scholarly journals Structural Equation Modeling of Latent Variables Affecting Stock Prices: Evidence from Nepal

2017 ◽  
Vol 31 (1-2) ◽  
pp. 25-44
Author(s):  
Dipendra Karki
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Latent Variables ◽  
Structural Equation ◽  
Statistical Significance ◽  
Investor Sentiment ◽  
Equation Modeling ◽  
Path Coefficient ◽  
Macroeconomic Factors ◽  
Path Diagram

This study uses the structural equation model in Nepali stock market, chooses investor sentiment, monetary and macroeconomic factors as latent variables, and selects a few observed variables which can explain the latent variables to study the influence on stock prices. Based on existing empirical research conclusion, influence path diagram is designed and gets its path coefficient and causal path diagram using maximum likelihood estimation. The statistical significance of the results indicated that the causal relationships of Nepal’s stock market as follows can be accepted; firstly, investor sentiment, macroeconomic indicators, and monetary factors have certain influence on stock prices, the investor sentiment has a positive correlation. Secondly, the investor sentiment has the major impact on stock prices; when investor sentiment is more stable and optimistic, stock prices will relatively be better. This research also provides certain reference to investors for rational investment decisions.

scholarly journals Structural Equation Modeling Applied to Socioeconomic Indicators in the Production of Sugarcane, in the State of Goiás

2019 ◽  
Vol 10 (5) ◽  
pp. 53
Author(s):  
Jean Marc Nacife ◽  
Frederico A. Loureiro Soares ◽  
Marconi Batista Teixeira ◽  
Leonardo Nazário S. dos Santos ◽  
Gustavo Castoldi
Keyword(s):  
Structural Equation Modeling ◽  
Production System ◽  
Structural Equation ◽  
Statistical Significance ◽  
Methodological Approach ◽  
Production Costs ◽  
Equation Modeling ◽  
Structural Quality ◽  
Sugarcane Production ◽  
Path Diagram

Agribusiness has played a strategic role for Brazil's development with the challenge of sustainable agriculture. It is proposed to determine, through Structural Equation Modeling (SEM), the validity and effects of the relationships between socioeconomic factors of the sugarcane production system in Quirinópolis, providing subsidies to the decision-making process of agricultural establishments. The research methodological approach was quantitative, applying techniques of normality statistics, hypothesis and multivariate analysis without statistical significance (P <0,05). A path diagram model was developed that presented structural quality adjustment and its validated explanatory equations, obtaining relevant R2. The results demonstrate that the Equation 1 (IBCcane = 0.02Rcane - 0.75ICcane – 0.46ISVO + 0.35ISPS + error) is explained in 73.7% of its variance (R2), in the Equation 2 (ICcane = 0.59ISVO – 0.45ISPS + 0.35SizeEstablis + error) successor vocation affects 42% on production costs and in the Equation 3 (Rcane = -0.40 AgroDistance – 0.16ISPS + error) the distance between farm and agribusiness influences 72% on the proposed revenue mix. The SEM analysis verified that social factors influence the economic factors that compose the sugarcane production system studied. The path diagram proved that the influence track relative to the costs in the proposed model is more representative than revenue for the economic results of rural sugarcane establishments. 

Barriers to radical innovation in research institutions in China

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jianhua Yang ◽  
Rafif Al-Sayed
Keyword(s):  
Theoretical Model ◽  
Latent Variables ◽  
Structural Equation ◽  
Business Models ◽  
Research Collaboration ◽  
Statistical Significance ◽  
Radical Innovation ◽  
Equation Modeling ◽  
Research Institutions ◽  
Content Type

Purpose This study aims to develop a better understanding of radical innovation performance and proposes a comprehensive and theoretical model of the barriers impeding radical innovation from the perspective of researchers working in research institutions in China. Both quantitative and qualitative techniques were used to test the hypotheses regarding barriers to radical innovation and the model proposed in this research. Design/methodology/approach The data was collected through questionnaires and semi-structured interviews with researchers from different research institutions across several cities in China. Next, the data was analyzed by deploying the structural equation modeling technique and calculating the statistical significance of correlations, regression and path coefficients among the latent variables. Findings The results indicated the major barriers impeding radical innovation in Chinese research institutes. Based on these findings, suggested policies, regulations and business models are put forward that can promote radical innovation in these institutes through increasing research freedom, enhancing organizational flexibility, attracting talented researchers and expanding research collaboration. Originality/value The research proposes a comprehensive and theoretical model of the barriers impeding radical innovation from the perspective of researchers working in research institutions in China.

scholarly journals Agents Affecting the Productivity of Pine Plantations on the Loess Plateau in China: A Study Based on Structural Equation Modeling

