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This study aims to investigate the interconnectedness across the risk appetite of distinct investor types in Borsa Istanbul. This study also examines the causal impact of global implied volatility indices on the risk appetite of these investor groups.

The authors use a novel time-varying frequency connectedness framework of Chatziantoniou et al. and a new time-varying Granger causality test with a recursive evolving procedure by Shi et al. over June 2008 and July 2022.

The results show a high level of interconnectedness across the risk appetite of different investor types. The sizable spillovers to domestic types of investors either occur from professional or foreign investors, indicating the long-term dominant effect of foreign and more qualified investors on the domestic investors in Borsa Istanbul. The authors provide significant evidence of causality from the global implied volatility to the Borsa Istanbul risk appetite indices, which are getting stronger after the COVID-19 outbreak.

Unlike the previous studies, the authors analyze the risk appetite sub-indices of various types of investors to reveal behavioral distinctions and interconnectedness across them. The authors use a novel econometric framework to assess investors’ risk appetite in different investment horizons in a time-varying system. Together with volatility index (VIX), the authors also use volatilities of oil (OVX), gold (GVZ) and currency (EVZ), considering the information transmission not only from stock markets but also energy, metals and currency markets. The present data set covers significant financial crises, socioeconomic events and the COVID-19 outbreak.

This study aims to investigate the connectivity among four principal implied volatility (“fear”) markets in the USA.

The empirical analysis relies on daily data (“fear gauge indices”) for the period 2017–2023 and the quantile vector autoregressive (QVAR) approach that allows connectivity (that is, the network topology of interrelated markets) to be quantile-dependent and time-varying.

Extreme increases in fear are transmitted with higher intensity relative to extreme decreases in it. The implied volatility markets for gold and for stocks are the main risk connectors in the network and also net transmitters of shocks to the implied volatility markets for crude oil and for the euro-dollar exchange rate. Major events such as the COVID-19 pandemic and the war in Ukraine increase connectivity; this increase, however, is likely to be more pronounced at the median than the extremes of the joint distribution of the four fear indices.

This is the first work that uses the QVAR approach to implied volatility markets. The empirical results provide useful insights into how fear spreads across stock and commodities markets, something that is important for risk management, option pricing and forecasting.

The primary objective of this research is to explore the potential of utilizing Global Consciousness Project (GCP) data as a tool for understanding and predicting market sentiment. Specifically, the study aims to assess whether incorporating GCP data into econometric models can enhance the comprehension of daily market movements, providing valuable insights for traders.

This study employs econometric models to investigate the correlation between the Standard & Poor's 500 Volatility Index (VIX), a common measure of market sentiment and data from the GCP. The focus is particularly on the largest daily composite GCP data value (Max[Z]) and its significant covariation with changes in VIX. The research employs interaction terms with VIX and daily returns from global markets, including Europe and Asia, to explore the relationship further.

The results reveal a significant relationship with the GCP data, particularly Max[Z] and VIX. Interaction terms with both VIX and daily returns from global markets are highly significant, explaining about one percent of the variance in the econometric model. This finding suggests that variations in GCP data can contribute to a better understanding of market dynamics and improve forecasting accuracy.

One limitation of this study is the potential for overfitting and P-hacking. To address this concern, the models undergo rigorous testing in an out-of-sample simulation study lasting for a predefined one-year period. This limitation underscores the need for cautious interpretation and application of the findings, recognizing the complexities and uncertainties inherent in market dynamics.

The study explores the practical implications of incorporating GCP data into trading strategies. Econometric models, both with and without GCP data, are subjected to an out-of-sample simulation where an artificial trader employs S&P 500 tracking instruments based on the model's one-day-ahead forecasts. The results suggest that GCP data can enhance daily forecasts, offering practical value for traders seeking improved decision-making tools.

Utilizing data from the GCP is found to be advantageous for traders as noteworthy correlations with market sentiment are found. This unanticipated finding challenges established paradigms in both economics and consciousness research, seamlessly integrating these domains of research. Traders can leverage this innovative tool, as it can be used to refine forecasting precision.

