Reconceptualizing Central Bank Transparency: A Multidimensional Index and Its Implications for International Equity Portfolio Allocation

Abstract

This paper examines the influence of Monetary Policy Transparency on Foreign Equity Portfolio Allocation by addressing the informational frictions that shape cross-border investment in Financial Markets. Building on recent developments in central bank communication, we construct a multidimensional measure of Monetary Policy Transparency that extends traditional frameworks by incorporating Accounting Information Transparency and Financial Stability Transparency. This enhanced index provides a more comprehensive representation of the informational environment faced by foreign investors. Using a panel of developed and emerging economies over a twenty-year period, the empirical analysis combines OLS and system GMM estimations to account for endogeneity, dynamic effects, and unobserved heterogeneity. The results indicate that higher levels of Monetary Policy Transparency significantly increase the attractiveness of domestic equity markets to foreign investors, with heterogeneous effects across country groups linked to differences in institutional credibility and financial integration. Overall, the findings highlight multidimensional transparency as a key determinant of Foreign Equity Portfolio Allocation, underscoring the strategic importance of Accounting Information Transparency and Financial Stability Transparency in shaping foreign equity portfolio allocation.

1. Introduction

Over the past two decades, central banks have profoundly reshaped their communication strategies and monetary policy frameworks, moving from a regime characterized by opacity to one defined by growing transparency (Geraats, 2006; Lehtimäki & Palmu, 2022). This transition has been reinforced by the increasing institutional independence of monetary authorities and by stronger accountability requirements imposed on them (N. Dincer et al., 2022). In an increasingly interconnected global environment, monetary policy transparency is now widely considered a fundamental tool for anchoring expectations, enhancing institutional credibility, and safeguarding financial stability (Ehrmann & Fratzscher, 2007; Weber, 2018).

Empirical studies consistently show that higher transparency reduces macroeconomic uncertainty, improves the predictability of policy actions, and influences international investors’ decision-making processes (Ehrmann et al., 2012; Papadamou et al., 2014). Transparency also mitigates information asymmetries, strengthens investor confidence, and dampens financial market volatility (Neuenkirch, 2014; Trabelsi, 2016). As such, it has become a central determinant of international financial integration and a key factor in shaping the attractiveness of open economies to foreign capital (Van Der Cruijsen & Demertzis, 2007).

Despite the growing scholarly and policy interest in transparency, existing indices remain largely grounded in the traditional conceptual frameworks developed by Geraats (2006) and subsequently expanded by N. N. Dincer and Eichengreen (2009). These widely used measures focus on macroeconomic assessments, policy objectives, decision-making processes, and forecasts. While influential, they do not fully capture the broader set of informational channels that matter for international investors operating in increasingly complex financial markets. Two dimensions, in particular, remain insufficiently represented in conventional transparency measures:

Accounting information transparency shapes the reliability, comparability, and robustness of the financial data disclosed by central banks’ critical signals through which investors assess institutional soundness and operational solvency (Barth, 2008; Bischof et al., 2024).

Financial stability transparency, which relates to communication practices concerning systemic risks, financial stress conditions, and macroprudential interventions, all of which increasingly influence cross-border investment behavior in a world exposed to recurrent financial shocks (Born et al., 2014; Horváth & Vaško, 2016; N. Dincer et al., 2022).

In today’s highly financialized global economy, where foreign equity portfolio allocation responds swiftly to both macroeconomic signals and perceived risk conditions, these two dimensions constitute essential informational channels. Their absence from traditional transparency indicators highlights an important conceptual and empirical gap in the literature.

This article addresses this gap by constructing a new multidimensional monetary policy transparency index that explicitly incorporates: Accounting information transparency, and Financial stability transparency.

Theoretically, the study extends and enriches the prevailing transparency frameworks by integrating informational dimensions that are increasingly decisive for international investors but have remained outside the scope of traditional indices. Empirically, the article evaluates the impact of this expanded index on international equity portfolio allocation using a panel of developed and emerging economies spanning twenty years. By combining a novel index with cross-country empirical analysis, the findings provide new evidence on how monetary policy transparency shapes portfolio decisions and international capital movements.

The remainder of the article is structured as follows. Section 2 examines the existing literature and elaborates on the theoretical foundations of the study. Section 3 outlines the methodology, dataset, and the development of the new transparency index. Section 4 presents and analyzes the empirical findings. Lastly, Section 5 concludes by highlighting the principal implications for monetary policy design and international investment behavior.

2. Literature Review and Theoretical Framework

The global financial crisis and its aftermath are a constant preoccupation for policy-makers. Financial markets are volatile at the international level, and governments are facing difficulties in financing investment in their national economies (Thapa & Poshakwale, 2012). An abundance of literature examines the constructive role of finance in economic development (McKinnon, 1973; Fry, 1989; Levine, 1992). Equity financing appears to be one of the most important forms of finance. The role of foreign investors in financing the needs of national economies is now more crucial than ever (Thapa & Poshakwale, 2012). According to Errunza (2001), foreign equity investors exert a significant positive influence on the development of domestic stock markets, which subsequently supports broader economic growth. Building on the role of portfolio investment in foreign equities, it becomes essential for policymakers to recognize the range of factors shaping the allocation decisions of international investors. This dynamic can be explained by investors’ pursuit of high portfolio returns combined with a dependable supply of fixed-income investment opportunities. F. Kwabi et al. (2025) argue that international capital tends to flow toward countries that maintain transparent monetary policy frameworks. This perspective reflects the notion that investors assess national policy environments and prefer those that promote investment and credit expansion, particularly where decision-making is not subject to concealed government interference.

Investors typically favor prudent macroeconomic management alongside well-regulated financial systems. Enhanced transparency is also regarded as a fundamental component of portfolio risk management. Empirical evidence indicates that central banks operating independently from political pressures contribute to economic stability (Alesina & Summers, 1993) and limit the use of monetary policy for political objectives, especially in proximity to election periods (Nordhaus, 1975). Research by Klomp and De Haan (2009) and F. Kwabi et al. (2025) further demonstrates that credible and stable macroeconomic policies implemented under transparent central banking frameworks support financial development. Notably, price stability is associated with improved equity portfolio performance. Horváth and Vaško (2016) show that transparent monetary policy also plays a meaningful role in strengthening financial stability. Central banks remain central actors in executing monetary policies aimed at economic stabilization (Bernhard, 1998; Fausch & Sigonius, 2018).

Moreover, transparent monetary frameworks encourage investor engagement in financial systems by fostering greater confidence. This outcome aligns with the understanding that information asymmetry can impede investment decisions.

Central bank transparency contributes to the implementation of transparent monetary policies (see Crowe & Meade, 2008; Gelos & Wei, 2005; Geraats, 2002; Van Der Cruijsen & Demertzis, 2007). Several studies indicate that transparent monetary frameworks influence investors’ expectations about future developments and shape their participation in financial markets (Blinder, 1999; S. C. W. Eijffinger & De Haan, 2000). Research by Eichler and Littke (2018) suggests that exchange rate volatility can be reduced when central banks adopt higher levels of monetary policy transparency. In line with the argument that transparency in monetary policy affects interest rate risk, inflation risk, exchange rate risk, and overall financial stability, these dynamics ultimately have consequences for equity portfolio returns.

