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This research paper aims to analyze the stock exchanges of developed, emerging and developing countries to investigate the volatility in stock markets and to evaluate the rate of mean reversion.

The stock exchanges included in the research are NASDAQ, Tokyo stock exchange, Shanghai stock exchange, Bombay stock exchange, Karachi stock exchange and Jakarta stock exchange. Secondary daily data from Bloomberg are used to conduct the research for the period from January 2011 to December 2018. Generalized autoregressive conditional heteroskedasticity (GARCH) (1,1) model was applied to examine volatility and the half-life formula was used to calculate mean reversion in days.

The research concluded that all the stock exchanges included in the research satisfy the assumptions of mean reversion. Developing countries have the lowest volatility while emerging countries have the highest volatility which means that the rate of mean reversion is fastest in developing countries and slowest in emerging countries.

Future studies can determine the reasons for fastest rate of mean reversion in developing countries and slowest rate of mean reversion in emerging countries.

Developing countries show the lowest mean reversion in days while the emerging countries show the highest mean reversion in days indicating that developing countries take less time to revert to their mean position.

The majority of previous studies on univariate volatility models are mostly on applications of the models. Only a few researchers have taken the robustness of the models into account when applying them in emerging countries and not in developed, developing and emerging countries in one place. This makes the current study unique and more rigorous.

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 study examines the determinants of idiosyncratic risk (IR) or unsystematic risk. The study also examines the determinants of IR by dividing the firms into different categories: beta-based firms, liquid and illiquid firms and financially constrained (FC) and unconstrained (FUC) firms.

The fixed effects static panel data model specifications are formulated based on Hausman (1978) test for BRICS (Brazil, Russia, India, China, and South Africa) member countries over the period 2000–2019. Moreover, the t-test is applied to see whether the returns of different types of portfolios are significantly different.

The portfolio analysis results show that, on average, high IR firms tend to be small in size, highly leveraged, have low competitiveness, low profitability, less dividend yield and low returns for all the sampled countries. The sample paired t-test also confirms that a significant difference exists between extreme portfolios: small and large size and low IR and high IR portfolios. The panel regression results show that firm size, market power, price-to-earnings ratio, return on equity (ROE) and dividend yield negatively relates to IR. Yet, both leverage and liquidity are positively related to IR. However, the sign of momentum returns is mostly positive for the entire sample. The coefficient values for high-beta, FC and illiquid firms are more significant and large than the firms' counterparts for all BRICS member countries. These results support the hypothesis of an under-diversified portfolio and suggest that the above-mentioned firm-specific variables are the significant determinants of unsystematic risk.

The securities exchange commission, as the supervisor of the public limited companies, needs to increase its role in investor protection related to the uncertainty of investment in the capital market. Accordingly, in making investment decisions in a stock exchange, investors can use the information that captures unsystematic risk for investment decision-making.

This study is the first to explore the determinants of IR in top emerging countries. Second, none of the existing studies has focused on the determinants of the IR based on different categories of firms.

In this paper, the authors investigate the impact of the confirmation bias on returns, expectations and hedging of optimistic and pessimistic traders in the cryptocurrencies, commodities and stock markets before and during COVID-19 periods.

The authors investigate the impact of the confirmation bias on the estimated returns and the expectations of optimistic and pessimistic traders by employing the financial stochastic model with confirmation bias. Indeed, the authors compute the optimal portfolio weights, the optimal hedge ratios and the hedging effectiveness.

The authors find that without confirmation bias, during the two sub periods, the expectations of optimistic and pessimistic trader’s seem to convergence toward zero. However, when confirmation bias is particularly strong, the average distance between these two expectations are farer. The authors further show that, with and without confirmation bias, the optimal weights (the optimal hedge ratios) are found to be lower (higher) for all pairs of financial market during the COVID-19 period as compared to the pre-COVID-19 period. The authors also document that the stronger the confirmation bias is, the lower the optimal weight and the higher the optimal hedge ratio. Moreover, results reveal that the values of the optimal hedge ratio for optimistic and pessimistic traders affected or not by the confirmation bias are higher during the COVID-19 period compared to the estimates for the pre-COVID period and inversely for the optimal hedge ratios and the hedging effectiveness index. Indeed, either for optimists or pessimists, the presence of confirmation bias leads to higher optimal hedge ratio, higher optimal weights and higher hedging effectiveness index.

