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This study aims to systematically review the literature in the fields of liquidity, informational efficiency and default risk. The authors outline the key research streams and provide possible pathways for future research.

The study adopts bibliographic mapping to identify the most influential studies in the research fields of liquidity, informational efficiency and default risk from 1984 to 2021.

The study identifies four key research themes that include efficiency and transparency of markets; corporate yield spreads; market interactions: bonds, stocks and cryptocurrencies; and corporate governance. By assessing publications published from 2018 to 2021, the authors also document seven key emerging research trends: cross markets, managerial learning and corporate governance, state ownership and government subsidies, international evidence, machine learning (FinTech approaches), environmental themes and financial crisis. Drawing on these emerging trends, the authors highlight the opportunities for future research.

Keyword searches have limitations since some studies might be overlooked if they do not match the specified search criteria, even though their relevance to the topic is under investigation. Adopt the R project to expand this review by incorporating more literature from other databases, such as the Scopus database could be a possible solution.

The four key research streams contribute to a comprehensive understanding of liquidity, informational efficiency and default risk. The emerging trends integrate existing knowledge and leave the chance for innovative research to expand the research frontier.

This study fulfills the systematic literature review streams in the fields of liquidity, informational efficiency and default risk, and provides fruitful opportunities for future research.

This study aims to measure the degree of volatility comovement between stock market liquidity and informational efficiency across Asia, Europe, North-South America, Africa, and the Pacific Ocean over three decades. In particular, the authors analyze the extent of the time-varying nexus between different aspects of stock market liquidity and multifractal scaling properties of the stock return series across various regions and diversified market conditions. This study further investigates several factors altering the degree of dynamic conditional correlations (DCCs) between the efficiency and liquidity of the domestic stock markets.

The study measures five aspects of stock market liquidity – tightness, depth, breadth, immediacy, and adjusted immediacy. The authors evaluate the multifractal scaling properties of the stock return series to measure the level of stock market efficiency across the regions and diversified market conditions. The study uses the dynamic conditional correlation-multivariate generalized autoregressive conditional heteroscedasticity framework to quantify the degree of volatility comovement between liquidity and efficiency over the period.

The study finds the presence of stronger volatility comovement between inefficiency and illiquidity due to the price impact characteristics of the stock markets irrespective of different regions and diversified market conditions. The extent of time-variation increased following the shock periods, indicating the significant role of the financial crisis in increasing the volatility comovement between inefficiency and illiquidity. The highest degree of time-varying correlation is observed in the developed stock markets of Northwestern and Northern Europe compared to the regional and emerging counterparts. On the other hand, weak DCCs are observed in the emerging stock markets of Europe.

The output of the present study assists investors in identifying diversification opportunities across the regions. Additionally, the study has significant implications for market regulators, aiding in predicting future troughs and peaks. The prediction, in turn, helps formulate capital market development plans during dynamic economic situations.

The primary purpose of this research is to identify and compare the multifractal behavior of different sectors during these crises and analyze their implications on market efficiency.

We used multifractal detrended fluctuation analysis (MF-DFA) to analyze stock returns from various sectors of the Moscow Stock Exchange (MOEX) in between two significant periods. The COVID-19 pandemic (January 1, 2020, to December 31, 2021) and the Russia–Ukraine conflict (RUC) (January 1, 2022, to June 30, 2023). This method witnesses multifractality in financial time series data and tests the persistency and efficiency levels of each sector to provide meaningful insights.

Results showcased persistent multifractal behavior across all sectors in between the COVID-19 pandemic and the RUC, spotting heightened arbitrage opportunities in the MOEX. The pandemic reported a greater speculative behavior, with the telecommunication and oil and gas sectors exhibiting reduced efficiency, recommending abnormal return potential. In contrast, financials and metals and mining sectors displayed increased efficiency, witnessing strong economic performance. Findings may enhance understanding of market dynamics during crises and provide strategic insights for the MOEX’s investors.

Understanding the multifractal properties and efficiency of different sectors during crisis periods is of paramount importance for investors and policymakers. The identified arbitrage opportunities and efficiency variations can aid investors in optimizing their investment strategies during such critical market conditions. Policymakers can also leverage these insights to implement measures that bolster economic stability and development during crisis periods.

This research contributes to the existing body of knowledge by providing a comprehensive analysis of multifractal properties and efficiency in the context of the MOEX during two major crises. The application of MF-DFA to sectoral stock returns during these events adds originality to the study. The findings offer valuable implications for practitioners, researchers and policymakers seeking to navigate financial markets during turbulent times and enhance overall market resilience.

This study aims to investigate how stock markets responded to corporate dividend policy changes during the COVID-19 pandemic in the Gulf Cooperation Council (GCC) countries. Likewise, it explores how efficiently market prices incorporate the news by examining the speed of stock price adjustment to various dividend announcements.