Forests ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1328
Author(s):  
Xuan Zhao ◽  
Yanjie Li ◽  
Hao Song ◽  
Yuhuan Jia ◽  
Jianjun Liu
Keyword(s):  
Indirect Effect ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Equation Modeling ◽  
Path Coefficient ◽  
Site Conditions ◽  
Population Stability ◽  
Forest Density ◽  
Weight Value

Stability and productivity are important indicators used to measure the state of forest ecosystems. Artificial forests populations with reasonable structures and strong stability are critical for ecosystem productivity. Previous studies have focused on individual factors, while the mechanisms of how multiple factors affect population productivity remain unknown. We used 57 plots in a Chinese pine (Pinus tabuliformis) plantation to investigate 23 stand factors and analyzed the relationships among site factors, population structure, population stability, and population productivity using partial least square-structural equation modeling (PLS-SEM). The results showed that the population productivity of the plantation was directly affected by the population stability latent variable but indirectly affected by the site conditions latent variables (indirect effect path coefficient = 0.249) and forest structure (indirect effect path coefficient = 0.222). However, the site conditions latent variable was the main factor directly affecting the population stability latent variables; the total effect was 0.511 (direct effect path coefficient = 0.307, indirect effect path coefficient = 0.204), and the influence of forest structure on population stability was lower than that of the site conditions latent variable (direct effect path coefficient = 0.454). The factor with the greatest weight among the site conditions latent variable was slope (0.747), indicating that slope contributes the most to latent variables related to forest population stability. Among all variables affecting the forest stability latent variables, forest density had the highest weight value (0.803), and the weight value of forest mortality was lower than that of forest density. The weights of the latent variables associated with population structure from high to low were canopy density, the uniform angle index, and the spatial competition index, indicating that competition for space had the lowest influence on the population stability latent variables. The results provide new insights and ideas for quantifying relationships among different driving factors and a basis for scientific and rational plantation management.

scholarly journals Structural Equation Modeling-Partial Least Square for Poverty Modeling in Papua Province

Jurnal Varian ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 79-90
Author(s):  
Wirajaya Kusuma ◽  
Rifani Nur Sindy Setiawan ◽  
Kirti Verma ◽  
Carina Firstca Utomo
Keyword(s):  
Structural Equation Modeling ◽  
Latent Variables ◽  
Structural Equation ◽  
Poverty Rate ◽  
Partial Least Square ◽  
Least Square ◽  
Equation Modeling ◽  
Path Coefficient ◽  
Path Coefficients ◽  
Health Variable

Poverty in Papua Province in 2018 has increased from the previous year. The poverty rate in Papua Province in March 2018 reached 27,74%. This study aims to analyze the factors that influence it so that it can be handled properly. The research method used in this research is Structural Equation Modeling (SEM) with the Partial Least Squares (PLS) approach. The research variables used consisted of 4 latent variables (Poverty, Economy, Human Resources (HR), and Health) with 16 indicators (manifest variables). Based on the analysis that has been done, it is found that economic and health variables have a negative and significant effect on poverty with path coefficients of -0,421 and -0,270, respectively. The health variable has a positive and significant effect on HR with a path coefficient of 0,496. Meanwhile, the HR variable has a positive and significant effect on the economy with a path coefficient of 0,801. It can be concluded that there are two variables that have a significant effect on poverty in Papua Province, including the economy and health.

scholarly journals Hidden Common Factors in Disaster Loss Statistics: A Case Study Analyzing the Data of Nepal

2018 ◽  
Vol 13 (6) ◽  
pp. 1032-1038
Author(s):  
Daisuke Sasaki ◽  
◽  
Kana Moriyama ◽  
Yuichi Ono
Keyword(s):  
Factor Analysis ◽  
Latent Variables ◽  
Structural Equation ◽  
Common Factors ◽  
Equation Modeling ◽  
Or Education ◽  
Path Diagram ◽  
Disaster Loss ◽  
Starting Point ◽  
Basic Services

This study aims to examine common hidden factors in disaster loss statistics and identify clues for verifying the fitness of the global targets of the Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR) to rule countries’ effort in reducing disaster risks. In this study, we first conducted an exploratory factor analysis (EFA), followed by a confirmatory factor analysis (CFA) using structural equation modeling (SEM). As a result of the EFA, we were able to extract three factors, namely Housing, Casualties or Education, and Relocation. In the analysis of SEM, we assumed three latent variables based on the results of the EFA. The relationship between the latent and observed variables was established in a manner that conformed to the implications of the EFA. According to the SEM results, we eventually identified three latent variables, namely Housing, Education and Relocation, as hidden common factors. Based on this identification, our judgment indicates that the latent variables appeared to be related to the following global targets of SFDRR: (b) those concerning the number of affected people and (d) those concerning damages to infrastructure and disruptions to basic services. It was found that relationships between variables could be clearly illustrated by using the path diagram. This study can be considered as a good example of introducing SEM to visualize hidden common factors and their relationships in an intelligible manner. Based on the results, we propose a starting point for discussing the fitness of SFDRR’s global targets by utilizing EFA and CFA (SEM) techniques. The path diagram can indicate the extent to which the indicators contribute to global targets that will be represented as latent variables. In the end, explicit reference should be made to the material data’s limitations in the disaster loss statistics. An effort to elaborate the input data themselves must be made in the near future.