The findings of the study provided additional evidence for investors, portfolio managers and financial analysts to exploit confirmation bias to make an optimal portfolio allocation especially during COVID-19 and non-COVID-19 periods. Moreover, the findings of this study might be useful for investors as they help them to make successful investment decision in potential hedging strategies.

First, this is the first scientific work that conducts a stochastic analysis about the impact of emotional biases on the estimated returns and the expectations of optimists and pessimists in cryptocurrency and commodity markets. Second, the originality of this study stems from the fact that the authors make a comparative analysis of hedging behavior across different markets and different periods with and without the impact of confirmation bias. Third, this paper pays attention to the impact of confirmation bias on the expectations and hedging behavior in cryptocurrencies and commodities markets in extremely stressful periods such as the recent COVID-19 pandemic.

Guided by resource-based theory, this study unpacks the relationship between an export entrepreneurial marketing orientation (EMO) and export performance. This is undertaken by investigating quadratic effects and the moderating role of export coopetition (cooperation amongst competitors in an international arena).

Survey responses were collected from a sample of 282 smaller-sized wine producers in Italy. This empirical context was ideal, as it hosted varying degrees of the constructs within the conceptual model. Put another way, it was suitable to test the underlying issues for theorising purposes. The hypotheses and control paths were tested through a three-step hierarchical regression analysis.

An export EMO had a non-linear (inverted U-shaped) association with export performance. Furthermore, this link was positively moderated by export coopetition. With too little of an export EMO, small enterprises might struggle to create value for their overseas customers. With too much of an export EMO, owner-managers could experience harmful performance outcomes. By cooperating with appropriate industry rivals, small companies can acquire new resources, capabilities and opportunities to help them to boost their export performance. That is, export coopetition can stabilise some of the potential dangers of employing an export EMO.

The empirical findings signified that an export EMO has potential dark-sides if these firm-wide behaviours are not implemented effectively. Nevertheless, cooperating with competitors in export markets can alleviate some of these concerns. Collectively, unique insights have emerged, whereby entrepreneurs are advantaged by being strategically flexible and collaborating with appropriate key stakeholders to enhance their export performance.

Customer incivility is a key phenomenon with various harmful consequences for businesses, particularly in the food and beverage industry. This study investigated the antecedents of this issue and explored its outcomes for frontline employees in Vietnam.

This study used quantitative methodology to survey 780 participants who frequently experienced customer incivility in cafés and bubble tea shops. Structural equation modeling was used to examine the data.

The results revealed three antecedents of customer incivility – employee incivility, selling and customer orientation – as well as outcomes such as revenge motivation, emotional exhaustion, service sabotage and job performance, along with two mediating effects.

Managers can create and enhance additional training classes with varied curricula for different staff groups to foster their perspectives and understand an organization's customer orientation, eliminating vengeance motives, emotional strain, service sabotage and workplace performance.

This study emphasizes the importance of customer incivility and how it can be minimized by examining its causes and consequences in Vietnamese cafés and bubble tea shops.

The aim of the study was to analyze the performance of Black-Litterman (BL) portfolios using a views estimation procedure that simulates investor forecasts based on technical analysis.

Ibovespa, S&P500, Bitcoin and interbank deposit rate (IDR) indexes were respectively considered proxies for the national, international, cryptocurrency and fixed income stock markets. Forecasts were made out of the sample aiming at incorporating them in the BL model, using several portfolio weighting methods from June 13, 2013 to August 30, 2022.

The Sharpe, Treynor and Omega ratios point out that the proposed model, considering only variable return assets, generates portfolios with performances superior to their traditionally calculated counterparts, with emphasis on the risk parity portfolio. Nonetheless, the inclusion of the IDR leads to performance losses, especially in scenarios with lower risk tolerance. And finally, given the impact of turnover, the naive portfolio was also detected as a viable alternative.

The results obtained can contribute to improve investors practices, specifically by validating both the performance improvement – when including foreign assets and cryptocurrencies –, and the application of the BL model for asset pricing.

The main contributions of the study are: performance analysis incorporating cryptocurrencies and international assets in an uncertain recent period; the use of a methodology to compute the views simulating the behavior of managers using technical analysis; and comparing the performance of portfolio management strategies based on the BL model, taking into account different levels of risk and uncertainty.

