Global Stock Market Research Articles

Challenging Institutional Path Dependence Through Field Configuring Events: Exploring the Collective Institutional Entrepreneurship of the Sustainable Stock Exchanges Initiative

ABSTRACTResearch Question/IssueHow can a global social movement, mobilized through a multi‐stakeholder initiative, promote the uptake of novel governance practices in distinct national institutional environments?Research Findings/InsightsUsing data from all 114 of the world's stock exchanges, covering over 99% of global stock market capitalization as of 2018, I find that exchanges in common‐law countries were more likely to become early partners of the Sustainable Stock Exchanges Initiative (SSEI) after its launch in 2012. However, exchanges based in civil‐law countries experienced a higher likelihood (i.e., risk) of adoption, as measured by the hazard ratio, particularly during a series of sustained field‐configuring events (FCEs) promoted by the SSEI. Over time, this increased hazard ratio indicated a rising likelihood of adoption for civil‐law‐based exchanges, eventually surpassing that of their common‐law counterparts.Theoretical/Academic ImplicationsWhile institutional environments' path dependency can facilitate or hinder the adoption of governance practices, collective institutional entrepreneurship (CIE) can moderate this path dependency and reveal new information regarding the structural fit between promoted governance practices and their institutional environments.Practitioner/Policy ImplicationsThe insights provided by this study can help practitioners understand why some practices face resistance in specific institutional environments, while others are more readily accepted. These insights also help practitioners communicate the elements of governance practices that implicitly align with the institutional environments in which they are seeking adoption.

Global Stock Markets during Covid-19: Did Rationality Prevail?

This study assesses the validity of the Efficient Markets Hypothesis (EMH) during the Covid-19 period by evaluating whether various stock markets around the world accurately predicted economic performance in their respective countries. The underlying premise is that stock prices should discount future company cash flows, and the projection of these cash flows can be proxied by investors' ability to forecast macroeconomic conditions. We assess stock market performance from January 2020 to February 2022 in 16 countries, equally split between developed and emerging markets. While results varied, the study suggests that stock markets generally predicted economic activity during Covid-19. The modeling test indicated that stock markets significantly predicted economic activity in 11 out of 16 countries, particularly in developed markets. Furthermore, incorporating the stock market as a variable, improved economic forecasts in all 16 countries. Unlike other studies that mainly focused on the characteristics of share price movements, this research judges the rationality of stock markets against an external criterion. Ultimately, it suggests that EMH was mostly vindicated during the Covid-19 period.

Impact of crude oil price innovations on global stock market volatility: Evidence across time and space

This study investigates the impact of crude oil price innovations on global stock market volatility through a ripple-spreading network model, incorporating four dimensions of crude oil price changes: realized volatility, implied volatility, variance risk premium, and realized skewness volatility. Additionally, we assess the effects of three types of crude oil shocks—oil-specific, aggregate demand, and oil supply shocks. The results indicate that while all four dimensions exhibit similar temporal diffusion patterns, their spatial impacts differ. Global stock markets demonstrate heightened sensitivity to implied volatility and variance risk premium, followed by realized volatility and, lastly, realized skewness volatility. Moreover, we find that realized volatility spreads through multiple transmission pathways, albeit at a slower pace compared to implied volatility and the variance risk premium. Among the crude oil shocks, oil-specific shock induces the most rapid volatility transmission across global markets, with aggregate demand shock following and oil supply shock exerting the smallest influence.

Prioritization of the IT Sector in the CEE Stock Markets: Investment Policies, Trends and Hidden Gems

Objective: The main purpose of the research is to show that the Central and Eastern European (CEE) market is an essential component of the global stock market. It displays similar patterns to developed countries, and there is a special emphasis on information technology. Research Design & Methods: The study is supported by various machine learning methods and economic analysis, in addition to using Python and R packages. In order to gain a more comprehensive insight into the developments in CEE-region trends, the paper considers a comparative portfolio that focuses on German business, and there is a simulation of the incorporation of the Ukrainian IT sector. Constructing a business strategy in accordance with the Fama–French approach is boosted by incorporating neural networks. Findings: An examination of the performance of Czech, Polish, and Hungarian firms on the stock exchanges suggests that the investment policy oriented towards software is a reasonable choice. The results yielded by the IT companies unmistakably reveal the substantial benefits of their stock market ventures, as well as indicating the ongoing trend of investor reliance. The problem of IT business valuation is highlighted as one of the pitfalls of investing. Implications & Recommendations: The research proposes that maintaining a priority focus on IT, even in challenging circumstances, ensures steady regional advancement. Contribution & Value added: This research brings a unique emphasis to the status and outlook of the IT sector, contributing to the existing toolkit, even in the face of repeated crises.

