COMPARATIVE ANALYSIS OF CORPORATE DISTRESS PREDICTION MODELS: A DYNAMIC PERFORMANCE EVALUATION FRAMEWORK

2016 ◽  
Author(s):  
Mohammad Mahdi Mousavi ◽  
Jamal Ouenniche
Keyword(s):  
Performance Evaluation ◽  
Comparative Analysis ◽  
Prediction Models ◽  
Dynamic Performance ◽  
Evaluation Framework ◽  
Distress Prediction ◽  
Corporate Distress

scholarly journals A comparative analysis of two-stage distress prediction models

2019 ◽  
Vol 119 ◽  
pp. 322-341 ◽  
Author(s):  
Mohammad Mahdi Mousavi ◽  
Jamal Ouenniche ◽  
Kaoru Tone
Keyword(s):  
Comparative Analysis ◽  
Prediction Models ◽  
Two Stage ◽  
Distress Prediction

A Dynamic Performance Evaluation Model for Target Capture by Robot Mechanisms, With the Consideration of Structural Flexibility

1999 ◽  
Author(s):  
J. Kövecses ◽  
W. L. Cleghorn ◽  
R. G. Fenton
Keyword(s):  
Performance Evaluation ◽  
Rigid Body ◽  
Dynamic System ◽  
Robot Manipulator ◽  
Dynamic Performance ◽  
Evaluation Framework ◽  
Robot Arm ◽  
Constrained Motion ◽  
Reference Element ◽  
Target Capture

Abstract In this paper a dynamic system consisting of a robot manipulator and a target is analyzed. The target is considered in a general way as a dynamic subsystem having finite mass and moments of inertia (e.g. a rigid body or a second robot). The situation investigated is when the robot establishes interaction with the target in such a way that it intercepts and captures a reference element of the target. The greatest attention is paid to the analysis of the phase of transition from from ‘free’ to constrained motion at the time of interception and capture (impulsive motion). Based on the use of impulsive constraints and the Jourdanian formulation of analytical dynamics, a novel approach is proposed for the modeling of target capture by a robot manipulator. The proposed approach is suitable to handle both finite and impulsive motions in a common analytical framework. Based on the dynamic model developed and using a geometric representation of the system’s dynamics, a detailed analysis and a performance evaluation framework are presented for the phase of transition. Both rigid and structurally flexible models of robots are considered. For the performance evaluation analyses, two main concepts are proposed and corresponding performance measures are derived. These tools may be used in the analysis, design and control of time varying robotic systems. The dynamic system of a three link robot arm capturing a rigid body is used to illustrate the material presented.

Evaluating the Operational Performance of Manufacturing Enterprises: An Evaluation Framework and Application to Pakistani Industry

1985 ◽  
Vol 24 (3-4) ◽  
pp. 703-719 ◽  
Author(s):  
Abdul Hafeez Shaikh
Keyword(s):  
Performance Evaluation ◽  
Final Section ◽  
Technology Development ◽  
Evaluation Framework ◽  
Relative Performance ◽  
Operational Performance ◽  
Special Focus ◽  
Manufacturing Enterprises ◽  
Private And Public

This study has two objectives; (i) to develop a framework for evaluating the operational performance of manufacturing enterprises, and (ii) to evaluate the trend in the performance of Pakistan's vegetable ghee industry for the 1970- 1980 period, with special focus on its relative performance under private and public ownerships. Section II is devoted to the vegetable ghee industry itself - its technology, development, pricing and distribution policies. In Section III a framework for performance evaluation is developed. In Section IV we evaluate in a series of steps - the performance of Pakistan's vegetable ghee industry. The final section is devoted to concluding comments.

scholarly journals SRPerf: a Performance Evaluation Framework for IPv6 Segment Routing

2020 ◽  
pp. 1-1
Author(s):  
Ahmed Abdelsalam ◽  
Pier Luigi Ventre ◽  
Carmine Scarpitta ◽  
Andrea Mayer ◽  
Stefano Salsano ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Evaluation Framework ◽  
A Performance

scholarly journals The Role of Board Independence and Ownership Structure in Improving the Efficacy of Corporate Financial Distress Prediction Model Evidence from India

2021 ◽  
Vol 14 (7) ◽  
pp. 333
Author(s):  
Shilpa H. Shetty ◽  
Theresa Nithila Vincent
Keyword(s):  
Prediction Model ◽  
Ownership Structure ◽  
Financial Distress ◽  
Prediction Models ◽  
Receiver Operating Curve ◽  
Financial Measures ◽  
Financial Variables ◽  
Financial Distress Prediction ◽  
Distress Prediction

The study aimed to investigate the role of non-financial measures in predicting corporate financial distress in the Indian industrial sector. The proportion of independent directors on the board and the proportion of the promoters’ share in the ownership structure of the business were the non-financial measures that were analysed, along with ten financial measures. For this, sample data consisted of 82 companies that had filed for bankruptcy under the Insolvency and Bankruptcy Code (IBC). An equal number of matching financially sound companies also constituted the sample. Therefore, the total sample size was 164 companies. Data for five years immediately preceding the bankruptcy filing was collected for the sample companies. The data of 120 companies evenly drawn from the two groups of companies were used for developing the model and the remaining data were used for validating the developed model. Two binary logistic regression models were developed, M1 and M2, where M1 was formulated with both financial and non-financial variables, and M2 only had financial variables as predictors. The diagnostic ability of the model was tested with the aid of the receiver operating curve (ROC), area under the curve (AUC), sensitivity, specificity and annual accuracy. The results of the study show that inclusion of the two non-financial variables improved the efficacy of the financial distress prediction model. This study made a unique attempt to provide empirical evidence on the role played by non-financial variables in improving the efficiency of corporate distress prediction models.

scholarly journals Financial Information Asymmetry: Using Deep Learning Algorithms to Predict Financial Distress

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 443
Author(s):  
Chyan-long Jan
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Information Asymmetry ◽  
Error Rate ◽  
Financial Distress ◽  
Prediction Models ◽  
High Accuracy ◽  
Financial Information ◽  
Financial Distress Prediction ◽  
Distress Prediction

Because of the financial information asymmetry, the stakeholders usually do not know a company’s real financial condition until financial distress occurs. Financial distress not only influences a company’s operational sustainability and damages the rights and interests of its stakeholders, it may also harm the national economy and society; hence, it is very important to build high-accuracy financial distress prediction models. The purpose of this study is to build high-accuracy and effective financial distress prediction models by two representative deep learning algorithms: Deep neural networks (DNN) and convolutional neural networks (CNN). In addition, important variables are selected by the chi-squared automatic interaction detector (CHAID). In this study, the data of Taiwan’s listed and OTC sample companies are taken from the Taiwan Economic Journal (TEJ) database during the period from 2000 to 2019, including 86 companies in financial distress and 258 not in financial distress, for a total of 344 companies. According to the empirical results, with the important variables selected by CHAID and modeling by CNN, the CHAID-CNN model has the highest financial distress prediction accuracy rate of 94.23%, and the lowest type I error rate and type II error rate, which are 0.96% and 4.81%, respectively.

Client-driven performance-evaluation framework for municipal infrastructure

2021 ◽  
pp. 1-18
Author(s):  
Khaled Shahata ◽  
Samer El-Zahab ◽  
Tarek Zayed ◽  
Ghasan Alfalah
Keyword(s):  
Performance Evaluation ◽  
Evaluation Framework
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