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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.

This study aims to provide one of the first empirical investigations of market efficiency for developed markets during the recent global financial crisis.

Using the Morgan Stanley Capital International (MSCI) country indices as proxies for national stock markets, the study conducts a battery of econometric tests in assessing weak-form market efficiency for the developed markets.

The inferential outcomes are consistent among the different tests. Specifically, the study finds that the majority of developed markets are weak-form efficient while the USA is the sole equity market to be commonly diagnosed as weak-form inefficient across the different tests when using full period data spanning the January 2008-November 2011 period. However, when basing the analysis on one-year subsamples over the identical time period, this study fails to reject weak-form market efficiency for all of the developed markets and presents evidence consistent with the Adaptive Market Hypothesis as described by Urquhart and Hudson (2013). When applying technical analysis for the case of the USA over the full study period, the results indicate that the return predictabilities can be exploited for some horizon of variable length moving average (VMA) trading rules.

This study provides one of the first empirical investigations of market efficiency for developed markets during the recent global financial crisis using an extended set of econometric tests. The study contributes to the existing body of empirical research that formally assesses the impact of a financial crisis on stock market efficiency and underlines the significance and relevance of examining market efficiency through subsample analysis.

This study aims to provide a review of corporate governance in China because effective and strong corporate governance is necessary for the efficient functioning and long-term sustainability of financial markets and corporations.

The author provides a literature review of corporate governance in China through themes such as the concentration of state ownership, the degree of independence among board directors, insider trading, quality of financial disclosures and the maturity of capital markets.

The author reviews empirical work surrounding key corporate governance variables and identifies avenues for future research. The author finds that corporate governance mechanisms exhibit implications for firm performance, fraud, capital retention, financial constraints, institutional investors, auditing and the quality of financial disclosures. In addition, the author reviews evidence documenting the importance of independent board directors in regulation and ethical conduct.

The literature review contributes to the growing literature on responsible corporate governance and provides further understanding of the importance of business ethics for promoting the integrity and long-term sustainability of China’s capital markets and corporations and to ensure that company assets are used efficiently and productively in the best interests of investors and other stakeholders. This study offers insights to policy-makers interested in enhancing the quality of corporate governance within their nation. In addition, it provides a macro-level perspective for executives of multinational firms to consider if they are considering making a direct investment in China.

This study aims to investigate the cross-sectional and time-series dynamics of realized Certified Emission Reduction (CER) credits issued and the role of investments for a seminal sample of China’s Clean Development Mechanism (CDM) projects specializing in the wind sector.

The study investigates the dynamics of realized CER credits issued and the role of investments using traditional cross-sectional and time-series regression analysis.

The study results find that the level of investment per megawatt (MW) of power generation is an important predictor for the expected number of realized CER credits issued in the cross-section of China’s wind CDM projects. Additionally, the study finds evidence of time trends and seasonality when examining the time series of realized monthly CER credits: CER credits issued are lower in the summer and higher in the winter.

The study results highlight the importance of financing CDM projects and suggest guidelines in which investors are able to better assess how much to invest based on the anticipated CER credits in the Project Design Document. Additionally, the results suggest opportunities for the CDM Executive Board surrounding the Project Design Document and the anticipated CER credits contained therein. The present study contributes to the literature on strategic tools for addressing climate change and offer insights that narrow the gap between empirical finance and sustainable business practice in the context of CDM projects.

The study systematically investigates persistence in performance for simulated trading among non-professional traders in the futures market.

In this study, the authors employ a novel data set from the Chicago Mercantile Exchange (CME) Group's Trading Challenges for years 2014 through 2018 and expand upon the empirical methodology of Malkiel (1995) through improved interval estimations in testing for persistence in performance. The authors implement Fama-MacBeth style regressions to understand the degree of persistence in performance and the extent to which non-professionals extrapolate from prior returns. They adjust returns for risk through the Fama and French (2015) five-factor model in understanding whether the sample of non-professionals is able to produce excess returns after expenses and whether there is evidence of excess gross to cover expenses.

The empirical analysis suggests strong evidence for performance persistence among non-professionals participating in the Preliminary Rounds. In the Championship Rounds, the authors find that the persistence effect becomes stronger in economic and statistical significance after accounting for expenses. The results suggest that competition and transaction costs help to distinguish between winners and losers. When conducting Fama-MacBeth style regressions, the authors present evidence that strongly supports the persistence effect and over-extrapolation. While the results of the multi-factor model analysis suggest that, after adjusting for risk, most teams are experiencing negative excess returns prior to expenses, the authors also uncover evidence of teams earning returns sufficient to cover their expenses.

The authors bridge the gap between the literature on performance persistence and the emerging literature on non-professionals in the financial markets. Data from the CME Group’s Trading Challenge provide a rich source in studying the beliefs of non-professionals, and this study is helpful for understanding how beliefs, operationalized in simulated trades, perform over short time horizons, thereby providing insights into the behavioral dynamics of the financial markets. The results provide new empirical evidence for performance persistence among non-professionals.

