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This paper aims to examine and compare the dividend ratios’ statistical and economic ability to predict the equity premium in the UK and US markets and two US sub-indices (S&P 500 Growth and S&P 500 Value).

In this paper, the authors use the linear regression models to examine the dividend ratios’ statistical ability to predict the equity premium. The in-sample and out-of-sample approaches, including Diebold and Mariano (1995) statistics, and Goyal and Welch’s (2003) graphical approach, are used. Also, the mean-variance analysis is used to test the economic significance.

The paper findings indicate that the dividend ratios have in-sample and out-of-sample predictive abilities in both UK and US markets and both US sub-indices. However, the results show that the dividend ratios have a less impressive predictive ability in the US market compared to the UK market and less in the US value index than the US growth index. This could indicate that there is no relation between the number of companies that distribute dividends in each index and the informativeness of dividends ratios. Furthermore, the tests show the dividend ratios’ predictive ability departure during particular periods and in some indices.

Results and implications of this research are exclusively applied to the US and UK markets. These results can also be applied with caution to other markets, taking into consideration the distinctive characteristics of these markets.

Results revealed in this paper imply that the investors in any of the indices may experience economic gain by adopting a dynamic trading strategy using the information content of the dividend ratios prediction models instead of the benchmark model, which is the prevailing simple moving average model.

This paper adds value through testing the prediction models’ economic significance in two well-developed markets, in addition to exploring the relationship between the number of companies distributing cash dividends and the dividends ratio prediction ability. Unlike most of the previous studies in which dividend ratios’ prediction ability is attributed to the number of companies that distribute dividends in the market, this paper denied this interpretation by studying two S&P 500 sub-indices. To the best of the authors’ knowledge, this is the first study to test the prediction models’ ability for these sub-indices.

The purpose of this paper is to analyze the hedging capacity of Bitcoin in relation to the S&P 500 index during the COVID-19 pandemic.

In order to investigate the hedging features of Bitcoin in relation to the S&P 500 index during the COVID-19 pandemic, the authors use the Granger causality applied on a daily sample of observations ranging from January 1st, 2019 to December 31st, 2020. As robustness checks, the authors use autoregressive models to test the validity of the findings.

Using time series of daily data from 1st January 2019 to 31st December 2020, the results show that Bitcoin is not considered as a safe haven because it moves at the same pace as the S&P 500. As a robustness check, the authors use the exponential GARCH model and confirm our previous findings. Overall, the study contributes to the debate on both COVID-19's impact on financial systems and the hypothesis of Bitcoin being a safe haven during extreme global crises.

The study contributes to the debate on both COVID-19's impact on financial systems and the hypothesis of Bitcoin being a safe haven during extreme global crises.

The purpose of this study is to investigate the value investors place on S&P index additions relative to uncertainty surrounding the firm and the market. Investors look for reassuring signals or tell-tale signs around uncertainty.

Variation in the market response to announcements of S&P additions to the 400, 500 and 600 indices is examined against measures of risk factors. Internal risk factors include firm size relative to the index, total firm risk and liquidity, and whether the firm is a brand new index entrant. External risk factors related to market uncertainty are measured by the Chicago Board of Exchange volatility index.

Firms with lower market capitalization relative to the index, higher total risk, lower trading volume and first-time entrants to any S&P index elicit a positive market reaction compared to firms with less pricing uncertainty. In times of increased market uncertainty, investors tend to place more value on signals from respected institutions such as S&P, and riskier firms benefit more from inclusion in the S&P index. Overall, this study finds that the market overreaction is explained by the degree of uncertainty surrounding the added firms, as well as by the degree of market uncertainty at the time of the announcement.

The findings of this study suggest that investors interpret the prospect of S&P index addition as an opportunity for firms to reduce uncertainty surrounding them, and thus partially hedge their exposure to market uncertainty by joining an index tracked by dozens of index funds. The value of such a hedging strategy rises for riskier firms during market turbulence.

The purpose of this paper is to investigate the initial public offerings (IPOs) of the firms that are eventually included in one of the S&P 400, the S&P 500, or the S&P 600 Indices. Do these firms have very different IPO features than the rest of the IPOs?

The control sample is formed of IPOs that are not included in the corresponding index, and the IPOs that end up in each S&P index are compared to this control sample. Logistic regressions are utilized to estimate the odds of inclusion into one of these indices.

The author finds that the IPO features, such as underpricing, offer price, underwriter's reputation, venture capital presence, and so on, are found to be substantially different for the index samples. The index firms are found to be “superstars” that deliver extremely high long‐run returns between their IPO date and their index inclusion date. The above results suggest that the quality of index firms has a persistent component to it that can be detected even during the IPO process. After estimating the determinants of the index inclusion, the author discovers that factors implying lower asymmetric information about firm's business (such as, the firm being a spinoff, or being certified by a venture capitalist or a prestigious underwriter, etc.) increase its odds of inclusion.

The paper proposes and tests two new hypotheses related to inclusion into an S&P index. Discoveries made in this paper can help someone recognize which IPOs could become “superstars” that end up in an S&P index.

The chapter reviews and extends the theory of exact and superlative index numbers. Exact index numbers are empirical index number formula that are equal to an underlying theoretical index, provided that the consumer has preferences that can be represented by certain functional forms. These exact indexes can be used to measure changes in a consumer's cost of living or welfare. Two cases are considered: the case of homothetic preferences and the case of nonhomothetic preferences. In the homothetic case, exact index numbers are obtained for square root quadratic preferences, quadratic mean of order r preferences, and normalized quadratic preferences. In the nonhomothetic case, exact indexes are obtained for various translog preferences.

