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This paper aims to investigate the intervalling effect bias in ETFs' systematic risk expressed by beta. The authors' findings reveal the existence of a significant intervalling effect on ETFs' beta obtained by the ordinary least squares method (OLS). Also investigated is the impact of ETFs' capitalization on beta. Results provide evidence that small cap ETFs have greater betas than large cap ETFs. Results also reveal that the OLS beta of all ETFs increases when the return interval is lengthened regardless of capitalization. The impact of ETFs' trading activity on systematic risk is assessed too. Findings give evidence that the OLS betas of the ETFs that trade infrequently are biased downwards while the beta of the frequently traded ETFs is biased upwards. Finally, the paper reveals a strong intervalling effect on ETFs' tracking error.

The authors employ a sample of 40 broad‐based ETFs listed on Nasdaq Stock Exchange to test whether beta estimates change when the return interval measurement changes. Their data cover a maximum period of ten years starting from September 16, 1998 using daily, weekly and monthly return data. The authors estimate beta applying three alternative methods: the market model applied with the OLS method, the Scholes and Williams model (SW beta) and the Dimson model (Dim beta).

Results indicate that the average beta of ETFs derived by the OLS method increases when the return interval increases. The differences among the daily, weekly and monthly OLS betas are statistically significant at the 1 per cent level. This finding implies a strong intervalling effect bias in ETFs' OLS beta. On the other hand, the authors did not find any statistically significant differences in daily, weekly and monthly Scholes and Williams and Dimson betas. Moreover, results show that the daily and weekly OLS and Scholes and Williams betas and weekly OLS and Dimson betas are significantly different from each other.

In this paper using a sample of 40 broad‐based ETFs listed on Nasdaq Stock Exchange, the authors have examined various issues concerning: the intervalling effect bias in ETFs' systematic risk, the relation between beta and capitalization of ETFs, the relation between beta and trading frequency of ETFs and, finally, the intervalling effect bias in ETFs' tracking error. While the literature on intervalling effect on securities' beta and the relation between systematic risk and market value and trading activity is voluminous, this is the first attempt to examine these issues with respect to ETFs.

We review the experimental evidence on risk aversion in controlled laboratory settings. We review the strengths and weaknesses of alternative elicitation procedures, the strengths and weaknesses of alternative estimation procedures, and finally the effect of controlling for risk attitudes on inferences in experiments.

The purpose of this paper is to examine people’s understanding and evaluation of uncertainty intervals produced by experts as part of a quality assurance procedure of large public projects.

Three samples of educated participants (employees in a large construction company, students attending courses in project management and judgment and decision making, and judges of district and appeal courts) answered questionnaires about cost estimates of a highway construction project, presented as a probability distribution.

The studies demonstrated additivity neglect of probabilities that are graphically displayed. People’s evaluations of the accuracy of interval estimates revealed a boundary (a “cliff”) effect, with a sharp drop in accuracy ratings for outcomes above an arbitrary maximum. Several common verbal phrases (what “can” happen, is “entirely possible” and “not surprising”) which might seem to indicate expected outcomes were regularly used to describe unlikely values near or at the top of the distribution (an extremity effect).

All judgments concerned a single case and were made by participants who were not stakeholders in this specific project. Further studies should compare judgments aided by a graph with conditions where the graph is changed or absent.

Experts and project managers cannot assume that readers of cost estimates understand a well-defined uncertainty interval as intended. They should also be aware of effects created by describing uncertain estimates in words.

The studies show how inconsistencies in judgment affect the understanding and evaluation of uncertainty intervals by well-informed and educated samples tested in a maximally transparent situation. Readers of cost estimates seem to believe that precise estimates are feasible and yet that costs are usually underestimated.

The purpose of this study was to review research published by Journal of Educational Administration (JEA) and the Educational Administration Quarterly (EAQ) over the past ten years to examine the type of research reported and to determine if confidence intervals and effect sizes were being reported as recommended by the American Psychological Association (APA) Publication Manual.

