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The purpose of this paper is to increase the accuracy of the efficient portfolios frontier and the capital market line using the Riskiness Index.

This paper will develop the mean-riskiness model for portfolio selection using the Riskiness Index.

This paper’s main result is establishing a mean-riskiness efficient set of portfolios. In addition, the paper presents two applications for the mean-riskiness portfolio management method: one that is based on the multi-normal distribution (which is identical to the MV model optimal portfolio) and one that is based on the multi-normal inverse Gaussian distribution (which increases the portfolio’s accuracy, as it includes the a-symmetry and tail-heaviness features in addition to the scale and diversification features of the MV model).

The Riskiness Index is not a coherent measurement of financial risk, and the mean-riskiness model application is based on a high-order approximation to the portfolio’s rate of return distribution.

The mean-riskiness model increases portfolio management accuracy using the Riskiness Index. As the approximation order increases, the portfolio’s accuracy increases as well. This result can lead to a more efficient asset allocation in the capital markets.

The purpose of this study is to estimate the convergence order of the Aumann–Serrano Riskiness Index.

This study uses the equivalent relation between the Aumann–Serrano Riskiness Index and the moment generating function and aggregately compares between each two statistical moments for statistical significance. Thus, this study enables to find the convergence order of the index to its stable value.

This study finds that the first-best estimation of the Aumann–Serrano Riskiness Index is reached in no less than its seventh statistical moment. However, this study also finds that its second-best approximation could be achieved with its second statistical moment.

The implications of this research support the standard deviation as a statistically sufficient approximation of Aumann–Serrano Riskiness Index, thus strengthening the CAPM methodology for asset pricing in the financial markets.

This research sheds a new light, both in theory and in practice, on understanding of the risk’s structure, as it may improve accuracy of asset pricing.

The literature has demonstrated that lump-sum (LS) outperforms dollar-cost averaging (DCA) in uptrend markets while DCA outperforms LS only when the asset price is mean-reverted or downtrend. To bridge the gap in the literature, this study aims to use both Sharpe ratio (SR) and economic performance measure (EPM) to compare the performance of DCA and LS under both accumulative and disaccumulative approaches when the asset price is simulated to be uptrend.

This study uses both disaccumulative and accumulative approaches to compare DCA with LS and uses both SR and EPM to evaluate their performance when the asset price is simulated to be uptrend. Instead of using the annualized returns that are commonly used by other DCA studies, we compute the holding-period returns in the comparison in this paper.

The simulation shows that no matter which approach is used, DCA outperforms LS in nearly all the cases in the less uptrend markets while DCA still performs better than LS in many cases of the uptrend markets, especially when the market is more volatile and investment horizon is long, regardless which approach the authors used. The authors also find more evidence supporting DCA over LS by using EPM, which is more suitable in the analysis because the returns generated by DCA are positive skewed and flat-tailed that are ignored when SR is used.

The authors conclude that DCA is a better trading strategy than LS for investment even in the uptrend market, especially on high risky assets.

Investors could consider choosing DCA instead of LS as their trading strategy, especially when they prefer long term investment and investing in high-risk assets.

Fund managers could consider recommending DCA to their customers, especially when they prefer long term investment and investing in high-risk assets.

This is the own study and, as far as the authors know, this is the first study in the literature uses both SR and EPM to compare the performance of DCA and LS under both accumulative and disaccumulative approaches when the asset price is simulated to be uptrend.

This paper aims to empirically examine the performance of the high-ESG (environment, social and governance) portfolio vis-à-vis the low-ESG portfolio at the Indian stock market before and during the Covid19 pandemic.

The absolute rate of return and several risk-adjusted performance measures, for instance, Sharpe ratio, Modigliani–Modigliani measure, Treynor ratio, Jensen’s alpha, information ratio, Fama’s decomposition measure and Fama and French’s three-factor model, have been used in this study along with the t-test.