scholarly journals Komparasi Faktor Fundamental Mikro dan Makro Ekonomi in Jakarta Islamic Index dan Lq-45

2021 ◽  
Vol 7 (1) ◽  
pp. 484
Author(s):  
Zelivieska Bintang Maharani
Keyword(s):  
Stock Returns ◽  
Capital Market ◽  
Stock Prices ◽  
Stock Price ◽  
Structural Equation ◽  
Stock Exchange ◽  
Equation Modeling ◽  
Macroeconomic Factors ◽  
Analysis Technique ◽  
Significant Difference

The capital market in Indonesia has developed rapidly in line with the entry of the Islamic capital market. On the Indonesia Stock Exchange, there are Islamic and conventional stocks. In this study, the object of research is the Jakarta Islamic Index and LQ-45 stocks in 2015-2017. The purpose of this research is to compare the fundamental micro and macroeconomic factors and their effects on stock prices and stock returns. In selecting the sample, this study used a purposive sampling method, where the number of samples in this study were 15 companies Jakarta Islamic index and 13 companies LQ-45. The test uses the analysis technique of Structural Equation Modeling (SEM) to see how much influence the variables have and the comparison test with the Mann Whitney test. The results of this study indicate that only microeconomic factors have a significant effect on stock prices in the Jakarta Islamic index and LQ-45. The results also show that there is a significant difference between the micro-fundamental factors in the Jakarta Islamic index and LQ-45. Meanwhile, there is no significant difference in macro factors. Keywords: Micro Economy, Macro Economy, Stock Price, Stock Return

Variation Sharing

2017 ◽  
Vol 8 (4) ◽  
pp. 46-68 ◽  
Author(s):  
Ned Kock ◽  
Shaun Sexton
Keyword(s):  
Measurement Error ◽  
Least Squares ◽  
Partial Least Squares ◽  
Latent Variables ◽  
Structural Equation ◽  
Least Squares Method ◽  
Fundamental Problem ◽  
Equation Modeling ◽  
Path Coefficient ◽  
Least Squares Regression

The most fundamental problem currently associated with structural equation modeling employing the partial least squares method is that it does not properly account for measurement error, which often leads to path coefficient estimates that asymptotically converge to values of lower magnitude than the true values. This attenuation phenomenon affects applications in the field of business data analytics; and is in fact a characteristic of composite-based models in general, where latent variables are modeled as exact linear combinations of their indicators. The underestimation is often of around 10% per path in models that meet generally accepted measurement quality assessment criteria. The authors propose a numeric solution to this problem, which they call the factor-based partial least squares regression (FPLSR) algorithm, whereby variation lost in composites is restored in proportion to measurement error and amount of attenuation. Six variations of the solution are developed based on different reliability measures, and contrasted in Monte Carlo simulations. The authors' solution is nonparametric and seems to perform generally well with small samples and severely non-normal data.

Meta-Accuracy and Perceived Reciprocity From the Perception-Meta-Perception Social Relations Model

2019 ◽  
Vol 50 (1) ◽  
pp. 24-37
Author(s):  
Ben Porter ◽  
Camilla S. Øverup ◽  
Julie A. Brunson ◽  
Paras D. Mehta
Keyword(s):  
Structural Equation Modeling ◽  
Social Relations ◽  
Latent Variables ◽  
Structural Equation ◽  
Multilevel Structural Equation Modeling ◽  
Equation Modeling ◽  
Complete Understanding ◽  
Social Relations Model ◽  
Flexible Framework ◽  
Multilevel Structural Equation

Abstract. Meta-accuracy and perceptions of reciprocity can be measured by covariances between latent variables in two social relations models examining perception and meta-perception. We propose a single unified model called the Perception-Meta-Perception Social Relations Model (PM-SRM). This model simultaneously estimates all possible parameters to provide a more complete understanding of the relationships between perception and meta-perception. We describe the components of the PM-SRM and present two pedagogical examples with code, openly available on https://osf.io/4ag5m . Using a new package in R (xxM), we estimated the model using multilevel structural equation modeling which provides an approachable and flexible framework for evaluating the PM-SRM. Further, we discuss possible expansions to the PM-SRM which can explore novel and exciting hypotheses.

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