The authors examined 95 volumes of the identified journals over the most recent ten‐year timeframe. Each empirical study was coded for decisions made during the analysis and information reported. A total of 473 journal articles were examined and included in the study. Descriptive measures were employed to provide insight into the use and frequency of reported confidence intervals, effect sizes, and the type of research that is currently published.

The results indicated that effect size was being reported in the majority of quantitative studies with limited interpretation. In contrast, no quantitative study examined over the ten‐year timeframe reported confidence intervals despite the recommendations of the APA Task Force on Statistical Inference and guidelines outlined in the most recent edition of the APA Publication Manual. Recommendations for statistical reform in reporting quantitative results in the JEA and EAQ are presented and future direction is discussed.

This paper is valuable for researchers wanting to place their findings in the proper context. By not reporting confidence intervals and effect size information, one does not know the true meaning of their results.

The purpose of this paper is to examine, whether or not, the residuals of the market model (MM) are conditionally heteroscedastic; to examine, whether or not, there exists an intervalling effect in conditional heteroscedasticity in the residuals of the MM; to propose a simple data‐driven conditional capital asset pricing model (CAPM); and to examine the effect of conditional heteroscedasticity on the estimation of systematic risk.

Systematic risk coefficients (betas) are estimated at first using data of various frequencies from the Athens stock exchange without taking into account conditional heteroscedasticity. The same procedure is repeated, but this time taking into consideration conditional heteroscedasticity, which is found to exist. The results of the two approaches are compared.

Empirical evidence is provided for the existence of: conditional heteroscedasticity in MM residuals; a pronounced intervalling effect on autoregressive conditional heteroscedasticity (ARCH) in MM residuals; and generalized autoregressive conditional heteroscedasticity in mean type of conditional heteroscedasticity for the majority of cases where ARCH was present in MM residuals. These findings are conducive to a conditional CAPM, which takes into account the effect of conditional variance on expected returns, rather than the standard CAPM.

Better estimates of financial risk.

The intervalling effect in ARCH in the residuals of the MM is examined for the first time.

This chapter investigates the cost of public debt for firms using a comprehensive sample consisting of 17,368 industrial bond issues from 1970 to 2011. The empirical evidence shows that yield spreads for seasoned bond issues are significantly lower than those for initial bond issues. This seasoning effect is robust across different sample periods, subsamples, and model specifications. On average, the yield spreads for seasoned bond issues are around 50 bps lower than those for initial bond issues. This difference cannot be explained by other bond and firm characteristics. The seasoning effect is more pronounced for firms with higher levels of uncertainty, lower information disclosure quality, and longer time intervals between the first and subsequent issues. Our empirical findings provide supportive evidence for the extant theories that aim to rationalize the information role in determining the cost of capital.

Many studies have proposed variant fuzzy time series models for uncertain and vague data. The purpose of this paper is to adapt a fuzzy time series combined with genetic algorithm (GA) to forecast tourist arrivals in Taiwan.

Different cases are studied to understand the effect of variation of fuzzy time series order, number of intervals and population size on the fitness function which decreases with increase in fuzzy time series order and number of fuzzy intervals, but do not have marginal effect due to change in population size.

Results based on an example of forecasting Taiwan’s tourism demand was used to verify the efficacy of proposed model and confirmed its superiority to existing models providing solutions for different orders of fuzzy time series, number of intervals and population size with a smaller forecasting error as measured by root mean square error.

This study provides a viable forecasting methodology, adapting a fuzzy time series combined with an evolutionary GA. The proposed hybridized framework of fuzzy time series and GA, where GA is used to calibrate fuzzy interval length, is flexible and replicable to many industrial situations.

The purpose of this paper is to focus on the risk decision making of entrepreneurial team, deconstruct the intermediate process mechanism of cognitive adaptability in promoting risk decision making and reveal the role of opportunity identification and entrepreneurial efficacy in the decision-making process, which clarifies how cognitive adaptability affects decision-making speed and effect.

