Traditional Ranking Research Articles

Testing for overconfidence statistically: A moment inequality approach

SummaryWe propose a moment inequality approach to test for the presence of overconfidence using data from ranking experiments where subjects rank themselves relative to other experimental participants. Although a ranking experiment is a typical way to collect data for the analysis of overconfidence, recent studies show that the resulting data may apparently indicate overconfidence even if participants are purely rational Bayesian updaters, in which case a set of inequalities hold. We apply state‐of‐the‐art tests of moment inequalities to test such a set of inequalities. We examine the data from a traditional ranking experiment as well as those from more sophisticated designs.

Empirical evidence on the profitability of momentum trading strategies using ETFs

PurposeWe extend empirical evidence on the profitability of momentum trading to the realm of plain-equity ETFs.Design/methodology/approachWe employ several ranking measures used in prior research, and for each we apply a traditional ranking based on total return, and a variation based only on the capital gain/loss portion of return.FindingsWhile we find that past momentum is not a strong predictor of future performance in our overall sample period, 2007 to June 2018, we find that the percent off 52-week high price results in positive performance in the recovery years following the financial crisis of 2008–2009.Research limitations/implicationsOur study is limited by the availability of ETF experience and data, and our test period covers just 2007 through June 2018. This period includes the financial crisis of 2008–2009, which previous research finding is associated with the momentum strategy's loss of profitability. When we exclude that period, we find evidence of a profitable momentum strategy based on the measure of percent off 52-week high price, enabling us to reject the null hypothesis that the momentum trading strategy is no longer profitable.Practical implicationsIt is profitable based on both return measures used in the rankings. Our finding of a profitable momentum trading strategy suggests that the null hypothesis that the momentum strategy is no longer profitable can be rejected.Originality/valueWhile perhaps not so strong as to reject the efficient markets hypothesis fully, our empirical findings are more consistent with a behavioral explanation and a market inefficiency. In view of the relative ease and low transactional costs of trading in ETFs, the markets have yet another opportunity to recognize an apparent mispricing and employ arbitrage based on it. To the extent that the relative ease of trading in ETFs makes momentum strategies easier to employ, the momentum anomaly might still be expected to disappear in an efficient market.

Improving Forecast Uncertainty Quantification by Incorporating Production History and Using a Modified Ranking Method of Geostatistical Realizations

Abstract The majority of the geostatistical realizations ranking methods disregard the production history in selection of realizations, due to its requirement of high simulation run time. They also ignore to consider the degree of linear relationship between the “ranks based on the ranking measure” and “ranks based on the performance parameter” in choosing the employed ranking measure. To address these concerns, we propose an uncertainty quantification workflow, which includes two sequential stages of history matching and realization selection. In the first stage, production data are incorporated in the uncertainty quantification procedure through a history matching process. A fast simulator is employed to find the realizations with consistent flow behavior with the production history data in shorter time, compared to a comprehensive simulator. The selected realizations are the input of the second stage of the workflow, which can be any type of the realization selection method. In this study, we used the most convenient realization selection method, i.e., ranking of the realizations. To select the most efficient ranking measure, we investigated the degree of the linear correlation between the ranks based on the several ranking measures and the performance parameter. In addition, due to the shortcomings of the traditional ranking methods in uncertainty quantiles identification, a modified ranking method is introduced. This modification increases the certainty in the probability of the selected realizations. The obtained results on 3D close-to-real synthetic reservoir models revealed the capability of the modified ranking method in more accurate quantification of the uncertainty in reservoir performance prediction.

New Data, Old Story: Molecular Data Illuminate the Tribal Relationships among Rove Beetles of the Subfamily Staphylininae (Coleoptera: Staphylinidae).

