Internal and external determinants of banks’ profitability

2016 ◽  
Vol 43 (1) ◽  
pp. 90-107 ◽  
Author(s):  
Maria Teresa Medeiros Garcia ◽  
João Pedro Silva Martins Guerreiro
Keyword(s):  
Time Series ◽  
Comparative Analysis ◽  
Fixed Effects ◽  
Design Methodology ◽  
Time Series Data ◽  
Ordinary Least Squares ◽  
Series Data ◽  
Content Type ◽  
Independent Variables ◽  
Net Interest Margin

Purpose – The purpose of this paper is to analyze the profitability of 27 universal banks in Portugal over the period from 2002 to 2011. Design/methodology/approach – The paper conducts ordinary least squares estimations with fixed effects using three measures of profitability: the return on average assets, the return on average equity and the net interest margin. Several independent variables were included concerning both bank-specific and macroeconomic and industry-specific characteristics which have not been considered in previous studies. In addition, the sub-sample between 2008 and 2011 was considered for comparative analysis. Findings – The authors concluded that the independent variables selected, with few exceptions, behaved accordingly to what was expected. Originality/value – To the best of the author’s knowledge, this is the first attempt to examine determinants of banks’ profitability in Portugal, both internal and external, using time series data, which have not been considered in previous studies.

China’s FDI inflow into Ethiopia; Does it have relation with bilateral trade?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hana Woldekidan Azmete ◽  
Kahsay Gerezihar Tsaedu
Keyword(s):  
Time Series ◽  
Design Methodology ◽  
Bilateral Trade ◽  
Time Series Data ◽  
Series Data ◽  
Individual Country ◽  
Content Type ◽  
Long Run ◽  
Trade Partner ◽  
Integration Analysis

Purpose The purpose of this study is to empirically analyze if a bilateral trade between two countries leads to a foreign direct investment (FDI) using a time series data spanning over the period 2000–2017. Design/methodology/approach The Engle-Granger method of co-integration analysis is applied to the data to estimate if China’s export to Ethiopia led to an inflow of FDI from China to Ethiopia over the long run. Findings The results indicated that bilateral trade (import from China) is a major determinant of Chinese FDI inflow to Ethiopia over the study period. Originality/value A number of studies have been conducted on the determinants of FDI in Ethiopia using time series data at different points of time. However, none of them tried to analyze what attracts FDI from an individual country. Accordingly, this study has concentrated on FDI from China and its relation with bilateral trade between China and Ethiopia as China is the number one FDI source and trade partner of Ethiopia.

Exploring the dynamic contribution behavior of editors in wikis based on time series analysis

2016 ◽  
Vol 50 (1) ◽  
pp. 41-57 ◽  
Author(s):  
Linghe Huang ◽  
Qinghua Zhu ◽  
Jia Tina Du ◽  
Baozhen Lee
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Time Of Day ◽  
Observation Time ◽  
Series Data ◽  
Clustering Methods ◽  
Content Type ◽  
Free Rider Problem ◽  
Series Analysis

Purpose – Wiki is a new form of information production and organization, which has become one of the most important knowledge resources. In recent years, with the increase of users in wikis, “free rider problem” has been serious. In order to motivate editors to contribute more to a wiki system, it is important to fully understand their contribution behavior. The purpose of this paper is to explore the law of dynamic contribution behavior of editors in wikis. Design/methodology/approach – After developing a dynamic model of contribution behavior, the authors employed both the metrological and clustering methods to process the time series data. The experimental data were collected from Baidu Baike, a renowned Chinese wiki system similar to Wikipedia. Findings – There are four categories of editors: “testers,” “dropouts,” “delayers” and “stickers.” Testers, who contribute the least content and stop contributing rapidly after editing a few articles. After editing a large amount of content, dropouts stop contributing completely. Delayers are the editors who do not stop contributing during the observation time, but they may stop contributing in the near future. Stickers, who keep contributing and edit the most content, are the core editors. In addition, there are significant time-of-day and holiday effects on the number of editors’ contributions. Originality/value – By using the method of time series analysis, some new characteristics of editors and editor types were found. Compared with the former studies, this research also had a larger sample. Therefore, the results are more scientific and representative and can help managers to better optimize the wiki systems and formulate incentive strategies for editors.

ANALISIS PERMINTAAN BERAS DI SUMATERA UTARA

2020 ◽  
Vol 1 (3) ◽  
Author(s):  
Hasyrul Aziz Harahap
Keyword(s):  
Time Series ◽  
Fixed Effects ◽  
Time Series Data ◽  
Food Prices ◽  
Series Data ◽  
Cross Point ◽  
Social Unrest ◽  
Corn Prices ◽  
North Sumatra ◽  
Global Food

Indonesia is often categorized as low food resilient nation, in the sense vulnerable to social unrest and rising global food prices. Where every year the number of requests or local domestic rice continue to increase along with the increasing number of people. This study aims to look at and determine how much influence the price of rice, corn prices and the number of population and GDP of the demand for rice in North Sumatra. Used in measuring and analyzing time series data (time series) and the cross-point (cross section) of the 25 districts / municipalities in the period from 2005 to 2010. Data analysis using fixed effects (fixed effect). The results showed a significant effect between the price of rice, the population and GDP of the demand for rice in North Sumatra. While corn prices do not influence of the demand for rice in North Sumatra. The magnitude of the effect is shown by the coefficient of independent variables, namely: -5.215489 for the variable price of rice, 13.08473 for the population, 4.736669 for the variable GDP.

