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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 investigate the impact of earnings predictability and truthfulness on nonprofessional investors’ investment willingness.

Earnings predictability is captured by quarterly earnings autocorrelation, and earnings truthfulness is indicated by real earnings management (REM). The average of investment attractiveness and willingness measures investment willingness. The authors use experiments to isolate the impact of quarterly earnings autocorrelation and REM on investors’ investment behaviors.

From the 2 × 2 design, the authors observe that investors weight more on earnings predictability than earnings truthfulness.

The generalization of the findings may be constrained for the following reasons. First, the authors use only one proxy, REM, to measure earnings truthfulness. In addition, the authors provide the participants, Amazon Mechanical Turk, with earnings predictability. Results may no longer hold if each participant has different understanding and analysis of earnings predictability.

In periods of unprecedented and severe financial uncertainty (i.e. the COVID-19 pandemic), investors rely more on earnings predictability than on earnings truthfulness. The study assists managers to strategically emphasize the predictability of earnings to attract investors, especially when firms face financial challenges or uncertainty.

This study contributes to understanding investor behavior and the critical role of earnings predictability and truthfulness in shaping investment decisions.

This paper contributes to the literature of earnings properties in financial reporting, particularly by shedding light on the nuanced interplay between earnings predictability and earnings truthfulness. The research also demonstrates that elevated earnings autocorrelation indirectly stimulates investment willingness by enhancing the investors’ perception of earnings persistence of targeted firms.

This paper examines whether there are differences in the nature of the price discovery process across established versus emerging stock markets using a twenty-country sample.

The authors analyse security returns for traces of predictability or non-randomness using variance ratio tests, Granger-Causality models and runs tests.

The findings pinpoint at predictabilities which seem inconsistent with market efficiency, and they suggest that the inherent cause of predictability differs across groups.

The authors present empirical evidence which may be used to attain a deeper understanding of the links between predictability and market efficiency, in view of the conflicting evidence in prior literature.

Whilst the pricing process in emerging markets may be hindered by delayed adjustments, in case of established markets it seems that there is a higher tendency for price reversals which could be due to prior over-reactions.

This study presents evidence of substantial differences in predictability across developed and emerging markets which was gleaned through the rigorous application of different empirical tests.

Respect for human rights is one of the most important criteria for the delivery of medical care in hospitals. Ethical predictability is useful to identify human rights concerns in health-care organizations. The hospital environment and the flow of its processes make the topic of predictability much more sensitive and, at the same time, more difficult than other organizations. The purpose of this paper is to determine and compare the ethical predictive factors in selected hospitals in Mazandaran province.

This cross-sectional survey using multilevel sampling (four hospitals, 938 patients, 186 staff) was conducted in the first half of 2017. The measurement instrument was a researcher-made questionnaire consisting of seven areas of service recipients’ rights, patient safety, patient satisfaction, human resources, governance, organizational and financial commitments. The analysis of the collected data was performed through SPSS V. 22 and one-way ANOVA and post hoc Tukey’s tests.

Ethical predictability was higher in social security hospitals compared to private and public hospitals, and patient safety and patient rights showed higher magnitudes compared to other dimensions. Financial domain, patient satisfaction, governance and organizational commitment formed the middle priorities in ethical predictability, and human resources had the least average in ethical predictability in the selected hospitals in the province.

Identifying the factors which influence ethical predictability, in addition to promoting service recipients’ rights and patient satisfaction, is of great help to the managers and health service authorities, so that they can have a better understanding of these factors and, consequently, make appropriate micro and macro-decisions to provide better services.

This paper aims to examine the cross-quantile correlation and causality-in-quantiles between green investments and energy commodities during the outbreak of COVID-19. To be specific, the authors aim to address the following questions: Is there any distributional predictability among green bonds and energy commodities during COVID-19? Is there exist any directional predictability between green investments and energy commodities during the global pandemic? Can green bonds hedge the risk of energy commodities during a period of the financial crisis.

