scholarly journals A model proposal for estimating banks’ future value: Evidence from Turkey

2021 ◽  
Vol 16 (4) ◽  
pp. 169-178
Author(s):  
Burhan Günay ◽  
Ayten Turan Kurtaran ◽  
Sara Faedfar
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Panel Data Analysis ◽  
Explanatory Power ◽  
Performance Criteria ◽  
Past Performance ◽  
Ols Regression ◽  
Significance Level ◽  
The Future ◽  
The Impact

Investors make solid decisions when evaluating their investments based on positive indicators the firm may show in the future, rather than based on its past performance. Accordingly, this study aims to investigate the relationship between performance criteria and the most significant value-based criterion; Economic Value Added (EVA). Further, it evaluates the impact of future EVA values on the bank value. Panel Data Analysis and the OLS Regression model are used to estimate the regression equation. The analysis is performed using data of 10 banks on the BIST Banks Index over the period 2011 to 2020. Furthermore, the EVA criterion was converted into standardized EVA(SEVA) by dividing EVA by total assets. The OLS regression analysis results revealed that the model’s explanatory power for the SEVA variable is 71.92%. The three variables that have positive correlation with SEVA are earnings per share (EPS) and TOBINQ rates at the 1% significance level and the price to sales growth rate with a degree of significance at 10%. Regarding the Panel Data Analysis results, while the explanatory power of the SEVA variable is 72.14%, its association with the EPS and TOBINQ criteria was found to be significant at the 1% significance level. The empirical investigations reveal that the model developed using the future SEVA as a proxy for bank value is found to be promising, and it is accepted that the SEVA variable can be used instead of the bank value.

An Examination of Audit Report Lag for Banks: A Panel Data Approach

2000 ◽  
Vol 19 (2) ◽  
pp. 159-174 ◽  
Author(s):  
B. Charlene Henderson ◽  
Steven E. Kaplan
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Panel Data Analysis ◽  
Explanatory Power ◽  
Omitted Variables ◽  
Audit Report ◽  
Cross Sectional ◽  
Cross Sectional Analysis ◽  
The Cross ◽  
Sectional Analysis

This study investigates the determinants of audit report lag (ARL) for a sample of banks. Researchers have been interested in the determinants of ARL, in part, because it impacts the timeliness of public disclosures. However, prior ARL research has relied exclusively on regression analysis of cross-sectional samples of companies from many industries. In addition to focusing exclusively on banks, panel data analysis is introduced and compared with cross-sectional analysis to demonstrate its power in dynamic settings and its potential to improve estimation. Results reveal important differences between cross-sectional analysis and panel data analysis. First, bank size is negatively related to ARL in cross-section but positively related to ARL using panel data analysis. The cross-sectional size estimate is subject to omitted variables bias, and furthermore, cross-sectional analysis fails to capture variation in size over time in relation to ARL. Panel data analysis both accounts for omitted variables and captures the dynamics of the relationship between size and ARL. As well, the panel data model's explanatory power far exceeds that of the cross-sectional model. This is primarily due to the panel model's use of firm-specific intercepts that both capture the role of reporting tradition and eliminate heterogeneity bias. Thus, panel data analysis proves to be a powerful tool in the analysis of ARL.

scholarly journals Impact of Digitalization on the Farmers in India: Evidence using Panel Data Analysis

2021 ◽  
Vol 6 (1) ◽  
pp. 5-12
Author(s):  
Rahul Singh Gautam ◽  
◽  
Venkata Mrudula Bhimavarapu ◽  
Dr. Shailesh Rastogi ◽  
◽  
...  
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Panel Data Analysis ◽  
Secondary Data ◽  
Development Model ◽  
Agriculture Sector ◽  
Economic Progress ◽  
New Development ◽  
The Impact ◽  
Financial Technology

The composition of digitalization and financial technology has brought about a new development model for the agriculture sector. What is the impact of digitization on India’s farmers? To answer this issue, this article examines the effects of digitalization on farmers in India using secondary data from 2018 to 2020, based on the idea of digitalization. It analyses the transmission of digitalization among Indian farmers using panel data analysis. The conclusions are as follows: Farmers' income can be significantly increased by digitalization, and farmers' digitization has resulted in agriculture sector development and contributed to economic progress.

