Performance Ranking Research Articles

Constructing Trading Strategies by Applying and Adapting Q-factor Model in the Australian Market

The Q-factor model is a quantitative pricing model similar to the Fama-French multi-factor model, which is an investment approach using investment (IA) and profitability (ROE) as the significant factors. This paper collects historical data (1987~2022) from Australian stocks and constructs long-short portfolios to investigate the effectiveness of applying this model in the Australian market. Net returns will be validated by Carhart regression. Finally, the optimal factor portfolios and trading strategies will be tested for factor substitution based on cumulative return trends and risk-return metrics. Overall, both the Q-factor model and the augmented Q-factor effectively analyzed investments in the Australian market. The difference between theoretical and practical-based long-short is mainly due to the abnormal ranking performance of individual factors, especially during the COVID-19 pandemic. In most cases, strategies based on actual data sorting generate better risk-return outcomes than theory-based approaches, suggesting that the models must be continually adapted to suit Australia's unique economic and market conditions.

Ranking Performance of MARCOS Method for Location Selection Problem in the Presence of Conflicting Criteria

The selection of the best location for a facility is generally a complex process especially when considering multiple criteria in the selection process. The costs associated with purchasing the land and related constructions makes the location selection problem a long-term investment decision. Hence, the problem should be analysed carefully with a reliable approach to avoid the consequences that follows awkward decisions. Many multiple criteria decision-making (MCDM) methods are developed recently which made the selection of a suitable MCDM method has become a confusing decision. In this study, measurement alternatives and ranking according to compromise solution (MARCOS) method is used to solve a location selection problem with conflicting criteria. The ranking produced by MARCOS method is analysed by a comparison with other common MCDM methods and a sensitivity analysis. The aim of the comparison to show that the method agreed the choice of the best alternative while the sensitivity analysis is employed to check the robustness and efficiency of this method. The sensitivity analysis includes the sensitivity of weight test and rank reversal (RR) test. The comparison of ranking with different methods showed that MARCOS method has strong ranking correlation with other methods. Moreover, the sensitivity analysis showed the reliability and robustness of MARCOS method as it produced high consistency of ranking through the tests. Thus, this study validates the applicability of MARCOS method to handle the presence of conflicting criteria in location selection problems.

Tourism Destination Competitiveness Ranking and Tourism Performance

This research note compares the impacts of the macroeconomic condition on the relationship between tourism destination competitiveness (TDC) ranking and tourism performance. This study utilized the cross-country analysis of the World Economic Forum (WEF) Travel and Tourism Competitiveness Index (TTCI) data among 115 countries. Macroeconomic conditions are conceptualized in high-income versus low-income nations, while the WEF TTCI ranking explains the TDC ranking. The Partial-least square-structural equation modelling (PLS-SEM) Multi-Group Analysis (MGA) results showed that TTCI ranking significantly influenced the low-income group’s tourism performance only but not for the developed ones. The results confirm the criticism of the TTCI ranking bias, reflecting the unbalanced competitiveness assessment distribution between advanced and low-income countries. The study findings augment the incomparability among countries on different levels of development and the arbitrary weighting of the TTCI ranking report, which may lead to inaccurate perceptions of the country and incorrect investment decisions.

A multi-model evaluation of probabilistic streamflow predictions via residual error modelling

Probabilistic streamflow predictions are valuable tools for predictive uncertainty estimation, hydrologic risk management, and support for decision-making in water resources. Usually, predictive uncertainty quantification is developed and assessed using only a single hydrological model, making it difficult to generalize to other model configurations. To tackle this issue, we assess changes in the model performance ranking of diverse streamflow models by applying a residual error model post-processing approach to multiple basins and multiple models. This assessment employed 141 basins from the Great Lakes watershed covering the USA and Canada, and 13 diverse streamflow models, which are evaluated using deterministic and probabilistic performance metrics. As the first study to implement probabilistic methods to diverse streamflow models applied to a multitude of basins, the analysis here examines the dependence of probabilistic streamflow estimation quality on model quality. Our findings show that streamflow model choice influences the robustness of probabilistic predictions. It was found that moving from deterministic to probabilistic predictions using a post-processing approach does not change the streamflow model performance ranking for the best and worst deterministic models, but models of intermediate rank in deterministic evaluation do not have consistent ranking when evaluated in probabilistic mode. Post-processing residual errors of long short-term memory (LSTM) network models are consistently the best-performing model in terms of deterministic and probabilistic metrics. This study highlights the significance of combining deterministic streamflow model predictions with residual error models for improving the quality and increasing the value of hydrological predictions, quantifying uncertainty, and facilitating decision-making in operational water management. It also clarifies the degree to which probabilistic predictions depend upon good model performance and can compensate for poor model performance.

