scholarly journals A TRIPLE SCREENING METHOD FOR THE IMPACT FACTORS OF HIGH STOCK TURNOVER

2021 ◽  
Author(s):  
YU-YANG PEI ◽  
XIAO-WEN XUE
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Screening Method ◽  
Influential Factors ◽  
Impact Factors ◽  
Feature Engineering ◽  
Analysis Method ◽  
Insufficient Information ◽  
Small Clusters ◽  
The Impact

Taking 2680 stocks selected from the market as the research object, this paper proposes a triple screening method for the influential factors of high transfer of stocks. Firstly, we preprocess the data and eliminate the variables with insufficient information. Secondly, cluster analysis was used to eliminate small clusters. Finally, according to the method of feature engineering, we further screen the features. In addition, this paper uses factor analysis method for reference to calculate the profitability of listed companies. The effect is remarkable.

scholarly journals Evaluation of Combined Use of Drought Indices in the Case of Konya Closed Basin

2020 ◽  
Author(s):  
Huseyin Yildirim Dalkilic
Keyword(s):  
Climate Change ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Reliability Analysis ◽  
Human Life ◽  
Standardized Precipitation Index ◽  
Aridity Index ◽  
Impact Factors ◽  
Drought Indices ◽  
The Impact

The climate covers a series of events that deeply affect human life. It is possible to understand these events through spatial and statistical analyzes. Today, climate change, which is one of the most important of these events and the impact factors of consequences of this change, become a current issue. Drought is cited as one of the consequences of climate change and it is important to examine it with various methods as it can give negative results to both the economy and the nature. In this study, the drought status of the regions where these stations are located and the effects of drought on climate change were statistically calculated and evaluated using Standardized Precipitation Index (SPI), Percentage of Normal Index (PNI), Aridity Index (AI) and Standardized Precipitation -Evopotranspiration Index (SPEI). The precipitation data from 1981 to 2010 were obtained from Cihanbeyli, Karapınar, Çumra, Seydişehir, Kulu, Ereğli, Niğde, Karaman, Beyşehir and Aksaray meteorology stations affiliated to Turkish State Meteorological Service. At the same time, factor analysis and validity-reliability analysis were conducted to test the computability of the indices used in the study as a single index and to determine the reliability of the operations. While using exploratory factor analysis, Kaiser-Meyer-Olkin (KMO) test and Barlett test for factor analysis; Cronbach's alpha coefficient was used for reliability analysis. In our study, K-Means Cluster Analysis method was performed to determine the cutoff values of indices. According to the result of cluster analysis for the new (common) index, new clusters were created and ANOVA test was conducted to determine whether there was a difference between clusters.

scholarly journals Exploring the Impact Factors of Tourists’ Intention to Choose Iran as a Traveling Destination

2020 ◽  
Vol 13 (3) ◽  
pp. 118
Author(s):  
Mona Mehrparvar ◽  
Xu Ming ◽  
Ahmad Saeedi
Keyword(s):  
Factor Analysis ◽  
Impact Factors ◽  
Chinese People ◽  
Online Questionnaire ◽  
Effective Factors ◽  
Political Risks ◽  
Confirmatory Factor ◽  
Main Factors ◽  
The Impact

The purpose of this paper is to explore the effective factors in attracting outbound tourists to choose Iran as a traveling destination. This survey has been done in China. The total number of respondents was 406, where 95% of respondents filled an online questionnaire and 5% filled it manually. The exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to analyze the questionnaire, and logistic regression was deployed to explore the effective factors in this survey. The questions were defined based on the theory of planned behavior (TPB) and the role of culture, custom, the source of traveling information, and perceived traveling risks in choosing Iran as a traveling destination. The outcome of this survey on Chinese people suggested that the attractions of Iran, environment, and political risks are the main factors which play an important role in choosing Iran as a traveling destination. The experience of traveling abroad also revealed a significant effect in decision making on traveling destination.

scholarly journals Geochemical Anomaly Separation by Statistical and Fractal Methods in the Sehezar Valley of Tonekabon – Northern Iran

2019 ◽  
Author(s):  
Meysam Yazdani ◽  
Firouz Alinia
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Multivariate Analysis ◽  
Pearson Correlation ◽  
Univariate Analysis ◽  
Screening Method ◽  
Bivariate Analysis ◽  
Geochemical Anomaly ◽  
Threshold Method ◽  
Fractal Methods