There is also a notable interaction between monetary policy transparency and the diversification of international equity portfolios, which can enhance market participation and support financial development (F. Kwabi et al., 2025). This perspective aligns with the view that increased involvement of foreign investors in domestic capital markets contributes to higher share prices, improved trading efficiency and technological progress, while also lowering transaction costs. Greater integration between domestic and foreign investors promotes risk-sharing and reduces the cost of capital. Additionally, transparent monetary policies strengthen investor confidence in financial markets by improving their understanding of future macroeconomic conditions.

A broad range of studies in the field of finance confirms that information asymmetry represents a significant barrier to investors’ decision-making. Consequently, some scholars argue that monetary policies implemented by central banks should be characterized not only by independence but also by transparency (Crowe & Meade, 2008; Gelos & Wei, 2005; Geraats, 2002). Providing accessible information and effectively communicating it to the public is therefore considered crucial for the success of monetary policy, as it helps guide the expectations and decisions of investors and financial market participants (Blinder, 1999; S. C. W. Eijffinger & De Haan, 2000). Consequently, monetary policy transparency helps to reduce informational frictions between monetary policy-makers and other economic agents, such as foreign investors and banks (Geraats, 2002). For example, according to Demertzis and Hallett (2007), greater transparency contributes to moderating inflation volatility, guiding investor expectations and making monetary policy decisions more predictable.

In the same vein, Eichler and Littke (2018) show that monetary policy is acutely transparent in order to reduce exchange rate volatility and therefore increase investment risk (Byrne & Davis, 2005) and the home bias in equities (Fidora et al., 2007; P. K. Mishra, 2011). Overall, through monetary policy, the central bank is likely to influence interest rate risk, inflation risk, exchange rate risk, price stability and financial stability and, consequently, the returns on foreign investors’ portfolios.

A study conducted by F. O. Kwabi et al. (2020) indicates that investors allocate more investment to countries with transparent central banks that promote the objectives and effectiveness of monetary policy through communication and the mitigation of uncertainty to guide investors’ future expectations. They indicate that transparency of monetary policy is an important factor in attracting foreign portfolio investment, implying that policymakers should consider increasing the transparency of central bank monetary policy.

Beyond monetary policy transparency, the literature highlights the central role of accounting information transparency in foreign equity portfolio allocation decisions. International investors, faced with heightened information asymmetries and higher information costs in institutional environments they only imperfectly master, attach particular importance to the quality, reliability, and comparability of financial information. Numerous studies show that transparent reporting systems reduce informational uncertainty, improve risk assessment, and promote cross-border equity investment (Leuz et al., 2003; Bushman et al., 2004; Daske et al., 2008; Florou & Pope, 2012). This transparency relies not only on balance sheet information but also on the full disclosure of off-balance-sheet commitments, such as contingent liabilities, public guarantees, derivative exposures, or off-balance-sheet structures, which can substantially alter the risk profile of financial institutions and national economies. The accounting literature shows that opacity surrounding these commitments increases information asymmetry and perceived investor risk, whereas detailed disclosure enables a more accurate assessment of solvency and exposure to extreme risks (Healy & Palepu, 2001; Barth et al., 2008; Ryan & Trahan, 2007). Moreover, the regular publication of independent audit reports constitutes a fundamental mechanism for enhancing the credibility of accounting information, by strengthening the reliability of financial statements and limiting opportunistic behavior. Several studies show that the quality and transparency of audit reports improve international investors’ confidence, reduce information risk, and foster foreign participation in equity markets (Fan & Wong, 2005; Francis et al., 2005; Defond & Zhang, 2014). Thus, enhanced transparency of accounting information explicitly integrating off-balance-sheet commitments and the publication of credible audit reports constitutes an essential determinant of higher and more efficient allocation of foreign equity portfolios.

Furthermore, financial stability transparency plays a decisive role in foreign equity portfolio allocation decisions, particularly in a context marked by recurrent macro-financial instability. Regular and detailed communication by central banks and prudential authorities regarding systemic risks, macro-financial imbalances, vulnerabilities in the banking sector, and stress test results helps reduce uncertainty and improve the readability of the financial environment. The literature shows that opacity surrounding systemic risks heightens international investors’ risk aversion and can trigger abrupt capital withdrawals, whereas greater transparency helps stabilize expectations and limit procyclical behavior (Gorton & Metrick, 2012; Acharya, 2009). Empirical and institutional studies also indicate that macroprudential transparency strengthens market discipline, improves the predictability of public policies, and supports foreign investors’ confidence in the resilience of the domestic financial system (Born et al., 2014; FSB, 2020). By enabling a better assessment of macro-financial risks and of the authorities’ capacity to manage episodes of instability, financial stability transparency thus promotes a more stable and sustained allocation of foreign equity portfolios.

These advances collectively demonstrate that traditional measures of monetary policy transparency are no longer sufficient to capture the full informational environment faced by international investors. Incorporating accounting transparency and financial stability transparency into the enriched index proposed in this study provides a more comprehensive and analytically relevant assessment, better aligned with the informational requirements of contemporary financial markets. This expanded conceptual framework is consistent with the recent evolution of central bank communication practices and strengthens the analytical relevance of the new index for examining foreign equity portfolio allocation.

Hypothesis 1.

Building on these theoretical and empirical insights, we posit that higher levels of monetary policy transparency including both traditional monetary dimensions and the additional dimensions of accounting transparency and financial stability transparency reduce uncertainty, reinforce institutional credibility, and enhance foreign investors’ ability to assess macro-financial risks. We therefore expect monetary policy transparency to exert a positive and significant effect on the foreign equity portfolio allocation.

3. Research Methodology and Data

3.1. Sample Definition

The empirical analysis relies on a balanced panel of 21 countries, structured around two groups of economies exhibiting distinct institutional and financial characteristics. The group of developed economies includes Australia, Canada, Denmark, Hong Kong, Japan, New Zealand, Norway, Singapore, Sweden, Switzerland, the United Kingdom, and the United States, while the group of emerging economies comprises Brazil, Chile, China, the Czech Republic, Egypt, Hungary, Indonesia, Poland, and the Russian Federation. The selection of these countries follows an analytical rationale closely linked to the objective of the study: they occupy a significant position in the allocation of foreign equity portfolios and display a sufficient degree of financial integration for institutional and informational mechanisms particularly those related to monetary policy transparency to effectively influence foreign investors’ decisions. The combination of developed and emerging economies further makes it possible to introduce substantial institutional and macro-financial heterogeneity, an essential condition for the empirical identification of differentiated effects of transparency according to the level of economic development, while ensuring the intertemporal and cross-sectional comparability of the macro-financial and institutional variables employed over the entire period of analysis.

