctly excluded from the model. In the research on the structure, governance, and stock market performance, the Sargan test statistic of chi2(27) = 28.29 with a Prob > chi2 = 0.248 indicates no statistical evidence against the null hypothesis at conventional significance levels. This suggests the instruments used are valid and support their use in the model. The results are shown in table 4.

These findings align with principles from seminal works by Sargan (1958) and Hansen (1982), emphasizing the importance of instrument validity for reliable IV and GMM estimations. Valid instruments ensure that the estimated effects of governance on market performance are not confounded by omitted variable bias or reverse causality, as highlighted by studies from La Porta et al. (1998, 2000), Fama and Jensen (1983), and Shleifer and Vishny (1997).

The acceptance of instruments through the Sargan test supports the reliability of findings on the impact of governance structures on stock market performance. This reinforces the theoretical framework linking corporate governance and equity returns, as discussed by Gompers et al. (2003), and contributes to ongoing debates on optimal governance practices and their implications for market efficiency and investor protection.

Hansen test

The Hansen test of over-identifying restrictions, with a chi-square statistic of 12.68 and a p-value of 0.971, suggests perfect instrument validity in the context of research on the structure, governance, and stock market performance. This result indicates no evidence against the null hypothesis of instrument exogeneity. Seminal works like Hansen (1982) emphasize that a non-significant result supports instrument validity. However, such an unusually high p-value might necessitate a re-evaluation of the model to ensure it captures the dynamics of governance and market performance accurately. The results are shown in table 4.

Studies by Milosevic et al. (2015) and Fama and French (1995) highlight the importance of valid instruments in establishing causal relationships between corporate governance and stock market outcomes. Thus, while the Hansen test result supports instrument validity, it also underscores the need for rigorous econometric verification and model assessment to ensure reliable findings in the complex interplay of governance and financial markets.

Table# 4 Author’s self calculation (AR(2) $& HT of over rid. Restrictions)

Test result

Z

Pr > z

Arellano-Bond test for AR (2)

H₀: Absence of second-order serial correlation in disturbances

1.29

0.198

Accept the null Hypothesis

Hansen test of over rid. restrictions

H₀: overidentification restrictions are valid

Chi2 = 12.68

0.971

Accept the null Hypothesis

Sargan test of overid. Restrictions

H₀: instruments are valid instruments

Chi2 = 28.29

0.248

Accept the null Hypothesis

*, ** and *** denote significance at the 1,5, or 10 percent level respectively.

7.2 Shareholder Suits Index

In second part of research we have taken shareholder suits index as a proxy for internal governance. Good stock market governance is essential for maintaining market stability and investor trust. This study uses the Shareholder Suits Index as a measure to evaluate the effectiveness of internal governance mechanisms in stock markets. By analysing the number and outcomes of shareholder lawsuits, this index reflects the efficacy of corporate governance structures in protecting shareholder interests. The research aims to understand governance dynamics within stock markets, identify strengths and weaknesses, and highlight the importance of addressing public grievances with holistic responses to improve governance and macroeconomic stability.

In the OLS regression models, both with and without lagged dependent variables, we observe distinct differences in explanatory power. For the model without the lagged dependent variable Eq. 6.2, the R-squared value is 0.2097, indicating that around 21% of the variability in stock return prices is explained by the independent variables. The inclusion of the Shareholder Suits Index results in a negative coefficient (-0.1121121), suggesting an inverse relationship with stock market returns; however, this relationship is non-significant, indicating that it might be due to random variation rather than a true effect. Market capitalization shows a positive coefficient (0.0011747), which aligns with the literature suggesting that larger firms might have higher returns, but this effect is also non-significant. Broad money displays a negative coefficient (-0.0196425), implying that increases in broad money supply may be associated with lower stock market returns, although this is non-significant. Trade has a positive coefficient (0.0112141), indicating a positive relationship with stock market returns, but again, this relationship is non-significant. The coefficient for stock market volatility is positive (0.804665) and highly significant (p < 0.001), indicating a strong positive relationship. GDP and FDI both show extremely small negative effects and are non-significant. The control of corruption index shows a positive and highly significant coefficient (0.2966497), indicating a substantial positive impact on stock market returns. Government effectiveness displays a negative coefficient (-0.0491856), which is non-significant. Political instability has a positive coefficient (0.1118948) and is highly significant (p < 0.01), indicating a positive impact. The rule of law shows a negative coefficient (-0.0103555), which is non-significant.