The megadiverse subfamily Staphylininae traditionally belonged to the best-defined rove beetle taxa, but the advent of molecular phylogenetics in the last decade has brought turbulent changes to the group’s classification. Here, we reevaluate the internal relationships among the tribes of Staphylininae by implementing tree inference methods that suppress common sources of systematic error. In congruence with morphological data, and in contrast to some previous phylogenetic studies, we unambiguously recover Staphylininae and Paederinae as monophyletic in the traditional sense. We show that the recently proposed subfamily Platyprosopinae (Arrowinus and Platyprosopus) is a phylogenetic artefact and reinstate Arrowinus as a member of Arrowinini stat. res. and Platyprosopus as a member of Platyprosopini stat. res. We show that several recent changes to the internal classification of the subfamily are phylogenetically unjustified and systematically unnecessary. We, therefore, reestablish Platyprosopini, Staphylinini, and Xantholinini as tribes within Staphylininae (all stat. res.) and recognize Coomaniini as a tribe (stat. nov.) rather than subfamily. Consequently, the traditional ranks of the subtribes Acylophorina, Afroquediina, Amblyopinina, Antimerina, †Baltognathina, Cyrtoquediina, Erichsoniina, Hyptiomina, Indoquediina, Quediina, and Tanygnathinina are restored (all stat. res.). We review the current classification of Staphylininae and discuss sources of incongruence in multigene phylogenies.

Scholars’ influence on their institutions: Reputation, prestige, and rankings

This research article examines the relationship of individual scholars to overall research production and develops new ranking schema upon the absence of top individual scholarship as demonstrations of individual scholarship relative to total rankings. The study creates a ranking system focused on the impact of individual scholars in public administration research. The contributions of this ranking system are to provide a different view than traditional ranking schemas at the institutional and departmental level. In addition, questions about the significance of scholarship within the discipline and for academic institutions outlines the reinforcing nature of systems aimed at identifying a “best” within public administration. Comparisons of findings are discussed in contrast to reputational and prestige ranking systems. Subsequent discussion is focused on the importance of individual scholarship to the institutional rankings through evaluating individual movement between institutions.

Empirical Investigation of Ranking vs Best-Worst Scaling Generated Preferences for Attributes of Quality of Life: One and the Same or Differentiable?

The objective of this study was to investigate the degree of inconsistency in quality-of-life attribute preference orderings generated via successive best-worst scaling (a form of ranking whereby the respondent chooses the best and worst attributes from a choice set, these attributes are then eliminated and the best and worst attributes from the reduced choice set are then chosen and this process is continued until all presented attributes are eliminated) and conventional ranking methods (whereby the respondent chooses the best, second best and third best from a choice set until all presented attributes are eliminated). An on-line survey was developed for administration to two general population samples comprising younger people (aged 18-64years) and older people (aged 65years and above). Data were analysed in STATA through an empirical examination of the relative level of choice inconsistency (randomness in responses or the variability in choice outcomes not explained by attributes and their associated preference weights) for successive best-worst in comparison with the conventional ranking method for the younger and older person samples. For the younger person sample, both methods were found to be similarly consistent. In contrast, for the older person sample, ranking performed relatively worse than best-worst scaling with more inconsistent responses (tau = 0.515, p < 0.01). These findings lend some support to the hypothesis initially propagated by the developers of best-worst scaling that it is a comparatively easier choice task for respondents to undertake than a traditional ranking task.

Supply Chain Quality Performance Evaluation Model Based on Intuitionistic Fuzzy PROMETHEE

Aiming at the ambiguity and uncertainty in subjective group decision-making of supply chain quality performance evaluation, the traditional preference ranking organization methods for enrichment evaluation (PROMETHEE) is extended to a fuzzy environment, and a new intuitive fuzzy PROMETHEE model is proposed. The model uses intuitionistic fuzzy numbers to express the decision-maker’s semantic evaluation information, and establishes a trust function based on the evaluation information to determine the decision-maker’s weight. Then, use the method of maximizing the net flow to obtain the attribute weight. Finally, gather the evaluation information to obtain the decision group’s pros and cons. The validity and feasibility of the model is verified by an example.

