scholarly journals Statistical and computational techniques for extraction of underlying systematic risk factors: a comparative in the Mexican stock exchange

2021 ◽  
Vol 13 (2) ◽  
pp. 237-268
Author(s):  
Rogelio ◽  
Salvador Torra Porras ◽  
Enric Monte Moreno

This paper compares the dimension reduction or feature extraction techniques, e.g., Principal Component Analysis, Factor Analysis, Independent Component Analysis and Neural Networks Principal Component Analysis, which are used as techniques for extracting the underlying systematic risk factors driving the returns on equities of the Mexican Stock Exchange, under a statistical approach to the Arbitrage Pricing Theory. We carry out our research according to two different perspectives. First, we evaluate them from a theoretical and matrix scope, making a parallelism among their particular mixing and demixing processes, as well as the attributes of the factors extracted by each method. Secondly, we accomplish an empirical study in order to measure the level of accuracy in the reconstruction of the original variables.

2021 ◽  
Vol 13 (2) ◽  
pp. 513-543
Author(s):  
Rogelio ◽  
Salvador Torra Porras ◽  
Enric Monte Moreno

This paper compares the dimension reduction or feature extraction techniques, e.g., Principal Component Analysis, Factor Analysis, Independent Component Analysis and Neural Networks Principal Component Analysis, which are used as techniques for extracting the underlying systematic risk factors driving the returns on equities of the Mexican Stock Exchange, under a statistical approach to the Arbitrage Pricing Theory. We carry out our research according to two different perspectives. First, we evaluate them from a theoretical and matrix scope, making a parallelism among their particular mixing and demixing processes, as well as the attributes of the factors extracted by each method. Secondly, we accomplish an empirical study in order to measure the level of accuracy in the reconstruction of the original variables.


2021 ◽  
Vol 16 (TNEA) ◽  
pp. 1-25
Author(s):  
Rogelio Ladrón de Guevara Cortés ◽  
Salvador Torra Porras ◽  
Enric Monte Moreno

The objective of this paper is to compare four dimension reduction techniques used for extracting the underlying systematic risk factors driving returns on equities of the Mexican Market. The methodology used compares the results of estimation produced by Principal Component Analysis (PCA), Factor Analysis (FA), Independent Component Analysis (ICA), and Neural Networks Principal Component Analysis (NNPCA) under three different perspectives. The results showed that in general: PCA, FA, and ICA produced similar systematic risk factors and betas; NNPCA and ICA produced the greatest number of fully accepted models in the econometric contrast; and, the interpretation of systematic risk factors across the four techniques was not constant. Additional research testing alternative extraction techniques, econometric contrast, and interpretation methodologies are recommended, considering the limitations derived from the scope of this work. The originality and main contribution of this paper lie in the comparison of these four techniques in both the financial and Mexican contexts. The main conclusion is that depending on the purpose of the analysis, one technique will be more suitable than another.


2016 ◽  
Vol 12 (7) ◽  
pp. 316
Author(s):  
Abeku Atta Asare-Kumi ◽  
Kwasi Adjepong Darkwah ◽  
Ezekiel N. N. Nortey ◽  
Charlotte Chapman-Wardy

Today, several factors has contributed to the profitability of Banks. Data collected on these factors often has several variables. It is a non-trivial exercise to determine which of the factors that significantly influences the profits of Banks. This paper adopts the use of Principal Component Analysis (PCA) on the several variables expected to influence the working capital management on the profits of banks of the Ghana Stock Exchange (GSE). Fifteen of the several variables captured by the GSE which affect working capital management on profit were grouped into four factors using the principal component analysis. Results of the PCA identifies Convertibility factors, Risk Factor, Short term Liquidity, Operational factors, and Credit Risk factors to be the determinants of bank profits. Consequently, these factors are used to fit a linear regression model in identifying the most significant factors. Apart from credit risk factors, all the other factors were found to be significant predictors of the profit of Ghanaian banks. Investors, stakeholders, and managers of banks can use these factors to monitor and evaluate their working capital in generating profits.


2022 ◽  
Author(s):  
Jaime González Maiz Jiménez ◽  
Adán Reyes Santiago

This research measures the systematic risk of 10 sectors in the American Stock Market, discerning the COVID-19 pandemic period. The novelty of this study is the use of the Principal Component Analysis (PCA) technique to measure the systematic risk of each sector, selecting five stocks per sector with the greatest market capitalization. The results show that the sectors that have the greatest increase in exposure to systematic risk during the pandemic are restaurants, clothing, and insurance, whereas the sectors that show the greatest decrease in terms of exposure to systematic risk are automakers and tobacco. Due to the results of this study, it seems advisable for practitioners to select stocks that belong to either the automakers or tobacco sector to get protection from health crises, such as COVID-19.