When the lagged dependent variable is included Eq. 6.4, the R-squared value rises to 0.4172, showing that the model now explains 42% of the variability in stock returns. This demonstrates the significant role of past stock market performance. In this model, the Shareholder Suits Index has a positive coefficient (0.1765595) but remains non-significant. Market capitalization also has a positive coefficient (0.003188), suggesting that larger firms might have higher subsequent returns, but this effect is non-significant. The effect of broad money remains negative (-0.0087221) but smaller and non-significant. Trade continues to show a positive relationship (0.0040259) with a smaller effect, which is non-significant. The coefficient for stock market volatility is positive (0.3649227) and highly significant (p < 0.001), though smaller than in the model without the lagged returns. GDP and FDI continue to show extremely small and non-significant negative effects. Government effectiveness maintains a negative coefficient (-0.0414192), which is non-significant. Political instability has a positive coefficient (0.0892587) and remains significant (p < 0.05), though the effect is slightly smaller compared to the model without the lagged returns. The rule of law changes to a positive coefficient (0.0165711) but is still non-significant.

Including a lagged dependent variable in OLS regression models significantly improves their explanatory power, highlighting the influence of past stock market performance on current returns. The shift from a negative to a positive coefficient for the Shareholder Suits Index when including the lagged dependent variable suggests that past returns influence the relationship between shareholder suits and stock market returns. This finding aligns with financial econometrics literature, indicating that stock returns often exhibit autocorrelation.

The study emphasizes the importance of governance factors, particularly the control of corruption and political instability, which show significant positive impacts on stock market returns. However, other factors like government effectiveness, rule of law, market capitalization, broad money, trade, GDP, and FDI do not show significant impacts, highlighting the complex and multifaceted nature of these relationships. The results underscore the importance of statistical significance in interpreting regression coefficients. Non-significant coefficients, despite aligning with theoretical expectations, indicate that observed relationships may be due to random variation rather than true underlying effects.

In conclusion, the study demonstrates that including lagged dependent variables can enhance the explanatory power of regression models, capturing the dynamic nature of stock market returns. It highlights the significance of certain governance factors in influencing market performance while emphasizing the need for rigorous econometric techniques to ensure reliable and meaningful findings in the complex domain of corporate governance and financial markets.

In the research examining stock market governance and performance, the use of the Shareholder Suits Index as a measure of internal governance mechanisms is pivotal. This index, reflecting the number and outcomes of shareholder lawsuits, provides insights into the efficacy of corporate governance structures in protecting shareholder interests. By analysing these governance dynamics, the study aims to identify strengths and weaknesses within stock markets, thereby emphasizing the importance of addressing public grievances to improve governance and macroeconomic stability.

In the context of OLS regression models, both with and without lagged dependent variables, notable differences in explanatory power are observed. For the model without the lagged dependent variable, the R-squared value is 0.2097, indicating that around 21% of the variability in stock return prices is explained by the independent variables. Here, the Shareholder Suits Index shows a negative coefficient (-0.1121121), suggesting an inverse relationship with stock market returns. However, this relationship is non-significant, indicating it might be due to random variation rather than a true effect. Market capitalization shows a positive coefficient (0.0011747), aligning with literature that suggests larger firms might have higher returns, though this effect is also non-significant. Broad money has a negative coefficient (-0.0196425), implying that increases in broad money supply may be associated with lower stock market returns, but again, this is non-significant. Trade presents a positive coefficient (0.0112141), indicating a positive relationship with stock market returns, but this relationship is non-significant. The coefficient for stock market volatility is positive (0.804665) and highly significant (p < 0.001), indicating a strong positive relationship. GDP and FDI show extremely small negative effects and are non-significant. The control of corruption index shows a positive and highly significant coefficient (0.2966497), indicating a substantial positive impact on stock market returns. Government effectiveness displays a negative coefficient (-0.0491856), which is non-significant. Political instability has a positive coefficient (0.1118948) and is highly significant (p < 0.01), indicating a positive impact. The rule of law shows a negative coefficient (-0.0103555), which is non-significant.

