scholarly journals Análise fatorial de séries temporais para medidas de liquidez no mercado brasileiro

2017 ◽  
Vol 16 (3) ◽  
pp. 1109-1132
Author(s):  
Vinicius Girardi Da Silveira ◽  
Kelmara Mendes Vieira ◽  
Reisoli Bender Filho ◽  
Daniel Arruda Coronel
Keyword(s):  
Factor Analysis ◽  
Model Data ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Times Series ◽  
De Se ◽  
Unique Factor ◽  
Liquidity Measures ◽  
The Times ◽  
Statistical Estimations

ResumoNeste estudo teve-se por objetivo sugerir o emprego de uma metodologia alternativa para mensuração da liquidez em mercados acionários. Para tanto, o procedimento empregado foi a Análise Fatorial de Séries Temporais (TSFA), proposta por Gilbert e Meijer (2005). Com base neste modelo, foram utilizados dados da negociação de ações da BM&FBOVESPA de dezembro de 1994 a junho de 2011 para cinco diferentes proxies de liquidez que foram empregadas na construção fatorial. No estudo permitiu-se observar a possibilidade de se combinarem diferentes medidas de liquidez para a formação de um único fator que demonstrou possuir uma forte associação com as proxies utilizadas em sua construção. Como vantagens, esse procedimento permite eliminar problemas de colinearidade em estimações estatísticas e possibilita que análises de investimento utilizem uma única variável para mensurar a liquidez ao invés de várias simultaneamente. Além disso, a construção fatorial pode ser replicada para novos dados, já que não é assumido um modelo dinâmico para os fatores, o qual é tratado simplesmente como um problema de mensuração.Palavras-chave: Liquidez. Análise Fatorial de Séries Temporais. Mercado acionário brasileiro. Times series factor analysis for liquidity measures on Brazilian stock marketAbstract The purpose of this paper is to suggest a new methodology to measure liquidity in stock markets.  For that, we use the Times Series Factor Analysis model (TSFA), proposed by Gilbert and Meijer (2005). We employed on that model, data from December, 1994 to June, 2011 of five liquidity proxies.  As findings, we observe that is possible to combine liquidity measures to create a unique factor, which show to have a high correlation with the liquidity proxies. The advantages of this procedure is to eliminate the problem of collinearity on statistical estimations and the possibility of use a single variable to measure liquidity in investment analysis. Furthermore, the factorial construction can be replicated for new datas, since is not assumed a dynamic model for the factors, which is treated simply as a measurement procedure.Keywords: Liquidity. Times Series Factor Analysis. Brazilian stock market.

Noniterative Estimation for the Multiple Battery Factor Analysis Model

1997 ◽  
Vol 24 (1) ◽  
pp. 3-18 ◽  
Author(s):  
Michael W. Browne ◽  
Krishna Tateneni
Keyword(s):  
Factor Analysis ◽  
Analysis Model ◽  
Factor Analysis Model

A Factor Analysis model of the instrumented Timed Up and Go test for physical capability assessment

2018 ◽  
Vol 66 ◽  
pp. S11-S12 ◽  
Author(s):  
A. Coni ◽  
S. Mellone ◽  
M. Colpo ◽  
S. Bandinelli ◽  
L. Chiari
Keyword(s):  
Factor Analysis ◽  
Analysis Model ◽  
Timed Up And Go ◽  
Factor Analysis Model ◽  
Physical Capability

scholarly journals Non-negative Independent Factor Analysis for single cell RNA-seq

2020 ◽  
Author(s):  
Weiguang Mao ◽  
Maziyar Baran Pouyan ◽  
Dennis Kostka ◽  
Maria Chikina
Keyword(s):  
Factor Analysis ◽  
Single Cell ◽  
Matrix Factorization ◽  
Component Analysis ◽  
Computational Techniques ◽  
Cell Type ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Type Identity ◽  
Wide Range

AbstractMotivationSingle cell RNA sequencing (scRNA-seq) enables transcriptional profiling at the level of individual cells. With the emergence of high-throughput platforms datasets comprising tens of thousands or more cells have become routine, and the technology is having an impact across a wide range of biomedical subject areas. However, scRNA-seq data are high-dimensional and affected by noise, so that scalable and robust computational techniques are needed for meaningful analysis, visualization and interpretation. Specifically, a range of matrix factorization techniques have been employed to aid scRNA-seq data analysis. In this context we note that sources contributing to biological variability between cells can be discrete (or multi-modal, for instance cell-types), or continuous (e.g. pathway activity). However, no current matrix factorization approach is set up to jointly infer such mixed sources of variability.ResultsTo address this shortcoming, we present a new probabilistic single-cell factor analysis model, Non-negative Independent Factor Analysis (NIFA), that combines features of complementary approaches like Independent Component Analysis (ICA), Principal Component Analysis (PCA), and Non-negative Matrix Factorization (NMF). NIFA simultaneously models uni- and multi-modal latent factors and can so isolate discrete cell-type identity and continuous pathway-level variations into separate components. Similar to NMF, NIFA constrains factor loadings to be non-negative in order to increase biological interpretability. We apply our approach to a range of data sets where cell-type identity is known, and we show that NIFA-derived factors outperform results from ICA, PCA and NMF in terms of cell-type identification and biological interpretability. Studying an immunotherapy dataset in detail, we show that NIFA identifies biomedically meaningful sources of variation, derive an improved expression signature for regulatory T-cells, and identify a novel myeloid cell subtype associated with treatment response. Overall, NIFA is a general approach advancing scRNA-seq analysis capabilities and it allows researchers to better take advantage of their data. NIFA is available at https://github.com/wgmao/[email protected]

scholarly journals Analysis of the Factors Affecting the Competitiveness of ASEAN-5 Countries

2021 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Tarwo Kusnarno ◽  
Eddy Suratman
Keyword(s):  
Exchange Rates ◽  
Labor Productivity ◽  
Series Data ◽  
Analysis Model ◽  
Factors Affecting ◽  
Times Series ◽  
Insignificant Effect ◽  
Net Exports ◽  
The Times ◽  
Net Export

This study analyzes the factors that affect the competitiveness of ASEAN-5 countries in terms of Labor Productivity, Net Exports, Investment, Inflation and Exchange Rates in two periods, namely the ASEAN internal crisis and the global crisis using the times series data from 1997-2017, then analyzed with the regression analysis model. The results showed that the labor productivity of the 1997-2008 period had a positive and significant effect on the competitiveness of ASEAN-5, as well as the period of 2008-2019, labor productivity had a positive and significant effect on the competitiveness of ASEAN-5. The net export period of 1997-2008 had a negative and not significant effect on the competitiveness of ASEAN-5, as well as the period of 2008-2017, the net export had a negative and not significant effect on the competitiveness of ASEAN-5. The investment period of 1997-2008 has a negative and not significant effect on the competitiveness of ASEAN-5, while the period 2008-2017 has a positive and not significant effect on the competitiveness of ASEAN-5. Inflation in the 1997-2017 period had a negative and insignificant effect on the competitiveness of ASEAN-5, as well as the 2008-2017 inflation period had a negative and not significant effect on the competitiveness of ASEAN-5. Exchange rates for the period 1997-2008 have a negative and insignificant influence on the competitiveness of ASEAN-5, as well as the 2008-2017 period, which has a negative and insignificant effect on the competitiveness of ASEAN-5.

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