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This research investigates Airbnb’s financial implications in emerging economies and their potential to influence stock market profitability.

Employing a multifaceted approach, the study combines parametric and nonparametric tests, robustness checks, and regression analysis to assess the impact of Airbnb’s announcements on emerging economy stock markets.

Airbnb’s announcements affect emerging economies' stock markets with a distinct pattern of cumulative abnormal returns (CAR): negative before the announcement and positive afterward. Informed investors strategically leverage this opportunity through short selling before the announcement and acquiring positions following it. Regression analysis validates these trends, revealing that stock index returns and inbound tourism affect CAR before announcements, while GDP growth influences CAR afterward. Announcements pertaining to emerging economies exert a more pronounced impact on stock indices compared to city-specific announcements, with COVID-19 period announcements demonstrating greater significance in abnormal returns than non-COVID-19 period announcements.

This study advances existing literature through a comprehensive range of statistical tests, differentiation between emerging countries and cities, introduction of five macroeconomic variables, and reliance on credible primary Airbnb data. It highlights the potential for investors to leverage Airbnb announcements in emerging markets for stock market profits, emphasizing the need for adaptive investment strategies considering broader macroeconomic factors.

To investigate the role of domestic and foreign economic policy uncertainty (EPU) in driving the corporate bond yields in emerging markets.

The study utilizes monthly data from January 2008 to June 2023 from the selected emerging economies. The data analysis is conducted using univariate, bivariate and multivariate statistical techniques. The study includes bond market liquidity and global volatility (VIX) as control variables.

Domestic EPU has a significant role in driving corporate bond yields in these markets. The study finds weak evidence to support the role of the USA EPU in influencing corporate bond yields in emerging economies. Domestic EPU holds more weight and influence than the EPU originating from the United States of America.

The findings provide useful insights to policymakers about the potential impact of policy uncertainty on corporate bond yields and enable them to make informed decisions regarding economic policies that maintains financial stability. Understanding the relationship between EPU and corporate bond yields enables investors to optimize their investment decisions in emerging market economies, opens the scope for further research on the interaction between EPU and volatility and other attributes of fixed income markets.

Focuses specifically on the emerging market economies in Asia, providing an in-depth analysis of the dynamics and challenges faced by these countries, Explores the influence of both domestic and the USA EPU on corporate bond yields in emerging markets, offering valuable insights into the transmission channels and impact of EPU from various sources.

This study proposes a portfolio of new venture signals that are likely to attract investors' attention in the context of an emerging market and examines how they work in combination to affect the likelihood of obtaining funding.

The authors use data on early-stage venture capital investments for high-tech start-ups in Turkey. The authors adopt a configurational approach and use fuzzy QCA and regression analysis.

The findings suggest that financing of new ventures in an emerging economy is shaped by signals of context-specific capabilities that are required to survive and thrive in this market environment alongside and in interaction with signals of general capabilities required for business success. Different combinations of these signals provide equifinal pathways to obtain funding. Furthermore, signals that differ in type and content interact in complex ways to affect investors' decisions.

The findings suggest that entrepreneurs with no prior experience in the emerging market context can increase their chances of obtaining funding by affiliating with a venture development organization. Another promising strategy is to form a founding team that includes members affiliated with a developed country together with members who have emerging market experience. Finally, entrepreneurs may consider combining signals of context-specific capabilities with signals of general capabilities as they work in a complementary way to attract funding.

This study addresses two major shortcomings of the literature on new venture signaling, first, by positing the emerging market context as a unique signaling environment and, second, by demonstrating the value of considering signals as portfolios with potential interdependencies.

Previous studies demonstrate that market-oriented reform has contributed significantly to China's economic growth from the efficiency-based economic view. But some argue that state-owned firms have access to policy information, scarce resources, and government support, and thus state-owned firms might foster innovation. This study tries to find out either market force or state ownership helps improve firms' R&D efficiency.

Using data from China's high-tech industry, we employed the fixed-effect stochastic frontier model and the spatial panel Han-Philips linear dynamic regression model to investigate the relationship between market-oriented reform and the dynamic evolution of R&D efficiency in both temporal and spatial dimensions. Moreover, we examined whether the relationship is affected in a state-owned economy and an industry protection environment.

The results indicate the following: (1) the R&D efficiency of China's high-tech industry has improved steadily and has converged gradually across its regions during the market-oriented reform; (2) the marketization degree is positively correlated with R&D efficiency and its regional convergence; (3) the state-owned economy and industry protection have significantly weakened the ability of market forces to shape R&D efficiency — i.e. they reduce, rather than enhance, R&D efficiency.