3.2. Data Sources and Measurement of Variables

This section outlines all variables employed in the empirical analysis. We begin by presenting the primary independent variable of interest, namely monetary policy transparency (MPTNWIX). We then introduce the selected dependent variable, foreign equity portfolio allocation (FEPA). Finally, we discuss and justify the control variables incorporated to account for their potential influence on the dependent variable. These control variables include inflation (Infl), GDP per capita growth (GDPPCG), rule of law (Law), and domestic credit (Dticcred).

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Monetary Policy Transparency New Index (MPTNWIX):

Monetary policy transparency is evaluated through the construction of a new composite index specifically developed for this study. The index builds upon the framework originally proposed by S. C. Eijffinger and Geraats (2006) and its subsequent practical extensions (N. N. Dincer & Eichengreen, 2009; N. Dincer et al., 2022). The initial benchmark index is structured around five dimensions: political, economic, procedural, operational, and policy transparency.

Our contribution to this development lies in the addition of two new original dimensions: financial stability transparency and accounting information transparency.

The Monetary Policy Transparency New Index (MPTNWIX) is embedded in a methodological derivation approach based on the monetary policy transparency index initially proposed by S. C. Eijffinger and Geraats (2006), as well as its subsequent extensions. The objective of this study is to enrich this reference framework by integrating new dimensions reflecting contemporary institutional and prudential challenges, while preserving the conceptual and methodological foundations that have ensured the coherence and diffusion of the index in the literature. From this perspective, maintaining equal weighting across dimensions appears as a natural and coherent choice. The constituent dimensions of the index, whether originating from the initial framework or newly introduced, are designed to capture distinct and complementary facets of monetary policy transparency. In the absence of a theoretical or empirical consensus allowing the establishment of an objective and stable hierarchy among these dimensions, the assignment of differentiated weights would introduce a degree of subjectivity that is difficult to justify and likely to undermine the internal balance of the derived index. By preserving the weighting logic adopted in the founding index, the MPTNWIX thus ensures methodological continuity, conceptual coherence, and the comparability of results, while allowing for a progressive and harmonious extension of the measurement of transparency. This approach ensures that the addition of new dimensions enriches the analysis without altering the fundamental structure of the index, and promotes a robust and homogeneous interpretation of the empirical results.

Our objective in extending the framework is to fill the gaps identified in the literature on macroprudential aspects and accounting information, two dimensions that are essential to a better understanding of the relationship between central banks and financial markets. Indeed, transparency of accounting information is particularly important in that it reduces information asymmetry and allows for better comparability of financial statements, thereby strengthening the credibility of institutions and promoting foreign equity portfolio allocation. Empirical studies have shown that the adoption of IFRS has improved the relevance and reliability of financial information, which has significantly increased the participation of international investors (Daske et al., 2008; Florou & Pope, 2012).

As for transparency in financial stability, this has become essential since the expansion of central banks’ macroprudential mandate. Indeed, the regular publication of financial stability reports and the dissemination of stress test results contribute in particular to reducing uncertainty, anchoring expectations, and controlling excessive market volatility (Nier, 2005; Born et al., 2014). By incorporating these two dimensions, we fill a gap in the previous index by broadening the assessment of central bank transparency to include contemporary issues of financial reporting and systemic stability. The new index now incorporates seven dimensions, each assessed on a standardized scale from 0 (total opacity) to 3 (optimal transparency), resulting in a total score between 0 and 21. Thanks to this new construction, monetary communication practices are measured in depth, accurately, and in a manner that is comparable internationally. Details of the operational specifications, coding principles, and internal and external validation procedures are provided in Appendix A.

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Foreign equity portfolio allocation:

We have chosen the foreign equity portfolio allocation of each country in our sample as the dependent variable. Aggregate annual data on equity allocation by country come from the IMF Coordinated Portfolio Investment Survey (CPIS). To construct the foreign portfolio allocation data, we use the annual aggregate CPIS dataset for 21 countries covering the period from 2000 to 2019. The CPIS provides data on all equities held on 76 stock markets. After first eliminating countries not included in our sample, as well as countries with outliers, inconsistent variables and missing data, we have limited our sample size to 21 countries. The data consists of the Morgan Stanley Capital International (MSCI) All Country Investable Index, which represents around 95% of the total assets and liabilities held by the CPIS.

The IMF requires all participating countries to submit a detailed breakdown of their equity portfolio investments. Following Cooper and Kaplanis (1986), we consider foreign equity portfolio allocation as the dependent variable. The FEPA from country i to country j is defined as follows:

$$W_{ijt}=log\left({\displaystyle \frac{{FPI}_{ijt}}{{\sum }_{j=1}^{21}{FPI}_{ijt}}}\right)$$

where $W_{ijt}$ represents the weight of the foreign equity portfolio allocation from country i to country j for year t, and ${FPI}_{ijt}$ represents the actual portfolio allocation of foreign investors in millions of USD.

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Control variables:

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Inflation rate (Infl):

Inflation describes a broad and sustained rise in the overall price level of goods and services within an economy, which consequently reduces the purchasing power of money. It is commonly assessed through price indices, particularly the consumer price index (CPI). As emphasized by Samuelson and Nordhaus (2009), inflation should not be interpreted merely as a temporary increase in certain prices, but rather as a persistent process that influences the entire economic system. Mishkin (2000) highlights the importance of inflation analysis, noting its direct implications for monetary policy, financial stability, and the expectations formed by economic agents.

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Rule of law (Law):

Law is defined as an institutional indicator of the extent to which the agents of a society, including citizens and governments, respect the laws and rules governing the country. This notion covers a number of major dimensions, including the following

✓

The quality of legal institutions: This includes the independence of the judiciary, the impartial application of laws and the absence of corruption in the judicial system.

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Respect for property rights: This involves determining whether individuals and companies enjoy legal protection of their property rights.

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Contract security: This measures the effectiveness of the legal system in enforcing contracts between parties (Kaufmann & Weber, 2010).

The Law is one of the six aspects of global governance developed by the World Bank as part of the World Governance Indicators (WGI). This indicator is based on data from multiple sources, such as company surveys, expert assessments and national databases. Its values are normalized between approximately −2.5 (weak rule of law) and +2.5 (strong rule of law). Researchers and policy-makers use this indicator to analyze the impact of institutions on economic performance, social stability and international investment flows. For example, a high level of rule of law is associated with less institutional uncertainty and greater investor confidence (North, 1990; Acemoglu et al., 2001).

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Domestic credit: It refers to all the financial resources that a country’s financial institutions grant to national economic agents, including governments, private and public sector companies and households. This includes bank loans, non-negotiable debt securities, trade credits and other types of financing that generate a claim. More specifically, the concept of Dticcred to the private sector is defined as all the financial resources granted by financial companies to the private sector in various forms: the granting of loans, the purchase of securities other than shares, the granting of commercial credits and the granting of other accounts receivable involving the obligation to honor the reimbursement. This variable is generally expressed as a percentage of GDP to assess the financial depth of a country (Beck et al., 2000a).