When a lagged dependent variable is included, the R-squared value rises to 0.4172, showing that the model now explains 42% of the variability in stock returns, demonstrating the significant role of past stock market performance. In this model, the Shareholder Suits Index has a positive coefficient (0.1765595) but remains non-significant. Market capitalization also has a positive coefficient (0.003188), suggesting larger firms might have higher subsequent returns, but this effect is non-significant. The effect of broad money remains negative (-0.0087221) but smaller and non-significant. Trade continues to show a positive relationship (0.0040259) with a smaller effect, which is non-significant. The coefficient for stock market volatility is positive (0.3649227) and highly significant (p < 0.001), though smaller than in the model without the lagged returns. GDP and FDI continue to show extremely small and non-significant negative effects. Government effectiveness maintains a negative coefficient (-0.0414192), which is non-significant. Political instability has a positive coefficient (0.0892587) and remains significant (p < 0.05), though the effect is slightly smaller compared to the model without the lagged returns. The rule of law changes to a positive coefficient (0.0165711) but is still non-significant.

The coefficient of 0.4534034 in the pooled OLS model suggests that a one-unit increase in the lagged stock market return is associated with an increase in the current stock market return by approximately 0.4534034 units, holding other factors constant. This relationship is highly statistically significant, with a p-value of 0.000, indicating strong evidence against the null hypothesis that the lagged stock market return has no effect on the current stock market return. The fixed effects model, with a coefficient of 0.3925809, similarly shows a highly significant positive relationship within each entity (e.g., country or firm), controlling for unobserved heterogeneity. The one-step difference GMM model, with a coefficient of 0.44578, also indicates a significant positive relationship, addressing potential endogeneity of the lagged stock market return.

The persistence of these coefficients across different models highlights the stability of the effect size of the lagged stock market return on current returns. The positive relationship underscores the phenomenon of momentum in financial markets, where past returns predict future returns due to factors like investor behaviour, herding, and the slow dissemination of information. This is consistent with the Efficient Market Hypothesis (EMH) in its weak form, suggesting that past price information might have some predictive power.

The analysis of stock market volatility across the models reveals a significant positive relationship with stock market returns. In the pooled OLS model, the coefficient is 0.3283855, highly significant with a p-value of 0.001. The fixed effects model shows an even stronger relationship, with a coefficient of 0.4654669 and a similarly significant p-value. The one-step difference GMM model, with a coefficient of 0.4835576, further supports this positive relationship, although the p-value is slightly higher at 0.016. These findings align with financial theory, which posits that higher risk (volatility) demands higher returns as compensation, reflecting the risk-return trade-off.

Overall, the significant and positive coefficients for both the lagged stock market return and stock market volatility highlight the importance of momentum and persistence in stock market returns. These results are consistent with economic theories suggesting that past performance can inform future returns due to market behaviour, investor sentiment, economic fundamentals, and trading strategies. The analysis underscores the complex interplay of various factors driving stock market dynamics, emphasizing the need for rigorous econometric techniques to ensure reliable and meaningful findings in the study of corporate governance and financial markets.

Table# 5 Author’s self-calculation (OLS with lagged dependent variable, OLS without lagged dependent variable)

Division of variables

Independent variables

Smr(OLS)

Smr(OLS) with lagged dependent variable

Lagged Dependent variable

Smr_t−1

0.4863599

(0.000) ***

Internal Governance

Ssi

-0.1121121

(0.778)

0.1765595

(0.608)

Size

Mcap

0.0011747

(0.868)

0.003188

(0.603)

Control variables

Bm

-0.0196425

(0.264)

-0.0087221

(0.566)

Trad

0.0112141

(0.492)

0.0040259

(0.779)

Smv

0.804665

(0.000) ***

0.3649227

(0.000) ***

GDP

-7.06e-13

(0.211)

-3.39e-13

(0.488)

Fdi

-1.21e-11

(0.227)

-8.08e-12

(0.348)

External Governance

Ccr

0.2966497

(0.000) ***

0.2106994

(0.009) ***

Gef

-0.0491856

(0.606)

-0.0414192

(0.619)

Psv

0.1118948

(0.005) ***

0.0892587

(0.011) **

Rq

-0.107872

(0.175)

-0.0887458

(0.199)

Va

-0.4258261

(0.000) ***

-0.3046833

(0.000) ***

Rl

-0.0103555

(0.808)

0.0165711

(0.655)

No. of Observation 1028 1012

R squared 0.2097 0.4172

*, ** and *** denote significance at the 1,5, or 10 percent level respectively.