War discourse and global equity returns

This study investigates the asset pricing implications of war risks in global stock markets. We employ a novel war discourse index developed by Hirshleifer et al. (2023a), which captures market attention to war through news. Extending this approach to both developed and emerging markets, we uncover a significantly positive relation between war risks and global stock market excess returns, which is robust to a range of sensitivity checks. Our findings indicate that investor attention to war risks significantly influences equity premium in global markets.

Artificial intelligence as a core of the new industrial revolution: prospects and limitations

The purpose of the article is to define prospects and limitations of artificial intelligence as a core of in the new industrial revolution. The definition of the concept of AI in the scientific community remains the subject of heated debate. At the same time, in the regulatory and legal plane, a trend is being formed towards unification of the concept of AI. Based on the analysis conducted and literary sources, the following prospects for AI can be identified on theoretical and practical levels. On theoretical level: (1) alienation of tacit knowledge from the individual (employee and entrepreneur); (2) optimization of the planning system; (3) revision of the socialist-calculation debate; (4) decreasing information asymmetry. On practical level: (1) formation of new products and markets; (2) increasing labor and capital productivity; (3) massive creation of new jobs; (4) optimization of business processes; (5) opportunity for rapid growth for small businesses and startups. Limitations: (1) long-term structural unemployment; (2) inflated expectations from AI and, as a consequence, the possible formation of a speculative bubble in the global stock market; (3) energy consumption of AI; (4) outdated pre-AI corporate culture and regulatory environment. Further improvement of AI (including the transition from AI to AGI) and the expansion of its use can make a significant contribution to solving problems related to economic calculation and minimizing information asymmetry, and therefore optimizing transaction costs in the economy. AI, certainly acting as a locally useful tool at the level of individual enterprises and organizations, causes the acceleration of attracting funds to the stock market, which can lead to the formation of a bubble on global level. If this bubble bursts, expectations about the economic efficiency of AI will be revised, and some AI-related companies will experience significant margin reductions (perhaps losses and bankruptcies). But this, in turn, will initiate the next stage of AI development, will accelerate its transition from the current narrow specialization to the creation of full-fledged general artificial intelligence (artificial general intelligence), which has a greater potential to change the economy at all levels. As a result, AI will become established as the core of the new industrial revolution.

Environmentally clean and dirty energy equities during extraordinary global conditions

I examine which extraordinary international events coincide with pronounced changes in the equity markets for some of the world’s largest publicly traded suppliers on opposite sides of the global energy mix — oil and environmentally clean energy companies. First, I adapt an intuitively appealing non-parametric filter to empirically timestamp unexpected and prominent increases and decreases in a wide range of global indicators relevant to the international energy market. Then, I use such extraordinary conditions to characterise the performance of oil and environmentally clean energy equities, and their relationships. My findings suggest that jumps in the global stock market, international crude oil market shocks, and the US dollar real effective exchange rate, are the indicators that define the financial landscape during which considerable gains, losses, and instability across both types of energy markets materialise. In contrast, major elevated uncertainties related to geo-political risk and climate policy reflect relative stability in the equities of both oil and environmentally clean energy companies. Although these results imply that both energy assets are potentially lucrative hedging strategies for investors to exploit during heightened geo-political and climate policy uncertainties, clean energy equities offer market participants the option to combine profit maximising and sustainability objectives while minimising global energy security risks.

