Search results

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.

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.

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.

The purpose of this paper is to directly examine the information hypothesis of S&P 500 index inclusion announcements by investigating the degree to which information beyond Standard & Poor's eight stated criteria enters the inclusion decision.

Isolating a sample of S&P 500 additions and their eligible candidates during 1987‐2004, this paper employs logistic analysis that identifies factors ex post beyond the stated criteria that help distinguish the type of information that influences the final selection decision and that is arguably priced at the inclusion announcements.

The evidence indicates that, when choosing among new S&P 500 candidates, the S&P's committee relies primarily on publicly available information related to enterprise risk and historical performance. Material, private insight into future value‐relevant information plays at most a small part in the selection.

The results suggest that index additions convey limited new information about added firms. Studies analysing index additions should start with the presumption that index inclusion announcements are information‐free events, and focus on the consequences of index inclusions such as liquidity, awareness or arbitrage risk, in their relation to index premia.

The results indicate that the previous evidence supporting the information hypothesis using the S&P 500 inclusions is not compelling.

Many papers have argued that there are long‐run downward‐sloping demand curves (LRDDC) for stocks. The purpose of this paper is to analyze this hypothesis using a new, unique, and ostensibly information‐free event: the re‐weighting of the Standard & Poor (S&P) 500 index from market based to free‐float based, which involves a significant shift in supply that, under the LRDDC, should result in significant and permanent price movements.

Event study methodology is used to examine abnormal returns and trading activity around the free‐float weight implementation dates for S&P 500 firms with various investable weight factors.

As a result of S&P 500 index re‐weighting, affected stocks experience statistically significant excess returns of −1.54 percent during the event week. This return is reversed during the following 30 days as trading volume returns to normal levels. These results are contrary to previous studies that analyze ostensibly informational events and/or different exchanges.

Results of this study indicate that arbitrage appears to be effective in eliminating a long‐term mispricing, which challenges the validity of the LRDDC hypothesis.

This study contributes to the body of literature on the S&P 500 index firms by providing supporting evidence for the price‐pressure hypothesis.

The purpose of this paper is to explore the relationship between multivariate process capability indices and loss functions for both nominal‐the‐best and smaller‐the‐better cases, so the likelihood and consequences resulting from the nonconforming of a manufacturing process or an environmental system can be evaluated simultaneously.

In this paper, the authors present a new approach of correlated risk assessment by linking the multiple process capability indices and loss functions, in which the multivariate process capability indices and multivariate loss functions describe the likelihood and consequences as a result of nonconformities in multivariate manufacturing or environmental system, respectively. Then, the associated relationship equations are developed using multivariate methods. Moreover, a step‐by‐step procedure is provided to facilitate the implementation of the correlated risk assessment.

Given the multivariate process capability indices, the authors show that the expected loss can be estimated by developed relationship equations and two numerical examples are also given, to demonstrate how the correlated manufacturing and environmental risks can be properly assessed by linking the multivariate process capability indices and multivariate loss function.

The risk information of likelihood and expected loss, classified in the four planning zones of a strategic planning matrix, provides practising managers and engineers with a decision‐making tool for prioritizing their quality improvement projects when conducting risk assessment for any multivariate process or environmental system.

Once the existing quality/environmental problems and their Key Performance Indicators are identified, one may conduct risk assessment by applying the relationship equations to evaluate the impact of correlated risk on manufacturing processes or multiple environmental emissions inside company; this can lead to the direction of continuous improvement for any industry.

In the past few years, several capability indices have been developed for evaluating the performance of multivariate manufacturing processes under the normality assumption. However, this assumption may not be true in most practical situations. Thus, the purpose of this paper is to develop new capability indices for evaluating the performance of multivariate processes subject to non-normal distributions.

In this paper, the authors propose three non-normal multivariate process capability indices (MPCIs) RNMC _p , RNMC _pm and RNMC _pu by relieving the normality assumption. Using the two normal MPCIs proposed by Pan and Lee, a weighted standard deviation method (WSD) is used to modify the NMC _p and NMC _pm indices for the-nominal-the-best case. Then the WSD method is applied to modify the multivariate ND index established by Niverthi and Dey for the-smaller-the-better case.

A simulation study compares the performance of the various multivariate indices. Simulation results show that the actual non-conforming rates can be correctly reflected by the proposed capability indices. The numerical example further demonstrates that the actual quality performance of a non-normal multivariate process can properly reflected by the proposed capability indices.

Process capability index is an important SPC tool for measuring the process performance. If the non-normal process data are mistreated as a normal one, it will result in an improper decision and thereby lead to an unnecessary quality loss. The new indices can provide practicing managers and engineers with a better decision-making tool for correctly measuring the performance for any multivariate process or environmental system.

Once the existing multivariate quality/environmental problems and their Key Performance Indicators are identified, one may apply the new capability indices to evaluate the performance of various multivariate processes subject to non-normal distributions.