An optimistic-pessimistic game cross-efficiency method based on a Gibbs entropy model for ranking decision making units

The game cross-efficiency method, a commonly utilized approach for ranking decision-making units in tie-breaking scenarios, is based on secondary goals. However, in certain data envelopment analysis ranking problems, the classical game cross-efficiency method may fail to differentiate all decision-making units effectively. To address this limitation, it is prudent to explore the development of a new method that can enhance the ranking performance of the classical game cross-efficiency approach. In this study, we propose a novel Gibbs entropy linear programming model that integrates both optimistic and pessimistic perspectives of the classical game cross-efficiency method for data envelopment analysis ranking problems. To validate the reliability and utility of our proposed method, we present three examples: the six nursing homes problem, numerical example 2, and an application involving twenty Thai provinces with cash crop data. The reliability of the proposed method is assessed using Spearman’s correlation coefficient (rs) on the numerical examples. The results demonstrate that the rs values for both the proposed method and the classical game crossefficiency method, specifically for the six nursing homes problem, numerical example 2, and the application involving twenty Thai provinces, are determined to be rs=0.998, 0.998, and 0.986 respectively.

Financial Performance and Bankruptcy Risk Analysis: An Application on Private Health Insurance Companies in Turkey

This research evaluates the correlation between the financial performances of seven insurance companies operating in Turkey from 2018 to 2022 and their risk of bankruptcy. In the study, data obtained from the year-end financial statements of the companies were used, weights were assigned to the criteria determined by the ENTROPY methodology, and the performance rankings of the companies were obtained using the WASPAS method. The Altman-Z model was applied to determine the risk of bankruptcy. The decisive criteria in the financial performance ranking are profitability ratios such as Asset Profitability, Equity Profitability, and Net Profit Margin. While Turkey Insurance and Anadolu Insurance's leadership positions in the sector were identified, Ray Insurance and Mapfre Insurance were determined as the companies showing the weakest performance. Although the research shows that financial performance affects the risk of bankruptcy, it also reveals that it is not the sole factor in determining the likelihood of bankruptcy. In this context, the evaluation of financial performance in the insurance industry should be considered as part of a more holistic risk assessment approach.

Rediscovery of Forgotten Dimensions of Pedagogical Practice from Continental Perspective

AbstractThis article critically assesses contemporary empirical educational research, directing attention toward overlooked facets of pedagogical practice. Here, Agnes Pfrang and Daniel Castner raise questions about predominant psychological approaches to empirical educational research, instead advocating for a holistic viewpoint that encompasses the subtleties of educational situations and experiences. They highlight the learning atmosphere and pedagogical relationships as crucial dimensions often neglected by researchers. By delving into the historical evolution of the relationship between educational research and empirical pedagogy, the article underscores the ramifications of an increasing emphasis on evidence‐based approaches. It contends that conventional control measures, focused on performance metrics and rankings, inadequately encapsulate the essence of pedagogy. Pfrang and Castner call for a paradigm shift, urging a more comprehensive and context‐sensitive framework in both educational research and practice. This article serves as a rallying cry for a nuanced understanding that embraces the multifaceted nature of educational interactions, advocating for a move away from traditional metrics and toward more inclusive perspectives.