The authors use the nonparametric causality in quantile and cross-quantilogram (CQ) correlation approaches as the estimation techniques to investigate the distributional and directional predictability between green investments and energy commodities respectively using daily spot prices from January 1, 2020, to March 26, 2021. The study uses daily closing price indices S&P Green Bond Index as a representative of the green bond market. In the case of energy commodities, the authors use S&P GSCI Natural Gas Spot, S&P GSCI Biofuel Spot, S&P GSCI Unleaded Gasoline Spot, S&P GSCI Gas Oil Spot, S&P GSCI Brent Crude Spot, S&P GSCI WTI, OPEC Oil Basket Price, Crude Oil Oman, Crude Oil Dubai Cash, S&P GSCI Heating Oil Spot, S&P Global Clean Energy, US Gulf Coast Kerosene and Los Angeles Low Sulfur CARB Diesel Spot.

From the CQ correlation results, there exists an overall negative directional predictability between green bonds and natural gas. The authors find that the directional predictability between green bonds and S&P GSCI Biofuel Spot, S&P GSCI Gas Oil Spot, S&P GSCI Brent Crude Spot, S&P GSCI WTI Spot, OPEC Oil Basket Spot, Crude Oil Oman Spot, Crude Oil Dubai Cash Spot, S&P GSCI Heating Oil Spot, US Gulf Coast Kerosene-Type Jet Fuel Spot Price and Los Angeles Low Sulfur CARB Diesel Spot Price is negative during normal market conditions and positive during extreme market conditions. Results from the non-parametric causality in the quantile approach show strong evidence of asymmetry in causality across quantiles and strong variations across markets.

The quantile time-varying dependence and predictability results documented in this paper can help market participants with different investment targets and horizons adopt better hedging strategies and portfolio diversification to aid optimal policy measures during volatile market conditions.

The outcome of this study will promote awareness regarding the environment and also increase investor’s participation in the green bond market. Further, it allows corporate institutions to fulfill their social commitment through the issuance of green bonds.

This paper differs from these previous studies in several aspects. First, the authors have included a wide range of energy commodities, comprising three green bond indices and 14 energy commodity indices. Second, the authors have explored the dependency between the two markets, particularly during COVID-19 pandemic. Third, the authors have applied CQ and causality-in-quantile methods on the given data set. Since the market of green and sustainable finance is growing drastically and the world is transmitting toward environment-friendly practices, it is essential and vital to understand the impact of green bonds on other financial markets. In this regard, the study contributes to the literature by documenting an in-depth connectedness between green bonds and crude oil, natural gas, petrol, kerosene, diesel, crude, heating oil, biofuels and other energy commodities.

Existing studies on corporate governance mainly focus on how a strong governance system enhances the valuation of firms with cash holding or free cash flow agency problem. The aims of this paper are threefold. First, it investigates the impact of surplus free cash flows (SFCF) on earnings predictability. Second, it investigates whether corporate governance variables moderate the negative impact of SFCF on earnings predictability. Finally, this study examines whether the ability of corporate governance to mitigate SFCF and improve the predictive value of earnings varies between large and small firms.

This paper uses heteroskedasticity-corrected least square regressions upon a sample of Malaysian listed firms.

This paper finds that firms with high SFCF experience less earnings predictability. It also indicates that earnings of firms with high SFCF are more predictable when institutional investors hold a large stake of shares and when a chairperson is independent. Finally, this paper reveals that the role of institutional and managerial ownership in mitigating agency conflict of free cash flow and improving earnings predictability is more prominent in larger firms. This study implies that investors still have reservations about the ability of boards to enhance earnings numbers in Malaysia, although efforts were taken to reform the corporate governance mechanisms following the Asian financial crisis.

This research is considered as the first attempt to examine the relationships between SFCF, corporate governance, firm size, and earnings predictability in a developing county such as Malaysia. The findings of this paper serve as a wake-up call to policy makers to evaluate the importance of governance structure in enhancing earnings predictability in emerging economies.

Risk aversion is considered as an important factor in predicting asset prices. Many studies have proved that there exists important price information spillover among crude oil, precious metals and agricultural markets. Then there naturally follows the question: Is the risk aversion of investors in crude oil market predictable for the returns of precious metals and agricultural products? The purpose of this paper is to answer this question. For this reason, the authors explore the directional predictability and the cross-quantile dependence between risk aversion of crude oil market investors and returns of precious metals and agricultural products.

To better describe the risk aversion of investors, this paper uses high-frequency data and model-free calculation method to obtain variance risk premium of crude oil. Then, this paper uses the cross-quantilogram method to investigate the directional predictability and cross-quantile dependence between risk aversion of crude oil market investors and returns of precious metals and agricultural products. Meanwhile, it employs the partial cross-quantilogram (PCQ) method to test the impact of control variables on the empirical results.