Sehezar area is located in southern Tonokabon in Mazandaran province, north of Iran, near the Tarom – Hashdtjin belt. The existence of granitoid masses in the region can be important in terms of the potential of mineralization. Geochemical anomaly separation from the background is one of the important steps in mineral exploration. In the past decades, geochemical anomalies have been identified by means of various methods. Some of these separation methods include: statistical analysis methods (like univariate, bivariate, multivariate statistics), spatial statistical methods and fractal and multi-fractal methods. To identify the anomalous area, 71 stream sediment samples were collected from the area and analyzed by the ICP-MS method, and then interpreted. Initially, data were normalized and afterwards, univariate analysis (threshold limit and screening (P.N) methods) was used, in which results of the probable and definite anomaly of the threshold method were confirmed by the P.N screening method. Finally, the maps of the anomal zones were drawn. Then, bivariate analysis (Pearson correlation coefficients) and multivariate analysis on normal data were performed on SPSS software, in which factor analysis and cluster analysis were used for multivariate analysis. As a result of using the factor analysis method, six factors were identified and factor maps were drawn by the Surfer software. Also, by using cluster analysis, the variables were divided into two groups. In order for a better separation of the geochemical anomaly from the background, in addition to the threshold method, the Concentration - Area fractal method was used. Here, the fractal geometry using full-logarithmic graphs of the Concentration - Area obtained is capable of separating the stairs of different sections (background, threshold, and anomaly) with respect to the angle coefficient of the Concentration - Area plot. Then, in conclusion, results of these methods were compared and investigated, and finally, the anomalies area maps of the Au, Ag, Cu, Fe, W elements were drawn by Concentration - Area fractal and threshold methods and anomalous zones were introduced.

Segmenting young adults based on financial management behavior in India

2019 ◽  
Vol 38 (2) ◽  
pp. 548-560
Author(s):  
Dhananjay Madhukar Bapat
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Young Adults ◽  
Young Adult ◽  
Financial Management ◽  
Emerging Market ◽  
Demographic Variables ◽  
Management Behavior ◽  
Content Type ◽  
The Impact

Purpose The profiling of young adult financial behavior can help financial service providers and financial advisors to target suitable marketing resources to specific customer segments. The purpose of this paper is to validate the scale for financial management behavior of young adults in an emerging market, segment these individuals and investigate the impact of demographic variables on key dimensions. Design/methodology/approach A structured questionnaire is used to validate a financial management behavior scale using data collected from 270 young adults in India. Based on dimensions obtained through factor analysis, cluster analysis is performed to identify young adult segments. Statistical techniques, such as the t-test and one-way analysis of variance, are used to examine the impact of demographic variables on financial management behavioral dimensions. Findings The factor analysis confirms three key financial management dimensions: cash management, credit management and savings management. Using cluster analysis, the young adults are segmented into three subgroups: responsible customers, credit-oriented customers and vulnerable customers. Young adults in these groups follow hierarchical patterns in terms of financial management behavior. Originality/value Since few studies are available from the standpoint of young adults in emerging markets, this study adds value to the literature by investigating the financial management behavior of young adults in India. Notably, it can serve as a reference for comparing similarities and differences on the basis of financial management behavior with other countries and customer segments.

Regional aspects of reputation capital valuation: The Volga Federal District case study

2020 ◽  
Vol 8 (8) ◽  
pp. 1531-1547
Author(s):  
A.S. Grunichev ◽  
L.A. El'shin ◽  
A.A. Abdukaeva
Keyword(s):  
Economic Growth ◽  
Factor Analysis ◽  
Federal District ◽  
Analysis Method ◽  
Index Numbers ◽  
Analysis Techniques ◽  
New Models ◽  
Methodological Approaches ◽  
The Impact

Subject. This article deals with the tools of factor analysis of the reputation capital of the region. Objectives. The article aims to make an explicit evaluation of the reputation capital of the regions based on the previously developed original methods and algorithms. Methods. For the study, we used the index numbers analysis method. Results. The article defines the values of the reputation capital index for the Volga Federal District regions. It offers a version of the implementation of the algorithm to quantify the reputation of the region. The use of factor analysis techniques makes it possible to determine the impact of regional reputation capital on the formation of its integral value. Conclusions. The methodological approaches developed and the practical results derived from them should be used in the development of new models of economic growth in the context of the increased importance of intangible factors of production.

Prediction of Mine Inrush Water Based on BP Neural Network Method

2014 ◽  
Vol 989-994 ◽  
pp. 1814-1820 ◽  
Author(s):  
Ai Jun Shao ◽  
Qing Xin Meng ◽  
Shi Wen Wang ◽  
Ying Liu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bp Neural Network ◽  
Mine Water ◽  
Influential Factors ◽  
Water Inflow ◽  
Impact Factors ◽  
Artificial Neural ◽  
Inrush Water ◽  
The Impact

Based on predictions of the mine inflow of water and the complexity of influential factors, a method of BP neural network is put forward for mine inrush water prediction in this paper. We chose proper impact factors and establish non-linear artificial neural network prediction model after analyzed the impact factors of mine water inflow in Shandong Heiwang iron, and also made one prediction with normal mine water inflow during the iron mining operation. It turned out that the result can match with the actual prediction data, which make it possible to predict the mine water inflow with the prediction of Artificial Neural Network.