Nonconvex Log-Sum Function-Based Majorization–Minimization Framework for Seismic Data Reconstruction

Because of the fact that complete seismic data can have a low rank in the frequency-space (f-x) domain, rank-reduction methods are classical techniques used for seismic data reconstruction. Models that employ nuclear-norm minimization signify convex relaxation methods in traditional rank minimization problems. However, the results obtained after solving the nuclear-norm minimization problem are usually suboptimal because the nuclear norm indicates a loose approximation of the rank function. To overcome the limitations of the nuclear norm, we propose a new method of seismic data reconstruction based on the log-sum function minimization, which is closer to the rank function than the convex nuclear norm. However, the problem based on the log-sum function is nonconvex. Consequently, a majorization–minimization framework has been adopted to solve the associated minimization problem. Numerical experiments performed using synthetic and real data demonstrate that the quality of reconstruction derived from our proposed algorithm is better than that of the singular value thresholding algorithm and the weighted nuclear norm method.

Expected Value From a Ranking of Alternatives for Personalized Quantifier

A novel model is presented in this article to derive the expected value from a ranking of alternatives, with which to tackle some uncertain problems, such as different types of att0itudes, in the creation of personalized quantifier caused by personal preference. There is no doubt that we consider here a reverse process of traditional ranking methods in decision making. More specifically, a representative sample of multi-attribute alternatives is prepared, and an involved person is asked to do nothing but provide, following personal preference or judgment on the whole, a ranking of alternatives of this sample. The relationship between the alternatives’ importance weights and their ranking positions is investigated. A TOPSIS-based model can then be established to derive his/her personal expected value from this ranking. The idea of ordered weighted averaging (OWA) aggregations is fully taken into account in the modeling. We use this technique to create a personalized quantifier, thus making the creation much more efficient and safer compared with what we did before. Experimental results show that different rankings of alternatives may lead to different expected values that eventually determine different types of attitudes. Thus, the developed technique could be used as a tool for uncertainty modeling in more complex situations in which various personality traits have to be taken into account, especially for decision making under uncertainty.

A novel target threat assessment method based on three-way decisions under intuitionistic fuzzy multi-attribute decision making environment

Target threat assessment aims to rank targets threat based on their attributes and state information, which provide decision support for subsequent military decisions, e.g. weapon-target optimal assignment. Most existing threat assessment methods can only obtain ranking results, decision-makers usually need to subjectively choose priority targets to attack or interfere based on the preset threat level and ordering results, which does not meet the requirements of complex battlefield situation and uncertain information processing. A method is urgently needed, which can objectively produce threat classification results and automatically provide priority targets for combat. Therefore, we propose a novel target threat assessment method based on three-way decisions under intuitionistic fuzzy multi-attribute decision making environment. The core parts are the conditional probability of each target is estimated by intuitionistic fuzzy TOPSIS and the decision thresholds of each target are constructed by intuitionistic fuzzy evaluation values. The results of two numerical examples show that the proposed method can effectively deal with dynamic uncertain situation information, turn the traditional ranking results of two-way decisions to the objective classification results of three-way decisions and can flexibly reflect the acquisition of situation information by setting the risk avoidance coefficient.