Analysis of the effect of Regional Stock Market on the Jakarta Composite Index using Markov Regime Switching Regression

Purpose: In the current era of information, global stock market interconnections significantly influence investment decisions. Changes in one market rapidly affect others. The co-movement of stock markets presents challenges and opportunities for investors, whereas volatility spillovers complicate risk management and investment strategies. Research Methodology: This study examines the influence of the Nikkei 225, Straits Times Index, and Shanghai Composite Index on the Jakarta Composite Index across the pre-pandemic, pandemic, and post-pandemic phases. Results: Utilizing the hidden Markov model with regime-switching regression, this study identifies changes in market behavior due to economic shifts during the pandemic, revealing two regimes: synchronization and desynchronization. Limitations: Pre-COVID-19, the Jakarta Composite Index shows strong synchronization with the Nikkei 225 and Straits Times Index, while the Shanghai Composite Index has an insignificant impact. During the COVID-19 pandemic, frequent desynchronization occurred due to high uncertainty and volatility, with only the Straits Times Index significantly influencing the Jakarta Composite Index. Post-pandemic, synchronization between the JCI and regional markets strengthened again. This study highlights the consistent influence of the Nikkei 225 and Straits Times Index, while the Shanghai Composite Index remains insignificant. Contributions: This study contributes significantly to the understanding of regional stock market relationships and offers valuable insights for academia and practice.

Global power and Stock market co-movements: A study of G20 markets

It is well understood that a handful of countries such as members of G7 and G20 influence the direction of the trade policy of nations across the world. Such influence places significant pressure on other countries to adapt their own policies to suit G7 and G20 as these two groups of countries control international agencies such as the World Bank and the United Nations. This influence is also evident from the deliberations of G7 and G20 member countries and how such more powerful nations seem to shape and influence the global economic agenda. This study aims to investigate the relationship between global power (as measured by the global power index GPI) and globalisation (as measured by the co-movements of stock markets time varying correlations). Previous studies have investigated various factors influencing stock market correlations; however, the relationship between GPI and stock market correlations has not been addressed thus far. To investigate this relationship, we created an index of correlations of each stock market with other stock markets in G20 countries. Our empirical results indicate that GPI has a positive and statistically significant impact on the stock market correlations in G20 nations. This is the first study to establish such relationship between GPI and change in relative stock market performance. In the past changes in relative stock market returns were mainly attributed to the economic factors and relative volatility of the underlying stock markets. As such this study makes an important contribution to body of knowledge by developing a theoretical argument to show how change in relative global power, influences changes in stock market correlations via changes in relative risk premium and returns. The findings of the study have implications for the development of global policies as global power influences stock market co-movements. The findings of this study may also have implications for investors who aim to construct globally diversified portfolios.

Investigating the relationship between Nifty and selected Global stock market indices

Investigating the relationship between Nifty and selected Global stock market indices

A novel central bank digital currency framework design for offline and foreign transactions based on blockchain

Purpose Fiat money production necessitates physical commodities, increasing costs and its flow is challenging to monitor, making it vulnerable to criminal exploitation. Cryptocurrencies offer decentralized solutions, but their decentralization has led to illegal activities. Current cross-border transactions face high costs, resource intensity and lack of instant currency transfers. Offline transactions are essential in unreliable networks. Design/methodology/approach Here, the authors proposed the methodology to perform offline transactions based on card, quick response (QR) code and a foreign transaction framework with universal identification (UID) to perform cross-border transactions using blockchain-dependent central bank digital currencies (CBDCs). Implications for the financial system are also analyzed. Findings The proposed CBDC framework reduces illegal transactions, corruption and the cost of producing fiat money; eases overseas transactions; and eventually increases international tourism, trade and business between countries. It also reduces the processing fees. Offline framework found useful for performing retail-level transactions. Research limitations/implications The research methodology may face limitations due to diplomatic relations, political instability, sanctions and the need for robust offline transaction infrastructure. Practical implications The proposed CBDC framework simplifies debt and insurance management, tax collection, international trade, tourism and global stock market participation. However, implementing CBDCs in low-income countries presents challenges like extensive training, infrastructure and user acceptance issues. Social implications The adoption of CBDCs can enhance financial stability by reducing corruption and illegal transactions through improved traceability and monitoring, thereby curbing activities like terrorism. Originality/value Common framework for foreign transactions is based on the UID, and offline transaction framework is based on the sender’s QR code for multiple user applications.

