Evaluating Impacts of a Seven Factor Regression Model on DHG Pharma Corporation Stock Price – A Case in Medicine & Pharma Industry in Vietnam

2020 ◽  
Vol 12 (7) ◽  
pp. 224-234
Author(s):  
Dr. Vu Xuan Thuy
Keyword(s):  
Regression Model ◽  
Stock Price ◽  
Factor Regression

scholarly journals Approaches to assessing the level of innovation development in manufacturing industries

2020 ◽  
Vol 220 ◽  
pp. 01027
Author(s):  
Vitaly Makoveev ◽  
Liliya Mukhametova
Keyword(s):  
Regression Model ◽  
Socioeconomic Factors ◽  
Manufacturing Industry ◽  
Energy Sector ◽  
Machine Building ◽  
Innovative Activity ◽  
Manufacturing Industries ◽  
Innovation Development ◽  
Factor Regression

Sustainable long-term development of the energy sector is impossible without a developed manufacturing industry and especially machine-building enterprises. The article offers a method for assessing the level of innovation development in the manufacturing industry and identifies the factors that have the greatest impact on the development of the process of creating and implementing innovations in this sector. A multi-factor regression model is constructed to determine the degree of influence of various socioeconomic factors on the level of development of innovative activity in manufacturing industries, as well as to develop proposals and recommendations for its activation.

scholarly journals Improvement of the innovation management system in the manufacturing industry

2020 ◽  
Vol 220 ◽  
pp. 01048
Author(s):  
Vitaly Makoveev ◽  
Liliya Mukhametova
Keyword(s):  
Regression Model ◽  
Manufacturing Industry ◽  
Innovation Management ◽  
Machine Building ◽  
Manufacturing Industries ◽  
Economic Factors ◽  
Innovation Development ◽  
Factor Regression ◽  
Socio Economic Factors

Sustainable long-term development of the energy sector is impossible without a developed manufacturing industry and especially machine-building enterprises. The article identifies the factors that have the greatest impact on the development of innovation in the manufacturing industry. A multi-factor regression model is constructed to determine the degree of influence of various socio-economic factors on the level of innovation development in manufacturing industries. An organizational and economic mechanism aimed at enhancing innovation in the manufacturing industry and increasing the competitiveness of the products of enterprises in this sector is proposed.

Impact of Exchange Rate and Jakarta Composite Index on Stock Price: A Case of Pharmaceutical Company in Indonesia

2015 ◽  
Vol 21 (4) ◽  
pp. 823-825
Author(s):  
Nino Manggala Prabha ◽  
Togar Alam Napitulu
Keyword(s):  
Exchange Rate ◽  
Pharmaceutical Industry ◽  
Stock Market ◽  
Regression Model ◽  
Pharmaceutical Company ◽  
Multiple Regression ◽  
Stock Price ◽  
Investment Decision ◽  
Composite Index ◽  
Multiple Regression Model

Stock market is growing in Indonesia and has become an important source of financing for industry in the country. This is true for pharmaceutical industry and as such, predicting the stock price in this industry is deemed very important in making investment decision. It is therefore necessary to know variables that affect stock price in this industry, in particular those that can be easily acquired and have relationship with the stock price. The objective of the study then is to find such variables. It was conjectured that exchange rate and the Jakarta Composite Index were among such variables. A linear multiple regression model was utilized to test such hypothesis. The results indicated that exchange rate positively affects stock price with a magnitude of 0.105 points. Similarly, the Jakarta Composite Index also positively affects stock price with magnitude of 0.417 points. The reliability of this model in predicting the stock price was 63%. Therefore, it is recommended to consider these variables in predicting stock price of the pharmaceutical company, hence important indicators for investors to be considered in making decision whether to buy or not to buy.

