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Many studies have shown that from a theoretical and empirical point of view, downside risk-based measures of risk are better than the traditional ones. Despite academic appeal and practical implications, downside risk has not been thoroughly examined in markets outside developed country markets. Using downside beta as a measure of downside risk, this study examines the relationship between downside beta and stock returns in Indian equity market, an emerging market with unique investor, asset and market characteristics.

This is an empirical study done by using ranked portfolio return analysis and regression analysis methodologies.

The study results show that downside risk, as measured by downside beta, is distinctly priced in the Indian equity market. There is a direct positive relationship between downside beta and contemporaneous realized returns, indicating a premium for downside risk. Downside risk carries a higher weightage than upside potential in the aggregate return of the stock portfolios. Downside beta is a better measure of systematic risk than conventional market beta and downside coskewness.

The empirical results support the adoption of downside beta in practice and provide a case for replacing traditional beta with downside beta in asset pricing applications, trading and investment strategies, and capital allocation decision-making.

This is one of the first in-depth studies examining downside beta in Indian equity markets using a broad sample of individual stock returns covering a wide time range of 22 years. To the best of our knowledge, this study is the first one to compare downside beta and downside coskewness using individual stock data from the Indian equity market.

This paper evaluates the performance of actively managed conventional and Islamic equity funds in a developing economy with a focus to assess the performance-growth puzzle posited by Gruber (1993) (a.k.a Gruber’s puzzle). Under the context of an emerging market of Pakistan, this study explores if actively managed equity fund (AMEF) managers have been able to add value by outperforming the market in terms of stock-selection and market-timing abilities; and the comparative performance analysis of Islamic versus conventional AMEFs is also carried out.

We employ Sharpe and Treynor ratios, Capital asset pricing model, Fama–French three factors model (1993), Carhart four-factor model (1997) and Hendrickson (1981) market timing models on 45 equity funds comprising of 23 conventional and 22 Islamic equity funds operating in Pakistan for a period of 10 years. The overall sample period (2008–2018) is divided into two 5 years sub-periods (i.e. 2009–2013 and 2014–2018) and three 3 years sub-periods (2009–2011, 2012–2014 and 2015–2017) to be viewed in conjunction with the country's macro-economic condition.

We report that the actively managed equity funds (AMEFs) were unable to beat the market index with their stock selection or market timing capabilities. However, AMEFs depicted improved performance in the post-global financial crisis period where both conventional and Islamic AMEFs generated substantial rewards for the given amount of risk. Also, conventional AMEFs outperformed Islamic AMEFs potentially due to their holdings in highly leveraged value and large-cap stocks, while Islamic AMEFS invest more cautiously in small-cap and value firms. Analysis of market timing skills revealed that the funds have not been able to select the undervalued stocks and adopted a defensive strategy in the post-global financial crisis recovery period.

Our findings shed some interesting insights and raise some pertinent questions for research, policy, and practice – specifically for developing countries’ context. The no ‘return-growth’ configuration defies its fit with the ‘Gruber puzzle’ and somewhat presents a case of what we call the ‘Inverse Grubber puzzle’. This novel notion of the ‘Inverse Grubber puzzle’ should inform policy and practice to reflect on their practices, institutional arrangement, regulatory framework and policy design in developing economies characterized by lacklustre performance and growth of AMEFs. For example, the regulatory design may consider focusing on stimulating financial inclusion and deepening by motivating low-cost Index tracker funds (ITFs) – with lower fund management costs, while allocating the avoided cost to flow towards effective marketing campaigns driving greater awareness, financial deepening, and investor base diversification. For future research, financial development researchers may explore the implications and appropriateness of AMEFs versus ITFs in other developing economies.

The work reported in this paper is original and constitutes a valuable asset for ethno-religious-sensitive investors. The research has not been published in any capacity and is not under consideration for publication elsewhere.

Biotech companies stand as key actors in pharmaceutical innovation. The high risk and long timelines inherent with their R&D investments might hinder their access to funding, potentially stifling innovation. This study aims to explore into the appeal of biotech companies to capital market investors, whose financial backing could bolster the growth of the biotechnology sector.

This paper uses a dataset of 774 US publicly listed biotech firms to investigate their risk and return characteristics by comparing them to pharmaceutical firms and a sample of matched non-biotech R&D-intensive firms over the sample period 1980–2021. Tests show that the conclusions remain consistent across diverse methodological approaches.

The paper shows that biotech companies are riskier than the average firm in the market index but outperform on a risk-adjusted basis both the market and a matched group of R&D-intensive firms. This is particularly true for large capitalization biotech, which is also shown to provide a diversification benefit by reducing the downside risk in past crisis periods.

This paper provides insight relevant to the current debate about the overall performance of the biotech industry in terms of policy changes and their impact on small, early-stage biotech firms. While small and early-stage biotech firms are playing an increasing role in scientific innovation, this study confirms their greater vulnerability to financial risks and the importance of access to capital markets in enabling those companies to survive and evolve into larger biotech.

The authors compare sentiment level with sentiment shock from different angles to determine which measure better captures the relationship between sentiment and stock returns.

