scholarly journals Predicting Sustainable Bank Performance in Nigeria Using Composite Financial Index

2021 ◽  
Vol 9 (9) ◽  
Author(s):  
Irem Collins Okechukwu ◽  
Aleke Stephen Friday ◽  
Nwele Anamalechi Ogai ◽  
Irem Nnaemeka Ekoyi
Keyword(s):  
Bank Performance ◽  
Financial Index ◽  
Composite Financial Index

scholarly journals Analyzing Financial Health of the SMES Listed in the AERO Market of Bucharest Stock Exchange Using Principal Component Analysis

2020 ◽  
Vol 12 (9) ◽  
pp. 3726
Author(s):  
Claudia Diana Sabău-Popa ◽  
Ramona Simut ◽  
Laurențiu Droj ◽  
Corneliu Cristian Bențe
Keyword(s):  
Principal Component Analysis ◽  
Stock Exchange ◽  
Small And Medium Enterprises ◽  
Score Function ◽  
Principal Component ◽  
Component Analysis ◽  
Financial Health ◽  
Rates Of Return ◽  
Financial Index ◽  
Composite Financial Index

In this paper we aimed to build a composite financial index for measuring the financial health of the companies listed in the AERO (Alternative exchange in Romania) market of the Bucharest Stock Exchange. We used a principal component analysis in order to build this composite financial index using the rates of return, liquidity and the management of 25 companies listed in the AERO market for the period 2011–2018. We conceived this composite indicator as a score function that established according to the numerical values that result from its application when a company was financially healthy, when it had a poor financial health and when it was financially stable. In order to test the financial health of the selected SMEs (small and medium enterprises), we used the one sample t-test under the model of the study and the three classifications of Z (Z < 0—companies with poor financial health, 0 ≤ Z ≤ 0.5—companies with good financial health and Z > 0.5—companies with very good financial health). In this study we also aimed to identify the possible correlations between the solvency rate and the financial health index and between solvency rate and the evolution of some economic and financial measures of the companies’ activities. The results of the regression analysis using panel data showed a positive and statistically significant relation between solvency and the three rates (rates of return, of liquidity and of management, respectively) determined using the analysis of the principal components. The former model of the solvency rate identified correctly 94.9% of the SMEs with poor financial health, 40% of the SMEs with stable financial health and 72.2% of the SMEs with good financial health.

scholarly journals COMPOSITE FINANCIAL PERFORMANCE INDEX PREDICTION – A NEURAL NETWORKS APPROACH

2021 ◽  
Vol 22 (2) ◽  
pp. 277-296
Author(s):  
Diana Claudia Sabău Popa ◽  
Dorina Nicoleta Popa ◽  
Victoria Bogdan ◽  
Ramona Simut
Keyword(s):  
Neural Networks ◽  
Financial Performance ◽  
Performance Index ◽  
Business Performance ◽  
Composite Index ◽  
Real Data ◽  
Measurement Model ◽  
Financial Index ◽  
Composite Financial Index ◽  
One Year

Financial indicators are the most used variables in measuring the business performance of companies, signaling about the financial position, comprehensive income, and other significant reporting aspects. In a competitive environment, the performance measurement model allows performing comparative analysis in the same industry and between industries. This paper aims to design a composite financial index to determine the financial performance of listed companies, further used in predicting business performance through neural networks. Principal components analysis was used to build a composite financial index, employing four traditional accounting indicators and four value-based indicators for the period 2011–2018. Five experiments were conducted to predict business performance through the composite financial index. The results showed that observations from two years, of the first three experiments, indicate a better predictive behavior than the same experiments using observations from one year. Therefore, we concluded that observations from more than one year are necessary to predict the value of the financial performance index. Findings led us to the conclusion that recurrent neural networks model predicted better financial performance composite index when taken into consideration more real data for the financial performance index (2012–2018) instead of just for one year (2018).

The performance of Islamic banking among the commercial banks in Malaysia

2008 ◽  
Vol 5 (1) ◽  
pp. 59
Author(s):  
Samsuwatd Zuha Mohd Abbas ◽  
Norli Ali ◽  
Aminah Mohd Abbas
Keyword(s):  
Commercial Banks ◽  
Positive Impact ◽  
Bank Performance ◽  
Islamic Banking ◽  
Islamic Banks ◽  
Return On Assets ◽  
Return On Equity ◽  
Accounting Performance ◽  
Significant Difference ◽  
The Mean

This paper examines the accounting performance of the Islamic banking among (??) commercial banks in Malaysia. A total of 18 commercial banks which include 4 Islamic banks are selected as samples covering the period of 2000 - 2006. Accounting performance is measured by the return on assets (ROA) and return on equity (ROE). The objective of the study is (1) to determine whether Islamic banking performance is at par with the conventional banking and (2) to investigate whether the type (Islamic or conventional bank) and age of bank influence the performance. Result of the independence t-test of the study shows that there is no significant difference in the performance of the Islamic and the conventional banking in Malaysia although the mean score for conventional banking is higher. The regression results show that the age of banks has a positive impact on the bank performance where as none of the types of banks influence performance.

