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This study aims to determine how changes in macroeconomic indicators and the housing prices index (HPI) are related. These factors can cause short-term and long-term changes in the housing market in Spain.

The study used cointegrating regression, fully modified ordinary least squares and dynamic ordinary least squares methodologies. The models are trained using quarterly time series data for these parameters from 2010 to 2022. A comprehensive examination is conducted to explore the relationship between macroeconomic issues and fluctuations in the HPI.

The results indicate statistically significant short-run effects (p < 0.05) of economic growth, inflation, Spanish stock indices, foreign trade and the interest rate on HPI. The inflation variables, Spain’s stock indices, interest rate and monetary rate, have statistically significant long-run effects (p < 0.05) on HPI. The exchange rate, unemployment and money supply have no substantial impact on HPI in Spain.

The study’s findings significantly contribute to increased information concerning the level of investing activity in the Spanish housing sector. After conducting an in-depth study of both the long-run and short-run connections with HPI, the study proved to be highly effective in formulating appropriate policies.

The paper analyzes the impact of bank opacity on financial stability in an emerging economy.

Based on a unique dataset of 31 Vietnamese commercial banks from 2007 to 2019, the paper captures earnings opacity via discretionary loan loss provisions and reflects individual bank stability through the accounting-based Z-score index and its disaggregate components. The least squares dummy variable corrected (LSDVC) approach is employed for empirical analysis.

In contradiction to most studies on developed economies, earnings management improves bank financial stability in Vietnam. Earnings management is more important for the financial stability of smaller banks. Further, the effect of financial information disclosure on bank stability is strengthened by unfavorable macroeconomic conditions, particularly economic downturns, the global financial crisis and uncertain times in banking.

This is the first study to shed light on how bank opacity influences bank financial stability in an emerging market. The evidence with the conditioning roles of bank size and macroeconomic factors, such as uncertainty in banking, is entirely novel in the related literature. Additionally, the paper contributes to a growing body of banking literature by using the LSDVC estimator to examine the association between bank opacity and bank stability.

Driven by the anticipated global stagflation, this straightforward yet novel study examines the cost of inflation as a macroeconomic factor by investigating its influence on stock market growth. Thus, this paper aims to examine the impact of inflation on the probability of initial public offering (IPO) withdrawal decision.

The paper employs a large dataset that covers the period January 1995–December 2019 and comprises 33,536 successful or withdrawn IPOs from 22 nations with various legal and cultural systems. This study applies a probit model utilizing version 15 of Stata statistical software.

This study finds that inflation is substantially and positively correlated with the likelihood of IPO withdrawal. Results of this study show that the IPO withdrawal decision increases up to 90% when the inflation rate climbs by 10%. Multiple robustness tests provide consistent findings.

This study's implications are important for researchers, investment banks, underwriters, issuers, regulators and stock exchanges. When processing IPO proposals, investment banks, underwriters and issuers must consider inflation projections to avoid negative effects, as demonstrated by the findings. In addition, regulators and stock exchanges must be aware of the detrimental impact of inflation on competitiveness in attracting new listings.

To the best of the authors’ knowledge, this study is the first to present convincing evidence of a major relationship between IPO withdrawal decision and inflation.

This paper aims to introduce a new perspective on long-term stock return predictability by focusing on the relative (individual and hybrid) informative power of a wide range of accounting (firm-related), technical and macroeconomic factors while considering the past performance of the stocks using machine learning algorithms.

The sample includes a panel data set of 94 non-financial firms listed in Egyptian Exchange 100 index from 2014: Q1 to 2019: Q4. Relativity has been investigated by comparing relevant factors’ individual and combined informative power and differentiating between losers and winners based on historical stock returns. To predict the quarterly stock returns, Gaussian process regression (GPR) has been used. The robustness of the results is examined through the out-of-sample test. This study also uses linear regression (LR) as a benchmark model.

The past performance and the presence of other predictors influence the informative power of relevant factors and hence their predictive ability. The out-of-sample results show a trade-off between GPR and LR with proven superiority to GPR in limited experiments. The individual informative power outperforms the hybrid power, in which macroeconomic indicators outperform the remaining sets of indicators for losers, while winners show mixed results in terms of various performance evaluation metrics. Prediction accuracy is generally higher for losers than for winners.

This study provides interesting insight into the dynamic nature of the predictor variables in terms of stock return predictability. Hence, this study also deepens the understanding of asset pricing in a way that directly contributes to practitioners’ portfolio diversification strategies.

