scholarly journals Stock returns and liquidity risk in Chile

2019 ◽  
Vol 17 (2) ◽  
Author(s):  
Francisco Javier Vásquez-Tejos ◽  
Hernán Pape-Larre ◽  
Juan Martín Ireta-Sánchez
Keyword(s):  
Stock Market ◽  
Emerging Markets ◽  
Regression Model ◽  
Stock Returns ◽  
Multiple Regression ◽  
Risk Measures ◽  
Liquidity Risk ◽  
Multiple Regression Model ◽  
Developed Markets ◽  
The Impact

This study analyzes the impact of liquidity risk on the return of shares in the Chilean stock market, during the period from January 2000 to July 2018. A large number of studies have focused on measuring this effect in developed markets and few in emerging markets, especially the Chilean one. To do this, we used 6 risk measures in a multiple regression model; four widely used in previous studies and two new proposed measures. We found evidence of the significance of the liquidity risk over the stock return.RESUMENEste estudio analiza el impacto del riesgo de liquidez sobre el retorno de las acciones en el mercado bursátil chileno, durante el periodo de enero de 2000 hasta julio de 2018. Gran cantidad de estudios se han centrado en medir este efecto en los mercados desarrollados y pocos en mercados emergentes, especialmente el chileno. Para ello, se utilizó un modelo de regresión múltiple 6 medidas de riesgo; cuatro utilizadas ampliamente en estudios anteriores y dos medidas nuevas propuestas. Encontramos evidencia de significancia del riesgo de liquidez sobre el retorno accionario.RESUMOEste estudo analisa o impacto do risco de liquidez no retorno das ações no mercado de ações chileno, durante o período de janeiro de 2000 a julho de 2018. Muitos estudos têm se concentrado em medir este efeito em mercados desenvolvidos e poucos nos mercados emergentes, especialmente o chileno. Para isso, utilizamos 6 medidas de risco em um modelo de regressão múltipla; quatro amplamente utilizados em estudos anteriores e duas novas medidas propostas. Encontramos evidências da significância do risco de liquidez sobre o retorno das ações.  

scholarly journals LIQUIDITY RISK AND STOCK RETURN IN LATIN AMERICAN EMERGING MARKETS

2021 ◽  
Vol 20 (2) ◽  
Author(s):  
Francisco Javier Vasquez-Tejos ◽  
Prosper Lamothe Fernandez
Keyword(s):  
Emerging Markets ◽  
Stock Returns ◽  
Latin American ◽  
Stock Return ◽  
Risk Measures ◽  
Liquidity Risk ◽  
Multiple Regression Model ◽  
Investment Strategies ◽  
Developed Markets ◽  
The Impact

This study analyzes the impact of liquidity risk on stock returns in four Latin American markets (Chile, Columbia, Mexico, and Peru) between January 1998 and July 2018. Several previous studies have focused on measuring this effect in developed markets and a few in emerging markets, such as Latin American stock markets. In the present study, five liquidity risk measures with a multiple regression model; three have been widely used in previous studies and two were from recently proposed measures. We found evidence of an inverse relationship between liquidity risk and stock performance, which indicates that there exist rewards for investing in less liquid positions and therefore originate new investment strategies. In general, lesser developed or smaller markets have a disadvantage for this type of study, due to lack of access to historical information on stock purchase and sales.

scholarly journals Top-down estimate of black carbon emissions for city cluster using ground observations: A case study in southern Jiangsu, China

2018 ◽  
Author(s):  
Xuefen Zhao ◽  
Yu Zhao ◽  
Dong Chen ◽  
Chunyan Li ◽  
Jie Zhang
Keyword(s):  
Regression Model ◽  
Black Carbon ◽  
Multiple Regression ◽  
Wet Deposition ◽  
Multiple Regression Model ◽  
Top Down ◽  
Bottom Up ◽  
Southern Jiangsu ◽  
City Cluster ◽  
The Impact

