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Climate change and digitisation are unquestionably the two defining features of this era. Both present immense challenges with unimaginable consequences for humankind while promising enormous rewards for those who can adequately address their adverse effects. These two critical factors must be considered while establishing strategies for the businesses' future operations. Hence, post-pandemic, especially with the rise of online commerce, packages and documents are delivered around the globe nearly every day, propelling the logistics industry's growth. This is not the critical challenge in logistics, the issue of sustainability, particularly as the returns are increasing exponentially, leading to a significant impact on transportation is and its reliance on fossil fuel has made it a prime target for society's growing environmental concerns. Thus, real-time visibility, collaboration and integration in reverse logistics (RL) are imperative for business sustainability. The most applicable Industry 4.0 technologies in RL are the Internet of Things (IoT), cloud computing, blockchain and digital twin that enable the defragmentation of the RL market.

This chapter analyses the technological impact of Industry 4.0 on RL. This research investigates the challenges faced by the logistics industry in the context of sustainability and how digital transformation can bring many potential benefits across the entire value chain. This chapter also presents a guidance for a framework based on the literature review that tends to favour the development of elastic logistics, implying improved company responsiveness to market conditions. The study contributes to the body of literature and the establishment of the framework for planning on the application of various Industry 4.0 technologies in developing eco-friendly and sustainable reverse logistics framework.

Environmental, social, and governance (ESG) issues have recently received much attention. This research investigates the daily performance of socially responsible investment (SRI). To do that, the authors construct portfolios consisting of the SRI, non-SRI, and matched non-SRI. The portfolios can be compared with the market benchmark based on α adjusted asset pricing models. Due to using high-frequency data, the authors use ARCH/GARCH to deal with time-varying volatility. Moreover, the authors also utilized Fama–MacBeth pooled regression to confront the SRI stocks and the non-SRI counterpart. In sum, the findings of this study confirm the superior performance of the value-weighted (VW) SRI portfolio against the market. On a head-to-head basis, the SRI yields a higher return than the non-SRI. The results are robust in the quarterly analysis. It is essential for investors that put their money in socially responsible (SR) portfolios to either promote sustainable development or chase a return on it.

Purpose: The present study aims to test the Quadratic Programming model for Optimal Portfolio selection empirically.

Need for the Study: All the investors who buy financial products are motivated to obtain higher profits or, in other words, to maximise their returns. However, the high returns are often accompanied by higher risks, and avoiding such risks has become the primary concern for all investors. There is a great need for such a model to maximise profits and minimise risk, which can help design an investment portfolio with minimum risk and maximum return. The Quadratic Programming model is one such model which can be applied for selected shares to build an optimised portfolio.

Methodology: This study optimises the stock samples using a two-level screening of correlation coefficient and coefficient of variation. The monthly closing prices of the NSE-listed Indian pharmaceutical stocks from December 2019 to January 2022 have been used as sample data. The Lagrange Multiplier method is used to apply the model to achieve the optimal portfolio solution. Based on the market reality, the transaction costs have also been considered. The Quadratic programming model is further optimised to achieve the optimal portfolio for the select stocks.

Findings: The traditional portfolio theory and the modified quadratic model gives similar and consistent results. In other words, the modified quadratic model asserts the accuracy of the conventional portfolio model. The portfolio constructed in the present study gives a return much higher than the return of the benchmark portfolio of Nifty Fifty, indicating the usefulness of applying the Quadratic Programming model.

Practical Implications: The construction of an optimal portfolio using the traditional or modified Quadratic model can help investors make rational investment decisions for better returns with lower risks.

In this chapter, we perform more detailed analyses and present the distribution characteristics and risk-return relationships of accounting-based financial returns (ROA) across different industry contexts and between periods with different economic conditions. We first display the frequency diagrams of the return measure (ROA) and its two components, net income and total assets, that show entirely different contours in the density graphs that must be reconciled. This is partially accomplished by analyzing the skewness, kurtosis, cross-sectional, and longitudinal risk-return characteristics of each of the three variables. The analyses further considers potential effects of accounting manipulation, and different organizational and executive traits, that identifies significant effects on the accounting-based return measures. We find extremely left-skewed return distributions with high negative correlations between the average return and risk measures, which reproduces the “Bowman paradox” as originally conceived. The same analysis is performed on net income and operating cash flows, the latter being less susceptible to accounting manipulation, which should display similar effects even though these performance distributions show positive skewness. We find negative but insignificant cross-sectional risk-return relations that nevertheless reappear in analyses performed within the specific industry contexts. The study further uncovers effects from prevailing economic conditions where left-skewness and kurtosis as well as negative risk-return correlations are much more significant during periods of high economic growth and business expansion where competition is more pronounced.

