scholarly journals Principal component analysis in determining representative financial ratios within non-life insurance sector in Serbia

2021 ◽  
Vol 69 (6-7) ◽  
pp. 306-317
Author(s):  
Vladimir Vasić ◽  
Jelena Kočović ◽  
Marija Koprivica
Keyword(s):  
Principal Component Analysis ◽  
Life Insurance ◽  
Financial Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Capital Adequacy ◽  
Financial Ratios ◽  
Insurance Companies ◽  
Insurance Sector ◽  
And Performance

The paper deals with the application of principal component analysis in determining financial ratios that are representative within non-life insurance sector. Starting from many financial indicators found in the literature in the field of insurance, the purpose of the study is to identify a smaller set of ratios that are most relevant for assessing the financial position and performance of non-life insurance companies in Serbia with a minimum loss of information. On the basis of financial reports of nonlife and composite insurers in the period 2010-2019, we calculated 38 financial ratios, grouped into seven categories (capital adequacy, asset quality, reinsurance risk and performance, adequacy of technical reserves, profitability, liquidity and management soundness). Using parallel analysis and Velicer's minimum average partial test, we found that it is possible to explain 85% of variability of the initial set of ratios with six financial ratios. The obtained results can be used for the purposes of efficient financial analysis of individual insurance companies and the entire nonlife insurance sector in Serbia.

Bankruptcy Prediction Using Principal Component Analysis Based Neural Network Models

2010 ◽  
pp. 138-156
Author(s):  
G. A. Rekha Pai ◽  
G. A. Vijayalakshmi Pai
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Prediction Models ◽  
Network Models ◽  
Principal Component ◽  
Component Analysis ◽  
Bankruptcy Prediction ◽  
Financial Ratios ◽  
Neural Network Models ◽  
The Individual

Industrial bankruptcy is a rampant problem which does not occur overnight and when it occurs can cause acute financial embarrassment to Governments and financial institutions as well as threaten the very viability of the firms. It is therefore essential to help industries identify the impending trouble early. Several statistical and soft computing based bankruptcy prediction models that make use of financial ratios as indicators have been proposed. Majority of these models make use of a selective set of financial ratios chosen according to some appropriate criteria framed by the individual investigators. In contrast, this study considers any number of financial ratios irrespective of the industrial category and size and makes use of Principal Component Analysis to extract their principal components, to be used as predictors, thereby dispensing with the cumbersome selection procedures used by its predecessors. An Evolutionary Neural Network (ENN) and a Backpropagation Neural Network with Levenberg Marquardt’s training rule (BPN) have been employed as classifiers and their performance has been compared using Receiver Operating Characteristics (ROC) analyses. Termed PCA-ENN and PCA-BPN models, the predictive potential of the two models have been analyzed over a financial database (1997-2000) pertaining to 34 sick and 38 non sick Indian manufacturing companies, with 21 financial ratios as predictor variables.

Shape of Western Australian dragon lizards (Agamidae)

2005 ◽  
Vol 26 (1) ◽  
pp. 73-85 ◽  
Author(s):  
Philip Withers ◽  
Graham Thompson
Keyword(s):  
Principal Component Analysis ◽  
Body Length ◽  
Size Effects ◽  
Principal Component ◽  
Component Analysis ◽  
Regression Residuals ◽  
Performance Traits ◽  
And Performance ◽  
Western Australian

AbstractFor 41 species of Western Australian agamid lizards, we found that most appendage lengths vary isometrically, so shape is largely independent of size. Of the three methods we used to quantitatively remove the effects of size on shape, the two that use principal component analysis (PCA; Jolicoeur, 1963; Somers, 1986; 1989) provided similar results, whereas regression residuals (against body length) provided a different interpretation. Somers' size-free PCA approach to remove the size-effects was the most useful because it provided 'size-free' scores for each species that were further analysed using other techniques, and its results seemed more biologically meaningful. Some, but not all, of the variation in size-free shape for these lizards could be related to phylogeny, retreat choice and performance traits.

scholarly journals Improved Approach for Identification of Real and Fake Smile using Chaos Theory and Principal Component Analysis

2019 ◽  
Vol 54 (5) ◽  
Author(s):  
Hayder Ansaf ◽  
Hayder Najm ◽  
Jasim Mohammed Atiyah ◽  
Oday A. Hassen
Keyword(s):  
Principal Component Analysis ◽  
Chaos Theory ◽  
Principal Component ◽  
Component Analysis ◽  
Human Beings ◽  
Multiple Parameters ◽  
Detection Approach ◽  
The Face ◽  
And Performance ◽  
Smile Detection