The sample includes 741 dividend announcements from 2017 to 2021 made by 326 firms listed in the stock markets of the GCC countries. A series of regression analyses examine how dividend announcements influence the market reaction during the COVID-19 pandemic, controlling for other well-documented firm characteristics.

This study reveals an adverse stock price reaction to all the dividend announcements in most GCC markets. The findings also show strong asymmetric effects of COVID-19 on how the markets react to different dividend changes. Likewise, the authors show that investors tend to underreact to the good news of dividend increases amid hard times of crises due to prevailing uncertainty and bearish sentiment. Besides, regression results reveal that firms with dividend reductions during the pandemic experience less adverse market reactions than dividend-decreasing firms prepandemic.

For firms, the findings confirm the role that corporate dividend policy can play in conveying signals to investors, especially during hard times of crises and turbulences, thereby affecting their share price. For policymakers, the results substantially affect market efficiency and firm valuation in the GCC markets.

This study is not only one of the first few attempts to scrutinize how the pandemic has affected the market reaction to changes in corporate dividend policies but also, to the best of the authors’ knowledge, it is the first to examine how corporate dividend policy could affect stock markets during COVID-19 in the context of GCC markets.

The present study reviews the literature on the history and evolution of investment strategies in the stock market for the period from 1900 to 2022. Conflicts and relationships arising from such diverse seminal studies have been identified to address the research gaps.

The studies for this review were identified and screened from electronic databases to compile a comprehensive list of 200 relevant studies for inclusion in this review and summarized for the cognizance of researchers.

The study finds a coherence to complex theoretical documentation of more than a century of evolution on investment strategy in stock markets, capturing the characteristics of time with a chronological study of events.

There were complications in locating unpublished studies leading to biases like publication bias, the reluctance of editors to publish studies, which do not reveal statistically significant differences, and English language bias.

Practitioners can refine investment strategies by incorporating behavioral finance insights and recognizing the influence of psychological biases. Strategies span value, growth, contrarian, or momentum indicators. Mitigating overconfidence bias supports effective risk management. Social media sentiment analysis facilitates real-time decision-making. Adapting to evolving market liquidity curbs volatility risks. Identifying biases guides investor education initiatives.

This paper is an original attempt to pictorially depict the seminal works in stock market investment strategies of more than a hundred years.

Sustainable development is considered a key factor in addressing environmental issues, global inequalities and poverty. This study aims to investigate the impact of stock market indicators on sustainable development across 16 emerging markets from 2003 to 2020.

The research uses causality and cointegration analyses to explore the relationships between stock market indicators and sustainable development.

Univariate causality analysis reveals a bidirectional causal relationship between the stock market turnover ratio and sustainable development, as well as a unidirectional relationship from sustainable development to stock market capitalization and total value traded. Panel-level cointegration analysis suggests that only stock market capitalization has a weak positive influence on sustainable development. However, the impact of stock market indicators on sustainable development varies significantly among countries, as revealed by country-level cointegration analysis.

While this study provides valuable insights, it is not without limitations. The findings are limited to the selected emerging markets and the specified timeframe (2003–2020). The complexity of factors influencing sustainable development suggests the need for further exploration in diverse contexts.

Understanding the nuanced relationships between stock market indicators and sustainable development can offer valuable insights for policymakers, investors and stakeholders.

This research contributes to the existing literature by examining the multifaceted connections between stock market indicators and sustainable development, focusing on country-specific causality relationships. The study highlights the reciprocal nature of this relationship, where financial market development can both influence and be influenced by a country's progress toward sustainability. This approach provides a more nuanced understanding of the complex interaction between stock market maturity and sustainability goals.

This study delves into the nuanced implications of short-sale constraints on stock prices within the context of stock market efficiency. While existing research has explored this relationship, inconsistencies persist in their findings. The purpose of this study is to conduct a comprehensive review of literature to elucidate the reasons behind these disparities.

A systematic review of existing theoretical and empirical studies was conducted following the PRISMA method. The analysis centered on discerning the factors contributing to the divergence in projected stock prices due to these constraints. Key areas explored included assumptions related to expectations homogeneity, revisions, information uncertainty, trading motivations and fluctuations in supply and demand of risky assets.

The review uncovered multifaceted reasons for the disparities in findings regarding the influence of short-sale constraints on stock prices. Variations in assumptions related to market expectations, coupled with fluctuations in perceived information uncertainty and trading motivations, were identified as pivotal factors contributing to differing projections. Empirical evidence disparities stemmed from the use of proxies for short-sale constraints, varied sample periods, market structure nuances, regulatory changes and the presence of option trading.

This study emphasizes the significance of not oversimplifying the impact of short-sale constraints on stock prices. It highlights the need to understand these effects within the broader context of market structure and methodological considerations. By delineating the intricate interplay of factors affecting stock prices under short-sale constraints, this review provides a nuanced perspective, contributing to a more comprehensive understanding in the field.