All three indices (CARBONEX, GREENEX and BSE 500) had better returns during Covid19 period as compared to the pre-Covid19 period. However, these returns were not statistically significant. During Covid19, the risk of the indices also rose, but they provided better returns for the additional risk taken. Finally, it is concluded that the performance of high-ESG and low-ESG stock portfolios did not differ significantly in both periods.

The study is relevant to individual and institutional investors, financial advisors, portfolio managers, corporations, policymakers, market regulators and society at large.

This study emphasized the need to expand the role of ESG investment in India for the benefit of people, communities and society as a whole.

This research is the first of its kind, to the best of the authors’ knowledge, that compares the performance of a high-ESG portfolio with a low-ESG portfolio both before and during the Covid19, particularly in the Indian context.

Over the last ten years there has been an enormous growth in research into share price behaviour. Most of the studies have been undertaken in the USA although there has recently been a growing interest in the British and European equity markets. This growth in research has been occasioned by the ignorance of the working of capital markets and by the advent of computer data banks which have facilitated large scale empirical investigations. Both completed research and future programmes represent large commitments in terms of manpower and computer time and thus there is a definite need to assess the potential benefits from such studies. This paper will point out some of the advantages that may accrue from a continuing study of share price behaviour.

This study examines co-movements between global Islamic index and heterogeneous rated/maturity sukuk. It tests the impact of financial uncertainty on these movements.

Firstly, we conduct a bivariate wavelet analysis to assess the co-movements between stocks and sukuk indexes. Secondly, we use General dynamic factor model and stochastic volatility to construct financial uncertainty index from Islamic stock indexes. Finally, we run regression analysis to determine the impact of uncertainty on the obtained correlations.

Our results suggest the absence of flight to quality phenomenon since correlations are positive especially at a short investment horizon. There is evidence of contagion phenomena across assets. Financial uncertainty may be considered as a determinant of stock-sukuk co-movements. Our results show that a rise in financial uncertainty induces correlation to move in the opposite direction in the short term, (exception for correlation with AA-Rated sukuk). However, the sign of stock market uncertainty becomes positive in the long term, which leads sukuk and stocks to move in the same direction (exception for 1–3 Year and AA Rated sukuk).

Investors may combine sukuk with 1–3 Year maturity and AA Rated when considering long holding periods. Further, all sukuk categories provide diversification benefit in time high financial uncertainty expectation for AA Rated sukuk when considering short holding periods.

To the best of our best knowledge, our study is the first investigation of the impact of financial uncertainty on Stock-sukuk co-movements and provides recommendation considering sukuk with different characteristics.

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.

This paper examines whether target returns act as specific goals that impact risk-taking when individuals make investment decisions.

Using an experimental setting, the authors assign either a low or a high target return to participants and ask them to make independent investment decisions as the risk-free rate fluctuates around their target return and, for some of them, becomes negative.

Building on cumulative prospect theory, the authors find that the prevailing reference point of participants is the target return, regardless of the level of the risk-free rate. This result still holds even when the risk-free rate is negative, suggesting that (1) the target return drives risk-taking more than does a zero-threshold and (2) negative rates are limited as a tool to stimulate appetites for risk. In a follow-up study, the authors show that these conclusions remain valid when the target return is endogenously determined.

The authors' original approach, which pioneers the use of target returns in both the positive and negative interest rate contexts, provides insightful results about the “reach for yield” among regular people.

This paper aims to give a brief review on behavioral economics and behavioral finance and discusses some of the previous research on agents' utility functions, applicable risk measures, diversification strategies and portfolio optimization.

The authors also cover related disciplines such as trading rules, contagion and various econometric aspects.

While scholars could first develop theoretical models in behavioral economics and behavioral finance, they subsequently may develop corresponding statistical and econometric models, this finally includes simulation studies to examine whether the estimators or statistics have good power and size. This all helps us to better understand financial and economic decision-making from a descriptive standpoint.

The research paper is original.