The smile detection approach is quite prominent with the face detection and thereby the enormous implementations are prevalent so that the higher degree of accuracy can be achieved. The face smile detection is widely associated to have the forensic of faces of human beings so that the future predictions can be done. In chaos theory, the main strategy is to have the cavernous analytics on the single change and then to predict the actual faces in the analysis. In addition, the integration of Principal Component Analysis (PCA) is integrated to have the predictions with more accuracy. This work proposes to use the analytics on the parallel integration of PCA and chaos theory to enable the face smile and fake identifications to be made possible. The projected work is analyzed using assorted parameters and it has been found that the deep learning integration approach for chaos and PCA is quite important and performance aware in the multiple parameters with the different datasets in evaluations.

scholarly journals Financial analysis of the South African life insurance sector - An empirical decomposition of economic value added

2016 ◽  
Vol 9 (3) ◽  
pp. 903-926
Author(s):  
Paul Alagidede ◽  
Takalani Mangenge
Keyword(s):  
Life Insurance ◽  
Asset Management ◽  
Financial Analysis ◽  
Insurance Industry ◽  
Economic Value ◽  
Value Added ◽  
Principal Component ◽  
Economic Value Added ◽  
Value Drivers ◽  
Insurance Sector

This article examines the determinants of economic value added (EVA) in insurance industries. It addresses the key components of EVA, the value drivers that are more important in managing economic value and the combination of these value drivers that best explain EVA as a group. The study covers the life insurance sector in South Africa, specifically focusing on the big five companies: Discovery Holdings, Liberty Holdings, MMI Holdings, Old Mutual plc, and Sanlam Ltd for the period 2004-2014. Variance and principal component analyses are used to identify the main drivers of EVA. Five main drivers were prominent, namely: underwriting, asset management, costs, opportunity cost and strategic investments. The implications of the results for best practice in the insurance industry are discussed.

Operational Risk Identification and Evaluation System of Power Grid Enterprises Based on Principal Component Analysis

2015 ◽  
Vol 738-739 ◽  
pp. 271-274
Author(s):  
Yi Xin Sun ◽  
Hong Xing Wei ◽  
Qing You Yan
Keyword(s):  
Principal Component Analysis ◽  
Asset Management ◽  
Evaluation System ◽  
Financial Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Risk Identification ◽  
Control Grid ◽  
Specific Analysis ◽  
Current Risk

This paper used financial analysis, facing on the current risk management of grid corporate assets, took the principal component analysis as the basic method. It identified five specific analysis of the control grid enterprise asset management risks, and choose the actual data for empirical analysis.

Branding efforts and SME performance – an empirical investigation of variations across firm sizes and business sectors

2017 ◽  
Vol 19 (1) ◽  
pp. 59-76 ◽  
Author(s):  
Raphael Odoom ◽  
Priscilla Mensah ◽  
George Asamoah
Keyword(s):  
Principal Component Analysis ◽  
Emerging Market ◽  
Pearson Correlation ◽  
Principal Component ◽  
Component Analysis ◽  
Content Type ◽  
Brand Building ◽  
Brand Marketing ◽  
And Performance ◽  
Stage Analysis

Purpose This paper aims to draw on the organizational ecology theory to examine variations in branding efforts and performance of small and medium-sized enterprises (SMEs) across enterprises sizes and business operating sectors. Design/methodology/approach A four-stage analysis involving principal component analysis, Pearson correlation, ANOVA and logistic regressions was used on a sample of 430 SMEs within an emerging market. Findings Principal component analysis identified four brand marketing efforts relevant to the SMEs. These efforts were used in fluctuating extents among small-sized versus medium-sized enterprises, as well as manufacturing versus services SMEs. Additionally, proportionate levels of performance corollaries were found to be accruable across the enterprise sizes and operating sectors. Originality/value The paper first identifies four brand-building efforts germane to SMEs within an emerging market and examines their precise contributions to firm performance within enterprise sizes and business operating sectors. It further reinforces the relevance of brand marketing programs to the growth of SMEs by establishing the likelihood and extent to which brand-building efforts impact on SME performance across enterprise sizes, as well as operating sectors. The study also presents issues of potential research and managerial interest from an emerging market, offering insightful implications to researchers and SME managers.

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