This study examines the impact of regional economic integration (REI) on stock market linkages in the BRICS (Brazil, Russia, India, China and South Africa) economic bloc. In this type of study, the BRICS framework is an appealing empirical case, given its uncommon characteristics. For example, BRICS member states come from remote geographic locations (Africa, Asia, Europe and South America) and have contrasting socioeconomic profiles.

An empirical design is framed from the perspective of bilateral trade between South Africa and BRIC. The author accepts trade intensity as a proxy of regional economic integration and then examines the resulting effect on the stock market co-movement within BRIC. The study applies a two-step econometric procedure of the BEKK-MGARCH and panel data models.

Overall, bilateral trade, as a proxy of economic inwctegration, is associated with an increase in stock market integration. This positive relationship is particularly observed during episodes of surplus trade, and more interestingly, was initiated three years after BRICS’ existence and continues to grow at an increasing rate.

The study outcome should benefit international trade practitioners and global investors interested in portfolio diversification or concerned with risk spillovers.

First, notwithstanding South Africa's significant economic presence in the African continent, to the best of the author’s knowledge, this is the first study to empirically evaluate the BRICS economic integration on their stock market linkages from the perspective of South Africa. The value of this contribution is that further work may investigate the bidirectional spillover impact conveyed by South Africa's trade interactions within the juxtaposition of Africa and BRICS economies. Second, given that research on REI and stock market integration has historically concentrated on mature regional blocs of Europe, Asia, South and North America, the current study advances knowledge while correcting the prevailing literature imbalance.

This study aims to examine the multiscale predictability power of COVID-19 deaths and confirmed cases on the S&P 500 index (USA), CAC30 index (France), BSE index (India), two strategic commodity futures (West Texas intermediate [WTI] crude oil and Gold) and five main uncertainty indices Equity Market Volatility Ticker (EMV), CBOE Volatility Index (VIX), US Economic Policy Uncertainty (EPU), CBOE Crude Oil Volatility Index (OVX) and CBOE ETF Gold Volatility Index (GVZ). Furthermore, the authors analyze the impact of uncertainty indices and COVID-19 deaths and confirmed cases on the price returns of stocks (S&P500, CAC300 and BSE), crude oil and gold.

The authors used the wavelet coherency method and quantile regression approach to achieve the objectives.

The results show strong multiscale comovements between the variables under investigation. Lead-lag relationships vary across frequencies. Finally, COVID-19 news is a powerful predictor of the uncertainty indices at intermediate (4–16 days) and low (32–64 days) frequencies for EPU and at low frequency for EMV, VIX, OVX and GVZ indices from January to April 2020. The S&P500, CAC30 and BSE indexes and gold prices comove with COVID-19 news at low frequencies during the sample period. By contrast, COVID-19 news and WTI oil moderately correlated at low frequencies. Finally, the returns on equity and commodity assets are influenced by uncertainty indices and are sensitive to market conditions.

This study contributes to the literature by exploring the time and frequency dependence between COVID-19 news (confirmed and death cases) on the returns of financial and commodity markets and uncertainty indexes. The findings can assist market participants and policymakers in considering the predictability of future prices and uncertainty over time and across frequencies when setting up regulations that aim to enhance market efficiency.

Based on stakeholder, agency and institutional theory, this study aims to examine the role of institutional ownership in the relationship between environmental, social and governance practices and CEO compensation.

Utilizing a fixed-effect panel regression analysis, this research utilized a panel data approach, analyzing data spanning from 2014 to 2021, focusing on US companies listed on the S&P500 stock market index. The dataset encompassed 219 companies, leading to a total of 1,533 observations.

The analysis identified that environmental scores significantly impact CEO equity-linked compensation, unlike social and governance scores. Additionally, it was found that institutional ownership acts as a moderating factor in the relationship between the environmental score and CEO equity-linked compensation, as well as the association between the social score and CEO equity-linked compensation. Interestingly, the direction of these moderating effects varied between the two relationships, suggesting a nuanced role of institutional ownership.

This research makes a unique contribution to the field of corporate governance by exploring the relatively understudied area of institutional ownership's influence on the ESG practices–CEO compensation nexus.

The purpose of this study is to investigate the moderating roles of innovation intensity and lenders’ monitoring on the relation between financial slack and performance.