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Gross domestic product (GDP) per capita (GDPPCG): It is defined as the annual growth rate of real GDP per person, which is expressed as a percentage. This index makes it possible to assess the average change in the economic wealth per person in an economy, while taking into account overall economic performance and demographic changes. It is calculated by dividing real GDP, adjusted to exclude the effects of inflation, by the total population. The growth of GDP per capita therefore reflects the gains or losses in economic production for each individual, providing a measure of the progression of the average standard of living.

3.3. Model Presentation

This section introduces the analytical framework used to examine in depth the influence of our newly developed monetary policy transparency index on a key determinant of financial markets, specifically foreign equity portfolio allocation. To empirically evaluate the theoretical model’s predictions, we employ panel data regression techniques, as specified in Equation (1), while accounting for cross-country heterogeneity and notable temporal fluctuations. The application of panel data enables us to simultaneously capture cross-sectional and time-series variations, as demonstrated in the equation.

$${∆FEPA}_{jt}=\alpha +{\beta }_1{.∆MPT}_{jt}{+\beta }_2{.∆Ctls}_{jt}{+\beta }_3{.TFE}_t{+\beta }_4{.CFE}_j{+ϵ}_{jt}$$

(1)

With: ${∆FEPA}_{jt}$ denotes the first difference in the flow of country j foreign equity portfolio allocation at time t. ${∆MPT}_{jt}$ test the first difference and the transparency of monetary policy. ${Ctls}_{jt}$ is a vector of country j’s control variables at time t. TFE and CFE are, respectively, the time and country fixed effects.

4. Empirical Results

This part of the study rigorously and thoroughly analyzes and interprets our empirical results in the light of our formulated hypotheses, as well as the main theoretical frameworks on which we have relied. With this in mind, we examine the robustness and reliability of our results, comparing them with those presented in similar studies. In addition, we highlight the impact of our new monetary policy transparency index on the foreign equity portfolio allocation.

4.1. Descriptive Statistics and Correlation Analysis

4.1.1. Summary Statistics

The examination of the descriptive statistics reveals pronounced structural differences between advanced and emerging economies, both in their macroeconomic dynamics and in the quality of their institutional frameworks. Regarding foreign equity portfolio allocation (FEPA), advanced economies exhibit a markedly higher mean value (0.082) compared with emerging economies (0.0012). This gap reflects the greater attractiveness of advanced financial markets, which benefit from deeper market structures, superior liquidity conditions, and lower execution risk. The substantially larger standard deviation observed among advanced economies further indicates a heightened sensitivity to financial cycles and monetary policy adjustments, in line with the evidence reported by Bekaert et al. (2023) on the structural volatility of cross-border capital flows.

Table 1. Descriptive statistics.

Developed Countries
Variable	Min	Max	Mean	Std. Dev	Skewness	Kurtosis	Unit Root
FEPA	0.0023238	0.5096771	0.082422	0.1253795	2.660246	8.739835	117.5209 (0.0000)
MPTNWIX	6.5	20.5	15	2.929043	−0.3190122	2.918962	186.7715 (0.0000)
Law	0.7965051	2.100738	1.729259	0.1975483	−1.122104	4.849847	68.8901 (0.0000)
GDPG	−5.693236	14.51975	2.277036	2.235169	0.6033541	8.846453	114.5168 (0.0000)
Infl	−2.983139	6.627782	1.636759	1.375062	0.0181651	3.922635	100.7567 (0.0000)
Dticcred	84.28706	236.7401	145.5399	31.01151	0.3220626	2.781661	145.6291 (0.0000)
Emerging Countries
Variable	Min	Max	Mean	Std. Dev	Skewness	Kurtosis	Unit Root
FEPA	3.87 × 10−6	0.009637	0.0012151	0.0019579	2.750969	9.730153	142.2128 (0.0000)
MPTNWIX	2	19.5	12.32749	4.937647	−0.3197976	1.871169	39.0673 (0.0028) -
Law	−1.083695	1.348704	0.1067131	0.726121	0.1467272	1.544717	166.0969 (0.0000)
GDPG	−7.799994	14.23086	4.146879	3.096439	−0.3846997	5.253571	43.1038 (0.0008) -
Infl	−0.8741259	29.50661	5.240058	4.430183	1.806916	8.319817	70.9657 (0.0000) -
Dticcred	12.82917	165.3904	57.0329	34.05665	1.364122	4.018678	105.8585 (0.0000)

Note: MPTNWIX corresponds to the value of our new monetary policy transparency index; FEPA indicates foreign equity portfolio allocation, i.e., the logarithmic value of the total foreign portfolio allocation made by country i to country j at time t (Wi, j, t); Infl is inflation; GDPPCG is GDP per capita growth; Law refers to the rule of law; Dticcred indicates domestic credit. Unit root tests were performed using Fisher’s Augmented Dickey–Fuller (ADF) method. This method enabled us to find that some variables are stationary in level, while others become stationary after a first difference. Before continuing with the econometric analyses, we differentiated the variables that are not level-stationary, to avoid problems linked to non-stationarity.

reports the descriptive statistics (minimum, maximum, mean, standard deviation, Skewness, Kurtosis and Unit Root) for the variables included in the model.

Turning to the expanded monetary policy transparency index (MPTNWIX), advanced economies display a higher average score (15 versus 12.33 for emerging economies), suggesting more regular communication practices, more comprehensive institutional frameworks, and a more systematic publication of accounting and macroprudential information. The relatively low standard deviation in advanced economies (2.93) points to a stable and institutionalized communication process, whereas the higher dispersion in emerging economies (4.94) reflects persistent institutional heterogeneity. The negative skewness observed in both groups indicates a concentration of observations toward higher transparency levels, signaling a broad improvement in monetary transparency over the sample period. Nevertheless, the higher kurtosis in advanced economies suggests occasional surges in communication or major reforms, consistent with the findings of concerning phases of monetary framework modernization.

Institutional variables similarly reveal pronounced divergences. The rule of law indicator (Law) is significantly higher in advanced economies, with a mean of 1.73 compared with 1.06 in emerging economies, reflecting more stable regulatory environments, stronger investor protection, and greater legal predictability. The low dispersion within advanced economies indicates institutional homogeneity, whereas the greater variability observed in emerging economies points to divergent institutional trajectories.

GDP per capita growth (GDPG) also highlights structural contrasts: emerging economies exhibit stronger average growth (4.15% versus 2.27% in advanced economies), yet with much higher dispersion and more frequent negative extremes, illustrating the inherent macroeconomic volatility of such economies. This instability aligns with Rey’s (2015) argument regarding the heightened exposure of emerging economies to global financial cycles and external shocks.