Table# 6 Author’s self-calculation (OLS with lagged dependent variable, OLS without lagged dependent variable)

Division of variables

Independent variables

Smr (pooled OLS)

Smr (Fixed effect model)

Smr (one step Difference GMM)

Lagged dependent variable

Smr_t−1

0.4534034

(0.000) ***

0.3925809

(0.000) ***

0.44578

(0.000) ***

Internal Governance

ssi

0.1241945

(0.735)

− .3891438

(0.589)

22.7369

(0.289)

Size

Mcap

0.003629

(0.833)

0 .0192731

(0.070) *

0.0121255

(0.373)

Control Variables

Bm

-0.008296

(0.496)

-0.0575134

(0.262)

0.1378

(0.352)

Trad

0.0045789

(0.679)

-0.0052087

(0.887)

-0.0230323

(0.776)

Smv

0.3283855

(0.001) ***

0.4654669

(0.001) ***

0.1946866

(0.016) **

GDP

-7.88e-14

(0.887)

-7.67e-13

(0.271)

-1.64e-12

(0.025)**

Fdi

-9.31e-12

(0.100)

1.39e-12

(0.839)

1.70e-10

(0.269)

External Governance

Ccr

0.1621512

(0.079)*

0.1706612

(0.406)

0.1001778

(0.745)

Gef

-0.0894272

(0.402)

-0.0352092

(0.892)

-0.4542412

(0.263)

Psv

0.0973263

(0.036)**

0.0330692

(0.737)

1.175984

(0.323)

Rq

-0.0689974

(0.404)

-0.2427788

(0.043)**

-0.4327808

(0.050)**

Rl

0.0063842

(0.878)

-0.0196134

(0.851)

-0.1473554

(0.707)

Va

-0.2419285

(0.002)***

-0.3606618

(0.014)*

-0.9035484

(0.061)*

No. of Observation 1012 1012 957

*, ** and *** denote significance at the 1,5, or 10 percent level respectively.

Arellano Bond test of Autocorrelation

The AR (2) model suggests that the current stock market return might be influenced by its values in the two previous periods. The z-statistic of 1.30 measures the number of standard deviations the estimated coefficient is from zero, testing the null hypothesis that the AR (2) term's coefficient is zero. With a p-value of 0.194, which is higher than typical significance thresholds (e.g., 0.01, 0.05, or 0.10), we fail to reject the null hypothesis. This indicates no statistically significant evidence that stock market returns from two periods ago affect the current return. Thus, from a statistical standpoint, the AR (2) term lacks significant predictive power for current stock market returns in this model. The results are shown in table 7.

Sargan Test

The Sargan test (or Sargan-Hansen test) checks the validity of instruments in a Generalized Method of Moments (GMM) estimation by testing whether the instruments are uncorrelated with the error term. In your case, the chi-square statistic is 34.16 with a p-value of 0.105. The null hypothesis, which posits that the instruments are valid, cannot be rejected at common significance levels (0.01, 0.05, or 0.10) because the p-value is greater than these thresholds. Therefore, you fail to reject the null hypothesis, indicating that there is no significant evidence against the validity of the instruments. This suggests that the instruments used in your GMM estimation are likely valid and uncorrelated with the error term, supporting the reliability of the estimated coefficients. Consequently, the GMM results are robust, and potential endogeneity has been appropriately addressed. The results are shown in table 7.

Hansen Test

The Hansen test (or Hansen J-test) is used in GMM estimation to assess the validity of instruments by checking if they are uncorrelated with the error term. In this case, the chi-square statistic is 19.85 with 25 degrees of freedom, and the p-value is 0.755. The null hypothesis, which states that the instruments are valid, cannot be rejected at any conventional significance level (e.g., 0.01, 0.05, 0.10) because the p-value is significantly higher than these thresholds. This indicates that there is no significant evidence against the validity of the instruments.