Assessing the Impact of the Turkey-Syria Earthquake on Global Stock Markets

Using the event study and cross-sectional analysis, we show that the impact of the Turkey-Syria earthquake on the leading stock market indices of 53 nations was not uniform. While the global, European, and developed markets exhibit negative returns on t+1 and t+4, positive returns were observed on t+2 and t+3, indicating that market participants later adjusted their perceptions and expectations of the event. The country-wise analysis suggests that investors' optimistic view leads to positive returns. We show that trade dependence and proximity to the event zone negatively impact returns, while past returns just before the event can predict returns during the event windows.

Investor heterogeneity and global stock market participation

To what extent does investor heterogeneity influence decision to invest in stock market? We address this question by drawing on the behavioural economics literature and analyse the impact of national culture on stock market data of forty seven countries spanning from 2000 to 2022. Our findings demonstrate that investors’ heterogeneity significantly accounts for the variation in stock market participation and the results remain consistent despite many robustness checks.

Asymmetric Impact of Russia–Ukraine War on Global Stock Markets

Russia’s invasion of Ukraine on February 24, 2022, emerged as Europe’s most significant military conflict post second world war, with global economic and geopolitical consequences. Using a broad (95-country) sample, the study examines the impact of the Russia–Ukraine war on global stock markets surrounding the war announcement. It applied the event study method and used short and long event windows to examine the war’s immediate and intermediate impacts. Global stock markets delivered negative 1.90% abnormal returns on the day of the war announcement, and Russia saw the biggest fall. However, after the initial adverse reaction, stock markets reacted asymmetrically. Stock markets of the countries in geographic proximity and high trade intensity with Russia and Ukraine, and net importers of energy and food grains negatively reacted more than the rest. The regional results show that Asia Pacific and Europe reported negative returns across event windows. In contrast, the Americas, Africa, and the Middle East did not react negatively, even in the shortest event window. Adverse war reactions moderated over time. Equity investors and portfolio managers who aim to protect their investments should buy stocks in countries that are net exporters of commodities made in war-torn countries and switch to stock markets geographically far from the war zone.

Choice between Sustainable versus Conventional Investments—Relative Efficiency Analysis from Global and Regional Stock Markets

We use the daily data from 1 October 2010 to 1 March 2021 on the stock prices of several global, regional, and country-specific ESG indices of the Dow Jones Sustainability Index family (DJSI) and MSCI family to analyse the relative performance of sustainable indices (ESG) and corresponding conventional benchmarks. In terms of classic risk and return characteristics and modern portfolio metrics, we report mixed evidence with some sustainability indices marginally outperforming the traditional benchmarks and others staying at parity. Regionally, the evidence is heterogeneous; the Pan Arab, the Middle East (including Israel), the United States, Emerging Markets, and Europe indices reported a slightly superior performance and those belonging to Asia Pacific, Emerging Africa, and Latin America marginally underperformed compared to their conventional counterparts. The findings of this study imply that sustainable investments appear to be an encouraging investment option, although their progress has not been substantial. For an appreciable outperformance of sustainable investments, a more conducive regulatory framework should be established, including robust incentivizing policies concerning tax rebates or low capital costs.

Forecasting realized volatility: Does anything beat linear models?

We evaluate the performance of several linear and nonlinear machine learning (ML) models in forecasting the realized volatility (RV) of ten global stock market indices in the period from January 2000 to December 2021. We train models using a dataset that includes past values of the RV and additional predictors, including lagged returns, implied volatility, macroeconomic and sentiment variables. We compare these models to widely used heterogeneous autoregressive (HAR) models. Our main conclusions are that (i) the additional predictors improve the out-of-sample forecasts at the daily and weekly forecast horizons; (ii) we find no evidence that nonlinear ML models can statistically outperform linear models in general; and (iii) in terms of the economic value that an investor would derive from monthly RV forecasts to build volatility-timing portfolios, simpler models without additional predictors work better.