This study examines the immediate impact of the Israel-Iran conflict on global stock markets and currency pairs, focusing on how these effects vary by market maturity and geographic region.

This study uses the event study method to examine the immediate effect of the Israel-Iran conflict. It uses the market model across a 252-day estimation window through −257, −6 trading days and an 11-day event window through −5, +5 trading days. The primary sample includes 73 stock market indices, 7 EURO currency pairs, 14 USD currency pairs, 6 GBP currency pairs, and 7 JPY currency pairs.

The findings suggest that (1) the global stock markets are adversely affected by the Israel-Iran conflict, (2) the JPY, GBP, and EURO currency pairs are least affected, (3) the USD currency pairs exhibit positive abnormal returns suggesting flight to safety, (4) the frontier and standalone markets experience most adverse effects, followed by developed and emerging markets, (5) the pan-American stock markets experience more pronounced effects of the conflict, followed by the Europe, Middle East, and African stock markets and the Asia Pacific stock markets.

The findings advise investors to manage risk during geopolitical uncertainty through diversification and hedging. Policymakers should monitor developments and enact responsive measures. Market participants can capitalize on insights for strategic investment.

The study contributes to the extant war literature by exploring the impact of the Israel-Iran conflict on global stock markets and currency pairs. This study serves as the first to examine the effects of the escalating conflict due to Iran’s attack on Israel.

This paper investigates the distinctive role of the US stock exchanges in the process of international consolidation. Besides the USA's leading role in financial markets, the focus on the country is motivated by its uniqueness within the stock exchange consolidation landscape, since, on the one hand, it has been involved in two different stock exchange mergers – with Nasdaq and NYSE – and, on the other hand, it has experienced a “reversal”, having joined and then left the Euronext-NYSE platform.

To investigate the effect of the NYSE-Euronext split on cross-border holdings and the role of the US as a member of the consolidated platform, we adopt a feasible Generalized Least Squares specification correcting for both heteroskedasticity and general correlation of observations across destination-countries, with standard errors adjusted for two-way clustering at the investing-country and year levels.

Differently from other mergers, we find a weak sensitivity of US inward and outward cross-border investments to stock exchange consolidation, and, consequently, to its reversal. The data suggest that the larger, the more liquid and the more visible the involved stock exchanges are, the less sensitive cross-border investment is to consolidation. Drawing on the cross-listing and cross-delisting literature, we formulate the conjecture that this evidence can be explained by decreasing returns of foreign investment to consolidation: the extraordinary large size, liquidity and visibility of the US stock exchanges diminishes the value of the role played by stock exchange consolidation in reducing cross-border barriers among member countries, so that it makes also the effects of its retreat non-significant.

This paper is the first, to best of our knowledge, to investigate the mirror phenomenon, that is, the “consolidation reversal” process of the NYSE stock exchange, the purpose being to understand its consequences for cross-border holdings. In the first part of this paper, we document no significant effect of the 2014 reversal on cross-border investments. The apparent absence of this effect could be due either to a level of cross-border investments remaining equally high (denoting persistence in investors' behavior) or to an equally non-significant effect of consolidation and reversal of the US stock exchanges on cross-border equity investments. The evidence supports the latter hypothesis and reveals an overall weak sensitivity of US cross-border investments (inward or outward) to stock exchange consolidation and, consequently, to its reversal. We formulate the conjecture, tested in the second part of the paper, that this evidence is due to the presence of diminishing returns of exchange consolidation's scale for foreign investors: the extraordinary large size, liquidity and visibility of the US stock exchanges makes the role of stock exchange consolidation less valuable in dampening cross-border barriers; consequently, also the reversal phenomenon presents no sizeable effects.

The study aims to explore how integrating recent fundamental values (RFVs) from conventional accounting studies enhances the accuracy of a machine learning (ML) model for predicting stock return movement in Indonesia.

The study uses multilayer perceptron (MLP) analysis, a deep learning model subset of the ML method. The model utilizes findings from conventional accounting studies from 2019 to 2021 and samples from 10 firms in the Indonesian stock market from September 2018 to August 2019.

Incorporating RFVs improves predictive accuracy in the MLP model, especially in long reporting data ranges. The accuracy of the RFVs is also higher than that of raw data and common accounting ratio inputs.

The study uses Indonesian firms as its sample. We believe our findings apply to other emerging Asian markets and add to the existing ML literature on stock prediction. Nevertheless, expanding to different samples could strengthen the results of this study.

Governments can regulate RFV-based artificial intelligence (AI) applications for stock prediction to enhance decision-making about stock investment. Also, practitioners, analysts and investors can be inspired to develop RFV-based AI tools.

Studies in the literature on ML-based stock prediction find limited use for fundamental values and mainly apply technical indicators. However, this study demonstrates that including RFV in the ML model improves investors’ decision-making and minimizes unethical data use and artificial intelligence-based fraud.