scholarly journals The effect of COVID-19 on the Egyptian exchange using principal component analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Heba M. Ezzat
Keyword(s):  
Principal Component Analysis ◽  
Principal Component Regression ◽  
Principal Component ◽  
Component Analysis ◽  
Content Type ◽  
Future Behavior ◽  
Data Variability ◽  
Food And Beverage ◽  
The Impact ◽  
Dimensionality Reduction Method

PurposeSince the beginning of 2020, economies faced many changes as a result of coronavirus disease 2019 (COVID-19) pandemic. The effect of COVID-19 on the Egyptian Exchange (EGX) is investigated in this research.Design/methodology/approachTo explore the impact of COVID-19, three periods were considered: (1) 17 months before the spread of COVID-19 and the start of the lockdown, (2) 17 months after the spread of COVID-19 and the during the lockdown and (3) 34 months comprehending the whole period (before and during COVID-19). Due to the large number of variables that could be considered, dimensionality reduction method, such as the principal component analysis (PCA) is followed. This method helps in determining the most individual stocks contributing to the main EGX index (EGX 30). The PCA, also, addresses the multicollinearity between the variables under investigation. Additionally, a principal component regression (PCR) model is developed to predict the future behavior of the EGX 30.FindingsThe results demonstrate that the first three principal components (PCs) could be considered to explain 89%, 85%, and 88% of data variability at (1) before COVID-19, (2) during COVID-19 and (3) the whole period, respectively. Furthermore, sectors of food and beverage, basic resources and real estate have not been affected by the COVID-19. The resulted Principal Component Regression (PCR) model performs very well. This could be concluded by comparing the observed values of EGX 30 with the predicted ones (R-squared estimated as 0.99).Originality/valueTo the best of our knowledge, no research has been conducted to investigate the effect of the COVID-19 on the EGX following an unsupervised machine learning method.

Evaluation of the Aging Behavior of High Density Polyethylene in Thermal Oxidative Environment by Principal Component Analysis

2017 ◽  
Vol 727 ◽  
pp. 447-449 ◽  
Author(s):  
Jun Dai ◽  
Hua Yan ◽  
Jian Jian Yang ◽  
Jun Jun Guo
Keyword(s):  
Principal Component Analysis ◽  
Impact Strength ◽  
High Density Polyethylene ◽  
Tensile Modulus ◽  
Principal Component ◽  
Component Analysis ◽  
High Density ◽  
Aging Behavior ◽  
Data Set ◽  
The Impact

To evaluate the aging behavior of high density polyethylene (HDPE) under an artificial accelerated environment, principal component analysis (PCA) was used to establish a non-dimensional expression Z from a data set of multiple degradation parameters of HDPE. In this study, HDPE samples were exposed to the accelerated thermal oxidative environment for different time intervals up to 64 days. The results showed that the combined evaluating parameter Z was characterized by three-stage changes. The combined evaluating parameter Z increased quickly in the first 16 days of exposure and then leveled off. After 40 days, it began to increase again. Among the 10 degradation parameters, branching degree, carbonyl index and hydroxyl index are strongly associated. The tensile modulus is highly correlated with the impact strength. The tensile strength, tensile modulus and impact strength are negatively correlated with the crystallinity.

A principal component analysis of the determinants of spatial disparity between rural and urban localities of Ghana

2017 ◽  
Vol 44 (6) ◽  
pp. 715-731 ◽  
Author(s):  
Ivy Drafor
Keyword(s):  
Principal Component Analysis ◽  
Rural Areas ◽  
Urban Areas ◽  
Principal Component ◽  
Component Analysis ◽  
Spatial Inequality ◽  
Necessary Condition ◽  
Spatial Disparity ◽  
Content Type ◽  
Rural And Urban

Purpose The purpose of this paper is to analyse the spatial disparity between rural and urban areas in Ghana using the Ghana Living Standards Survey’s (GLSS) rounds 5 and 6 data to advance the assertion that an endowed rural sector is necessary to promote agricultural development in Ghana. This analysis helps us to know the factors that contribute to the depravity of the rural sectors to inform policy towards development targeting. Design/methodology/approach A multivariate principal component analysis (PCA) and hierarchical cluster analysis were applied to data from the GLSS-5 and GLSS-6 to determine the characteristics of the rural-urban divide in Ghana. Findings The findings reveal that the rural poor also spend 60.3 per cent of their income on food, while the urban dwellers spend 49 per cent, which is an indication of food production capacity. They have low access to information technology facilities, have larger household sizes and lower levels of education. Rural areas depend a lot on firewood for cooking and use solar/dry cell energies and kerosene for lighting which have implications for conserving the environment. Practical implications Developing the rural areas to strengthen agricultural growth and productivity is a necessary condition for eliminating spatial disparities and promoting overall economic development in Ghana. Addressing rural deprivation is important for conserving the environment due to its increased use of fuelwood for cooking. Absence of alternatives to the use of fuelwood weakens the efforts to reduce deforestation. Originality/value The application of PCA to show the factors that contribute to spatial inequality in Ghana using the GLSS-5 and GLSS-6 data is unique. The study provides insights into redefining the framework for national poverty reduction efforts.