This investigation helps understand the drivers of R&D efficiency in transition economies, and the findings are also helpful in defining the boundaries and constraints of market forces.

The data economy, emerging from the current hyper-technological landscape, is a global digital ecosystem where data is gathered, organized and exchanged to create economic value. This paper aims to shed light on the interplay of the different topics involved in the data economy, as found in the literature. The study research provides a comprehensive understanding of the opportunities, challenges and implications of the data economy for businesses, governments, individuals and society at large, while investigating its impact on business value creation, knowledge and digital business transformation.

The authors conducted a literature review that generated a conceptual map of the data economy by analyzing a corpus of research papers through a combination of machine learning algorithms, text mining techniques and a qualitative research approach.

The study findings revealed eight topics that collectively represent the essential features of data economy in the current literature, namely (1) Data Security, (2) Technology Enablers, (3) Business Implications, (4) Social Implications, (5) Political Framework, (6) Legal Enablers, (7) Privacy Concerns and (8) Data Marketplace. The study resulting model may help researchers and practitioners to develop the concept of data economy in a structured way and provide a subset of specific areas that require further research exploration.

Practically, this paper offers managers and marketers valuable insights to comprehend how to manage the opportunities deriving from a constantly changing competitive arena whose value is today also generated by the data economy.

Socially, the authors also reveal insights explaining how the data economy features may be exploited to build a better society.

This is the first paper exploring the data economy opportunity for business value creation from a critical perspective.

The crude oil market plays a key role in addressing the issue of energy economics. This paper aims to detect the causality relationship between the crude oil market and economy based on the financial system.

This paper used the static and dynamic Hatemi-J Bootstrap Toda–Yamamoto and Diebold–Yilmaz connectedness index. The Hatemi-J Bootstrap Toda-Yamamoto approach allows researchers to use nonstationary data and that method is robust to nonnormal distribution and heteroscedasticity. The Diebold–Yilmaz connectedness index model provides researchers to detect the power of connectedness besides linkage direction. The analyzed period is the span from January 3, 2005 to October 3, 2022.

The results show bidirectional causality in the full sample but unidirectional causality before and after the 2008 financial crisis. During the 2008 financial crisis period and the COVID-19 period, there was a bidirectional and unidirectional causality, respectively. The connectedness approach indicates that the crude oil market affects financial stress through investors’ risk preferences.

The Diebold–Yilmaz spillover index model is based on vector autoregression methods with a stationarity precondition. However, some of the five dimensions that constitute the financial stress index (FSI) are nonstationary in level. Therefore, the authors takes the first difference of the nonstationary data.

The linkage between the crude oil market and the FSI provides useful information for investors and policymakers. For instance, this paper indicates that an investor wanted to forecast future value of the crude oil (financial stress) should consider the current and past values of financial stress (crude oil). Moreover, policymaker should consider the crude oil market (FSI) to make a policy proposal for financial system (crude oil market).

Recently, indicators of economic activity levels (economic policy uncertainty, implied volatility index) have begun to be considered to analyze the relationship between energy and the economy but very little is known in the literature about the leading and lagging roles of data in subsample periods and the linkage channel. The other originality of this research is using the new econometric approaches.

This paper aims to examine whether the volatility of returns in commodity (gold, oil), bond and forex markets is related over time to the volatility of returns in equity markets of Bangladesh, Indonesia, Pakistan, Philippines, Turkey and Vietnam. In addition, the authors analyze the integration of the commodity, bond, forex and equity markets across these markets.

The dynamic conditional correlation GARCH (DCC-GARCH) model is used to capture the time-varying conditional correlation among markets. The authors use daily data of stock prices, oil prices, gold prices, exchange rates and 10 years' bond yields of the six countries from Datastream and investing.com from January 2001 to April 2021.

Findings reveal that the parameters of dynamic correlation are statistically significant which indicates the importance of time-varying co-movements. Estimation of the DCC-GARCH model suggests that the stock market is significantly correlated with bond, forex, gold and oil markets in all six countries.

This study has practical implications for policymakers and investment professionals. A better understanding of dynamic linkages among the markets would help in constructing effective hedging and portfolio diversification strategies. Policy makers can get insight to build proper strategies in order to insulate the economy from factors that cause volatility.

Several studies have investigated the linkage between commodity and stock markets and the volatility spillover effect, but very little attention is given to study the interrelationship between groups of market segments of different economies. No study has comparatively examined the dynamic relationship of multiple markets of a group of emerging countries simultaneously.

This paper aims to investigate the dynamic volatility connectedness among 13 G20 countries by using the volatility indices.

