Exchange Rate Risk in the U.S. Stock Market: A Pooled Panel Data Regression Approach

2018 ◽  
Author(s):  
Cheng-Few Lee ◽  
Cindy S.H. Wang ◽  
Andrew Y.M. Xie
Keyword(s):  
Exchange Rate ◽  
Panel Data ◽  
Stock Market ◽  
Exchange Rate Risk ◽  
Rate Risk ◽  
Panel Data Regression ◽  
Data Regression ◽  
Regression Approach ◽  
The U.S

scholarly journals PENGARUH INFLASI, KURS DAN SUKU BUNGA TERHADAP RISIKO SISTEMATIS SEBAGAI VARIABEL MEDIASI DAN IMPLIKASINYA TERHADAP HARGA SAHAM DENGAN PENERAPAN REGRESI DATA PANEL COMMON EFFECT

Kinerja ◽  
2020 ◽  
Vol 2 (01) ◽  
pp. 133-152
Author(s):  
Sunaryo
Keyword(s):  
Exchange Rate ◽  
Panel Data ◽  
Path Analysis ◽  
Stock Price ◽  
Systematic Risk ◽  
Significant Negative Effect ◽  
Common Effect ◽  
Panel Data Regression ◽  
Data Regression ◽  
Negative Effect

The aims of this research are to examine the direct effect and indirect effect of Inflation, Exchange Rate, Interest Rate on Stock Price with Systematic Risk as an intervening variable. This research sample is shared in the sharia category in the JII group (Jakarta Islamic Index) listed on the Indonesia Stock Exchange (IDX) period 2013-2018 by using a purposive sampling method. There were 41 stocks selected as samples. The method of analysis used is Path Analysis, the development of panel data regression common effect. Using panel data regression with common effect analysis, it is known that the Inflation and Exchange Rate has a positive significant effect on Systematic Risk. However, Interest Rates have a significant negative effect on Systematic Risk. Systematic Risk has a significant negative effect on Stock Price. The path analysis results show that Systematic Risk mediates the effect of Inflation and Exchange Rates on the Stock price.

scholarly journals Determinan Volume Pembiayaan Bank Umum Syariah Indonesia

IKONOMIKA ◽  
2017 ◽  
Vol 2 (1) ◽  
Author(s):  
Masudah Masudah
Keyword(s):  
Exchange Rate ◽  
Panel Data ◽  
Interest Rate ◽  
Operational Efficiency ◽  
Islamic Banking ◽  
Islamic Banks ◽  
Efficiency Ratio ◽  
Independent Variables ◽  
Panel Data Regression ◽  
Data Regression

As a financial intermediary institution, Islamic banking should give a financing to deficit units. The aim of this study is to emphasize the factors that had an influence to financing in Islamic banks. This research using panel data regression with 11 Islamic full-pledge banks from 2011 until 2015. The result shows that deposit funds, exchange rate, operational efficiency ratio and interest rate had an impact on Islamic bank’s financing, but other variables such as non-performing financing and inflation doesn't had an impact. From F-test shows that all variables simultaneously had an impact on Islamic bank’s financing. Besides that, the value of adjusted R-square shows that all of the independent variables can explain the model about 78.5%, the others explained by other variables outside the model.

scholarly journals Modeling the Effect of Bank Specific Factors on Financial Performance of Commercial Banks in Nigeria: Panel Data Regression Approach

2021 ◽  
Vol 28 (1) ◽  
pp. 40-47
Author(s):  
Y. Yakubu ◽  
S.M. Egopija
Keyword(s):  
Panel Data ◽  
Financial Performance ◽  
Commercial Banks ◽  
Management Quality ◽  
Panel Data Regression ◽  
Data Regression ◽  
Regression Approach ◽  
Specific Factors ◽  
Key Indicators ◽  
Bank Specific Factors

Periodic checking and evaluation of financial performance of the banking sector is a way of sustaining the development of a nation’s economy. The key indicators of the banks’ financial performance are their return on assets (ROA) and return on equity (ROE). A bank’s financial performance is affected by some specific factors like capital adequacy ratio (CAR), credit risk (CRISK), management quality, liquidity ratio (LIQ.RAT.) and bank size. This work first compares average financial performance of some sampled commercial banks in Nigeria (UBA, GTB, ZENITH, FIRST, and ACCESS banks) based on the key indicators and the bank specific factors. It then models the effect of these factors on the overall financial performance of the sampled banks using panel data regression approach. The results showed that the GTB had the highest average ROA, ROE and CAR throughout the period under review while Zenith bank was the best in terms of credit risk, management quality and liquidity ratio. The fitted ROA model accounted for 83% of the total variability in the data and revealed that CAR, CRISK, and LIQ.RAT were significant at both 1% and 5% levels while the ROE model accounted for 69% and revealed that CRISK and LIQ.RAT were significant. Keywords: Financial Performance, Commercial Banks, Evaluation, Panel Data, Economy

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