Factorial analysis of stress factors among the sample of Lebanese police officers

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fadi Afif Fayyad ◽  
Filip Vladimir Kukić ◽  
Nemanja Ćopić ◽  
Nenad Koropanovski ◽  
Milivoj Dopsaj
Keyword(s):  
Principal Component Analysis ◽  
Police Officers ◽  
Psychological Health ◽  
Social Life ◽  
Principal Component ◽  
Component Analysis ◽  
Stress Factors ◽  
Research Approach ◽  
Content Type ◽  
Stress Management Training

PurposeThe purpose of the study is to determine the prevalence of stress and to identify the occupational stressors among Lebanese police officers.Design/methodology/approachOperational Police Stress Questionnaire (PSQ-op) was addressed to 100 randomly selected male Lebanese Police officers. Twenty items from the PSQ-op were run through the principal component analysis to determine the most significant factors of stress and loading within each of the factors.FindingsThe results indicated that 59% of officers reported moderate stress level and 41% reported strenuous stress. Principal component analysis identified six independent factors or stress among Lebanese police officers explaining in total 72.1% of the total variance: excessive workload (30.6%), social-life time management (12.8%), occupational fitness (9.1%), success-related stress (8.6%), physical and psychological health (5.8%), and working alone at night (5.2%).Research limitations/implicationsThis research approach encountered some limitations so further research must: use a larger sample size, include female gender and identify other sources of stressors mainly organizational or job context stressors.Originality/valueAddressing and understanding stress factors among Lebanese police officers helps improving awareness and developing individualized treatment strategies leading police officers to engage in stress-management training to learn coping strategies and use effective tools for preventing stress before it becomes chronic.

Use of Chemometrics: Principal Component Analysis (PCA) and Principal Component Regression (PCR) for the Authentication of Orange Juice

1999 ◽  
Vol 32 (15) ◽  
pp. 3131-3141 ◽  
Author(s):  
Stella Vaira ◽  
Víctor. E. Mantovani ◽  
Juan C. Robles ◽  
Juan C. Sanchis ◽  
Héctor C. Goicoechea
Keyword(s):  
Principal Component Analysis ◽  
Orange Juice ◽  
Principal Component Regression ◽  
Principal Component ◽  
Component Analysis

scholarly journals Global money laundering appeal index: application of principal component analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Milind Tiwari ◽  
Adrian Gepp ◽  
Kuldeep Kumar
Keyword(s):  
Principal Component Analysis ◽  
Money Laundering ◽  
Principal Component ◽  
Component Analysis ◽  
Economic Feasibility ◽  
Ranking System ◽  
Single Measure ◽  
Financial Flows ◽  
Content Type ◽  
To Come

Purpose The paper aims at developing a global ranking system determining a country's appeal as a destination for money laundering. Design/methodology/approach This paper uses principal component analysis (PCA), with a mix of standardised and unstandardised components relating to attractiveness, economic freedom and money laundering risk to come up with an index of money laundering appeal. Findings Four components relating to economic feasibility, financial liberty, government spending and tax regime are critical in influencing a country's money laundering appeal. Research limitations/implications This paper attempts to use a standardised and replicable methodology to condense into a single measure the complex and multifaceted phenomenon of a country's appeal as a destination for money laundering, thus avoiding the difficulty associated with precisely calculating illicit financial flows. Practical implications The ranking system could be used to determine the destinations attractive for laundering money. Such information can be used to come up with more effective preventative strategies to combat phenomena responsible for the stagnation of economic growth through tax evasion, corruption and creation of non-competitive markets. Originality/value It is the first attempt to use a statistical technique to understand the underlying components of a country's money laundering appeal.

scholarly journals METODE STATISTIK PRINCIPAL COMPONENT REGRESSION DALAM ANALISIS HUBUNGAN KUANTITATIF STRUKTUR DAN AKTIVITAS PENGHAMBAT LIPOKSIGENASE SENYAWA TURUNAN KURKUMIN

2018 ◽  
Vol 1 (1) ◽  
pp. 60
Author(s):  
Didi Nurhadi
Keyword(s):  
Principal Component Analysis ◽  
Principal Component Regression ◽  
Principal Component ◽  
Component Analysis