Comparative Analysis of Spillover Effects in the Global Stock Market under Normal and Extreme Market Conditions

Using the volatility spillover index method based on the quantile vector autoregression (QVAR) model, this paper systematically examines structural changes and corresponding spillover effects within 20 major stock markets under both extreme and normal market conditions, using data spanning from January 2005 to January 2023. The results show that, compared to the traditional volatility spillover index method, which focuses mainly on average spillover effects, the QVAR model-based spillover index better captures spillover effects under extreme and various market conditions among global stock markets. The connections between stock markets are closer in extreme market conditions. The total spillover index of major global stock markets significantly increases in extreme conditions compared to normal conditions. In extreme market conditions, inflow indices show varying degrees of increase, with emerging economy stock markets displaying more significant increases. The outflow indices exhibit heterogeneity; emerging economies show consistent increases, while developed economies show mixed changes.

Risk Modeling and Connectedness Across Global and Industrial US Fintech Stock Market: Evidence from the COVID‑19 Crisis

The main purpose of this paper is to test the performance of GARCH models in estimating and forecasting VaR (value at risk) of the US Fintech stock market from July 20, 2016, to December 31, 2021. In addition, this study examines the impact of COVID‑19 on the risk spillover between the adequate VaR series of the US global KFTX index and the five Fintech industries. Specifically, we compare different VaR estimates (862 in‑sample daily returns) and predictions (550 out‑of‑sample daily returns) of several GARCH model specifications under a normal and Student‑t distribution with 1% and 5% significance. The Backtesting results indicate that I‑GARCH with Student‑t distribution is a good model for estimating and forecasting VaR of the US Fintech stock market before and during COVID-19. Moreover, the total connectedness results suggest that global and each Fintech industry increases significantly under turbulent market conditions. Given these considerations, this paper provides policymakers and regulators with a better understanding of risk in the Fintech industry without inhibiting innovation.

Stock market pattern recognition using symbol entropy analysis

Market uncertainty and asset pricing are closely related. The emergence of global and local shocks provokes price patterns in stock markets that threaten overall market stability. Using a combination of symbol entropy analysis, regression models, and forecast variance decomposition estimates, we examine the relationships between the frequency of states associated with rising and falling price patterns of the S&P 500 index, its implied volatility, and the fluctuations in the global stock market uncertainty. We find a positive (negative) relationship between the increase (decrease) in global uncertainty and the frequency of extreme bear (bull) episodes. Similarly, we observe the same relationship between increases (decreases) in the implied volatility and the frequency of extreme bear (bull) episodes. Furthermore, joint increases (decreases) in implied volatility and global uncertainty are positively (negatively) associated with the frequency of bear (bull) episodes in the S&P 500 index. Finally, interconnectedness studies confirm the relevance of the mutual influence of the phenomenon and its dynamic nature, which increases the probability of observing shock waves of financial instability in global and local stock markets. Our work highlights the need to consider price patterns and their links to uncertainty as factors of stock market instability. For policymakers, governments, and practitioners, our findings are a call to include events with high global/local impact in the radar of threats to financial stability.

Forecasting global stock market volatilities: A shrinkage heterogeneous autoregressive (HAR) model with a large cross-market predictor set

We propose a shrinkage heterogeneous autoregressive (HAR) model to explore the predictability of global stock market volatilities. To this end, we construct a big data environment using international stock market data, which comprises over 200 cross-market predictors, including realized variances (RVs), continuous components, jumps, leverages, overnight returns, and uncertainty indices. Two shrinkage models (LASSO and ENet) are considered for volatility modeling and forecasting. The results demonstrate that these models, notably LASSO, consistently improve predictive performance across global stock markets and daily, weekly, and monthly horizons. Their superiority extends to directional and density forecasting as well as asset allocation. There are several empirical findings of shrinkage: (i) the shrinkage degree varies significantly across forecast horizons, with longer horizons implying more predictors are needed for accurate estimation; (ii) no country/region's predictors show absolute superiority and utilize cross-market information leads to larger predictive gains, particularly for longer horizons; (iii) the predictors except for RVs and continuous components are more powerful, especially over longer horizons; and (iv) the uncertainty indices are the strongest predictors for all horizons.