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PurposeThe beta coefficient used for the cost of equity calculation is at the heart of the valuation process. This study conducts comparative analyses of the classical capital asset pricing model (CAPM) and downside CAPM risk parameters to gain further insight into which risk parameter leads to better performing risk measures at explaining stock returns.Design/methodology/approachThe study conducts a comparative analysis of 16 risk measures at explaining the stock returns of 4531 companies of 20 developed and 25 emerging market index for 2000–2018. The analyses are conducted using both the global and local indices and both USD and local currency returns. Calculated risk measures are analyzed in a panel data setup using a univariate model. Results are investigated in country-specific and model-specific subsets.FindingsThe results show that (1) downside betas are better than CAPM betas at explaining the stock returns, (2) both risk measure groups perform better for emerging markets, (3) global downside beta model performs better than global beta model, implying the existence of the contagion effect, (4) high significance levels of total risk and unsystematic risk measures further support the shortfall of CAPM betas and (5) higher correlation of markets after negative shocks such as pandemics puts global CAPM based downside beta to a more reliable position.Research limitations/implicationsThe data are limited to the index securities as beta could be time varying.Practical implicationsResults overall provide insight into the cost of equity calculation and emerging market assets valuation.Originality/valueThe framework and methodology enable us to compare and contrast CAPM and downside-CAPM risk measures at the firm level, at the global/local level and in terms of the level of market development.

Purpose Considering the different motivation for the creation of each of these cryptocurrencies, the purpose of this paper is to examine whether there is a dominant external factor in the cryptocurrency world. Using a novel two-step time and frequency independent methodology, the authors examine a large scope of cryptocurrencies and external factors within the same period, and analytical framework. Design/methodology/approach The examined cryptocurrencies are Bitcoin, Ethereum, Ripple, Litecoin, Monero and Dash. In total, 18 external factors from 5 factor families are selected based on the mining motivation of these cryptocurrencies. The study first examines discrete wavelet transform-based (WTB) correlations, reduce the dimension and focuson relevant pairs. Selected pairs are further examined by wavelet coherence to capture the intermittent nature of the relationships allowing the most needed “Flexibility of frequency and time domains”. Findings Each coin appears to operate as a unique character with the exception of Bitcoin and Litecoin. There is no prominent external driver. The cryptocurrency market is not a clear substitute for a specific factor or market. Two-step WTB filtered wavelet coherence analysis help us to analyze a large number of factor without the loss of focus. The co-movements within the cryptocurrencies spillover from Ethereum to altcoins and later to Bitcoin. Originality/value The study presents one of the first examples of two-step WTB filtered wavelet coherence analysis. The methodology suggests an approach for simultaneous examination of large number of variables. The scope of the study provides a rather holistic view of the co-movements of external factors and major cryptocurrencies.

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indices. 55 Type A, and 77 Type B Funds were included in the analysis. In order to test whether four different indices make similar ranking, Spearman rank correlation analysis was utilized. Secondly, Wilcoxon Signed-Rank test was applied to test the significance of the differences in Sharpe indices of alternative investment instruments included in the analysis.Analysis revealed that different criteria rank the portfolios similarly. But more importantly it was found that, the best investment over the entire analysis period as well as in the sub-periods was T-Bills, which was followed by ISE-100 index, Type B Funds, and Type A funds respectively. This finding makes the merits of the efforts spent by funds managers, over the analysis period, to outperform the market highly questionable.