Fraud and Bank Performance in Nigeria – Var Granger Causality Analysis

e-Finanse ◽  
2020 ◽  
Vol 16 (1) ◽  
pp. 20-26
Author(s):  
Taiwo A. Muritala ◽  
Muftau A. Ijaiya ◽  
Olatanwa H. Afolabi ◽  
Abdulrasheed B. Yinus
Keyword(s):  
Control Systems ◽  
Granger Causality ◽  
Internal Control ◽  
Banking Sector ◽  
Bank Performance ◽  
Causality Analysis ◽  
Vector Autoregressive ◽  
Granger Causality Analysis ◽  
Long Run ◽  
Multivariate Cointegration

AbstractThis paper examines the causality between fraud and bank performance in Nigeria over the period 2000-2016 for quarterly financial data using Johansen’s Multivariate Cointegration Model and Vector Autoregressive (VAR) Granger Causality analysis. The results show a long-run relationship between the variables. Bank performance was found to be linked to Granger fraud variables and vice versa at 10% significant level. This study reveals that there was a direct causal relationship between bank performance and fraud because increase in fraudulent activities in the banking sector leads to reduction in bank performance. Hence, this study recommends that internal control systems of banks should be strengthened so as to detect and prevent fraud. In this way, bank assets would be protected.

Effects of Financial Inclusion on Startup Establishment in OECD Countries

2018 ◽  
Vol 34 (1) ◽  
Author(s):  
Dinh Thi Thanh Van ◽  
Nguyen Thuc Trang
Keyword(s):  
Success Factors ◽  
Oecd Countries ◽  
Financial Inclusion ◽  
Economic Research ◽  
Mobile Banking ◽  
Policy Makers ◽  
Academic Researchers ◽  
Financial Index ◽  
Enterprise Survey

Financial inclusion and startup are two topics, which recently get attention of academic researchers and policy makers in Vietnam. One of the important factors for setting up a successful startup is the financial capability of the owners. Therefore, financial inclusion has a strong correlation with startup establishment. This article tested the effects of several factors in financial index (findex) developed by World Bank on startup establishment in some OECD countries. The result showed that borrowing from friends and relatives along with from credit institutions and opening a debit account at banks have  significant impacts on startup establishment in these countries. Finally, the article presented several recommendations for policy makers to stimulate the startup growth in Vietnam in the next time. Key words startup, financial inclusion, startup establishment References 1. Colman Msoka (2015), “Financial inclusion and microfinance in Tanzania”, Inclusive growth: Tanzania Country Report2. Endeavor-GEM, 2011, “High-Impact Entrepreneurship Global Report”3. Eric Ries, 2012, “The Lean Startup” book”, http://www.stpia.ir/files/The%20Lean%20Startup%20.pdf 4. European Startup Monitor, 2015, “European Startup Monitor 2015”, http://europeanstartupmonitor.com/fileadmin/presse/download/esm_2015.pdf 5. Jennifer Dahlin Ivarsson (2014), “Mobile-banking and entrepreneurship: Is there a link? A case study on South Africa”, Nationalekonomiska Institutionen, Box 7082, ISSN 0283 – 15896. Maher Al-Mahouq (2010), “Success factors of small and medium-sized enterprises(SMEs): The case of Jordan”, Anadol University jourmal of social sciences, Cilt/Vol.: 10 – Say/No:1-16 (2010)7. Mohammed S.Chowdhury (2013), “Success factors of entrepreneurs of small and medium sized enterprises: Evidence from Banladesh”, Business and Economic Research, ISSN 2162 – 4860, 2013, Vol.3, No.2.8. OECD, 2015, “Entrepreneurship at a Glance 2015”9. Roman Angela, 2011, “SME’s sector access to finance: An overview”10. Yao Wang, 2014, “What are the biggest obstacles to growth of SMEs in developing countries? An empirical evidence from an enterprise survey”, JED 210 Paper

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