The high p-value suggests that the instruments are appropriate for the GMM estimation, as they do not show significant correlation with the error term. This result implies that the potential endogeneity has been adequately addressed, and the GMM results are robust. The Hansen test, with a chi-square statistic of 19.85 and a p-value of 0.755, reinforces the validity of the instruments used, supporting the reliability of the estimated coefficients and the conclusions drawn about the relationship between governance indicators and stock market performance. This provides a solid foundation for the study's findings, confirming that the instruments do not overfit the model and are indeed valid. The results are shown in table 7.

The findings of our study underscore the urgent need for nations and regulatory bodies to design and enforce effective disclosure policies. Such policies are crucial not only for maintaining transparency in financial markets but also for ensuring their efficiency and stability. Transparency is the foundation of investor trust, reducing information asymmetry that can lead to market inefficiencies and unethical practices. Therefore, regulatory bodies must strike a delicate balance between promoting transparency and preserving market efficiency by avoiding overly burdensome regulations that could hinder innovation and competitiveness.

To achieve this, comprehensive and timely reporting of financial data, governance practices, risk factors, and corporate strategies should be mandated. These measures will not only enhance market integrity but also empower investors to make informed decisions, contributing to more stable and robust financial markets.

Furthermore, safeguarding shareholder rights is of paramount importance. Establishing and enforcing optimal governance standards is essential for fostering healthy, efficient, and effective stock market operations. Strong governance standards ensure that management acts in the best interests of shareholders, protecting them from corporate malfeasance. This involves implementing stringent regulations and mechanisms for shareholders to hold management accountable, such as voting rights, transparent election processes for board members, and avenues for redress in case of disputes.

A regulatory framework that optimizes governance standards should incorporate best practices in corporate governance, including the independence of the board of directors, clear separation of roles between the CEO and board chair, and robust internal controls and auditing processes. Such measures are instrumental in ensuring the smooth and effective functioning of stock markets, which in turn supports economic growth and bolsters investor confidence.

Our study also sheds light on the complex dynamics influencing stock market returns, highlighting the role of various control variables. Stock market performance is not solely a reflection of the broader economic environment but also the result of specific sectoral and corporate activities. Among these factors, inflation stands out as particularly complex, affecting investment returns and strategies in diverse ways. Inflation can erode purchasing power and influence interest rates, which in turn impact corporate profits, investor behaviour, and overall market performance.

For investors, managing the risks and opportunities presented by inflation is crucial for safeguarding their portfolios. This requires a strategic approach to investment, often involving consultation with financial experts to make well-informed decisions. Investors must consider the potential impact of inflation on different asset classes and sectors, adjusting their portfolios to mitigate risks and capitalize on opportunities. For instance, inflation-linked bonds, real assets like real estate, and commodities can serve as hedges against inflation.

Adopting a cautious and informed investment approach, grounded in a thorough understanding of market dynamics and regulatory environments, enables investors to navigate the intricacies of the stock market more effectively. This strategy allows them to make more judicious investment choices, optimizing their financial outcomes amidst market volatilities and economic fluctuations. Continuous monitoring of economic indicators, regulatory changes, and market trends is essential for adjusting strategies as necessary to remain resilient in the face of uncertainty.

In conclusion, the implementation of effective disclosure policies and robust governance frameworks is essential for maintaining transparency and efficiency in financial markets. Protecting shareholder rights and understanding the factors influencing stock market returns are critical for fostering a healthy investment environment. By managing the risks and opportunities presented by inflation and other economic variables, investors can safeguard their portfolios and achieve optimal financial outcomes. Collaboration among regulatory bodies, companies, and investors is necessary to ensure that markets remain transparent, efficient, and resilient, supporting sustained economic growth and stability.

Following the outcomes of our study, it becomes imperative for nations and regulatory bodies to craft and implement effective disclosure policies. These policies are essential for maintaining transparency in financial markets, thereby ensuring their efficiency and fostering investor confidence. Transparency serves as the bedrock of investor trust, as it provides clarity and reduces the information asymmetry that can lead to market inefficiencies and misconduct.