Abstract. We combined a chemistry transport model (CTM), a multiple regression model and available ground observations, to derive top-down estimate of black carbon (BC) emissions and to reduce deviations between simulations and observations for southern Jiangsu city cluster, a typical developed region of eastern China. Scaled from a high-resolution inventory for 2012 based on changes in activity levels, the BC emissions in southern Jiangsu were calculated at 27.0 Gg/yr for 2015 (JS-prior). The annual mean concentration of BC at Xianlin Campus of Nanjing University (NJU, a suburban site) was simulated at 3.4 μg/m3, 11 % lower than the observed 3.8 μg/m3. In contrast, it was simulated at 3.4 μg/m3 at Jiangsu Provincial Academy of Environmental Science (PAES, an urban site), 36 % higher than the observed 2.5 μg/m3. The discrepancies at the two sites implied the uncertainty of the bottom-up inventory of BC emissions. Assuming a near-linear response of BC concentrations to emission changes, we applied a multiple regression model to fit the hourly surface concentrations of BC at the two sites, based on the detailed source contributions to ambient BC levels from brute-force simulation. Constrained with this top-down method, BC emissions were estimated at 13.4 Gg/yr (JS-posterior), 50 % smaller than the bottom-up estimate, and stronger seasonal variations were found. Biases between simulations and observations were reduced for most months at the two sites when JS-posterior was applied. At PAES, in particular, the simulated annual mean was elevated to 2.6 μg/m3 and the annual normalized mean error (NME) decreased from 72.0 % to 57.6 %. However, application of JS-posterior slightly enhanced NMEs in July and October at NJU where simulated concentrations with JS-prior were lower than observations, implying that reduction in total emissions could not correct CTM underestimation. The effects of numbers and spatial representativeness of observation sites on top-down estimate were further quantified. The best CTM performance was obtained when observations of both sites were used with their difference in spatial functions considered in emission constraining. Given the limited BC observation data in the area, therefore, more measurements with better spatiotemporal coverage were recommended for constraining BC emissions effectively. Top-down estimates derived from JS-prior and the Multi-resolution Emission Inventory for China (MEIC) were compared to test the sensitivity of the method to initial emission input. The differences in emission levels, spatial distributions and CTM performances were largely reduced after constraining, implying that the impact of initial inventory was limited on top-down estimate. Sensitivity analysis proved the rationality of near linearity assumption between emissions and concentrations, and the impact of wet deposition on the multiple regression model was demonstrated moderate through data screening based on simulated wet deposition and satellite-derived precipitation.

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.

scholarly journals Multiple Regression Analysis used in Analysis of Private Consumption and Public Final Consumption Evolution, case of Albanian Economy

2020 ◽  
Vol 3 (1) ◽  
pp. 48
Author(s):  
Ilva Isa ◽  
Bederiana Shyti ◽  
Kamen Spassov
Keyword(s):  
Regression Model ◽  
Multiple Regression ◽  
Private Consumption ◽  
Influence Factors ◽  
Multiple Regression Model ◽  
Public And Private ◽  
The Public ◽  
Final Consumption ◽  
Public And Private Sector ◽  
The Impact

This paper approaches the evolution of the final consumption recorded at the level of Albanian economy. According to statistical methodology the public and private consumption are two of the components of the final consumption. The main variable of our study is final consumption, which is set to be influenced by at least two independent variables, such as public and private consumption. Lately, Albanian economy has been presented with a new and different macro economic policy, a new form of partnership of investments between public and private sector. We are highly interested in the impact of these changes on final consumption .The correlation between the main parameter and its influence factors is analyzed through a regression model. Eviews is the software that the data will be processed under standard methods. The model and the results are part of the paper. To be emphasized is that the reliability of the multiple regression model does not exclude the possibility to analyze the single correlation between the parameters, in parallel.

Impact of Leadership Style on Organizational Effectiveness

2005 ◽  
Vol 30 (1) ◽  
pp. 90-103
Author(s):  
Bandana Nayak ◽  
B. B. Mishra
Keyword(s):  
Regression Model ◽  
Leadership Styles ◽  
Multiple Regression ◽  
Organizational Effectiveness ◽  
Leadership Style ◽  
Multiple Regression Model ◽  
Steel Plant ◽  
Model Data ◽  
The Impact ◽  
Relation Matrix

The present paper examines the impact of leadership styles on organizational effectiveness by using Pearson's co-relation matrix and multiple regression model. Data were collected from 10 departments of Rourkela Steel Plant. Participants included 68 supervisors and 241 managers. Managerial Behaviour Questionnaire (MBQ) and Organizational Effectiveness Scale (OES), the standardized questionnaires were used for the study. The study reveals that leadership styles of managers and supervisors highly influence the organizational effectiveness.

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