2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Subhashish Agarwal ◽  
David R. Jacobs ◽  
Dhananjay Vaidya ◽  
Christopher T. Sibley ◽  
Neal W. Jorgensen ◽  
...  

Background. The NCEP metabolic syndrome (MetS) is a combination of dichotomized interrelated risk factors from predominantly Caucasian populations. We propose a continuous MetS score based on principal component analysis (PCA) of the same risk factors in a multiethnic cohort and compare prediction of incident CVD events with NCEP MetS definition. Additionally, we replicated these analyses in the Health, Aging, and Body composition (Health ABC) study cohort.Methods and Results. We performed PCA of the MetS elements (waist circumference, HDL, TG, fasting blood glucose, SBP, and DBP) in 2610 Caucasian Americans, 801 Chinese Americans, 1875 African Americans, and 1494 Hispanic Americans in the multiethnic study of atherosclerosis (MESA) cohort. We selected the first principal component as a continuous MetS score (MetS-PC). Cox proportional hazards models were used to examine the association between MetS-PC and 5.5 years of CVD events (n=377) adjusting for age, gender, race, smoking and LDL-C, overall and by ethnicity. To facilitate comparison of MetS-PC with the binary NCEP definition, a MetS-PC cut point was chosen to yield the same 37% prevalence of MetS as the NCEP definition (37%) in the MESA cohort. Hazard ratio (HR) for CVD events were estimated using the NCEP and Mets-PC-derived binary definitions. In Cox proportional models, the HR (95% CI) for CVD events for 1-SD (standard deviation) of MetS-PC was 1.71 (1.54–1.90) (P<0.0001) overall after adjusting for potential confounders, and for each ethnicity, HRs were: Caucasian, 1.64 (1.39–1.94), Chinese, 1.39 (1.06–1.83), African, 1.67 (1.37–2.02), and Hispanic, 2.10 (1.66-2.65). Finally, when binary definitions were compared, HR for CVD events was 2.34 (1.91–2.87) for MetS-PC versus 1.79 (1.46–2.20) for NCEP MetS. In the Health ABC cohort, in a fully adjusted model, MetS-PC per 1-SD (Health ABC) remained associated with CVD events (HR=1.21, 95%CI 1.12–1.32) overall, and for each ethnicity, Caucasian (HR=1.24, 95%CI 1.12–1.39) and African Americans (HR=1.16, 95%CI 1.01–1.32). Finally, when using a binary definition of MetS-PC (cut point 0.505) designed to match the NCEP definition in terms of prevalence in the Health ABC cohort (35%), the fully adjusted HR for CVD events was 1.39, 95%CI 1.17–1.64 compared with 1.46, 95%CI 1.23–1.72 using the NCEP definition.Conclusion. MetS-PC is a continuous measure of metabolic syndrome and was a better predictor of CVD events overall and in individual ethnicities. Additionally, a binary MetS-PC definition was better than the NCEP MetS definition in predicting incident CVD events in the MESA cohort, but this superiority was not evident in the Health ABC cohort.


Author(s):  
Víctor Pérez-Segura ◽  
Raquel Caro-Carretero ◽  
Antonio Rua

It has been more than one year since Chinese authorities identified a deadly new strain of coronavirus, SARS-CoV-2. Since then, the scientific work regarding the transmission risk factors of COVID-19 has been intense. The relationship between COVID-19 and environmental conditions is becoming an increasingly popular research topic. Based on the findings of the early research, we focused on the community of Madrid, Spain, which is one of the world’s most significant pandemic hotspots. We employed different multivariate statistical analyses, including principal component analysis, analysis of variance, clustering, and linear regression models. Principal component analysis was employed in order to reduce the number of risk factors down to three new components that explained 71% of the original variance. Cluster analysis was used to delimit the territory of Madrid according to these new risk components. An ANOVA test revealed different incidence rates between the territories delimited by the previously identified components. Finally, a set of linear models was applied to demonstrate how environmental factors present a greater influence on COVID-19 infections than socioeconomic dimensions. This type of local research provides valuable information that could help societies become more resilient in the face of future pandemics.


Sign in / Sign up

Export Citation Format

Share Document