The connectedness approach based on the time-varying parameter vector autoregression model has been used to investigate the linkage. The period of study is from 1 January 2014 to 20 April 2023.

This analysis revealed that volatility connectedness among the countries during COVID-19 and Russia–Ukraine conflict had increased significantly. Furthermore, analysis has indicated that investors had not anticipated the World Health Organization announcement of COVID-19 as a global pandemic. Contrarily, investors had anticipated the Russian invasion of Ukraine, evident in a significant rise in volatility before and after the invasion. In addition, the transmission of volatility is from developed to developing countries. Developed countries are NET volatility transmitters, whereas developing countries are NET volatility receivers. Finally, the ordinary least square regression result suggests that the volatility connectedness index is informative of stock market dynamics.

The connectedness approach has been widely used to estimate the dynamic connectedness among market indices, cryptocurrencies, sectoral indices, enegy commodities and metals. To the best of the authors’ knowledge, none of the previous studies have directly used the volatility indices to measure the volatility connectedness. Hence, this study is the first of its kind that has used volatility indices to measure the volatility connectedness among the countries.

Despite its devastating nature, the COVID-19 pandemic has also catalyzed a substantial surge in the adoption and integration of technological tools within economies, exerting a profound influence on the dissemination of information among participants in stock markets. Consequently, this present study delves into the ramifications of post-pandemic dynamics on stock market behavior. It also examines the relationship between investors' sentiments, underlying behavioral drivers and their collective impact on global stock markets.

Drawing upon data spanning from 2012 to 2023 and encompassing major world indices classified by Morgan Stanley Capital International’s (MSCI) market and regional taxonomy, this study employs a threshold regression model. This model effectively distinguishes the thresholds within these influential factors. To evaluate the statistical significance of variances across these thresholds, a Wald coefficient analysis was applied.

The empirical results highlighted the substantive role that investors' sentiments and behavioral determinants play in shaping the predictability of returns on a global scale. However, their influence on developed economies and the continents of America appears comparatively lower compared with the Asia–Pacific markets. Similarly, the regions characterized by a more pronounced influence of behavioral factors seem to reduce their reliance on these factors in the post-pandemic landscape and vice versa. Interestingly, the post COVID-19 technological advancements also appear to exert a lesser impact on developed nations.

This study pioneers the investigation of these contextual dissimilarities, thereby charting new avenues for subsequent research studies. These insights shed valuable light on the contextualized nexus between technology, societal dynamics, behavioral biases and their collective impact on stock markets. Furthermore, the study's revelations offer a unique vantage point for addressing market inefficiencies by pinpointing the pivotal factors driving such behavioral patterns.

This study aims to analyze the volatility spillover effects of crude oil, gold price, interest rate (yield) and the exchange rate (USD (United States Dollar)/INR (Indian National Rupee)) on inflation volatility in India.

This study uses the multivariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models (Baba, Engle, Kraft and Kroner [BEKK]-GARCH and dynamic conditional correlation [DCC]-GARCH) to examine the volatility spillover effect of macroeconomic indicators and strategic commodities on inflation in India. The monthly data are collected from January 2000 till December 2020 for the crude oil price, gold price, interest rate (5-year Indian bond yield), exchange rate (USD/INR) and inflation (wholesale price index [WPI] and consumer price index [CPI]).

In BEKK-GARCH, the results reveal that crude oil price volatility has a long time spillover effect on inflation (WPI). Furthermore, no significant short-term volatility effect exists from crude oil market to inflation (WPI). However, the short-term volatility effect exists from crude oil to inflation while considering CPI as inflation. Gold price volatility has a bidirectional and negative spillover effect on inflation in the case of WPI. However, there is no price volatility spillover effect from gold to inflation in the case of CPI. The price volatility in the exchange rate also has a negative spillover effect on inflation (but only on CPI). Furthermore, volatility of interest rates has no spillover effect on inflation in WPI or CPI. In DCC-GARCH, a short-term volatility impact from all four macroeconomic indicators to inflation is found. Only crude oil and exchange rate have long-term volatility effect on inflation (CPI).

In an economy, inflation management is an essential task. The findings of the current study can be beneficial in this endeavor. The knowledge of the volatility spillover effect of all the four markets undertaken in the study can be significantly helpful in inflation management, especially for inflation-targeting policy.

It is observed that no other study has addressed this issue. We do not find any other research which studies the volatility spillover effect of gold, crude oil, interest rate and exchange rate on the inflation volatility. The current study is novel with a significant contribution to the vast knowledge in this context.