Inflation (Infl) follows a similar pattern. Advanced economies display lower and more stable inflation rates (1.64%) relative to emerging economies (5.24%). The elevated kurtosis among emerging economies suggests recurrent episodes of inflationary pressure, which may undermine monetary credibility and increase perceived uncertainty among international investors.

Finally, domestic credit (Dticred) is substantially higher on average in advanced economies (145.5 versus 57.03 in emerging economies), indicating deeper financial systems. The lower dispersion among advanced economies reflects more mature credit markets, consistent with the financial development hypothesis advanced by Levine et al. (2020).

Taken together, these descriptive statistics confirm the presence of deeply differentiated institutional and macrofinancial profiles across the two groups of countries, thereby fully justifying the distinction between advanced and emerging economies in the subsequent econometric analysis.

4.1.2. Correlation Analysis

The analysis of the correlation matrix constitutes a crucial step in assessing the structure of linear relationships among the model’s variables and ensuring the absence of multicollinearity that could undermine the reliability and consistency of the estimated coefficients.

Table 2. Pearson’s pairwise correlation coefficients between the dependent, key independent and control variables.

Developed Countries
	FEPA	MPTNWIX	Law	GDPG	Infl	Dticcred
FEPA	1.0000
MPTNWIX	−0.0960	1.0000
Law	−0.3339	0.4733	1.0000
GDPG	−0.0722	−0.0764	0.0058	1.0000
Infl	0.0953	0.1591	0.2022	0.1177	1.0000
Dticcred	0.4035	−0.1979	−0.2189	−0.2872	−0.0033	1.0000
Emerging Countries
	FEPA	MPTNWIX	Law	GDPG	Infl	Dticcred
FEPA	1.0000
MPTNWIX	0.3078	1.0000
Law	0.5269	0.6503	1.0000
GDPG	−0.0921	−0.3958	−0.2766	1.0000
Infl	−0.2564	−0.3682	−0.4952	−0.0590	1.0000
Dticcred	0.5320	−0.1932	0.1109	0.2771	−0.3708	1.0000

Note: MPTNWIX corresponds to the value of our new monetary policy transparency index; FEPA indicates foreign equity portfolio allocation, i.e., the logarithmic value of the total foreign portfolio allocation made by country i to country j at time t (Wi, j, t); Infl is inflation; GDPPCG is GDP per capita growth; Law refers to the rule of law; Dticcred indicates domestic credit.

presents the Pearson pairwise correlation matrix, providing an overview of the linear relationships between the dependent variable, the key independent variable, and the control variables included in the empirical analysis.

The correlation coefficients observed for both groups of countries remain consistently below the conventional critical threshold of 0.8, thereby confirming the absence of sufficiently strong linear relationships that could give rise to problematic collinearity (Gujarati & Porter, 2009; Wooldridge, 2016).

In advanced economies, the correlation between monetary policy transparency (MPTNWIX) and foreign equity portfolio allocation (FEPA) is weak, ensuring the degree of statistical independence required for a robust identification of the causal effect. Correlations involving institutional, macroeconomic, and financial variables are moderate and align with the structural stability commonly documented in the literature on advanced economies (Ghirelli et al., 2023). This pattern suggests that the underlying transmission channels remain distinct, thereby strengthening the credibility and internal validity of the subsequent econometric estimations.

In emerging economies, correlations are somewhat stronger, particularly between monetary transparency and institutional indicators an outcome consistent with recent findings emphasizing their heightened reliance on institutional quality and financial depth (Bekaert et al., 2023). Nevertheless, even the highest coefficients fall well below the 0.8 threshold, ruling out any serious threat of multicollinearity. The negative correlations with inflation and GDP growth reflect the structural vulnerabilities characteristic of emerging economies and their greater exposure to informational frictions.

Taken together, the overall correlation structure is fully consistent with international finance predictions: low interdependence among institutional determinants in advanced economies, moderate yet non-critical linkages in emerging economies, and no linear relationships sufficiently strong to distort coefficient estimates. These results validate the statistical soundness of the dataset and confirm that both OLS and GMM models can be estimated under satisfactory econometric conditions.

4.2. Classical Econometric Analysis and Preliminary Results: Ordinary Least Squares (OLS)

The main objective of this econometric OLS estimation with random effects is to analyze the impact of MPTNWIX on FEPA, while taking into account macroeconomic Infl, GDPPCG, financial Dticcred and institutional Law factors. Our results reveal the existence of distinct dynamics between advanced and emerging economies. We thus validate the hypothesis that MPTNWIX operates according to the macro-financial context. The results we obtained corroborate this hypothesis, while introducing an important deepening on the specificities between the advanced economies and the emerging economies.

The estimates reported in

Table 3. Static panel estimation (OLS results).

	Advanced Economies		Emerging Economies
	Random Effects		Random Effects
FEPA	Coef	P > |z|	Coef	P > |z|
MPTNWIX	0.004898	(0.083) *	0.0000971	(0.001) ***
Law	−0.214874	(0.000) ***	0.0011428	(0.000) ***
GDPG	0.0013978	0.682	−0.0000258	0.474
Infl	0.0131119	0.015	0.0001244	(0.000) ***
Dticcred	0.0014543	(0.000) ***	0.0000373	(0.000) ***
cons	0.1442273	(0.089) ***	−0.0027739	(0.000) ***
Number of observations	228		171
Country random effects	Yes	Yes	Yes	Yes
Year random effects	Yes	Yes	Yes	Yes
Wald chi2(5)	79.74		231.37
Prob > chi2	0.0000		0.0000

Note: MPTNWIX corresponds to the value of our new monetary policy transparency index; FEPA indicates foreign equity portfolio allocation, i.e., the logarithmic value of the total foreign portfolio allocation made by country i to country j at time t (Wi, j, t); Infl is inflation; GDPPCG is GDP per capita growth; Law refers to the rule of law; Dticcred indicates domestic credit. Statistical significance is reported at the 10% (*), 5% (**) and 1% (***) significance levels, respectively.

are based on a static panel model (OLS with random effects), whose inferential credibility is first supported by the joint significance of the regressors: the Wald tests strongly reject the null hypothesis of overall non-relevance for both advanced economies (Wald χ²(5) = 79.74; Prob > χ² = 0.0000) and emerging economies (Wald χ²(5) = 231.37; Prob > χ² = 0.0000). From a strictly econometric standpoint, this strong joint significance validates the internal coherence of the specification and reduces the risk of an under-specified model in the sense of joint restriction tests; it does not “prove” the total absence of bias (endogeneity, simultaneity), but it confirms that the selected variables carry statistically exploitable information to account for variations in foreign equity portfolio allocation (FEPA). Within this framework, and in line with the literature on international allocation decisions, the coefficients should be interpreted as conditional marginal effects capturing the manner in which investors arbitrate among informational signals (monetary policy transparency), the institutional environment, macroeconomic fundamentals, and financial depth, with expected heterogeneity between advanced and emerging countries (Bekaert & Harvey, 2000; Obstfeld, 1994).