Regulatory bodies are tasked with the critical job of determining the appropriate form and level of regulatory requirements. This involves a delicate balance: on one hand, promoting transparency through stringent disclosure requirements; on the other, preserving market efficiency by avoiding overly burdensome regulations that could stifle innovation and competitiveness. Effective disclosure policies should mandate comprehensive and timely reporting of financial data, governance practices, risk factors, and corporate strategies. Such policies not only enhance market integrity but also empower investors to make informed decisions, thereby contributing to more stable and robust financial markets.

Moreover, safeguarding shareholder rights emerges as a paramount concern. Establishing and enforcing an optimal balance of governance standards is crucial for fostering healthy, efficient, and effective operations of stock markets. Strong governance standards protect shareholders from corporate malfeasance and ensure that management acts in the best interests of the owners. This involves not only stringent regulations but also mechanisms for shareholders to hold management accountable, such as voting rights, transparent election processes for board members, and avenues for redress in case of disputes.

The protection of shareholder rights also involves creating a regulatory framework that optimizes governance standards. This framework should incorporate best practices in corporate governance, including the independence of the board of directors, clear separation of roles between the CEO and board chair, and robust internal controls and auditing processes. Such measures are instrumental in ensuring the smooth, efficient, and effective functioning of stock markets, which in turn supports economic growth and bolsters investor confidence.

Our study also illuminates the complex dynamics influencing stock market returns, highlighting the role of various control variables. Stock market performance is not merely a reflection of the broader economic environment but also the result of specific sectoral and corporate activities. Among these factors, inflation stands out as particularly complex, impacting investment returns and strategies in diverse ways. Inflation can erode purchasing power and affect interest rates, which in turn influence corporate profits, investor behavior, and overall market performance.

For investors, adeptly managing the risks and opportunities presented by inflation is crucial for safeguarding their portfolios. This necessitates a strategic approach to investment, often involving consultation with financial experts to make well-informed decisions. Investors must consider the potential impact of inflation on different asset classes and sectors, adjusting their portfolios to mitigate risks and capitalize on opportunities. For example, inflation-linked bonds, real assets like real estate, and commodities can serve as hedges against inflation.

By adopting a cautious and informed approach to investment, grounded in a thorough understanding of market dynamics and regulatory environments, investors can better navigate the intricacies of the stock market. This strategy enables them to make more judicious investment choices, optimizing their financial outcomes amidst market volatilities and economic fluctuations. Investors should continuously monitor economic indicators, regulatory changes, and market trends, adjusting their strategies as necessary to remain resilient in the face of uncertainty.

In conclusion, effective disclosure policies and robust governance frameworks are essential for maintaining transparency and efficiency in financial markets. Protecting shareholder rights and understanding the dynamics influencing stock market returns are critical for fostering a healthy investment environment. By managing the risks and opportunities presented by factors such as inflation, investors can safeguard their portfolios and achieve optimal financial outcomes. Regulatory bodies, companies, and investors must collaborate to ensure that markets remain transparent, efficient, and resilient, supporting sustained economic growth and stability.

Author Contribution

I would like to express my sincere gratitude to my supervisor, Dr Muhammad Jamil, for their invaluable guidance and support throughout the course of this research. Their expertise and insights were instrumental in shaping the direction and depth of this study. Specifically, Dr Muhammad Jamil contributed significantly in the following areas:Providing critical feedback and suggestions during the conceptualization and design of the research framework.Assisting in the development of the methodological approach, ensuring robustness and accuracy in the analysis.Offering continuous encouragement and mentorship, which were essential in navigating the challenges encountered during the research process.Reviewing multiple drafts of the manuscript, providing constructive critiques that greatly improved the quality of the final submission.Dr Muhammad Jamil dedication and commitment to excellence have been a constant source of inspiration, and I am deeply thankful for their contributions to this work. Their involvement has not only enhanced the scholarly quality of this article but has also significantly enriched my academic and professional growth.

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No competing interests reported.

AnnexAB.docx

Dynamics of Governance Structure and Stock market performance

Status:

Version 1

Abstract

Figures

1. Introduction

2. Literature Review

3. Theoretical Framework

4. Methodology

5. Data

6. Estimation technique

7. Results and Conclusion

7.1 Business Disclosure Index

7.2 Shareholder Suits Index

8. Conclusion and Policy suggestions

Declarations

Author Contribution

References

Additional Declarations

Supplementary Files

Status:

Version 1