In concern of the chaos of factors in the literature and its accompanying misleading conclusions, this study takes another look at the approach that studies stock return predictability. To the best of the authors’ knowledge, this is the first study in the Egyptian context that re-examines the predictive power of the previously discovered factors from a different perspective that highlights their relative nature.

This paper aims to examine if the market risk premiums of the Gulf Cooperation Council (GCC) countries are particularly higher on prescheduled US monetary policy announcement days. The findings shed light on the causality relationship from the state of the global economy to the GCC equity markets as well as their integration with the rest of the world.

The author takes the standard event-study approach, following Fama et al. (1969). As the announcement days are prescheduled, the impact of the announcements on the GCC markets' risk premia allows for test of causality, while other studies address predictability and association.

The author finds that excess returns are higher, both economically and statistically, on announcement days in most individual GCC countries and the region overall. Moreover, additional compensations may not appear on the exact days of announcement in a few countries; rather, on the days right before or after announcements, possibly due to information leakage or gradual diffusion. My results show that there is a causal relationship from the state of the global economy to the GCC equity markets' risk premia. This new evidence supports integration between the Gulf region's and the world's financial markets.

The evidence of risk–return transmission from US monetary policy announcements to GCC countries' equity indices supports integration between the region's and the world's financial markets. The study results will help guide investors' and corporations' investing, capital budgeting and portfolio evaluation decisions.

This paper extends the announcement literature (Savor and Wilson 2013, 2014) by examining the responses of the GCC countries, the major players of the global oil markets. The empirical analysis documents a causal relationship from the state of the global economy, as revealed by US monetary policy announcements, to the GCC equity indices. This new evidence supports increased integration between the Gulf region and the world, a finding that investors and corporations should consider when making investing, capital budgeting and portfolio evaluation decisions.

Understanding which market to invest in for a well-diversified portfolio is fundamental in economies that are highly vulnerable to fluctuations in exchange rates. Extant literature that has considered phenomenon hardly juxtapose the markets. The purpose of this study is to examine the effects of exchange rate volatility on the Stock and Real Estate market of South Africa. The essence is to determine whether the fluctuations in the exchange rate influence the markets prices differently.

The Generalised Autoregressive Conditional Heteroskedasticity [GARCH (1.1)] model was used in establishing the effect of exchange rate volatility on both markets. This study used monthly South African data between 2000 and 2020.

The results of this study showed that increased exchange rate volatility increases stock market volatility but decreases real-estate market volatility, both of which revealed weak influences from the exchange rates volatility.

This study has implication for policy in using the exchange rate as a policy tool to attract foreign portfolio investment. The weak volatility transmission from the exchange rate market to the stock and real estate market indicates that there is prospect for foreign investors to diversify their investments in these two markets.

This study investigated which of the assets market, stock or housing market do better in volatile exchange rate conditions in South Africa.

Over the last few years, the European Central Bank (ECB) has adopted unconventional monetary policies. These measures aim to boost economic growth and increase inflation through the bond market. The purpose of this paper is to study the impact of the ECB’s quantitative easing (QE) on the investor’s behavior in the stock market.

First, the authors theoretically identify the transmission channels of the QE shocks to the stock market. Then, the authors empirically assess the financial market’s responses to QE shocks in a data-rich environment using a factor augmented VAR (FAVAR).

The results show that the ECB’s unconventional monetary policy positively affects the stock market. A QE shock leads to an increase in stock prices and a drop in the realized volatility and the implied risk premium. The authors also suggest that the ECB’s QE is transmitted to the stock market through five main channels: the liquidity, the expectation, the portfolio reallocation, the interest rates and the risk premium channels.

The findings help to better understand the behavior of stock market assets in a data-rich economic context and guide investors and policymakers in the presence of unconventional monetary tools. For instance, decision-makers and investors should consider the short-term effect of the QE interventions and the changing behavior of the financial actors over time. In addition, high stock market returns can increase risk appetite. This can lead investors to underestimate the market risk. Decision-makers and market participants should take into consideration the impact of the large injection of money through the QE, which may raise the risk of a speculative bubble in the financial market.

To the best of the authors’ knowledge, this is the first study that incorporates a theoretical and empirical analysis to explore QE transmission to the stock market in the European context. Unlike previous studies, the authors use the shadow rate proposed by Wu and Xia (2017) to quantify the effect of the ECB’s QE in a data-rich environment. The authors also include two key risk indicators – the stock market risk premium and the realized volatility – to capture investors’ behavior in the stock market following QE shocks.