The dependent variable, foreign equity portfolio allocation, refers to a highly reversible and information-sensitive allocation decision; this is precisely why the hypothesis of an “information–credibility–reallocation” channel is testable within a static framework at the first stage. In this context, the central variable MPTNWIX is positively associated with FEPA in both country groups, but with differentiated statistical robustness that is itself economically informative. For advanced economies, the coefficient of MPTNWIX is positive (0.004898) and significant at the 10% level (p = 0.083), suggesting a marginal effect of monetary policy transparency in environments where markets are already deep, information already abundant, and institutional credibility generally high; in other words, transparency primarily acts as a mechanism for reducing residual uncertainty and refining expectations (the “policy predictability” channel), which is consistent with the foundational framework of monetary policy transparency (S. C. Eijffinger & Geraats, 2006) and its empirical extensions showing that transparency improves the incorporation of monetary signals into asset prices, particularly in mature markets (N. N. Dincer & Eichengreen, 2014; F. O. Kwabi et al., 2020). For emerging economies, MPTNWIX remains positive (0.0000971) and highly significant at the 1% level (p = 0.001), indicating that, where informational frictions and belief dispersion are stronger, monetary policy transparency constitutes a more “scarce” signal and is therefore more highly valued by international investors. This asymmetry between advanced and emerging countries is classically interpreted as a difference in the marginal value of public information: when the environment is more uncertain, improvements in transparency more strongly reduce information asymmetry and the opacity-related risk premium, thereby reinforcing equity allocations (Gelos & Wei, 2005).

The natural transition toward institutional quality (Law) then makes it possible to test a complementarity hypothesis: transparency is fully “monetizable” into portfolio flows only if investors anticipate credible legal protection and contract enforcement. The results reveal marked heterogeneity. In emerging economies, Law is positive (0.0011428) and highly significant (p = 0.000), which is consistent with the standard institutional mechanism: an improvement in the rule of law reduces risks of expropriation, regulatory arbitrariness, and contractual insecurity, thereby directly increasing the attractiveness of domestic assets for non-residents (North, 1990; Porta et al., 1998; Acemoglu et al., 2001). In advanced economies, the estimated coefficient is negative (−0.214874) and highly significant (p = 0.000). A rigorous (and non-rhetorical) reading is to interpret this sign as a marginal effect within an “institutionally saturated” zone: when legal quality is already high and varies little, the Law indicator may capture dimensions associated with regulatory density, compliance costs, or legal rigidities that can reduce portfolio capital mobility at the margin. In other words, the negative sign does not contradict general institutional theory; it suggests a plausible non-linearity and a composition effect specific to advanced economies, often discussed when variation in an institutional indicator is limited and it absorbs aspects of “regulatory burden” rather than investor protection in the strict sense (Porta et al., 1998; North, 1990). This comparison between advanced and emerging economies strengthens economic plausibility: in emerging economies, Law is a first-order determinant because it reduces a fundamental risk; in advanced economies, the marginal effect may be dominated by regulatory frictions.

Macroeconomic fundamentals (GDPG and Infl) are then introduced not to “narrate” the table, but to qualify the hierarchy of signals: do portfolio investors arbitrate on cyclical macroeconomic performance, or on credibility, institutions, and financial depth? The estimates indicate that growth (GDPG) is not statistically significant in either advanced economies (β = 0.0013978; p = 0.682) or emerging economies (β = −0.0000258; p = 0.474). This result is economically consistent with the classic distinction between foreign direct investment and portfolio investment: growth is a long-term signal more relevant for FDI, whereas portfolio flows, being more liquid and more risk-sensitive, respond more strongly to information, market liquidity, and institutional quality than to cyclical variations in growth (Obstfeld, 1994; Henry, 2000; Bekaert & Harvey, 2000). With respect to inflation, technical precision is required: in advanced economies, Infl is positive (β = 0.0131119) and statistically significant at the 5% level (p = 0.015). A robust (and non-simplistic) interpretation is that, in advanced economies, observed inflation may capture a macroeconomic regime in which inflation remains “contained” and correlated with robust demand, without undermining monetary credibility; in such a case, inflation is not necessarily a proxy for de-anchoring of expectations and does not automatically translate into portfolio capital outflows. In other words, the positive sign is not an “anomaly” if inflation remains within a corridor compatible with credibility, which aligns with the literature emphasizing that the effect of inflation on foreign equity portfolio allocation depends on the regime, the anchoring of expectations, and the quality of monetary institutions (Bekaert & Harvey, 1997; Clarida et al., 2001). In emerging economies, Infl is positive (β = 0.0001244) and highly significant (p = 0.000), but the order of magnitude is very small, leading to an economically disciplined reading: inflation carries statistical information, but its marginal economic impact is quantitatively limited relative to credibility, institutional, and financial variables; this corresponds to the idea that, in emerging countries, inflation may be endogenous to broader macro-financial conditions, and that investors primarily arbitrate on more structurally salient signals (Bekaert & Harvey, 2000). The comparison between advanced and emerging economies is therefore clear: inflation is significant in both groups of countries, but its economic significance is not symmetric—more substantial in advanced economies (larger coefficient), more “secondary” in emerging economies (very small coefficient despite high statistical precision).

Finally, the financial depth variable Dticcred closes the explanatory chain through a microstructural mechanism: foreign equity portfolio allocation requires markets capable of absorbing orders, providing liquidity, and limiting transaction costs and market impact. The results show a positive and highly significant effect in both advanced economies (β = 0.0014543; p = 0.000) and emerging economies (β = 0.0000373; p = 0.000), confirming that financial depth and domestic intermediation capacity constitute a robust determinant of attractiveness for foreign investors. This result is directly aligned with the financial development literature: a deeper financial system improves liquidity, reduces frictions, and facilitates international diversification, thereby increasing both the likelihood and the magnitude of foreign equity allocations (Rajan & Zingales, 1998; Beck et al., 2000b). The difference in magnitude between groups is consistent with heterogeneity in market scale and structure: in advanced economies, variation in Dticcred may capture marginal changes in financial conditions within an already deep market; in emerging economies, even a small coefficient may reflect a strong structural constraint, whereby any improvement in intermediation enhances the capacity to absorb flows.

The OLS method used in our study allowed us to confirm the influence of MPTNWIX on foreign equity portfolio allocation. Nevertheless, this approach does not allow us to address the potential problems of endogeneity, the dynamics of investment flows (past flows affect future flows), and the autocorrelation of errors that may bias our results. This encourages us to use a GMM (Generalized Method of Moments) estimate, which will enable us to test the robustness of our results and to refine our analysis of the underlying mechanisms establishing a link between MPTNWIX and FEPA.