Given their interrelationship, international market selection (IMS) and entry mode selection (EMS) must be considered jointly if an optimal entry strategy is to be realized. However, researchers in the field of international construction have the tendency to consider IMS and EMS independently or sequentially. Therefore, this paper aims to explore a holistic framework that can accommodate IMS and EMS concurrently and test it using empirical data.

his study includes theoretical and empirical research. In theoretical part, an integrated decision model of IMS and EMS is proposed adopting literature review and theoretical derivation, then hypotheses are developed for the impact of decision-making factors. In the latter part, the IMS and EMS of 54 Chinese contractors in 67 countries were investigated, empirical data are collected according to hypotheses, an ordinal logistic regression model is established for statistics analysis. Finally, findings are drawn by comparing literature-based hypotheses with data-based analysis results.

Results show that empirical data fit theoretical model well. Findings are: IMS and EMS can be integrated into a holistic decision-making framework when be properly sequenced. When IMS and EMS are determined simultaneously, the decision can benefit from a sharing of common information. And the roles of at least 13 common factors are empirically demonstrated in this study.

The integrated decision sequence proposed in this study is applicable for a specific market, and cannot compare multiple alternative markets directly. The decision-making factors identified in this paper do not cover the enterprise strategic objectives and some other factors. Empirical data and some theoretical assumptions are based on the international market entry strategy of Chinese contractors. Therefore, the conclusions may not be completely applicable to global contractors though have certain reference value.

Based on the idea of holistic decision-making of IMS and EMS, this study proposes an international market entry strategy (IMES) sequence and an explicit model for determinants, then tests them with empirical data. This paper provides a new idea to manage IMS and EMS concurrently, which can improve the efficiency of IMES decision-making and avoid missing optimal alternatives. This study paves the way for a practical model and provides reference for contractors' international market entry strategy.

Fabrics, which are one of the raw materials of the clothing industry, constitute approximately 40–45% of the total cost of an apparel product. Due to the labor-intensive nature of this industry and failure to apply scientific methods along with the manufacturing processes, the wastes in the raw materials, including fabrics, become higher. Besides, quality deficiencies are encountered due to the same reasons. This study aims to determine the optimum total fabric layer height based on the fabric type during the cutting process with a straight knife cutting machine, which provided a decrease in the cutting errors.

Frequently used fabric types in an enterprise operating in organic cotton knitwear were listed. During the cutting tests, the straight knife cutting machine was used as the cutting device. The weight and thickness values of the fabrics were obtained to provide a comparison basis. Two different algorithms were created to evaluate the defective pieces according to fabric type, cutting height and error placement. Cutting resistances of these fabrics were also determined to evaluate the defect reasons. In the end, optimum total fabric layer count and total cutting height suggestions were proposed for each fabric type for a minimum cutting error.

At the end of this study, the error-free layers were identified per fabric type. At the same time, the optimum cutting height was suggested for each fabric basis. For 40/1 single jersey fabrics, the cutting height should be between 2.10 cm and 10.40 cm; for 30/1 single jersey fabrics, between 1.65 cm and 5.70 cm; for 20/1 single jersey fabrics, between 1.83 cm and 6.70 cm; for two-thread fleece fabrics, between 2.13 cm and 4.70 cm; and for three-thread fleece fabrics, between 0 cm and 4.90 cm.

Within the scope of the study, since the products made of knitted fabric were produced more frequently and in large quantities, the study was carried out with 15 different types of knitted fabrics at 10 different layers. The same methods should be applied for woven, denim and nonwoven fabric types, which would shed light on the following studies.

Due to scarce research carried out on the cutting procedure of the clothing industry in regards to sustainability, this study aims to contribute to this area. The main difference between this study and the studies that mostly make mathematical predictions about the cutting procedure is that it is practice-oriented.

The paper aims to examine the nature and scale of the sustainability value of car sharing and to identify, through consumer analysis, the contextual and consumer factors of success of car subscription as a business model.

The study evaluates the car sharing model against the sustainable development goals defined by the United Nations in 2019. Individual interviews were performed for preliminary understanding of the factors affecting consumers' choices. Subsequently, through two phases of data collection, factor analysis and path model analysis were performed to identify and confirm latent factors. Consumer market segmentation was performed using cluster analysis.

Car sharing was found to have an overall positive net impact, with certain potential negative dimensions. Willingness, financial affordability, location and experience were identified as the key factors of consumers opting for car subscriptions. The findings further highlight the significant business potentialities of car subscription in India, consequent also to consumers' attitudes toward car ownership.

The research has substantial implications for both society and business, with the former being presented with an innovative sustainable means of transportation, and the latter with the elements of success of an entrepreneurial business model to support the former.

The study is a pioneer in objectively evaluating and prescribing positive social and business value creation for and through car subscription in India, based on consumer analysis.