With the development of Web2.0 and publishing digitalization, traditional libraries and evaluation citation system can no longer indicate academic paper influence validly. Therefore, it is necessary to construct smart library and find the evaluation effect of Internet metrics-Usage.

This study puts forward four indexes of scholars’ evaluation based on Usage (total Usage (U), average Usage rate (U/N), h_u-index and p_u-index), which refer to citation indexes, takes the 35 high-output scholars in the field of library and information science in the WoS database as examples, analyzes performance of different scholars evaluation indexes based on Usage and compares the differences and correlations between “citation indicators” and “usage indicators.”

This study results show that p_u-index is the strongest index to evaluate scholars. Second, there is a high correlation and strong mechanism based on time dependence and interactions between Usage and citation. Third, compared to “citation indicators”, the “usage indicators” has a larger numerical value and wider measurement range, which can break the time limitation of citation, and scientifically evaluate young scholars and newly published paper by scholars.

This paper proposes the p_u-index – a relatively superior mathematical model for Usage and provides reference for the scholars’ evaluation policy of the smart library. This model can not only provide fair evaluation conditions for young scientists but also shorten the evaluation effect of the time lag of cited indicators. In addition, the “usage indicators” in this paper are new scientific evaluation indicators generated in the network environment. Applying it to the academic evaluation system will make the research papers widely accepted by the public and will also encourage scientists to follow the development of the Internet age and pursue research with equal emphasis on quantity and quality.

The purpose of this paper is to reveal the limitations of h-index in assessing research performance through citation analysis and suggest two new indexes called prime index (P-index) and value added index (V-index), which are simpler to compute than g-index and more informative. For more serious research performance evaluation, analytic hierarchy process (AHP) methodology is proposed.

The methodology adopted is to compare existing indexes for citation-based research assessment and identify their limitations, particularly the h-index, which is most commonly employed. It gives advantages of g-index over h-index and then proposes P-index which is simpler to compute than g-index but is more powerful in information content than g-index. Another V-index is proposed on a similar philosophy as P-index by considering total number of citations/author. For serious evaluation of finite candidates for awards/recognitions, a seven-criteria-based AHP is proposed. All new approaches have been illustrated by drawing raw data from Google scholar-powered website H-POP.

This paper demonstrates over-hype about use of h-index over g-index. However, it shows that newly proposed P-index is much simpler in computation than g but better than g-index. V-index is a quick way to ascertain the value added by a research scientist in multiple-authored research papers. P-index gives a value 3–4 percent higher than g and it is holistic index as it uses complete data of citations. AHP is a very powerful multi-criteria approach and it also shows g-index to be a more important factor, whereas h-index is the least important but frequently used approach. It is hoped that the findings of this paper will help in rectifying the misplaced emphasis on h-index alone.

The research focus has been to suggest new faster, better methods of research assessment. However, a detailed comparison of all existing approaches with the new approaches will call for testing these over a large number of data sets. Its limitation is that it has tested the approaches on 5 academics for illustrating AHP and 20 researchers for comparing new indexes with some of the existing indexes. All existing indexes are also not covered.

The outcomes of this research may have major practical applications for research assessment of academics/researchers and rectify the imbalance in assessment by reducing over-hype on h-index. For more serious evaluation of research performance of academics, the seven-criteria AHP approach will be more comprehensive and holistic in comparison with a single criterion citation metric. One hopes that the findings of this paper will receive much attention/debate.

Research assessment based on proposed approaches is likely to lead to greater satisfaction among those evaluated and higher confidence in the evaluation criteria.

P- and V-indexes are original. Application of AHP for multi-criteria assessment of research through citation analysis is also a new idea.

This paper investigates the static and dynamic directional return spillovers and dependence among green investments, carbon markets, financial markets and commodity markets from January 2013 to September 2020.

This study employed both the quantile vector autoregression (QVAR) and time-varying parameter VAR (TVP-VAR) technique to examine the magnitude of static and dynamic directional spillovers and dependence of markets.

Results show that the magnitude of connectedness is extremely higher at quantile levels (q = 0.05 and q = 0.95) compared to those in the mean of the conditional distribution. This connotes that connectedness between green bonds and other assets increases with shock size for both negative and positive shocks. This further indicates that return shocks spread at a higher magnitude during extreme market conditions relative to normal periods. Additional analyses show the behavior of return transmission between green bond and other assets is asymmetric.

The findings of this study offer significant implications for portfolio investors, policymakers, regulatory authorities and investment community in terms of carefully assessing the unique characteristics offered by each markets in terms of return spillovers and dependence and diversifying the portfolios.

The study, first, uses a relatively new statistical technique, the QVAR advanced by Ando et al. (2018), to capture upper and lower tails’ quantile price connectedness and directional spillover. Therefore, the results possess adequate power against departure from mean-based conditional connectedness. Second, using a portfolio of green investments, carbon markets, financial markets and commodity markets, the uniqueness of this study lies in the examination of the static and dynamic dependence of the markets examined.

US studies show significant price effects when shares enter or leave an index during index revisions. Studies on other markets generally yield similar results with smaller price reactions. This study aims to examine the price effects resulting from revisions to the Australian S&P/ASX 100 and 300 indices.

The event study methodology is used to examine abnormal price and volume effects around the announcement dates and implementation dates of index revisions.

In contrast with studies on US index changes, this study shows no abnormal returns for additions to or deletions from the S&P/ASX 100 index and only a weak effect for the S&P/ASX 300, which showed a median abnormal return of  + 1.06 per cent on the implementation date for additions and −2.78 per cent for deletions.

These results give a cautionary warning to those who wish to speculate on the changes to index constituents on the Australian market, or other similar markets where the strength of the index effect has not been clearly quantified.

This study adds to the body of knowledge on the index effect by providing Australian evidence.