A carbon oxidation factor regression model of coal-fired power plants in China

2017 ◽  
Vol 142 ◽  
pp. 4403-4411 ◽  
Author(s):  
Handong Wu ◽  
Wei Han ◽  
Dandan Wang ◽  
Lin Gao
Keyword(s):  
Regression Model ◽  
Power Plants ◽  
Carbon Oxidation ◽  
Factor Regression

scholarly journals Use of the multi-factor regression model for temperature control in structurally isolated rooms of the railway stations

2021 ◽  
Vol 1151 (1) ◽  
pp. 012044
Author(s):  
V S Tynchenko ◽  
I V Markevich ◽  
A R Ogol ◽  
O V Baryshnikova ◽  
D V Rogova ◽  
...  
Keyword(s):  
Regression Model ◽  
Temperature Control ◽  
Railway Stations ◽  
Factor Regression

scholarly journals The Influence of Ownership Structure, Asset Structure, and Earning Volatility on Debt Policy in Indonesia

2019 ◽  
Vol 2 (1) ◽  
pp. 93-106
Author(s):  
Saiful Anwar
Keyword(s):  
Regression Model ◽  
Ownership Structure ◽  
Stock Price ◽  
Stepwise Regression ◽  
Stock Exchange ◽  
Secondary Data ◽  
Institutional Ownership ◽  
Pharmaceutical Companies ◽  
Managerial Ownership ◽  
Debt Policy

The higher the proportion of debt, the higher the stock price, but at a certain point, the increase in debt will reduce the value of the company because the benefits obtained from the use of debt are smaller than the costs incurred. The purpose of this study is to analyze the influence of ownership structure – managerial ownership and institutional ownership, asset structure, and earning volatility on debt policy. The data used in this study are secondary data in the form of managerial ownership data, institutional ownership, asset structure, earning volatility and debt policy in pharmaceutical companies that go public on the Indonesia Stock Exchange 2013-2017. The statistical method used is the Stepwise Regression, because there is high multicollinearity in managerial ownership variables, institutional ownership, assets structure, earning volatility. Based on the results of the Stepwise Regression shows that the variables entered into the regression model are earning volatility. Other variables such as managerial ownership, institutional ownership, asset structures are not included in the Stepwise Regression so that conclusion is only earning volatility variable that influences the debt policy earning volatility variables that affect debt policy.

scholarly journals AN EMPIRICAL STUDY OF PRICE CLUSTERING ON THE NAIROBI SECURITIES EXCHANGE

2017 ◽  
Vol 2 (2) ◽  
pp. 23
Author(s):  
Lugongo Maurice Wafula ◽  
Dr. Sifunjo E Kisaka
Keyword(s):  
Empirical Study ◽  
Regression Model ◽  
Stock Price ◽  
Multivariate Regression ◽  
The Other ◽  
Multivariate Regression Model ◽  
Stock Volatility ◽  
Market Capitalization ◽  
Secondary Sources ◽  
Price Clustering

Purpose: The purpose of this study was to empirically investigate price clustering phenomenon on the Nairobi Securities Exchange for the period 2009 to 2013.Materials and methods: The study used secondary sources of data obtained from the Nairobi Securities exchange. The study revealed that there has been a preference by investors for stock whose prices end with the digit 5 and this accounted for 67.88 percent of all the stocks examined and was followed by stocks whose prices ended with the digit 0 which accounted for 4.55 percent. In order to establish the determinants of this observed behavior a multivariate regression model used by Harris (1991) was adopted where price clustering was regressed against stock volatility, number of trades, market capitalization, and own stock price.Results: The regression results indicated that the number of trades as well as Market Capitalization was positive and significantly related to price clustering. The study also found the stock price to be negative and significantly related to price clustering. On the other hand, Stock volatility was established to be an insignificant predictor of price clustering. The multivariate regression model was found to be significant in explaining the observed relationship and that 15.4 percent of the variance in price clustering was explained by number of trades, stock volatility, own stock price and the market capitalization. The study finds that there is a tendency of prices to cluster around certain numbers as evidenced by the 67.88 percent of numbers clustering around the number 5 and that price clustering is positively related to number of tradesRecommendations: It is thus recommended that if firms are to increase the number of trades of their shares they should consider pricing their shares according to the preferences of investors who prefer shares or stocks whose prices ends with 5 or 0.

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