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Finance theory such as Merton’s ICAPM suggests that there should be a positive relationship between the expected return and risk. Empirical evidence on this relationship, however, is far from conclusive. Building on the recent econometric research on this topic such as Lundblad (2007) and Hedegaard and Hodrick (2016), we estimate the risk-return relation implied in the ICAPM using a long sample (1962~2016) of daily, weekly, and monthly excess stock returns in Korea. More specifically, we estimate various volatility models including GARCH-M using the overlapping data inference (ODIN) method suggested by Hedegaard and Hodrick (2016), as well as the traditional maximum likelihood estimation methodology. For the full sample period, we fail to find a positive risk-return relationship that is significant and robust. For the subsample period from 1998 to 2016, however, we find a significantly positive risk-return relation for GARCH-M model regardless of return intervals and estimation methods. This result is also robust to using other specifications such as EGARCH-M which includes the leverage effect of the variance process and EGARCH-M-GED whose conditional distribution has fatter tails. Our findings suggest that there is indeed a positive relationship between the expected return and risk in the Korean stock market, at least for the period after 1998.

This paper aims to examine the relationship between cash holdings (CH) and expected equity return in a sample of firms of Pacific alliance countries.

This paper constructed a panel of Pacific alliance firms for the period ranging from 2010 to 2016. This paper estimated different specification models using multivariate regression, and the statistical technique used to validate the hypothesis was panel data.

Results showed that there is a positive relationship between CH and expected equity return (r). The relationship between CH and systematic risk (ß) was estimated and this paper found a positive and statistically significant association. Findings suggest that corporate liquidity contains underlying information that contributes to explain the expected equity return, which, if ignored, can produce quite misleading results.

The results of this study have both academic and practical implications. First, the findings of the research contribute to a better understanding of the asset pricing models in emerging countries. On the other hand, the results obtained in this study can serve shareholders to make better estimations of the expected equity return, so investors can improve the risk-return trade-off due to the model allow a better estimation of the risk-return relation.

This study investigates an under-researched yet fundamental question of how a developed country multinational enterprises (DMNE) perceives and manages political risks when undertaking infrastructure projects in the emerging markets (EMs).

The authors use an abduction-based qualitative research approach to analyze six international project operations of a multinational enterprise originating from Finland in five EMs.

The findings suggest that the overall nature of political risks in EMs is not the same, except few political risk factors that are visible in most EMs. Consequently, the applied risk management mechanisms vary between EMs, except with few common mechanisms. The authors develop an integrative analytical framework of political risk management based on the findings.

This paper is one of the first studies to identify political risk factors for western MNEs while undertaking international project operations and link them to reduction mechanisms used by them. The authors go beyond the notion of risk being conceptualized at a general level and evaluate 20 specific political risk factors referred to in extant literature. The authors further link these political risk factors with both social exchange and transaction cost theories conceptually as well as empirically. Finally, the authors develop a relatively comprehensive analytical framework of political risk management based on the case projects' findings that combine several strands of literature, including the social exchange theory, transaction cost theory, international market entry, project management and finance literature streams.

Are financially illiterate individuals all the same? This study aims to answer this question. Specifically, the authors investigate whether people answering incorrectly and “do not know” to the big five questions about financial knowledge (FK), all identified by previous literature as financially illiterate, are two sides of the same coin, or rather individuals with different socio-economic and demographic characteristics, and whether this leads to different levels of risk of falling victim to financial fraud.

Using a large and representative sample of Italian adults, the authors run both ordered probit and probit regressions to test the determinants of financially illiterate individuals, distinguishing between those answering FK questions incorrectly and those answering “do not know”. The authors also measure the probability of falling victim to financial fraud for the two groups. To check the robustness of our results, the authors run a multinomial regression, a structural equation model and an instrumental variable regression model.