Feature-enriched author ranking in incomplete networks

Evaluating scientists based on their scientific production is a controversial topic. Nevertheless, bibliometrics and algorithmic approaches can assist traditional peer review in numerous tasks, such as attributing research grants, deciding scientific committees, or choosing faculty promotions. Traditional bibliometrics rank individual entities (e.g., researchers, journals, faculties) without looking at the whole data (i.e., the whole network). Network algorithms, such as PageRank, have been used to measure node importance in a network, and have been applied to author ranking. However, traditional PageRank only uses network topology and ignores relevant features of scientific collaborations. Multiple extensions of PageRank have been proposed, more suited for author ranking. These methods enrich the network with information about the author’s productivity or the venue and year of the publication/citation. Most state-of-the-art (STOA) feature-enriched methods either ignore or do not combine effectively this information. Furthermore, STOA algorithms typically disregard that the full network is not known for most real-world cases.Here we describe OTARIOS, an author ranking method recently developed by us, which combines multiple publication/citation criteria (i.e., features) to evaluate authors. OTARIOS divides the original network into two subnetworks, insiders and outsiders, which is an adequate representation of citation networks with missing information. We evaluate OTARIOS on a set of five real networks, each with publications in distinct areas of Computer Science, and compare it against STOA methods. When matching OTARIOS’ produced ranking with a ground-truth ranking (comprised of best paper award nominations), we observe that OTARIOS is >30% more accurate than traditional PageRank (i.e., topology based method) and >20% more accurate than STOA (i.e., competing feature enriched methods). We obtain the best results when OTARIOS considers (i) the author’s publication volume and publication recency, (ii) how recently the author’s work is being cited by outsiders, and (iii) how recently the author’s work is being cited by insiders and how individual he is. Our results showcase (a) the importance of efficiently combining relevant features and (b) how to adequately perform author ranking in incomplete networks.

Digital Media and the Changing Nature of Labor Action

Precarious working conditions resulting from neoliberal policies of the Indian state have placed an overwhelmingly young and mobile industrial workforce under a lot of duress. Traditional forms of organizations and modes of resistance such as labor strikes that were thought to be effective are now increasingly seen as inadequate against the speed and complexity of contemporary production processes, forcing the workers to devise commensurate responses. In this article, I discuss some of the newer strategies of resistance gaining prominence among industrial workers, especially as they are mediated through digital media. Focusing on online self-work underpinning worker agitations, I argue that contemporary labor movement should devise creative strategies using new media tools, to which the millennial worker has unprecedented access, in addition to their traditional rank and file struggles, to counter contemporary challenges.

Deep Top-$k$ Ranking for Image–Sentence Matching

Image-sentence matching is a challenging task for the heterogeneity-gap between different modalities. Ranking-based methods have achieved excellent performance in this task in past decades. Given an image query, these methods typically assume that the correct matched image-sentence pair must rank before all other mismatched ones. However, this assumption may be too strict and prone to the overfitting problem, especially when some sentences in a massive database are similar and confusable with one another. In this paper, we relax the traditional ranking loss and propose a novel deep multi-modal network with a top-k ranking loss to mitigate the data ambiguity problem. With this strategy, query results will not be penalized unless the index of ground truth is outside the range of top-k query results. Considering the non-smoothness and non-convexity of the initial top-k ranking loss, we exploit a tight convex upper bound to approximate the loss and then utilize the traditional back-propagation algorithm to optimize the deep multi-modal network. Finally, we apply the method on three benchmark datasets, namely, Flickr8k, Flickr30k, and MSCOCO. Empirical results on metrics R@K (K = 1, 5, 10) show that our method achieves comparable performance in comparison to state-of-the-art methods.

An Integrated Fuzzy AHP and Fuzzy TOPSIS Approach to Assess Sustainable Urban Development in an Emerging Economy.