4.3. Robustness Checks and Additional Tests

In a setting where the international allocation of equity portfolios exhibits strong intertemporal persistence and where institutional and macroeconomic determinants are likely to be endogenous, Ordinary Least Squares and fixed-effects estimators should be regarded as descriptive benchmarks rather than as credible frameworks for structural inference. The statistical significance of the lagged dependent variable reveals a dynamic adjustment process in which portfolio positions in one period exert a substantial influence on subsequent allocation decisions, thereby exposing static estimators to the well-known dynamic panel bias documented in short-T settings (Nickell, 1981; Bond, 2002). Moreover, monetary policy transparency, financial development, and several macroeconomic indicators may themselves respond contemporaneously to foreign equity portfolio allocation, generating simultaneity and omitted-variable biases that OLS cannot correct. These limitations fully justify the use of the system GMM estimator, which is particularly suitable for panels with low time dimensions and moderate cross-sectional size, and which are characterized by strong persistence and structural endogeneity of the regressors. The Arellano and Bover (1995) and Blundell and Bond (1998) estimator jointly exploits moment conditions in differences and in levels, instrumenting potentially endogenous variables with their own lags in order to purge unobserved specific effects and to correct simultaneously for dynamic bias, simultaneity, and time-invariant heterogeneity. The instrument matrix is constructed parsimoniously by restricting lag depth and employing collapsed instruments, following Roodman’s (2009) recommendations, so as to prevent instrument proliferation and preserve the power of overidentification tests.

Given the moderate cross-sectional dimension of the panel, system GMM estimation could, in the absence of appropriate precautions, expose the model to a risk of instrument proliferation likely to weaken the informative power of over-identification tests and to lead to overfitting. In order to control this risk, the instrumentation strategy is deliberately parsimonious: the depth of lags used as instruments is strictly limited, and instruments are collapsed so as to contain their number and maintain a reasonable ratio between instruments and cross-sectional units. This instrumentation discipline, in line with standard recommendations for panels of limited size, aims to preserve reliable identification while addressing potential endogeneity and the dynamic nature of the model. The standard econometric diagnostics reported below (tests of autocorrelation and tests of instrument validity) confirm that the estimates are not affected by model overfitting and support the robustness of the results.

Empirical diagnostics namely the absence of second-order autocorrelation and the non-rejection of the Hansen test validate this estimation strategy and establish the system GMM framework as a rigorous methodological extension of the baseline linear model for identifying the structural effect of monetary policy transparency on the foreign equity portfolio allocation in an environment shaped by persistence, endogeneity, and institutional complexity.

The system GMM estimates provide a substantive reappraisal of the relationship between monetary policy transparency and the foreign equity portfolio allocation by correcting for endogeneity biases, dynamic persistence, and unobserved heterogeneity that limited the reliability of the OLS estimates. The results reveal markedly different economic mechanisms across advanced and emerging economies, consistent with the recent literature on the transmission of monetary signals and the sensitivity of cross-border portfolio flows to institutional fundamentals (Bernanke & Gertler, 1995; Obstfeld, 2013; Kraay & Nehru, 2006).

The estimates reported in

Table 4. Dynamic panel estimation (GMM system results).

Advanced Economies
L.FEPA	Coef.	Std. Err.	t	P > |t|	[95% Conf.	Interval]
FEPA	1.148159	0.047579	24.13	(0.000) ***	1.043439	1.25288
MPTNWIX	0.0027051	0.0004813	5.62	(0.000) ***	0.0016458	0.0037643
Law	0.0105979	0.0033767	3.14	(0.009) ***	0.0031658	0.0180301
GDPG	−0.0018652	0.0004463	−4.18	(0.002) ***	−0.0028476	−0.0008828
Infl	−0.0091674	0.0029605	−3.10	(0.010) *	−0.0156835	−0.0026513
Dticcred	−0.0001394	0.0000458	−3.04	(0.011) *	−0.0002402	−0.0000386
cons	−0.0295476	0.0092613	−3.19	(0.009) ***	−0.0499315	−0.0091637
Arellano-Bond test for AR(2) in first differences: z = −0.04 Pr > z = 0.970
Hansen test of overid. restrictions: chi2(6) = 4.04 Prob > chi2 = 0.671
F(6, 11) = 2477.62
Prob > F = 0.000
Number of observations = 216
Emerging Economies
L.FEPA	Coef.	Std. Err.	t	P > |t|	[95% Conf.	Interval]
FEPA	0.8188102	0.244348	3.35	(0.010) *	0.2553426	1.382278
MPTNWIX	0.0000964	0.0000474	2.04	(0.076) *	−0.0000128	0.0002057
Law	−0.0006141	0.0004397	−1.40	(0.200)	−0.0016281	0.0003999
GDPG	−0.0001028	0.0000237	−4.33	(0.003) **	−0.0001575	−0.000048
Infl	4.56 × 10−6	0.0000258	0.18	(0.864)	−0.0000549	0.000064
Dticcred	−0.0000117	8.71e-06	−1.34	(0.218)	−0.0000317	8.43e-06
cons	0.0001652	0.0006904	0.24	(0.817)	−0.0014269	0.0017572
Arellano-Bond test for AR(2) in first differences: z = −1.03 Pr > z = 0.303
Hansen test of overid. restrictions: chi2(4) = 1.98 Prob > chi2 = 0.740
F(6, 8) = 671.84 Prob > F = 0.000
Number of observations = 162

Note: MPTNWIX corresponds to the value of our new monetary policy transparency index; FEPA indicates foreign equity portfolio allocation, i.e., the logarithmic value of the total foreign portfolio allocation made by country i to country j at time t (Wi, j, t); Infl is inflation; GDPPCG is GDP per capita growth; Law refers to the rule of law; Dticcred indicates domestic credit. Statistical significance is reported at the 10% (*), 5% (**) and 1% (***) significance levels, respectively.

employ a System GMM estimator in order to simultaneously address three major sources of bias present in static models: the adjustment dynamics of portfolio allocations, the potential endogeneity of macro-financial and institutional variables, and unobservable heterogeneity (Arellano & Bover, 1995; Blundell & Bond, 1998; Roodman, 2009). The credibility of the inferences is confirmed by standard diagnostics: absence of second-order autocorrelation (AR(2) advanced economies: z = −0.04; p = 0.970; emerging economies: z = −1.03; p = 0.303), overall validity of the instruments (Hansen advanced economies: χ²(6) = 4.04; p = 0.671; emerging economies: χ²(4) = 1.98; p = 0.740), and joint significance of the specifications (Fisher advanced economies: F(6, 11) = 2477.62; p = 0.000; emerging economies: F(6, 8) = 671.84; p = 0.000). These results support the relevance of the dynamic framework for analyzing foreign equity portfolio allocation, which is characterized by strong inertia and simultaneous interactions between economic policy, institutions, and financial conditions.