There is ample evidence that financial market development leads to economic growth. If improving labor rights can be shown to positively influence equity markets, then that, in turn, will lead to economic growth. The finance literature has examined the impact of a broader metric, namely, the Economic Freedom Index, on equity returns worldwide, and the evidence is mixed. This study focuses on one dimension of economic freedom: labor rights. Specifically, the study analyzes the impact of labor rights on national equity market indexes using the Labor Rights Index developed by the Organization for Economic Co-operation and Development (OECD) and the Fraser Institute (FI). Using panel regression analysis for 49 countries (for the OECD Index) and 76 countries (for the FI Index) over the period 1985–2014, the study finds that changes in labor rights have a statistically significant positive impact on equity returns, after controlling for business-cycle effects and time-fixed effects. The study also finds significant differences in the labor–rights–equity returns relationship between developed and less developed economies.

This study focuses on listed and over-the-counter (OTC) companies in the Taiwan Stock Exchange. It found that an increase in the ownership proportion of institutional investors (INs), including foreign investors, investment trusts, and dealers can enhance the informativeness of stock prices. The relationship between these factors follows an inverted U-shaped pattern, indicating that excessively high ownership ratios can actually lead to a decrease in the informativeness of stock prices. Additionally, increasing the ownership proportions of foreign investors and investment trusts can reduce the risk of stock price collapse, while dealers show no significant relationship in this regard. This study also reveals that the technical variable of the price deviation rate is an important explanatory factor for post-collapse returns. It is positively correlated with the magnitude of the price decline after a collapse, meaning that stocks with weaker pre-collapse performance experience larger post-collapse declines. When the data during the 2020 pandemic period are excluded, changes in foreign ownership ratios show a significant positive correlation with postcrash returns in both the long and short term. The significant correlation in the short term may be due to a high proportion of foreign ownership. Any reduction in this could put pressure on stock prices, and retail investors may follow suit and sell-off, using foreign investors as a reference. The significant correlation in the long term might be due to foreign investors themselves possibly also trying to avoid the pressure that their own short-term sell-offs could exert on stock prices. The changes in the ownership ratios of investment trusts and dealers indicate that medium and long-term changes have a significant impact on postcrash returns, while the changes in the major players' ownership show no significant correlation. When data from 2020 are included in the analysis, the significance of all INs decreases.

While COVID-19 temporarily created worldwide immobility, the gradual opening up of borders spurred one of the largest return migration episodes ever, and it continues to this day. Disappearing jobs, decreasing wages, inadequate social protection systems and networks, xenophobia, wage theft and overall uncertainty are among the prominent factors that have influenced this movement. Emigrants from the Gulf-India Migration Corridor were particularly affected by these forces and returned en masse, uncertain of their future. When people come back to their home country after living abroad, particularly due to exogenous shocks, it raises concerns about whether their decision to return was truly voluntary, their ability to adjust to being back home and the long-term effects on their reintegration. Additionally, it is uncertain what kind of impact return migrants have on their home country’s development. In this chapter, the authors examine the recent trend of return migration since the outbreak of COVID-19 and how it affects the Gulf-India corridor. The authors also take a closer look at the state of Kerala through a unique survey conducted by the authors and provide possible future scenarios for emigration in this region, along with recommendations for policy.

This chapter introduces empirical studies of firm performance and related risk outcomes conducted in the management and finance fields presenting underlying theoretical rationales as they have evolved over time. Early finance studies of market-based returns predominantly found positively skewed return distributions that conform to assumptions about higher returns associated with more risky investments. Subsequent studies found that performance outcomes measured as accounting-based financial returns generally display left-skewed distributions that reflect negative risk-return relationships. This artifact was first observed by Bowman (1980), thus often referred to as the “Bowman paradox” because it contravened the conventional assumptions in finance. The management studies have largely confirmed the inverse risk-return observations but often following rather confined research streams. A contingency perspective inspired by prospect theory and behavioral rationales have investigated the lagged effects of performance on risk outcomes and vice versa. Another stream has focused on the spurious relationships between negatively skewed performance distributions and the inverse risk-return associations. A third approach considered the performance and risk outcomes as deriving from the firms responding in distinct ways to exogenous changes. These studies reach comparable results but underpinned by very different rationales. The finance studies observe deviations from the pure doctrine of positive risk-return associations embedded in the widely adopted capital asset pricing model (CAPM) and note deficiencies with alternative interpretations that even question the validity of CAPM. A more recent strain of studies in behavioral finance observes how many (even professional) investment managers have biases that lead to inverse relationships between perceived risk and return outcomes. While these diverse fields of study have different starting points, they uncover an increasing number of interesting commonalities that can inspire the ongoing search for explanations to observed left-skewed financial returns and negative risk-return correlations across firms.

Using a GED-GARCH model to estimate monthly data from January 1990 to February 2022, we test whether gold acts as a hedge or safe haven asset in 10 countries. With a downturn of the stock market, gold can be viewed as a hedge and safe haven asset in the G7 countries. In the case of inflation, gold acts as a hedge and safe haven asset in the United States, United Kingdom, Canada, China, and Indonesia. For currency depreciation, oil price shock, economic policy uncertainty, and US volatility spillover, evidence finds that gold acts as a hedge and safe haven for all countries.