This paper proposes a new multi-dimensional financial inclusion index.

The authors employ two-stage principal component analysis (PCA) and aggregating indicators of availability, access and use. The paper first assesses the cross-country variations in the index and analyses trends over time for a sample of countries members of the Union for the Mediterranean (UfM) from 2010–2018. Second, it investigates factors that could explain the level of financial inclusion across countries.

The financial inclusion index shows a downward trend for the full sample over the period under investigation; however when splitting the sample by income group, it appears that high- and middle–income countries did not register the same trend. When examining the determinants of financial inclusion for the UfM countries, the authors find that macroeconomic, social and governance factors, as well as banking conditions, matter. Policy-makers in low- and middle-income economies should consider the importance of digital financial inclusion, which is substituting the role to traditional banking system, to close the gap and accelerate its development.

First, the authors provide a new measure of financial inclusion using a three-dimensional index: availability, access and use, for which weights are assigned using PCA. It uses data available for the UfM sample by combining data from different databases in order to include most indicators considered in the literature, as the majority of studies only use single measures (number of bank branches, ownership of a bank account, ratio of credits or deposits to gross domestic product [GDP], etc.). Second, by focussing on UfM countries, the study covers a region that includes both large developed and small developing economies that are connected via financial and trade ties, whilst previous studies generally give global evidence from an international sample with little or no economic ties. Third, splitting the sample by country income groups, the paper presents a more comprehensive representation of the cross-country variation in financial inclusion levels between high- and middle-income economies for this region.

In the context of investment decisions, the intricate interplay between exogenous shocks and their influence on investor confidence significantly shapes their behaviors and, consequently, their outcomes. Investment decisions are influenced by uncertainties, exogenous shocks as well as the sentiments and confidence of investors, factors typically overlooked by decision-makers. This study will meticulously examine these multifaceted influences and discern their intricate hierarchical nuances in the sentiments of industrial entrepreneurs during the COVID-19 pandemic.

Employing the robust framework of the generalized linear latent and mixed models (GLLAMM), this research will thoroughly investigate individual and group idiosyncrasies present in diverse data compilations. Additionally, it will delve deeply into the exogeneity of disturbances across different sectors and regions.

Relevant insights gleaned from this research elucidate the adverse influence of exogenous forces, including pandemics and financial crises, on the confidence of industrial entrepreneurs. Furthermore, a significant discovery emerges in the regional analysis, revealing a notable homogeneity in the propagation patterns of industrial entrepreneurs' perceptions within the sectoral and regional context. This finding suggests a mitigation of regional effects in situations of global exogenous shocks.

Within the realm of academic inquiry, this study offers an innovative perspective in unveiling the intricate interaction between external shocks and their significant impacts on the sentiment of industrial entrepreneurs. Furthermore, the utilization of the robust GLLAMM captures the hierarchical dimension of this relationship, enhancing the precision of analyses. This approach provides a significant impetus for data-informed strategic directions.

The financial system has witnessed the substantial growth of financial technology (fintech) firms. One of the strategies that banks have adopted to cope with this emergence is to cooperate with fintech firms. This study empirically investigated whether cooperation between banks and fintech companies would improve banks’ risk-adjusted returns.

We developed a novel index of bank–fintech cooperation across various fintech sectors. A system generalized method of moments (GMM) was used to examine this relationship using a sample of Vietnamese banks from 2007 to 2019.

The findings show that the diversity of bank–fintech cooperation across seven sectors tends to enhance banks’ risk-adjusted returns. The results also highlight that this relationship may depend on the types of fintech sectors and bank ownership. More specifically, the positive association between this cooperation and banks’ risk-adjusted returns only holds in the comparison sector of fintech, whereas there is a negative relationship between them in the payments and mobile wallets sector. Furthermore, state-owned commercial banks that engage in more bank–fintech cooperation tend to generate greater earnings. If we look at listed banks, the positive effect of bank–fintech partnerships on risk-adjusted returns still holds. A similar result was also found in the case of large banks.

Our empirical evidence provides motivations for incumbent banks to implement appropriate strategies toward diversity in bank–fintech partnerships when fintech firms have engaged in various financial segments.

This study adds more evidence to the existing literature on the relationship between bank–fintech cooperation and bank performance.