ABSTRAK Hubungan Kuantitatif Struktur dan Aktivitas (HKSA) pada suatu seri senyawa turunan kurkumin telah dikaji dengan menggunakan data muatan bersih atom hasil perhitungan semi empirik AM1 dengan pendekatan Principal Component Regression PCR. Pengkajian dilakukan terhadap data aktivitas antiinflamasi yang menghambat lipoksigenase (log (1/IC50)) sebagai fungsi linear dan variable laten (Tx) hasil transformasi data muatan bersih atom menggunakan Principal Component Analysis (PCA). Persamaan HKSA ditentukan berdasar kontribusi komponen yang terpilih dan selanjutnya dianalisis dengan pendekatan Model persamaan HKSA yang diperoleh adalah: log (1/IC50) = -0,669-1,816.T1+1,697.T2 –3,643.T3 Persamaan tersebut mempunyai tingkat kepercayaan 95 % dengan parameter statistik n =9,  r2 = 0.700,  SE = 0,355, Fhitung/Ftabel=1,19 dan PRESS = 0,082.  Kata kunci : HKSA, kurkumin, lipoksigenase, PCA, muatan bersih atom

Sociodemographic characteristics and dietary patterns in cardiometabolic risk subjects

2019 ◽  
Vol 121 (11) ◽  
pp. 2780-2790 ◽  
Author(s):  
Brenda Kelly Souza Silveira ◽  
Juliana Farias de Novaes ◽  
Sarah Aparecida Vieira ◽  
Daniela Mayumi Usuda Prado Rocha ◽  
Arieta Carla Gualandi Leal ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Dietary Patterns ◽  
Cardiovascular Health ◽  
Cardiometabolic Risk ◽  
Principal Component ◽  
Component Analysis ◽  
Unhealthy Lifestyle ◽  
Cross Sectional ◽  
Content Type ◽  
Traditional Pattern

Purpose The purpose of this paper is to examine the associations of dietary patterns with sociodemographic and lifestyle characteristics in a cardiometabolic risk population. Design/methodology/approach In this cross-sectional study data from 295 (n=123 men/172 women, 42±16 years) participants in a Cardiovascular Health Care Program were included. After a 24-hour recall interview the dietary patterns were determined using principal component analysis. Sociodemographic, clinical and lifestyle data were collected by medical records. Findings Subjects with diabetes and hypertension had a higher adherence in the “traditional” pattern (rice, beans, tubers, oils and meats). Poisson regression models showed that male subjects with low schooling and smokers had greater adherence to the “traditional” pattern. Also, students, women, and those with higher schooling and sleeping =7 h/night showed higher adherence to healthy patterns (whole grains, nuts, fruits and dairy). Women, young adults and those with higher schooling and fewer sleep hours had greater adherence to healthy dietary patterns. Those with low schooling and unhealthy lifestyle showed more adherence to the “traditional” pattern. Social implications The results indicate the importance to personalized nutritional therapy and education against cardiometabolic risk, considering the dietary patterns specific to each population. Originality/value Socioeconomic and lifestyle characteristics can influence dietary patterns and this is one of the few studies that investigated this relationship performing principal component analysis.

Quantitative measurement of complex substances dissolved in an ionic liquid using IR spectroscopy and chemometrics

2017 ◽  
Vol 84 (1) ◽  
Author(s):  
Johannes Kiefer ◽  
Andreas Bösmann ◽  
Peter Wasserscheid
Keyword(s):  
Principal Component Analysis ◽  
Ionic Liquid ◽  
Principal Component Regression ◽  
Principal Component ◽  
Component Analysis ◽  
Least Squares Regression ◽  
Catalytically Active ◽  
Regression Techniques ◽  
Active Substances ◽  
Better Than

AbstractIn the past two decades, ionic liquids have found many applications as solvents for complex solutes. Prominent examples are the dissolution of biomass and carbohydrates as well as catalytically active substances. The chemical analysis of such solutions, however, is still a challenge due to the molecular complexity. In the present work, the use of infrared spectroscopy for quantifying the concentration of different solutes dissolved in an imidazolium-based ionic liquid is investigated. Binary solutions of glucose, cellubiose, and Wilkinson's catalyst in 1-ethyl-3-methylimidazolium acetate are studied as examples. For this purpose, different chemometric approaches (principal component analysis (PCA), partial least-squares regression (PLSR), and principal component regression (PCR)) for analyzing the spectra are tested. Principal component analysis was found to be suitable for classifying the different solutions. Both regression techniques were capable of deriving accurate concentration values. The performance of PLSR was slightly better than that of PCR for the same number of components.

Sign in / Sign up

Export Citation Format

Share Document