Simulation Analysis on Dynamic Load of Air-Conditioning System of Ocean-Going Ship

2014 ◽  
Vol 672-674 ◽  
pp. 1693-1699 ◽  
Author(s):  
Zhong Chao Zhao ◽  
Hua Cheng ◽  
Wei Xian Feng ◽  
Bo Jin Qin
Keyword(s):  
Dynamic Load ◽  
Air Conditioning ◽  
Simulation Analysis ◽  
Mathematic Model ◽  
Maximum Load ◽  
Impact Factors ◽  
Analysis Method ◽  
Total Load ◽  
Air Conditioning System ◽  
The Impact

In this paper, a dynamic load mathematic model for ship’s multifunctional cabins was developed using the “black box model” theory. The multifunctional cabins’ dynamic loads for a ship navigating in a typical Eurasian route were calculated through this mathematic model. The research results show that the air-conditioning load of cabins varies with navigation areas and sailing time significantly. Ventilation load accounts for 51.92% of the total load, while heat conduction and radiation load only account for 7.47% and 6.68%, respectively. For a same navigating area, the maximum load for different cabins is 2.648 times of the minimum load at the same time. In addition, the impact factors were analyzed by regression analysis method, and that the 91.72% of variation of the dynamic load results from the outside temperature changing

Concrete Curved Box Girders Interacted with Vehicles in Braking or Acceleration

2018 ◽  
Vol 18 (04) ◽  
pp. 1850053 ◽  
Author(s):  
Xinyi Huang ◽  
Chung C. Fu ◽  
Weidong Zhuo ◽  
Quanzhe Yan ◽  
Ying Sun
Keyword(s):  
Spatial Analysis ◽  
Bending Moment ◽  
Considerable Effect ◽  
Dynamic Responses ◽  
Impact Factors ◽  
Analysis Method ◽  
Box Girders ◽  
Girder Bridge ◽  
Spatial Analysis Method ◽  
The Impact

In this study, an experimentally validated spatial analysis method for the vehicle–bridge interaction system was modified to include the features of vehicle braking and accelerating. The effect of braking or accelerating was considered as external force acting on the vehicular center of gravity and was quasi-statically distributed to every tandem, for which the formulae of load redistribution were derived. The effect of centrifugal force was also incorporated in the model. Based on the modified spatial analysis method, the dynamic responses of a three-span continuous concrete box girder bridge due to vehicle braking and accelerating were studied. Impact factors, including deflection, bending moment, torsional moment and shear force, were examined. The results show that vehicle braking has considerable effect on dynamic responses and the impact factors are related to braking rise time and braking position, but cases of vehicle braking do not always cause larger effects. While the increase in initial speed can produce higher maximum dynamic responses and corresponding impact factors, the dynamic responses in the first span of a multi-span bridge are smaller than those in other spans due to vehicle accelerating.

scholarly journals PENGARUH PENDIDIKAN dan PELATIHAN, KEPEMIMPINAN, MOTIVASI KERJA TERHADAP KINERJA KARYAWAN di PT JASA KONSTRUKSI JAKARTA

ETIKONOMI ◽  
2015 ◽  
Vol 14 (1) ◽  
Author(s):  
Lili Supriyadi
Keyword(s):  
Factor Analysis ◽  
Positive Impact ◽  
Linear Regression Analysis ◽  
Sampling Technique ◽  
Multiple Linear Regression Analysis ◽  
Analysis Method ◽  
Validity And Reliability ◽  
Performance Study ◽  
Leadership Motivation ◽  
The Impact

The aim of this research is to discover the impact of training, leadership, motivation to employees’ performance (study at PT. Jasa Konstruksi Jakarta). This research was carried out by doing survey to 95 employees at PT. Jasa Konstruksi Jakarta. The sampling technique used in the research was purposive sampling with data examination technique that covered the validaty test with factor analysis, reliability test by Alpha Cronbach. Classical assumption test, multiple linear regression analysis, t test were taken to prove research hypothesis. After the collected data which was tested its validity and reliability by factor analysis method and Alpha Cronbach was done the data was stated as a valid and reliable data. The result of the analysis indicated that: training gives positive impact to employees’ performance, leadership gives positive impact to employees’ performance, and motivation give positive Impact to employees’ performance

scholarly journals Using Principal Component Analysis for Factor Analysis in Indonesian Automotive Industries

2020 ◽  
Vol 8 (2) ◽  
pp. 62
Author(s):  
Ulil Hamida
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Automotive Industry ◽  
Principal Component ◽  
Component Analysis ◽  
Influential Factors ◽  
Analysis Method ◽  
Vehicle Ownership ◽  
Principal Component Analysis Algorithm

Policies related to the automotive industry have become significant for the Ministry of Industry. The problem in determining these policies is the determination of important factors for the automotive industry so that the policies formulated are right on target. The search for these important factors can be done by using the factor analysis method. So far, no studies have been conducted to examine the factors that influence the growth of the automotive industry. In this study, factor analysis is performed on factors in the automotive industry using the principal component analysis algorithm. The algorithm seeks to describe independently the aspects that become the main factors in determining the automotive industry. Based on an analysis of factors in the automotive industry production, the most influential factors are foreign investment, vehicle ownership ratios, and at last the change in GDP.

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