This paper examines the relationship between investor sentiment and contemporaneous stock returns. It also proposes a model of systems science to explain the empirical findings.

The authors find that sentiment shock has a higher explanatory power on stock returns than sentiment itself, and sentiment shock beta exhibits a much higher statistical significance than sentiment beta. Compared with sentiment level, sentiment shock has a more robust linkage to the market factors and the sentiment shock is more responsive to stock returns.

This is the first study to compare sentiment level and sentiment shock. It concludes that sentiment shock is a better indicator of the relationship between investor sentiment and contemporary stock returns.

This article aims to analyse the relationship between the environmental, social and governance (ESG) score and the cost of capital of 600 large, mid and small capitalization companies across 17 countries that are component of the EURO STOXX 600 Index. By examining whether ESG has an impact on the cost of capital, this article contributes to the solutions to improve the impact of organizations and societies on sustainable development. The article further examines whether the effect is because of the environmental, social and/or governance components. In addition, the article analyses which WACC component (i.e. the cost of equity, the cost of debt, the beta or the leverage ratio) is affected. Furthermore, this article analyses whether a high ESG score can substitute for a weaker legal environment.

The results were obtained by using ordinary least squares panel data modelling to analyse the relationship between the ESG score and the cost of capital. The sample consists of companies that are part of the STOXX Europe 600 Index over the period 2018–2021, which is composed of 600 companies, including large, mid and small capitalization firms listed across 17 countries. The sample finally includes 1,960 firm-year observations.

Companies with a higher ESG score tend to have a lower cost of capital, but this relationship holds only for firms domiciled in countries with a weaker legal environment. In addition, these firms should not only increase their ESG score to create a more sustainable environment but also to reduce their cost of debt. Environmental and social factors have a significantly negative impact on the cost of capital only in countries with a weaker legal environment, while the governance component positively impacts the cost of capital by allowing firms to borrow more.

There is not yet a standardized taxonomy to define ESG, making the study dependent on commercial data providers.

The new insights can be used by companies domiciled in countries with weaker legal environments to reduce their cost of capital. The results also allow us to know on which components of the ESG score to focus. It can also help policymakers, specifically those in countries with a weaker legal environment, to provide incentives to further stimulate ESG investments and disclosure, thereby contributing to a more sustainable society.

To achieve the sustainable development goals put forward by the United Nations, it is important for firms to invest in ESG projects. It is nevertheless insightful to know whether these ESG investments, which are currently observed as a cost, also provide benefits to firms and in which countries. If firms clearly see the advantages of investing in ESG projects, they are likely to proactively engage in them.

This article is the first, to the best of the authors’ knowledge, to focus on 17 European countries, thereby capturing divergent legal environments. This setting allows us to answer the main novel research question, namely, whether the ESG score can act as a substitute for the legal environment in which the company is domiciled. The article also goes further than previous articles by examining whether the effect is because of the environmental, social and/or governance component and whether these impact the components of the weighted cost of capital, namely, the cost of equity, the cost of debt, the beta or the leverage ratio of the companies.

We aim to clarify the effect of economic uncertainty on Chinese stock market fluctuations. We extend the understanding of the asymmetric connectedness between economic uncertainty and stock market risk and provide different characteristics of spillovers from economic uncertainty to both upside and downside risk. Furthermore, we aim to provide the different impact patterns of stock market volatility following several exogenous shocks.

We construct a Chinese economic uncertainty index using a Factor-Augmented Variable Auto-Regressive Stochastic Volatility (FAVAR-SV) model for high-dimensional data. We then examine the asymmetric impact of realized volatility and economic uncertainty on the long-term volatility components of the stock market through the asymmetric Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling (GARCH-MIDAS) model.

Negative news, including negative return-related volatility and higher economic uncertainty, has a greater impact on the long-term volatility components than positive news. During the financial crisis of 2008, economic uncertainty and realized volatility had a significant impact on long-term volatility components but did not constitute long-term volatility components during the 2015 A-share stock market crash and the 2020 COVID-19 pandemic. The two-factor asymmetric GARCH-MIDAS model outperformed the other two models in terms of explanatory power, fitting ability and out-of-sample forecasting ability for the long-term volatility component.

Many GARCH series models can also combine the GARCH series model with the MIDAS method, including but not limited to Exponential GARCH (EGARCH) and Threshold GARCH (TGARCH). These diverse models may exhibit distinct reactions to economic uncertainty. Consequently, further research should be undertaken to juxtapose alternative models for assessing the stock market response.

Our conclusions have important implications for stakeholders, including policymakers, market regulators and investors, to promote market stability. Understanding the asymmetric shock arising from economic uncertainty on volatility enables market participants to assess the potential repercussions of negative news, engage in timely and effective volatility prediction, implement risk management strategies and offer a reference for financial regulators to preemptively address and mitigate systemic financial risks.

First, in the face of domestic and international uncertainties and challenges, policymakers must increase communication with the market and improve policy transparency to effectively guide market expectations. Second, stock market authorities should improve the basic regulatory system of the capital market and optimize investor structure. Third, investors should gradually shift to long-term value investment concepts and jointly promote market stability.