Sustainable urban development (SUD) requires a balance between economic growth, social well-being, and environmental protection. Oftentimes, urban policy makers can hardly fulfill all SUD goals due to inadequacy of resources to support SUD programs. Therefore, the process of allocating scarce resources to achieve and balance various SUD goals becomes a critical challenge for policy makers and researchers. To solve this problem, this study adopts fuzzy Analytic Hierarchy Process (AHP) and fuzzy Technique for Order Preference by Similarity to Ideal Situation (TOPSIS) to assess and rank different indicators of SUD and evaluate different cities in an emerging economy (Vietnam). Fuzzy methods exhibit more advantages than traditional ranking methods. Fuzzy AHP is an extension of AHP, whereas fuzzy TOPSIS is an extension of TOPSIS. Fuzzy methods are used to overcome disadvantages of traditional methods and are beneficial techniques for solving complicated decision problems with a realistic solution. Using a valid sample data of ten experts in the field of SUD, empirical results show that education, healthcare, quality of life, and social democracy are the most important indicators of SUD. By contrast, social diversity, social maturity, and energy consumption are the least important indicators of SUD. For social sustainability, social democracy and quality of life are the two most important criteria, whereas social maturity and social diversity are the two least important criteria. For economic sustainability, education and healthcare are the two most important criteria, whereas infrastructure and income are the two least important criteria. For environmental sustainability, water quality and waste disposal are the two most important criteria, whereas energy consumption and ecological conservation are the two least important criteria. Furthermore, fuzzy TOPSIS results reveal the best and the worst cities in Vietnam with regard to overall SUD and its three components. This study provides evidence for researchers and policy makers to better understand the importance of different goals of SUD and efficiently allocate scarce resources to achieve and balance different SUD goals. Furthermore, researchers and policy makers should further focus on indicators such as social democracy, quality of life, education, healthcare, water quality, and waste disposal. These indicators will help obtain the goals of SUD.

College Rankings: Creating an Equitable Model of Transformation and Institutional Effectiveness

Colleges that serve working-class students show up poorly in traditional rankings of US colleges. Without appropriate outcome measures, measures of ‘quality’ of inputs drive most current ranking systems. The trouble is that quality is often just a measure of pre-existing privilege (e.g., selectivity, average SAT scores). In this article, I demonstrate the viability of a model that uses economic returns data while factoring in the relative lack of privilege of students attending any particular institution as a way of ranking that institution’s transformative efficacy and institutional effectiveness. The model was then tested on a diverse sample of 655 US colleges and universities for whom reliable economic returns and institutional effectiveness data are available. Unlike widely used rankings models, this proposed alternative model can distinguish between reproducing privilege (high economic returns as expected, low defaults, timely year to degree and fewer incompleters) and facilitating social mobility (higher returns and persistence than would be expected given the incoming characteristics of students). The article concludes with a discussion of the uses to which such a model could be best put.

Random walk-based ranking in signed social networks: model and algorithms

How can we rank nodes in signed social networks? Relationships between nodes in a signed network are represented as positive (trust) or negative (distrust) edges. Many social networks have adopted signed networks to express trust between users. Consequently, ranking friends or enemies in signed networks has received much attention from the data mining community. The ranking problem, however, is challenging because it is difficult to interpret negative edges. Traditional random walk-based methods such as PageRank and random walk with restart cannot provide effective rankings in signed networks since they assume only positive edges. Although several methods have been proposed by modifying traditional ranking models, they also fail to account for proper rankings due to the lack of ability to consider complex edge relations. In this paper, we propose Signed Random Walk with Restart (SRWR), a novel model for personalized ranking in signed networks. We introduce a signed random surfer so that she considers negative edges by changing her sign for walking. Our model provides proper rankings considering signed edges based on the signed random walk. We develop two methods for computing SRWR scores: SRWR-Iter and SRWR-Pre which are iterative and preprocessing methods, respectively. SRWR-Iter naturally follows the definition of SRWR, and iteratively updates SRWR scores until convergence. SRWR-Pre enables fast ranking computation which is important for the performance of applications of SRWR. Through extensive experiments, we demonstrate that SRWR achieves the best accuracy for link prediction, predicts trolls $$4\times $$ more accurately, and shows a satisfactory performance for inferring missing signs of edges compared to other competitors. In terms of efficiency, SRWR-Pre preprocesses a signed network $$4.5 \times $$ faster and requires $$11 \times $$ less memory space than other preprocessing methods; furthermore, SRWR-Pre computes SRWR scores up to $$14 \times $$ faster than other methods in the query phase.