This study establishes a relationship model among entrepreneurial team’s cognitive adaptability, opportunity identification, entrepreneurial efficacy and risk decision making, and selects 316 entrepreneurial teams to empirically study the relationship among core variables using Bootstrap analysis and Johnson–Neyman technology.

Cognitive adaptability though has no direct impact on risk decision-making speed, whereas it directly affects risk decision-making effect; opportunity identification has a full mediating effect between cognitive adaptability and decision-making speed, and a partial mediating effect between cognitive adaptability and decision-making effect; entrepreneurial efficacy plays a moderating role between opportunity identification and decision-making speed, and a same role between opportunity identification and decision-making effect.

First, in setting the research model, the study does not take other moderators into consideration, which might be improved. Second, the study ignores the origin and formation of entrepreneurial team’s cognitive adaptability, the predisposing factors of which might be discussed in the future research.

The practical implication of this paper is to guide the entrepreneurial team to turn their focus on the impact of highly implicit cognitive adaptability on decision making, which might be divided into two aspects: the first is to enhance the cognitive adaptability of the entrepreneurial team, cultivate team members’ self-examination awareness and self-monitoring habits. The second is to strengthen team’s psychological capital and value the cultivation of entrepreneurial efficacy.

This paper breaks through the team process and structure perspectives, explores the driving mechanism of entrepreneurial team risk decision making from team cognition perspective, and deconstructs the logical framework of cognitive adaptability’s influence on risk decision making. This paper applies Johnson–Neyman technology to quantify the mediating effect entrepreneurial efficacy exerts on cognitive adaptability and decision-making speed, as well as on cognitive adaptability and decision-making effect.

The purpose of this paper is to investigate the effects of parameters on the liquid phase migration (LPM) during the freeze-form extrusion fabrication (FEF) process.

To carry out this study, three factors were systematically investigated using orthogonal design of experiments. These three parameters are the extrusion velocity, the extrusion interval time and the extrusion head length. An orthogonal array with nine test units was selected for the experiments. Range analysis and analysis of variance were used to analyze the data obtained by the orthogonal experiments to identify the order of significant factors on LPM.

It was found that the LPM decreased with the increase of extrusion velocity and increased with the lengthening of extrusion interval time and the length of the extrusion nozzle. The order of significant factors for the LPM were found to be extrusion velocity > extrusion nozzle length > extrusion interval time.

Using an orthogonal design of experiments and a statistical analysis method, the liquid content of extrudate can be predicted and appropriate process parameter values can be selected. This leads to the minimization of LPM during the FEF process. Also, this analysis method could be used to study the LPM in other paste extrusion processes.

This paper suggests that the factors have significant impact on LPM during FEF process. The following analysis in this paper is useful for FEF users when prediction of LPM is needed. This methodology could be easily applied to different materials and initial conditions for optimization of other FEF-type processes. The research can also help to get better understanding of LPM during the FEF process.

This purpose of this paper is to identify the possible sources of variation of results in prior studies linking corporate social responsibility (CSR) with corporate financial performance (CFP).

A meta‐analysis was performed on 51 prior studies included in Orlitzky et al. in order to ensure compatibility with previous results. The meta‐analysis is based on sub‐groups of papers in five‐year time intervals focusing on sample size and methodology employed as the sources of variation concerning the nexus between CSR and CFP.

The major finding of the study is that sample size and methodology are significant sources of variation in measuring the link between CSR and CFP.

The findings are likely to help develop a structural framework towards broadening and deepening our understanding of the debate regarding the sources of variation in the measurement of CSR and CFP link. This research is limited to papers published up to 1999 as included in Orlitzky et al. Future research can update the findings by using data beyond 1999.

This paper can be considered an advance on the previous research as it contributes to broadening our understanding of the possible source of causes of variation in results of studies linking CSR with CFP.