scholarly journals Corporate Governance Codes and Intellectual Capital: Evaluating the Performance Impacts among Government-Linked Companies in Malaysia and Singapore – A Panel Data Analysis

2021 ◽  
Vol 124 ◽  
pp. 08004
Author(s):  
Yen Wen Chang ◽  
Ng Ching Yat David ◽  
Suet Cheng Low ◽  
Peck Ling Tee
Keyword(s):  
Corporate Governance ◽  
Data Analysis ◽  
Panel Data ◽  
Intellectual Capital ◽  
Stock Exchange ◽  
Panel Data Analysis ◽  
Policy Makers ◽  
Corporate Governance Codes ◽  
The Government ◽  
The Impact

The objective of this study was to examine and compare the effects of corporate governance (CG) and intellectual capital (IC) between Malaysia Government-Linked Companies’ (M-GLCs) and Singapore Government-Linked Companies’ (S-GLCs) firm performance (FP). Panel data analysis was employed to analyse the impact of CG’s variables and IC’s variables on FP. FP was measured by Return on Total Assets (ROA), Tobin’s Q and Earnings Per Share (EPS). Data was gathered from the website of Bursa Malaysia and the Stock Exchange of Singapore from 2005 to 2018. The sample size of this research was 60 GLCs which comprised of 34 M-GLCs and 26 S-GLCs. There were a total 840 firm year observations. Results indicated that CGs of S-GLCs have greater impact on FP when compared to M-GLCs while the findings of the IC of M-GLCs have greater impact on FP compared to S-GLCs. This research was helpful in offering further insights of CG practices and IC efficiency to the Government, Board of Directors, policy makers, shareholders and stakeholders.

Effects of content characteristics on stages of customer engagement in social media: investigating European wine brands

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Aspasia Vlachvei ◽  
Ourania Notta ◽  
Eirini Koronaki
Keyword(s):  
Social Media ◽  
Data Analysis ◽  
Panel Data ◽  
Panel Data Analysis ◽  
Customer Engagement ◽  
Content Type ◽  
Interactive Engagement ◽  
Long Run ◽  
Three Stages ◽  
The Impact

PurposeThis study advances knowledge of interactive marketing strategies by examining the effect of different content types on the three stages of customer engagement (CE) in social media, namely, relationship formation, engagement creation and engagement contribution, for European wine brands.Design/methodology/approachBoth quantitative and qualitative content analyses are conducted; a panel data analysis validates the impact of content type on the three stages of CE in social media.FindingsThe results indicate that remunerative content is the most consistent and promising strategy for enhancing all three stages of CE in social media. Social content motivates consumers to interact with wine brands by commenting, which is the most demanding and time-consuming form of engagement.Practical implicationsThe empirical results offer valuable directions for managers and marketers of European wine brands on creating and maintaining optimal interactive engagement in all three stages with their Facebook communities over the long run.Originality/valueThis study is one of the first to empirically examine, through objective measurement, how content type affects the three stages of CE in social media. The case of European wine brands is examined, over time, through a panel data analysis.

Financial development and self-employment

2020 ◽  
Vol 19 (3) ◽  
pp. 339-357
Author(s):  
Papar Kananurak ◽  
Aeggarchat Sirisankanan
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Financial Development ◽  
Panel Data Analysis ◽  
Statistical Significance ◽  
Random Effect Model ◽  
Direct Examination ◽  
Content Type ◽  
Self Employment ◽  
The Impact

Purpose There are several different factors that can influence self-employment. However, there is little evidence stemming from direct examination of the impact of financial development (FD) on self-employment. This study aims to formulate empirical specification models to examine the effect of FD on self-employment. Design/methodology/approach Panel data analysis of 136 sample countries was performed during the period from 2000 to 2017. This study initially implemented the new financial index developed by the International Monetary Fund (IMF) to examine the impact of FD on self-employment. Panel data analysis including the pooled model, fixed effect and random effect model has been carried out. Findings The empirical results show that the financial institutions index has a negative significant impact on self-employment by a considerable magnitude, whereas the financial markets index does not show any statistical significance. The results also find that the government effectiveness index is negative and statistically significant on self-employment. Originality/value There are several different factors which can influence self-employment. Nevertheless, there is little evidence for the direct examination of the impact of FD on self-employment. This study investigated the impact of FD on self-employment by using the new FD index created by the IMF. The finding may help policymakers to implement FD along with other institutional policies to control self-employment.

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