A comparative analysis of the OECD/INFE financial knowledge assessment using the Rasch model

Based on Item Response Theory, and more specifically the Rasch model, the financial knowledge domain included in the OECD/INFE adult financial literacy assessment conducted in 2015 was evaluated. This was done in order to determine whether the measurement instrument, in its existing design, could be classified as an International Large-scale assessment (ILSA), suitable for within countries and for comparison across countries. The development cycle of the OECD/INFE assessment was briefly presented to portray the conditions necessary to ensure that successful measurement would lead to action. Based on the first phase of the analysis, the suitability of the data for the Rasch model was established and the applicability of the instrument to country-specific analysis was confirmed. However, the differential item function (DIF) exploration determined that the assumption that item difficulties are homogeneous across the various countries does not hold, therefore confirming the utility of this study. The results highlighted the greater risk associated with the traditional ranking of results rather than with sophisticated analyses, as traditional approaches could result in misdiagnosis of problem areas on instruments which might not be comparable across countries. Based on the results, it does not seem that the OECD/INFE adult financial knowledge assessment adhere to the requirements of being classified an ILSA.

SignRank: A Novel Random Walking Based Ranking Algorithm in Signed Networks

Social networks have become an indispensable part of modern life. Signed networks, a class of social network with positive and negative edges, are becoming increasingly important. Many social networks have adopted the use of signed networks to model like (trust) or dislike (distrust) relationships. Consequently, how to rank nodes from positive and negative views has become an open issue of social network data mining. Traditional ranking algorithms usually separate the signed network into positive and negative graphs so as to rank positive and negative scores separately. However, much global information of signed network gets lost during the use of such methods, e.g., the influence of a friend’s enemy. In this paper, we propose a novel ranking algorithm that computes a positive score and a negative score for each node in a signed network. We introduce a random walking model for signed network which considers the walker has a negative or positive emotion. The steady state probability of the walker visiting a node with negative or positive emotion represents the positive score or negative score. In order to evaluate our algorithm, we use it to solve sign prediction problem, and the result shows that our algorithm has a higher prediction accuracy compared with some well-known ranking algorithms.

Ranking Meets Distance Education

University ranking systems are being implemented with the aim of assessing and comparing higher education institutions at a global level. Despite their being increasingly used, rankings are often strongly criticized for their social and economic implications, as well as for limitations in their technical implementation. One of these limitations is that they do not consider the specific characteristics of online education. This study used a participatory approach to define a set of criteria and indicators suitable to reflect the specific nature of distance education. This endeavour will help evaluate and rank online higher education institutions more appropriately than in current practice, where indicators are devised for traditional universities. To this end, several stakeholders and informants were involved in a Delphi study in an attempt to reach the broader higher education institutions (HEI) community. According to the study participants, apart from students’ achievements and general quantitative measures of HEI performance, which are quite common in traditional ranking systems, teaching and student learning experience turned out to be the most important criteria. Student support, teacher support, technological infrastructure, research and organization were deemed middle ground criteria, while sustainability and reputation were regarded as the least important criteria.

Regional disaster risk assessment of china based on self-organizing map: Clustering, visualization and ranking

Regional disaster risk assessment is of practical value for disaster prevention and mitigation in emergency management. It remains an ongoing challenge due to the multiple criteria associated with the decision making problem. In this paper we propose a new multi-criteria decision making method to evaluate the natural disaster risk of China at the region scale. The evaluation index system is composed of 28 indicators reflecting the danger of natural disasters and the vulnerability of the affected body. The method involves a combination of Analytic Hierarchy Process (AHP), Self-Organizing Map (SOM), Isometric Feature Mapping (Isomap), and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to achieve the clustering, visualization, and ranking of Chinese regions. Compared with the traditional ranking methods in regional disaster risk assessment, the proposed method is able to recognize the similarity of regions, exploit the embedded clustering structures, visualize the high dimensional regional data, examine the influence of indicators, and rank the regions in terms of the disaster risk. It offers a comprehensive and visual way for policy makers or related organizations to understand the studied data from different perspectives and thus mitigate the disaster risk in the Chinese regions.