The authors demonstrate that the socio-demographic and socio-economic characteristics of individuals selecting incorrect responses to FK questions are different from those of individuals selecting the “do not know” option. Moreover, the results show that the former are more likely to be victims of financial frauds.

The “one-size-fits-all” approach is not suitable for financial education. It is important to consider socio-demographic and socio-economic characteristics of individuals in order to identify specific targets of education programmes aiming to reduce insecurity and excessive self-confidence as well as to increase objective FK. The study’s findings also identify vulnerable groups to which financial fraud prevention schemes should be targeted.

To date, financial illiteracy has been measured as the sum of incorrect and “do not know” responses given to FK questions. This approach does not allow to observe the socio-demographic and socio-economic differences between people choosing the “do not know” option and those answering incorrectly. The paper aims to overcome this limit by investigating the socio-demographic and socio-economic characteristics of individuals selecting “do not know” and incorrect responses, respectively. The authors also investigate whether the two groups have different probabilities of being victims of financial fraud.

The purpose of this paper is to examine the impact of structural breaks on the conditional variance of carbon emission allowance prices.

The authors employ the symmetric GARCH model, and two asymmetric models, namely the exponential GARCH and the threshold GARCH.

The authors show that the forecast performance of GARCH models improves after accounting for potential structural changes. Importantly, we observe a significant drop in the volatility persistence of emission prices. In addition, the effects of positive and negative shocks on carbon market volatility increase when breaks are taken into account. Overall, the findings reveal that when structural breaks are ignored in the emission price risk, the volatility persistence is overestimated and the news impact is underestimated.

The authors are the first to examine how the conditional variance of carbon emission allowance prices reacts to structural breaks.

The outbreak and the spreading of the COVID-19 pandemic have impacted the global financial sector, including the alternative clean and renewable energy sector. This paper aims to assess the impact of the pandemic, COVID-19 on the stock market indices of the clean energy sector using quantile regression methods.

This study utilized daily data sets on the four major categories of stocks: (1) Morgan Stanley Capital International Global Alternative Energy Index, (2) WilderHill Clean Energy Index, (3) Renewable Energy Industrial Index (RENIXX) and (4) the S&P 500 Global Clean Index. The study adopts a multifactor capital asset pricing model.

Clean and alternative energy stocks are powerful instruments for diversification. However, the impact of the volatility index induced by infectious disease is negative and significant across quantiles.

For investors and policymakers, considering how the uncertainty caused by COVID-19 and the geopolitical index influences renewable energy markets is of great practical importance. For investors, it throws insights into portfolio diversification. For policy makers, it helps to devise strategies to reboot the economy along the lines of the deployment of renewables. This study sheds light on a global green-energy transition and has practical implications for renewable energy resilience in post-pandemic times.

This paper can be considered as a pioneer that explores the nexus between oil prices, interest rates, volatility index, and geopolitical risk upon the stock indices of clean and alternative sources of (renewable) energy in the COVID-19 pandemic situation. The results have important insights into the area of energy and policy decision-making. Additionally, the paper's novelty lies in using the explanatory variables associated with the Covid 19 pandemic.

This study investigates the impact of uncertainty on the mean-variance relationship. We find that the stock market's expected excess return is positively related to the market's conditional variances and implied variance during low uncertainty periods but unrelated or negatively related to conditional variances and implied variance during high uncertainty periods. Our empirical evidence is consistent with investors' attitudes toward uncertainty and risk, firms' fundamentals and leverage effects varying with uncertainty. Additionally, we discover that the negative relationship between returns and contemporaneous innovations of conditional variance and the positive relationship between returns and contemporaneous innovations of implied variance are significant during low uncertainty periods. Furthermore, our results are robust to changing the base assets to mimic the uncertainty factor and removing the effect of investor sentiment.

This paper aims to verify the presence of a management model that confirms or not the one size fits all hypothesis expressed in terms of risk-return. This study will test the existence of stickiness phenomena and discuss the relevance of business model analysis integration with the risk assessment process.