Evaluating the effectiveness of machine learning models for performance forecasting in basketball: a comparative study

Sports analytics (SA) incorporate machine learning (ML) techniques and models for performance prediction. Researchers have previously evaluated ML models applied on a variety of basketball statistics. This paper aims to benchmark the forecasting performance of 14 ML models, based on 18 advanced basketball statistics and key performance indicators (KPIs). The models were applied on a filtered pool of 90 high-performance players. This study developed individual forecasting scenarios per player and experimented using all 14 models. The models’ performance ranking was developed using a bespoke evaluation metric, called weighted average percentage error (WAPE), formulated from the weighted mean absolute percentage error (MAPE) evaluation results of each forecasted statistic and model. Moreover, we employed a comprehensive forecasting approach to improve KPI's results. Results showed that Tree-based models, namely Extra Trees, Random Forest, and Decision Tree, are the best performers in most of the forecasted performance indicators, with the best performance achieved by Extra Trees with a WAPE of 34.14%. In conclusion, we achieved a 3.6% MAPE improvement for the selected KPI with our approach on unseen data.

Combining Multiple Supervision for Robust Zero-Shot Dense Retrieval

Recently, dense retrieval (DR) models, which represent queries and documents with fixed-width vectors and retrieve relevant ones via nearest neighbor search, have drawn increasing attention from the IR community. However, previous studies have shown that the effectiveness of DR critically relies on sufficient training signals, which leads to severe performance degradation when applied in out-of-domain scenarios, where large-scale training data are usually unavailable. To solve this problem, existing studies adopt a data-augmentation-plus-joint-training paradigm to construct weak/pseudo supervisions on the target domain and combine them with the large-scale human annotated data on the source domain to train the DR models. However, they don't explicitly distinguish the data and the supervision signals in the training process and simply assume that the DR models are mighty enough to capture and memorize different domain knowledge and relevance matching patterns without guidance, which, as shown in this paper, is not true. Based on this observation, we propose a Robust Multi-Supervision Combining strategy (RMSC) that decouples the domain and supervision signals by explicitly telling the DR models how the domain data and supervision signals are combined in the training data with specially designed soft tokens. With the extra soft tokens to store the domain-specific and supervision-specific knowledge, RMSC allows the DR models to conduct retrieval based on human-like relevance matching patterns and target-specific language distribution on the target domain without human annotations. Extensive experiments on zero-shot DR benchmarks show that RMSC significantly improves the ranking performance on the target domain compared to strong DR baselines and domain adaptation methods, while being stable during training and can be combined with query generation or second-stage pre-training.

Explicable prioritization of genetic variants by integration of rule-based and machine learning algorithms for diagnosis of rare Mendelian disorders

BackgroundIn the process of finding the causative variant of rare diseases, accurate assessment and prioritization of genetic variants is essential. Previous variant prioritization tools mainly depend on the in-silico prediction of the pathogenicity of variants, which results in low sensitivity and difficulty in interpreting the prioritization result. In this study, we propose an explainable algorithm for variant prioritization, named 3ASC, with higher sensitivity and ability to annotate evidence used for prioritization. 3ASC annotates each variant with the 28 criteria defined by the ACMG/AMP genome interpretation guidelines and features related to the clinical interpretation of the variants. The system can explain the result based on annotated evidence and feature contributions.ResultsWe trained various machine learning algorithms using in-house patient data. The performance of variant ranking was assessed using the recall rate of identifying causative variants in the top-ranked variants. The best practice model was a random forest classifier that showed top 1 recall of 85.6% and top 3 recall of 94.4%. The 3ASC annotates the ACMG/AMP criteria for each genetic variant of a patient so that clinical geneticists can interpret the result as in the CAGI6 SickKids challenge. In the challenge, 3ASC identified causal genes for 10 out of 14 patient cases, with evidence of decreased gene expression for 6 cases. Among them, two genes (HDAC8 and CASK) had decreased gene expression profiles confirmed by transcriptome data.Conclusions3ASC can prioritize genetic variants with higher sensitivity compared to previous methods by integrating various features related to clinical interpretation, including features related to false positive risk such as quality control and disease inheritance pattern. The system allows interpretation of each variant based on the ACMG/AMP criteria and feature contribution assessed using explainable AI techniques.