Firstly, risk aversion of crude oil market investors has directional predictability for returns of precious metals and agricultural products. Secondly, different degrees of risk aversion of crude oil market investors have different impacts on returns of precious metals and agricultural products. A low (high) degree of crude oil market investors' risk aversion has negative (positive) predictability for returns of precious metals and agricultural products. Finally, during the sample period, the returns of precious metals are more affected by risk aversion of crude oil market investors than returns of agricultural products.

First of all, this paper studies the impact of risk aversion of crude oil market investors on returns of precious metals and agricultural products. It updates previous relevant studies on the factors influencing the prices of precious metals and agricultural products, and provides a new idea for the forecast of those commodity returns. Secondly, this paper provides the evidence that different degrees of risk aversion of investors have different effects on the returns of commodities, and expands the research on the topic of commodity returns prediction. Finally, high-frequency data are employed in this paper to better capture the risk aversion of investors than commonly used daily data.

Although several studies have aimed to present models to predict conflict outcomes, fewer methods have been developed to analyze conflict manageability and provide management strategies based on prediction models. This research pitches into the manageability analysis of conflicts occur during the implementation of a proposed change in construction projects. In this way, a framework has been developed by defining two parameters: the predictability index and the preventability index.

Within this framework, the predictability index determines how many outcomes of the prediction model can be used for conflict management based on the degree of clarity. The preventability index demonstrates how preventive measures for conflict management can be identified. Eventually, three preventive measures can be determined: (1) identifying weaknesses of decision-making patterns and organizational culture, (2) identifying events that may be prevented using soft skills and (3) identifying differences among similar change-implementation scenarios and evaluating causes of the differences. To demonstrate the capabilities of proposed framework, a practical example has been analyzed.

The results show that the behavior of the project parties can be psychologically analyzed, and psychological conflicts can be distinguished from technical conflicts. Moreover, identifying the weaknesses of parties' decision-making patterns and their organizational culture is the most effective measure to prevent the conflicts.

This research contributes to the construction body of knowledge by quantifying the predictability and preventability of conflicts between the project parties in a construction project based on: (1) the certainty level of the conflict occurrence and (2) the level of alignment between predicted outcomes of the conflict occurrence and the issued change request and/or change order.

The authors examine how different exchange patterns affect structurally disadvantaged actors' interactional justice evaluations and group identification in situations characterized by reciprocal and negotiated exchange.

Laboratory experiment.

Although results replicate prior work finding that disadvantaged individuals view their exchange partners as less fair when exchanging via negotiation rather than reciprocation, they also show the value of considering the pattern of exchange. Indeed, both the form of exchange and the pattern of exchange prompt exchange behaviors that shape how disadvantaged actors view the exchange experience, such that much of the direct effect of the form of exchange is offset by indirect paths, especially when the disadvantaged actor remains committed to their more advantaged partner. These fairness evaluations matter because as the authors show, they affect perceptions of group identification.

Future work should more explicitly consider how emotions as well as different levels of inequality might modify the processes described.

This chapter highlights the need to consider both the form of exchange and the relative stability of exchange when considering the fairness perceptions and group identification of disadvantaged individuals.

The main objective of this study is to obtain new empirical evidence on non-synchronous trading effects through modelling the predictability of market indices.

The authors test for lead-lag effects between the Indian Nifty and Nifty Junior indices using Pesaran–Timmermann tests and Granger-Causality. Then, a simple test on overnight returns is proposed to infer whether the observed predictability is mainly attributable to non-synchronous trading or some form of inefficiency.

The evidence suggests that non-synchronous trading is a better explanation for the observed predictability in the Indian Stock Market.

The indication that non-synchronous trading effects become more pronounced in high-frequency data suggests that prior studies using daily data may underestimate the impacts of non-synchronicity.

The originality of the paper rests on various important contributions: overnight returns is looked at to infer whether predictability is more attributable to non-synchronous trading or to some form of inefficiency; the impacts of non-synchronicity are investigated in terms of lead-lag effects rather than serial correlation; and high-frequency data is used which gauges the impacts of non-synchronicity during less active parts of the trading day.