The sample consists of 60 credit institutions operating in Europe for 20 years of observations. This study proposes a classification of banks’ business models (BMs) based on an agglomerative hierarchical clustering algorithm analyzing their performance according to risk and return dimensions. To confirm BM stickiness, the authors verify the tendency and frequency with which a bank migrates to other BMs after exogenous events.

The results show that it is impossible to define a single model that responds to the one size fits all logic, and there is a tendency to adapt the BM to exogenous factors. In this context, there is a propensity for smaller- and medium-sized institutions to change their BM more frequently than larger institutions.

Quantitative metrics seem to be only able to represent partially the intrinsic dynamics of BMs, and to include these metrics, it is necessary to resort to a holistic view of the BM.

This paper provides evidence that BMs’ stickiness indicated in the literature seems to weaken in conjunction with extraordinary events that can undermine institutions’ margins.

The purpose of this paper is to implement a genetic algorithmic geared toward building an optimized investment portfolio exploring data set from stocks of firms listed on the Nigerian exchange market. To provide a research-driven guide toward portfolio business assessment and implementation for optimal risk-return.

The approach was to formulate the portfolio selection problem as a mathematical programming problem to optimize returns of portfolio; calculated by a Sharpe ratio. A genetic algorithm (GA) is then applied to solve the formulated model. The GA lead to an optimized portfolio, suggesting an effective asset allocation to achieve the optimized returns.

The approach enables an investor to take a calculated risk in selecting and investing in an investment portfolio best minimizes the risks and maximizes returns. The investor can make a sound investment decision based on expected returns suggested from the optimal portfolio.

The data used for the GA model building and implementation GA was limited to stock market prices. Thus, portfolio investment that which to combines another capital market instrument was used.

Investment managers can implement this GA method to solve the usual bottleneck in selecting or determining which stock to advise potential investors to invest in, and also advise on which capital sharing ratio to reduce risk and attain optimal portfolio-mix targeted at achieving an optimal return on investment.

The value proposition of this paper is due to its exhaustiveness in considering the very important measures in the selection of an optimal portfolio such as risk, liquidity ratio, returns, diversification and asset allocation.

This study aims to examine the mediating effect of four antecedents of competitive advantage on the linkage of risky strategy to firm performance, measured by revenue dynamics. It considers the roots of competitive advantage to highlight different patterns and foundations of achieving superior performance. It investigates whether pursuing a risky strategy fosters revenue dynamics growth and whether different mediators are included in that relationship.

Path analysis (structural equation modeling) method is used to analyze data from 122 companies of various sizes and industries. All respondents were responsible for executing strategic management processes. The paper used the subjective perspective, which is based on the individual opinion of senior company managers and owners.

The authors find a positive relationship between risky strategy and firm performance, but no evidence of a mediating role of competitive advantage and dynamic growth in this relationship. Competitive advantage should be perceived as a set of integrated factors that can be analyzed from an aggregated perspective. Integrating all antecedents requires a holistic and systematic approach and the development of a particular mindset. Aggregated competitive advantage is related to setting dynamic growth as a priority. However, no relationship between risky strategy and achieving competitive advantage, or between implementing a risky strategy and setting dynamic growth as a priority, is observed, which was assumed to explain the revenue dynamics growth.

Secondary data should be analyzed to explore how risky strategies are manifested, and which managerial decisions are reflected in high-level risk. A multidimensional scale could be developed to check how risk shapes the constructs’ interdependence. Therefore, the dynamic capabilities approach could be further expanded.

This research offers insights into the short-term relationship between risky strategy and revenue dynamics, although competitive advantage does not mediate that relationship. Special attention should be paid to the selected antecedents of competitive advantage, as they influence dynamic growth.

This work provides insights into different antecedents of competitive advantage, which is not necessarily based on making risky decisions, and into factors that facilitate firm performance measured by revenue dynamics.