Effect of Rim Seal Geometry on Rotationally-Driven Ingestion

Abstract This study investigates turbine rim seal geometry effects within the rotationally-driven ingestion regime. Computations were performed with a wall-resolved unsteady Reynolds-averaged Navier–Stokes (URANS) model and a large-eddy simulation (LES) model including near-wall boundary layer modeling, that is, wall-modeled LES (WMLES). Use of simplified rim sealing models is proposed as an efficient method of ranking seal designs and investigating sensitivity to seal geometry. Four rim seal configurations, two chute seals, an axial seal and a radial seal which are representative of those used in gas turbines and in previous research were investigated. Furthermore, hybrid seals combining geometric characteristics from both the chute and radial seal were considered. Significant sensitivities of sealing performance to turbulence modeling are identified, but URANS and WMLES show similar trends in ranking of seal performance, and these are consistent with previous experimental work. The addition of an outer radial clearance section to a chute seal is effective in reducing ingestion levels.

Local champions and change of governments: a longitudinal analysis of firms’ political ties in Gaziantep, Turkey

Abstract This article examines business–politics ties during a shift from multi-party politics to competitive-authoritarian rule in Turkey. We conducted a longitudinal investigation of the political ties and performance ranking of top manufacturing firms in a provincial industrial centre, Gaziantep. The analysis demonstrates that major power transitions in centre politics elicited variegated local responses and intra-group contestations. The leading business elites sustained political capital through a multi-scalar diversification of political ties. Using an agent, network, and institutions framework, we highlight the political dynamics behind sub-national growth trajectories, and contribute to scholarship on urban party politics and elite localism in economic geography.

SABER: Stochastic-Aware Bootstrap Ensemble Ranking for portfolio management

Portfolio management (PM) is a central subject of finance that presents many unresolved challenges. Although solutions have been proposed since the 1950’s, recent advances in machine learning are revolutionizing investments. Whereas supervised paradigms such as regression and classification are established in this direction, emerging research suggests that the learning-to-rank (LtR) paradigm can be more effective. Yet, these works are not many and overlook some important aspects. These aspects include the risk inherent in stocks, feature representation, portfolio cardinality and allocation. Addressing these gaps, this paper presents Stochastic-Aware Bootstrap Ensemble Ranking (SABER), an LtR method that directly minimizes the uncertainty and improves ranking performance. In addition, we propose Merged Bootstrap Selection for dynamic portfolio cardinality optimization. Trained on macroeconomic, fundamental and market indicators, the proposed methods outperform a state-of-the-art LtR approach, reaching annual returns and volatility of 83.48% and 29.89% across 6 years. The former also considers renowned weight allocation methods and achieves Sharpe and Sortino ratios of 2.79 and 5.28 respectively. Finally, the computational complexity of the proposed method is proven comparable to that of current methods.

SpaceGrow: efficient shape-based virtual screening of billion-sized combinatorial fragment spaces

The growing size of make-on-demand chemical libraries is posing new challenges to cheminformatics. These ultra-large chemical libraries became too large for exhaustive enumeration. Using a combinatorial approach instead, the resource requirement scales approximately with the number of synthons instead of the number of molecules. This gives access to billions or trillions of compounds as so-called chemical spaces with moderate hardware and in a reasonable time frame. While extremely performant ligand-based 2D methods exist in this context, 3D methods still largely rely on exhaustive enumeration and therefore fail to apply. Here, we present SpaceGrow: a novel shape-based 3D approach for ligand-based virtual screening of billions of compounds within hours on a single CPU. Compared to a conventional superposition tool, SpaceGrow shows comparable pose reproduction capacity based on RMSD and superior ranking performance while being orders of magnitude faster. Result assessment of two differently sized subsets of the eXplore space reveals a higher probability of finding superior results in larger spaces highlighting the potential of searching in ultra-large spaces. Furthermore, the application of SpaceGrow in a drug discovery workflow was investigated in four examples involving G protein-coupled receptors (GPCRs) with the aim to identify compounds with similar binding capabilities and molecular novelty.Graphical abstractSpaceGrow descriptor comparison for an example cut in the molecule of interest. Scoring scheme is implied for one fragment of this cut.