This study adopts an empirical method using data from firms listed in both the Compustat S&P500 and Boardex for the period 2010 to 2019 to analyze the effects of innovation intensity and lenders’ monitoring on the relation between financial slack and performance.

The authors find that financial slack is positively related to performance, and this relation is stronger as innovation intensity increases. Furthermore, we demonstrate that lenders’ monitoring strengthens the positive relationship between financial slack and performance.

First, this study focuses on the effects of financial slack, research and development (R&D) intensity and lenders’ monitoring on financial performance. Future research might extend this study by investigating the effects of these variables on non-financial performance. Second, the data and results do not provide insights into the reasons for firms to accumulate financial slack. Future research might conduct a longitudinal field study to understand why firms build financial slack. Finally, this study only uses R&D intensity and lenders’ monitoring as the moderating variables. Future studies might incorporate other contingency variables such as firms’ budgeting and budget-based compensation systems to provide useful insights into the relationship between financial slack and performance.

This study provides important insights into the value of financial slack for firms that invest heavily in R&D activities. This study also provides useful insight into the benefits of lenders’ monitoring to mitigate managers’ unethical behavior.

This study provides useful insights for companies that invest heavily in innovation activities by showing that financial slack is beneficial for this company and lenders’ monitoring is needed to discipline managers in using the slack resources.

This study is the first to investigate the moderating effects of innovation intensity and lenders’ monitoring on the relation between financial slack and performance. Previous studies focus their investigations on the direct effect of financial slack and performance.

The purpose of this paper is to study the impact of the 2016 presidential election polls on the stock market.

The empirical model includes daily stock returns as the dependent variable and past asset prices, 10-year treasury rates, opinion polls and VIX (market uncertainty) as explanatory variables with a one-year lag. The model was estimated using two sets of daily polling data: from July 1, 2015, to November 8, 2016, and from June 1, 2016, to November 8, 2016. Additional descriptive statistics, such as means and standard deviations, were also calculated.

The estimated results did not reveal any statistically significant effects of opinion polls in favor of one candidate over another on stock returns. Simple statistical tests, however, show that the market performed better when Trump held a polling advantage over Clinton.

To the best of the authors’ knowledge, this is the only study that has examined the effects of the 2016 presidential election polls on the US stock market. This study adds value to the understanding of the relationship between election polls and the stock market in the USA.

The average U.S. farm size has risen dramatically over the last three decades. Motives for this trend are the subject of a large body of literature. This study incorporates farm size risk and return analysis into this research stream. In this paper, cross-sectional and temporal relations between farm size and returns are examined and characterized.

Relying on farm level panel data from Kansas Farm Management Association (KFMA) for 140 farms from 1996 to 2018, this article examines the relationship between farm size and returns and investigates whether farm size is related to risk. Two measures of farm returns are used: excess return on equity and risk-adjusted return on equity. Value of farm production and total farm acres are used as measures of farm size.

Findings suggest a significant and positive relationship between farm size and excess return on equity as well as farm size and risk-adjusted return on equity. However, this return premium associated with farm size is not associated with additional risk. Stated differently, farm size can be viewed as a farm characteristic that is associated with higher return without additional risk.

These findings provide further support for ongoing farm consolidation.

The results suggest the trend towards consolidation in production agriculture is likely to continue. Larger farms bear less risk.

Design/Methodology/Approach: This chapter applies tests associated with the adaptive market hypothesis (AMH) and Johansen cointegration test. AMH acknowledges the views of the efficient market hypothesis and behavioural finance approach.

Purpose: Cryptocurrencies are considered a new asset class by multiasset portfolio managers. Hence, we examine the AMH and cointegration in the cryptocurrency market to know whether select cryptocurrencies can be diversified.

Findings: We find that cryptocurrencies are efficient and there is a long-run relationship among constituent series, and there is no short-run causality derived from bitcoin, Ethereum and litecoin to bitcoin, while stellar and Dogecoin have short-run causality to bitcoin.

Originality/Value: This chapter is different from the existing one as this is the first study in which the AMH and Johansen cointegration test are applied to check the efficiency and relationship of Bitcoin, Ethereum, and Monero, Stellar, litecoin and Dogecoin.