This study offers a novel perspective on incorporating a Chinese economic uncertainty index constructed by a high-dimensional FAVAR-SV model into the asymmetric GARCH-MIDAS model.

This study aims to investigate the variation in overvaluation proxies and volatility across industry sectors and time.

Using industry sector data from the S&P Capital IQ database, this study applies traditional cross-sectional regressions to investigate the relationship between overvaluation and volatility over the 2001–2020 time period.

This study finds that the most volatile industry sectors generally do not coincide with overvalued industry sectors in the cross-section, implying that there are limitations to price-multiple methods for forecasting future volatility. Rather, this study finds that historical volatility significantly increases the goodness-of-fit when modeling volatility in the cross section of industry sectors. The findings of this study imply that firms should increase disclosures and transparency about corporate practices to decrease downside risk that stems from bad news. In addition, the findings underline the consistency between market efficiency and high levels of volatility in periods of significant uncertainty.

This study proposes a novel approach to examining the cross section of volatility across time for industry sectors.

In this study, we investigate what drives the MAX effect in the South Korean stock market. We find that the MAX effect is significant only for overpriced stocks categorized by the composite mispricing index. Our results suggest that investors' demand for the lottery and the arbitrage risk effect of MAX may overlap and negate each other. Furthermore, MAX itself has independent information apart from idiosyncratic volatility (IVOL), which assures that the high positive correlation between IVOL and MAX does not directly cause our empirical findings. Finally, by analyzing the direct trading behavior of investors, our results suggest that investors' buying pressure for lottery-like stocks is concentrated among overpriced stocks.

The purpose of this study is to evaluate whether the classification of the equity mutual fund depends on the performance measure used.

The sample for this study includes stock mutual funds for the USA, Europe and emerging market economies covering the period 2010 to 2020. Using more than 20 performance measures the results are compared using the Sharpe ratio as the reference.

The results show that performance measures based on absolute reward–risk ratios like Sortino, Treynor, etc. have similar rankings, because in general the numerator (mean excess return) is the same. However, when the authors employ other types of performance measures, results may be significantly different, especially in the case of metrics for “incremental returns”, i.e. alphas. Focussing on markets, their results show that choosing performance measures is more relevant for emerging markets.

The sample is only limited to the USA, Europe and the emerging market, and there are other performance metrics in the literature which have not been covered in this work.

The ordering of equity mutual funds depends on the measure used, specially if investors employ factor models to measure excess returns (alphas). Hence, policy formulation on disclosure of mutual fund performance should encourage the use of several metrics from different families. Investors must be aware of the different rankings made and the most appropriate metrics based on their preferences.

This paper focusses specifically on the effect that performance metrics have on relative fund performance. Previous studies have ignored alpha metrics to rank funds, which are commonly employed by investors. The authors’ study performs an analysis for three different markets considering the two main developed ones (the American and European equity markets), as well as the emerging one, largely ignored until now.

In the contemporary period, numerous businesses undergo significant adjustments, such as evaluating critical components of the corporate operations and relying on technology to keep operations running while conforming to an ever-changing set of norms and new tactics. The present study aims to (1) explore the relationship between Industry 4.0 (I4.0) tools and their impact on organizational performance and (2) find evidence supporting the moderating role of remote working and organizational agility (OA) in enhancing organizational performance.

The study employed the quantitative research method, and the data were collected from individuals working in different Asian IT firms using the previously established questionnaire. The data were examined using SPSS v22. Different statistical tests have been performed to find the relationship among constructs.

This study uncovers that I4.0 tools impact organizational performance, especially in the IT sector, with a particular emphasis on the moderating influence of remote work and OA. I4.0 tools encompass pivotal components such as artificial intelligence (AI), big data (BD), cloud computing (CC) and Internet of Things (IoT) indeed augment organizational performance. It can be referenced that I4.0 tools play the role of a driving force that equips organizations with the knowledge to augment their performance.

Companies should encourage remote work and use I4.0 technology to support and manage it. Enabling people to work from any location, lowering the requirement for physical infrastructure and enabling a more flexible and responsive organizational structure can increase OA. In conclusion, firms in Asia may increase the performance and agility using I4.0 technology. Organizations may innovate by putting money into these technologies, encouraging remote work and creating an innovative culture.

In this dynamic and technologically advanced environment, every industry is forced to look for latest tools, i.e. I4.0, tools to augment the performance. It has been concluded that I4.0 tools are “better practices” for boosting organizational performance; hence, the findings benefit firms working in the IT sector. The verdicts of this research can assist organizations in making decisions regarding the implementation of I4.0 tools.

To the best of the authors' knowledge, no specific study could be found in which the relationship among these constructs had been investigated earlier in the IT sector. This research work acts as value addition to the literature as it illustrates technological advancements may increase organizational performance, especially in Asia. This research work adds to the body of knowledge by amplifying the effect of latest technologies on organizational performance, via remote work and OA.