Performance ranking, regulatory penalty, and improper risk adjustment behavior of fund managers

This study is based on a sample dataset of Chinese open-end funds, empirically examining the relationship between performance ranking, regulatory penalty, and improper risk adjustment behavior of fund managers. The results indicate that class B penalties related to criminal offenses could mitigate the improper risk adjustment behavior resulting from agency issues among fund managers, thereby exerting an indirect deterrent effect. However, if the fund experiences a lower ranking in the mid-term evaluation or the top 20 stocks of a sample fund are subjected to regulatory penalties in the first half of the year, the impact is entirely reversed. Secondly, there is a positive correlation between the intensity of regulatory penalties and the degree of inhibition of fund managers’ improper risk adjustment behavior. The larger the expected gap and historically expected surplus, the more pronounced the trend of increasing improper risk-taking becomes. Finally, this paper conducted a collective study on the characteristics of fund managers and discovered that highly educated fund managers are significantly influenced by class B penalties, resulting in an indirect deterrent effect.

Investigation of LAS-based fatigue evaluation methods for high-viscosity modified asphalt binders with high-content polymers

The objective of this paper is to investigate the microstructure of high-viscosity modified asphalt binder (HVA) containing high-content polymers and its effect on fatigue performance, as well as the applicability of different analytical methods and failure criteria of linear amplitude sweep (LAS) test to HVA. In this paper, two types of HVAs with different polymer contents, a commercial HVA, a conventional polymer-modified asphalt binder, and a base asphalt binder were first prepared or selected. Fluorescence microscopy tests, time sweep tests, and LAS fatigue tests were then performed on these binders. Finally, the fatigue properties of the different binders were analyzed in depth using different analytical methods and failure criteria. The results show that the polymers in HVA crosslink with each other to form a strong cross-linking structure, and the phase inversion of the polymers renders them into a continuous phase, which significantly improves the fatigue damage resistance of the HVA; moreover, the evolutions of stress with shear strain for the fatigue process of HVAs is related to the polymer network. Besides, the two-dimensional damage characteristic curve (DCC) cannot accurately reflect the evolution of the fatigue performance of HVA, and this paper suggests the use of a three-dimensional DCC curve with strain information. Furthermore, the 35% reduction in C0 in the dissipative energy method is not a suitable failure criterion, a more accurate fatigue performance ranking can be obtained by using the yield stress as the failure criterion. Although the fatigue resistance of HVA after yield stress is theoretically considered using the pseudo-strain energy approach, HVA does not show a peak of stored pseudo-strain energy even when the strain is increased to 60% in the LAS test. Therefore, the maximum strain value (30%) in the standard LAS test is not sufficient in characterizing high-polymer HVAs because there is not enough fatigue accumulation of HVAs in this range, consequently, the maximum shear strain parameter in the LAS experiment needs to be reconsidered.

Assessment of adequacy of single number quantities for wall insulation performance based on the Zwicker’s loudness of transmitted indoor noise

Previous work suggested a method that makes use of the Zwicker’s loudness value of transmitted sound to validate single number quantities (SNQs) determining the airborne sound insulation of walls in dwellings. The correlation between the ranking of walls based on a SNQ, and the one based on transmitted sound loudness was used to qualify the adequacy of that SNQ. The underlying idea is that Zwicker’s loudness adequately represents people’s perception of loudness and that evaluations based on calculated loudness can substitute listening tests. The current work compares 16 SNQs: eight standardized or proposed in past studies, and eight new ones. The results are more robust, since more (56) walls were included, among which also highly isolating ones. SNQs are assessed for the indoor noise stimulus recently suggested by CSTB, France, and also for four filtered variants, and for two levels, the ‘usual’ or ‘normal’ level (as reported by CSTB), and that level increased by 10 dB, expressing the less common but psychologically and socially relevant situation of excessive neighbour noise. The adequacy of SNQs is analysed per wall category, (1) lightweight, (2) heavyweight, (3) CLT-based and (4) combined walls and per stimulus type. Three approaches are used to quantify the correlation between SNQ-based wall insulation performance rankings and Loudness based ones: (i) Spearman’s correlation coefficient, (ii) correlation and (iii) inverse RMS residual of fitting with a parametrized function that has been empirically conceived so as to describe as good as possible the correlation SNQ-Loudness correlation behaviour for the considered population of walls.