This article aims to relate investments in human capital to the United Nations Sustainable Development Goals (UN SDGs), and examine the spending levels necessary to achieve high performance in related SDG sectors for Azerbaijan.

Employing data from the World Bank, the empirical approach undertaken in this study relies on peer analysis by examining spending levels for nations exhibiting similar income levels and geographical proximity to Azerbaijan.

This study estimates that total spending in education would need to increase by 0.4 percentage points of GDP by 2030, while total spending in health would need to increase by 5.9 percentage points of GDP by 2030 for Azerbaijan.

This study contributes to the literature by conducting an empirical analysis in which other nations can emulate in measuring their relative progress on human capital investments and related UN SDGs.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2023-0137

The purpose of this paper is to examine the time-series dynamics of entrepreneurship rates for different race classifications based on household characteristics over the 1996 through 2013 period.

Using microdata from the Kauffman Foundation, this study investigates the roles of unemployment, homeownership, income, immigration, education, age, gender and marital status in relation to entrepreneurship rates for different race classifications through ridge regression analysis.

Results suggest that the time-series variation in entrepreneurship rates for different race classifications are variable-dependent, moreover, the economic and statistical significance of the candidate explanatory variables are sensitive to the time period under analysis. Unemployment, homeownership, education, age and marital status are significant variables for whites while unemployment, income, immigration and gender variables are significant for blacks. For the case of Native Americans and Asians, the candidate explanatory variables do not explain the time-series variation in entrepreneurship rates for the sample periods in this study.

This study exhibits implications for public policy in helping to promote entrepreneurship at the individual level and help stimulate entrepreneurial activity as a mechanism for promoting economic growth.

The findings suggest the importance of examining entrepreneurship rates across time based on race classifications. This study highlights the importance of conducting ridge regression analysis for different sub-periods in time when assessing entrepreneurship rates.

The study aims to provide an empirical investigation of social enterprises in the context of experiential learning. Specifically, the study aims to investigate the interplay between faith-based principles and the processes of opportunity recognition and exploitation through an in-depth, qualitative study of social enterprises offered through the Global Social Benefit Institute (GSBI).

In this study, student experiences with social entrepreneurship are examined and their subsequent reflections are analyzed. Applying the Gioia methodology to the sample of student reflection data, this study enriches the growing literature on sense-making by looking closely at how student entrepreneurs engage their own faith-based education in helping their teams, beneficiaries and stakeholders “make sense” of a social change opportunity.

This study finds evidence of variability in the elaboration of the faith-based principles when sampling on the social needs of affection, behavioral confirmation and status.

The results suggest a role for faith-based sense-making when confronting the realities of social change opportunities.

The purpose of this paper is to investigate green exchange‐traded funds (ETFs) and propose a market‐wide proxy for green returns and a green volatility factor.

Identifying a unique sample of green funds, this paper investigates the time‐series behavior of returns for these investment vehicles and their associated conditional volatility dynamics via GARCH methodology. In this study, green ETFs are defined as index funds replicating market indices that invest in stocks exhibiting positive environmental, social, and governance characteristics.

Cumulative market‐wide green returns are found to be positive from inception year 2005 through 2008. Estimating a t‐GARCH (1,1) specification, the author finds strong evidence in favor of volatility persistence for the 15 green ETFs identified in this study. Additionally, the results suggest that a 1 percent volatility‐based value‐at‐risk forecast ranges from $24,150 through $26,000 on a daily basis. Finally, the empirical evidence provides support for weak‐form market efficiency when examining the green universe of stocks.

Green ETFs are relatively recent financial instruments and exhibit important implications for volatility timing strategies and the cross‐section of green stock returns.

Knowledge of green price behavior is important in constructing optimal hedging and risk management strategies.

Green ETFs provide a natural channel for sustainable investing.

Green ETFs exhibit the advantage of including many companies that have undertaken positive measures towards the global environment in their respective business models. This paper provides the first investigation of green ETFs.

The purpose of this paper is to investigate the return performance of different investment strategies in the hedge fund sector, with a particular emphasis on the recent US financial crisis of 2007‐2010. Additionally, the paper aims to investigate the comovement of hedge fund index returns.

The paper identifies broad hedge fund investment strategies using data from the Dow Jones Credit Suisse Hedge Fund Database. It examines the return comovement using the cross‐sectional volatility, covariance, and correlation metrics proposed in Adrian (2007). In addition, the paper examines whether correlations and covariance are important determinants of future volatility via traditional time‐series regressions.

The paper finds that the majority of the broad hedge fund investment strategies incurred record level losses and gains during the 2007‐2010 period. In addition, it finds that the crisis period was preceded by high correlations, attributed primarily to a rise in cross‐sectional hedge fund covariances. However, during the crisis period, a decrease in average correlations, stemming from an increase in hedge fund volatility, is documented. The time‐series regressions are supportive of a strong relationship between cross‐sectional covariances and subsequent volatility, suggesting that systemic risk occurs in the hedge fund sector when returns move significantly in dollar terms.

This study is one of the first investigations that focus on the comovement and volatility of hedge fund index returns during the US financial crisis of 2007‐2010.