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The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).

The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.

The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.

In light of its manifold conceptual and empirical limitations, the authors advise against the use of the CEI. Its adoption and the routine use of equal weights estimation could adversely affect the validity of measurement and structural model results and understate structural model predictive accuracy. Although this study shows that the CEI is an unsuitable metric to decide between equal weights and differentiated weights, it does not propose another means for such a comparison.

The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive assessment of the CEI’s usefulness. The results provide guidance for researchers considering using equal indicator weights instead of PLS-SEM-based weighted indicators.

This study aims to investigate the risk-return trade-off in the Indian equity market at both the aggregate equity market level and in the cross-sections of stock return using alternative risk measures.

The study uses weekly and monthly data of 3,085 Bombay Stock Exchange-listed stocks spanning over 20 years from January 2000 to December 2019. The study evaluates the risk-return trade-off at the aggregate equity market level using the value-weighted and the equal-weighted broader portfolios. Eight different risk proxies belonging to the conventional, downside and extreme risk categories are considered to analyse the cross-sectional risk-return relationship.

The results show a positive equity premium on the value-weighted portfolio; however, the equal-weighted portfolio of these stocks shows an average return lower than the return on the 91-day Treasury Bills. The inverted size premium mainly causes this anomaly in the Indian equity market as the small stocks have lower returns than big stocks. The study presents a strong negative risk-return relationship across different risk proxies. However, under the subsample of more liquid stocks, the low-risk anomaly regarding other risk proxies becomes moderate except the beta-anomaly. This anomalous relationship seems to be caused by small and less liquid stocks having low institutional ownership and higher short-selling constraints.

The findings have important implications for investors, managers and practitioners. Investors can incorporate the effects of different highlighted anomalies in their investment strategies to fetch higher returns. Managers can also use these findings in their capital budgeting decisions, resource allocations and other diverse range of direct and indirect decisions, particularly in emerging markets such as India. The findings provide insights to practitioners while valuing the firms.

The study is among the earlier attempts to examine the risk-return trade-off in an emerging equity market at both the aggregate equity market level and in the cross-sections of stock returns using alternative measures of risk and expected returns.

In estimating the inputs into the modern portfolio theory (MPT) portfolio optimisation problem, it is usual to use equal weighted historic data. Equal weighting of the data, however, does not take account of the current state of the market. Consequently this approach is unlikely to perform well in any subsequent period as the data is still reflecting market conditions that are no longer valid. The need for some return weighting scheme that gives greater weight to the most recent data would seem desirable. Therefore, this study uses returns data which are weighted to give greater weight to the most recent observations to see if such a weighting scheme can offer improved ex ante performance over that based on unweighted data.

Most composite indicators of national performance limit their scope to only economic performance criteria and aggregate primitive performance data using subjective fixed weight values applied uniformly to all countries. This paper proposes a weighting method to correct for biases inherent in the use of fixed and uniform weights, and it presents a composite performance indicator that encompasses both economic and non‐economic performance criteria.

The paper presents a method that endogenously determines country‐specific weights that explicitly take account of a country's own choices and achievements across primitive dimensions of performance. The method is then used to construct a composite inclusive index that combines economic performance with two other performance dimensions: environmental sustainability and governance.

Comparison of the endogenous weight method with the method of using fixed and uniform weights indicates a bias in the latter that penalizes countries, in terms of indicating lower relative performance, which are more diverse in their achievements among primitive performance dimensions. When the endogenous weight method is used, the performance ranking of countries is altered such that countries with greater diversity improve their relative performance while the relative performance of countries having less diversity may either rise or fall.

The weighting method discussed in this paper: is applicable at any level of analysis (e.g. nations, companies, business units, etc.), obviates objections about the “importance” of alternative primitive dimensions that can arise when subjective fixed weights are used; and indicates more accurately relative performance since each unit of analysis is first allowed to obtain its best performance before relative performance is assessed. The method can therefore assist policy makers, companies, etc. to more accurately benchmark performance and it can, in particular, assist companies to respond to perceptions of low performance or compliance among different performance dimensions when performance has been determined using the traditional method of fixed and uniform weights.

The purpose of this paper is to compare various linear opinion pooling models for aggregating probability judgments and to determine whether Cooke's performance weighting model can sift out better calibrated experts and produce better aggregated distribution.

The leave‐one‐out cross‐validation technique is adopted to perform an out‐of‐sample comparison of Cooke's classical model, the equal weight linear pooling method, and the best expert approach.

Both aggregation models significantly outperform the best expert approach, indicating the need for inputs from multiple experts. The performance score for Cooke's classical model drops considerably in out‐of‐sample analysis, indicating that Cooke's performance weight approach might have been slightly overrated before, and the performance weight aggregation method no longer dominantly outperforms the equal weight linear opinion pool.

The results show that using seed questions to sift out better calibrated experts may still be a feasible approach. However, because the superiority of Cooke's model as discussed in previous studies can no longer be claimed, whether the cost of extra efforts used in generating and evaluating seed questions is justifiable remains a question.

Understanding the performance of various models for aggregating experts' probability judgments is critical for decision and risk analysis. Furthermore, the leave‐one‐out cross‐validation technique used in this study achieves more objective evaluations than previous studies.

The purpose of this paper is to deal with methodological issues in the Index of Economic Freedom (IEF) building by using principal components analysis (PCA) and benefit of the doubt (BOD) methods to generate component- and country-specific weights in computing the scores.

The paper uses endogenous model and country-specific weight system to generate country-specific score unlike the equal weight used by the Heritage Foundation.

The PCA and BOD analyses provide consistent results that differ dramatically with the baseline ones (results using equal weights).

The limitation of the paper is that the results change depending on the method used.