The first structuring result concerns the persistence of foreign equity portfolio allocation. The coefficient of the lagged dependent variable is positive and highly significant in advanced economies (L.FEPA = 1.148159; p = 0.000) and remains significant in emerging economies (L.FEPF = 0.8188102; p = 0.010). This inertia reflects a mechanism of gradual investor adjustment (reallocation costs, benchmark-tracking constraints, liquidity frictions), which is typical of foreign equity portfolio allocation (Bekaert & Harvey, 2000; Gelos & Wei, 2005). Consequently, the effects of contemporaneous determinants should be interpreted as factors that modify the trajectory of allocations beyond a strongly self-sustained dynamic.

Within this framework, monetary policy transparency (MPTNWIX) retains an economically central role, albeit in a heterogeneous manner depending on the level of development. In advanced economies, MPTNWIX is positive and highly significant (0.0027051; p = 0.000), indicating that an improvement in transparency robustly increases foreign equity portfolio allocation even after controlling for dynamics and endogeneity. From an economic standpoint, this result is consistent with a credibility and expectations-formation channel: clearer communication reduces uncertainty, tightens the distribution of expectations, improves the predictability of the interest rate path, and lowers the informational risk premium, thereby enhancing the attractiveness of domestic assets (Clarida et al., 2001; Weber, 2018). In emerging economies, MPTNWIX remains positive but is significant only at the 10% level (0.0000964; p = 0.076), suggesting that transparency remains a favorable signal but is less decisive when dominant risks—exchange rate risk, institutional instability, liquidity constraints, exposure to global financial conditions—weigh more heavily on portfolio arbitrage decisions (Rey, 2015; Kraay & Nehru, 2006). This asymmetry between advanced and emerging countries thus indicates that the effectiveness of transparency is conditioned by the capacity of the financial and institutional system to absorb and capitalize on public information.

The comparison logically extends to institutional quality (Law), which informs the investor protection environment. In advanced economies, Law is positive and significant (0.0105979; p = 0.009), confirming that legal security, regulatory predictability, and contract enforcement strengthen the attractiveness of equity markets for foreign investors even in environments that are already institutionally developed (North, 1990; Porta et al., 1998). By contrast, in emerging economies, Law becomes non-significant (−0.0006141; p = 0.200) once dynamics and endogeneity are corrected for, suggesting that the aggregate rule-of-law indicator captures heterogeneity in implementation (effectiveness versus formal norms) and that, in these markets, other more immediate risks may dominate the average effect of perceived legal quality (Acemoglu et al., 2001).

Macroeconomic fundamentals then reflect an important requalification specific to the dynamic framework. Growth (GDPG) is negative and significant in both advanced economies (−0.0018652; p = 0.002) and emerging economies (−0.0001028; p = 0.003). This sign does not imply that growth mechanically “discourages” investment, but rather that, conditional on other factors and on allocation persistence, expansionary phases may be associated with expectations of monetary tightening, rising risk premia, or increased asset volatility, leading some investors to reduce equity exposure or rebalance toward other markets; this mechanism is consistent with the literature on financial cycles and the sensitivity of foreign equity portfolio allocation to risk conditions rather than to growth levels alone (Bekaert & Harvey, 2000; Rey, 2015). Inflation, in turn, displays marked heterogeneity: it is negative and significant in advanced economies (−0.0091674; p = 0.010), indicating that higher inflation deteriorates expected real returns and reinforces expectations of monetary tightening, thereby reducing the attractiveness of domestic assets (Bekaert & Harvey, 1997). By contrast, inflation is not significant in emerging economies (4.56 × 10⁻⁶; p = 0.864), indicating that once endogeneity and dynamics are taken into account, inflation does not constitute an average discriminating signal in the presence of more salient structural risks.

Finally, the behavior of domestic financial development/credit (Dticcred) precisely illustrates the contribution of System GMM relative to static approaches: in advanced economies, Dticcred becomes negative and significant (−0.0001394; p = 0.011). This inversion/qualification suggests that credit expansion, once simultaneity biases are neutralized, may be interpreted by investors as an indicator of financial vulnerabilities (leverage, overheating of the credit cycle) rather than as a simple signal of financial deepening, which is consistent with the literature on financial instability associated with credit booms (Reinhart & Rogoff, 2004). In emerging economies, Dticcred is not significant (−0.0000117; p = 0.218), a result compatible with strong structural heterogeneity: depending on the country, credit expansion may reflect either favorable financial deepening or the accumulation of macroprudential imbalances, neutralizing the average effect once endogeneity is controlled for.

Taken together, these findings demonstrate that monetary policy transparency is a fundamental though not uniform determinant of foreign equity portfolio allocation. Its effectiveness depends critically on the institutional quality, financial depth, and macroeconomic stability of the issuing countries. In advanced economies, transparency operates as a powerful signal that reduces uncertainty; in emerging economies, its role remains positive but is substantially conditioned by persistent structural vulnerabilities.

5. Conclusions

This study revisits the role of monetary policy transparency in shaping the allocation of foreign equity portfolios by developing an expanded conceptual framework that incorporates two previously overlooked dimensions: accounting information transparency and financial stability transparency. The enhanced index introduced in this research rests on the premise that, in a globalized financial system exposed to interconnected systemic risks, international investors evaluate not only conventional monetary policy signals but also the integrity, coherence, and informational granularity of the institutional disclosures issued by central banks.

Drawing on a panel of twenty-one advanced and emerging economies over the period 2000–2019, our estimations reveal a significant relationship between this broadened form of monetary policy transparency and the allocation of foreign equity portfolios. In advanced economies, improvements in the quality of accounting and macroprudential information emerge as key determinants of reduced informational uncertainty, enhanced credibility, and more stable investor expectations and findings that align with recent evidence emphasizing the crucial role of informational infrastructures in the transmission of monetary signals. In emerging economies, the effects are more heterogeneous, underscoring that the influence of transparency is strongly mediated by institutional robustness, financial market depth, and the capacity of authorities to mitigate information frictions an observation consistent with contemporary work on institutional fragmentation and heightened exposure to global shocks (Bekaert et al., 2023).

Theoretically, the principal contribution of this study lies in demonstrating that monetary policy transparency should be conceptualized as a multidimensional informational architecture, far exceeding the mere disclosure of decisions or forecasts. Accounting transparency and financial stability transparency constitute essential pillars of this architecture, as they shape investors’ perception of systemic risks, the readability of prudential frameworks, and ultimately their allocation choices between domestic and foreign equity markets.

Nonetheless, several limitations warrant cautious interpretation. The occasional incompleteness of CPIS data, the sensitivity of GMM estimations to instrument proliferation, and the relatively small panel size all suggest that the empirical results, while informative, should be viewed with prudence. These limitations do not invalidate the analytical contribution but indicate the need for more granular empirical approaches and external validation of the proposed index.

Promising avenues for future research naturally emerge from this framework. These include inter-coder validation of the index components, the use of more refined micro-bilateral datasets, the exploration of non-linear dynamics between institutional transparency and foreign equity portfolio allocation, and the investigation of the endogeneity linking monetary credibility, financial stability, and international capital mobility. Collectively, these extensions reinforce the central role of informational infrastructures in shaping the contemporary architecture of global financial integration.