A comparative analysis of missing data imputation techniques on sedimentation data

Sediment data pertains to various hydrological variables with complex sediment hydrodynamics such as sedimentation rates which are often incompletely presented. Thus, the availability of sedimentation data is of utmost necessity for data accessibility. A comparative analysis on the missing fine sediment data imputation performance was made based on four different techniques, namely the k-Nearest Neighbourhood (k-NN), Support Vector Regression (SVR), Multiple Regression (MR), and Artificial Neural Network (ANN), under the single imputation (SI) and multiple imputation (MI) regimes. Across different missing data proportions (10%-50%), the ANN demonstrated optimal results with consistent performance metrics recorded over both SI and MI regimes. For the highest missing data proportion (50%), the ANN presented the best imputation performance with a reported root mean squared error (RMSE) 0.000882, mean absolute error (MAE) 0.000595, coefficient of determination (R2) 71%, and Kling-Gupta Efficiency (KGE) 72%. The imputation performance ranking is as follows: ANN, SVR, MR, and k-NN.

Prediction of optimal dimensions and arrangements of circular tubes of a fin and tube heat exchanger for an improved thermal-hydraulic performance

In the present work, the thermal-hydraulic performance of a fin and tube compact heat exchanger (FTHE) with different spatial arrangements of tubes and variations of tube diameter is investigated numerically. The three types of spatial arrangement of the tubes, namely inline, staggered row (SR), and staggered column (SC) arrangements, and five different tube diameters starting from Dc = 6.51 mm to Dc = 10.51 mm considered with a constant increment of 1 mm, are investigated for the augmentation of the heat transfer. The entire analysis is conducted under the low inlet velocity ranging from 0.7 m/s to 1.1 m/s. The results indicate that FTHEs (flow-through heat exchangers) have better thermal-hydraulic performance when arranged in staggered column configurations compared to inline and staggered row arrangements. However, it was observed that the pressure drop increased in staggered column arrangements compared to inline and staggered row arrangements. The performance ranking method MOORA is applied to optimize the spatial arrangements of tubes. The study’s findings suggest that the performance of FTHE is better in the case of staggered column arrangements, which offer a fair agreement with performance evaluation criteria (PEC3 = Q/(Pf +Pp )). After achieving the staggered column arrangement as an optimum spatial arrangement of the tubes, the influence of tube diameter on the performance of FTHEs is investigated for this arrangement only. The findings indicate that when the tube diameter is increased, there is a corresponding increase in both the characteristics like heat transfer coefficient and the pressure drop penalty. The performance ranking method MOORA is applied to get an optimum tube diameter, which is cross-checked by the TOPSIS method. Based on PEC3 and two optimization approaches, MOORA and TOPSIS, it is revealed that a larger tube diameter leads to better overall performance of the FTHEs.

Risk Assessment for Electricity Generation Management Process with SWARA Based Fuzzy TOPSIS Method

In the successful maintenance of electricity generation management processes in power generation plants, it is of great importance to determine the risks that may arise during the operation of the relevant processes, take measures to minimize these risks, and take the necessary actions. In this study, common risks in the electricity generation management process in HEPPs were identified and these risks were rated by experts (decision-makers) within each power plant itself. Since this rating is made by the experts of each power plant, the impact and probability values of the same risk may differ, and accordingly, different risk levels may arise for the same risk. In the study, the SWARA method was used to compare the risk levels of common risks in the electricity generation process in different power plants and calculate the final weight values of the related risks. As a result of the measures determined for each risk in the electricity generation management processes in the power plants and the actions taken for these measures, it was determined whether the relevant risks were reduced to acceptable levels by looking at the results of the internal audits. In the internal audits, the performance of HEPPs in eliminating the related risks is evaluated with fuzzy expressions separately for each risk. The risk weight values obtained by the SWARA method and the fuzzy expressions obtained as a result of the risk assessment were analyzed with the Fuzzy TOPSIS method, and the performance values of the power plants in eliminating the risks were calculated, then the performance ranking was made in the light of these values.