Given results provided by the PCA and BOD analysis, the IEF would receive broad legitimacy basing the calculation of its scores on endogenous weighting models.

As composite indicators are essential in public debates and policies, their construction must be objective and well-known by a large public, making the methodological matters in composite indexes building one of the big challenge to researchers and a major democratic issue.

The originality of the paper is to use endogenous approach to generate weights and countries’ scores.

The recent development of the European debt sovereign crisis showed that sovereign debt is not “risk free.” The traditional index bond management used during the last two decades such as the market-capitalization weighting scheme has been severely called into question. In order to overcome these drawbacks, alternative weighting schemes have recently prompted attention, both from academic researchers and from market practitioners. One of the key developments was the introduction of passive funds using economic fundamental indicators.

In this chapter, the authors introduced models with economic drivers with an aim of investigating whether the fundamental approaches outperformed the other models on risk-adjusted returns and on other terms.

The authors did this by constructing five portfolios composed of the Eurozone sovereigns bonds. The models are the Market-Capitalization RP, GDP model RP, Ratings RP model, Fundamental-Ranking RP, and Fundamental-Weighted RP models. These models were created exclusively for this chapter. Both Fundamental models are using a range of 10 country fundamentals. A variation from other studies is that this dissertation applied the risk parity concept which is an allocation technique that aims to equalize risk across different assets. This concept has been applied by assuming the credit default swap as proxy for sovereign credit risk. The models were run using the Generalized Reduced Gradient (GRG) method as the optimization model, together with the Lagrange Multipliers as techniques and the Karush–Kuhn–Tucker conditions. This led to the comparison of all the models mentioned above in terms of performance, risk-adjusted returns, concentration, and weighted average ratings.

By analyzing the whole period between 2006 and 2014, it was found that both the fundamental models gave very appealing results in terms of risk-adjusted returns. The best results were returned by the Fundamental-Ranking RP model followed by the Fundamental-Weighting RP model. However, better results for the mixed performance and risk-adjusted returns were achieved on a yearly basis and when sub-dividing the whole period in three equal periods. Moreover, the authors concluded that over the long term, the fundamental bond indexing triumphed over the other approaches by offering superior return and risk characteristics. Thus, one can use the fundamental indexation as an alternative to other traditional models.

When comparing and evaluating performance, decision-makers are concerned with providing a range of effective, efficient, and fair measures that can yield representative relative rankings for the units being evaluated. In this chapter, we apply three multicriteria benchmarking modeling techniques – weighted linear combination, data envelopment analysis (DEA), and ordered weighted average (OWA) – to an example dataset to provide a quantitative assessment of performance. Evaluation of the results demonstrates that each of these techniques has relative strengths and shortcomings. To take advantage of the relative strengths, and avoid some of the shortcomings that we observed, we develop and assess a promising new methodological approach, the order rated effectiveness (ORE) model. ORE uses the OWA unit ratings within a DEA optimization framework to provide an overall relative performance assessment.

Most firms select their information technology outsourcing (ITO) vendors based on the two methods of the weighted-criteria evaluation technique – the “qualification score plus the lowest bid price for the highest price score (QS-LBHPS)” and the “qualification score plus the average bid price for the middle price score (QS-ABMPS).” This paper aims to understand whether these two methods provide the same or different results of vendor selection and how the proportional weights of a vendor's qualification and bid price affect the vendor selection results under the two methods.

–In total, 1,000 experimental tests were carried out using the developed spreadsheet template to examine vendor selection results of the two methods (QS-LBHPS and QS-ABMPS) and compare the vendor selection results under three conditions of vendors’ qualification and price weights. A correspondence analysis was also used to determine the proximal relationships among the selection results of the weighted criteria technique under the comparable methods.

The results indicate that, when using the two methods of the weighted criteria technique for a vendor selection, the selection results are significantly correspondent. In addition, the proportions of qualification and price weights affect the selection results under the two methods. The different proportions of qualification and price weights under the two methods yield the same selection results rather than different results.

This study fills the gap in ITO literatures concerning the vendor selection strategy. No empirical studies have been undertaken to compare the results of vendor selection under the two methods of the weighted-criteria evaluation technique. The findings enable a firm's selection team to apply the weighted-criteria evaluation technique effectively and realize that vendor selection results are altered based on the predefined proportions of qualification and price weights.

The purpose of this paper is to propose a new approach in which cost‐based process weights are used to determine a unique weighted‐defects per million opportunity (DPMO) and its corresponding overall sigma level in order to classify an organization as either “world‐class,” “industry average” or “non‐competitive.”

In order to achieve this objective, the proposed approach uses both internal and external performances of the products and processes in terms of costs involved to determine cost‐based process weights. These weights are then incorporated into the respective DPMOs for computing weighted‐DPMOs. Finally, a unique weighted‐DPMO and its corresponding sigma level are found.

The proposed method is a new one and it involves various costs for determining process weights. The findings reveal that the weight‐based overall sigma level is more realistic than the one that is calculated without weights. Further, the results of this study could provide interesting feedback to six‐sigma practitioners, as they are particular about DPMOs and return on investments in project implementations.

The results of this paper are based on the weights of respective processes and their products that are calculated using various cost aspects. Determining such weights by means of any other process and product factors incorporating the effects of various marketing activities, if any, could extend its generality and fulfil the gap.

The proposed method is simple to implement and the required data can be collected without any additional commitments. Also, it is more generic so that it can be adapted by organizations of any nature. This paper recommends change in the practice from simply using the DPMOs with equal importance to using the weight‐based DPMOs for evaluating overall sigma level (performance) of an organization.

The proposed approach would have a high value among six‐sigma quality practitioners and researchers as it provides a new and more realistic measure for overall performance of an organization during the evaluation process.