scholarly journals PENGARUH NET INTEREST MARGIN (NIM), BEBAN OPERASIONAL PENDAPATAN OPERASIONAL (BOPO), CAPITAL ADEQUACY RATIO (CAR), DAN NON PERFORMING LOAN (NPL) TERHADAP PROFITABILITAS PERUSAHAAN PERBANKAN BURSA EFEK DI NEGARA INDONESIA, MALAYSIA, DAN THAILAND

2017 ◽  
Vol 29 (1) ◽  
pp. 9-19
Author(s):  
Juhasdi Susono
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Operating Cost ◽  
Capital Adequacy ◽  
Series Data ◽  
Independent Variables ◽  
Pooled Data ◽  
Net Interest Margin ◽  
Capital Adequacy Ratio ◽  
Interest Margin

This study aims to determine the effect of Net Interest Margin (NIM), Operational Income Operating Cost (BOPO), Capital Adequacy Ratio (CAR), and Non-Performing Loan (NPL) on banking stock exchange company profitability in Indonesia, Malaysia and Thailand. This research was a quantitative, aimed to work out a systematically explain on the facts and properties of object in the research then merger was done between related variables in it with the presentation of secondary data from the financial statements of banking companies in Indonesia, Malaysia and Thailand. The population used in this study was banking company listed in Indonesia, Malaysia and Thailand stock exchanges in the period of 2010 to 2016. The sample used in this study as many as 24 banking companies in Indonesia, Malaysia and Thailand using purpose sampling method to obtain a representative sample that matches the criteria that have been made. In this study, data analysis method used was panel data (pooled data) which is a combination of time-series data and data between individuals or space (cross section) in banking companies in Indonesia, Malaysia and Thailand. Research Results for banking companies in Indonesia gained value of R square model of 0.222 percent, means that the variation of the profit that can be explained by the independent variables in the analysis of NIM, BOPO, CAR and NPL of 22.20 percent of the remaining 78.80 percent explained by other factors not studied here. Next, In Malaysia R value of this model square of 0.335 percent means that the variation of the profit that can be explained by the independent variables in the analysis of NIM, BOPO, CAR and NPL of 33.50 percent on the remaining 66.50 percent explained by other factors not included in the study this. While in Thailand, R square value of this model was 0.266 percent means that the variation of the profit that can be explained by the independent variables in the analysis of NIM, BOPO, CAR and NPL of 26.60 percent of 73.40 percent was explained by other factors not discussed in this study.   Abstrak   Penelitian ini bertujuan untuk untuk mengetahui pengaruh Net Interest Margin (NIM), Biaya Operasional Pendapatan Operasional (BOPO), Capital Adequacy Ratio (CAR), dan Non Performing Loan (NPL) terhadap pofitabilitas perbankan di negara indonesia, malaysia, dan thailand. Penelitian ini merupakan penelitian kuantitatif yang tujuanya untuk mengerjakan suatu yang di jelaskan secara sistematis tentang fakta-fakta serta sifat dalam suatu objek dalam penelitian kemudian melakukan penggabungan antar variabel yang terkait di dalamnya dengan penyajian data sekunder dari laporan keuangan dari perusahaan perbankan di negara indonesia, malaysia dan thailand. Populasi yang di gunakan pada penelitian ini adalah perusahaan perbankan yang terdaftar di bursa efek indonesia, malaysia dan thailand dalam kurun waktu 2010 sampai 2016. Sampel yang di gunakan dalam penelitian ini sebanyak 24 perusahaan perbankan di negara indonesia, malaysia, dan thailand dengan menggunakan metode purpose sampling tujuanya untuk memperoleh sampel representatif yang sesuai kriteria yang sudah di pastikan. Pada penelitian ini, metode analisa data yang digunakan adalah data panel (pooled data) yang merupakan gabungan dari data antar waktu (time series) dan data antar individu atau ruang (cross section) di perusahaan perbankan di negara indonesia, malaysia dan thailand. Hasil Penelitian untuk perusahaan perbankan di negara indonesia Nilai R square model ini sebesar 0,222 persen artinya bahwa variasi dari profit yang dapat dijelaskan oleh variabel bebas yang di analisis yaitu NIM, BOPO, CAR dan NPL sebesar 22.20 persen sisanya sebesar 78.80 persen dijelaskan oleh faktor lain yang tidak dimasukkan dalam penelitian ini. Selanjutnya Di negara malaysia Nilai R square model ini sebesar 0,335 persen artinya bahwa variasi dari profit yang dapat dijelaskan oleh variabel bebas yang di analisis yaitu NIM, BOPO, CAR dan NPL sebesar 33.50 persen sisanya sebesar 66.50 persen dijelaskan oleh faktor lain yang tidak dimasukkan dalam penelitian ini. Sedangkan di negara thailand. Nilai R square model ini sebesar 0,266 persen artinya bahwa variasi dari profit yang dapat dijelaskan oleh variabel bebas yang di analisis yaitu NIM, BOPO, CAR dan NPL sebesar 26.60 persen sisanya sebesar 73.40 persen dijelaskan oleh faktor lain yang tidak dimasukkan dalam penelitian ini.

scholarly journals PENGARUH ZIS (ZAKAT, INFAK, SEDEKAH) DAN ZAKAT FITRAH TERHADAP PENURUNAN KEMISKINAN DI INDONESIA PERIODE 1998 - 2010

Media Ekonomi ◽  
2017 ◽  
Vol 20 (1) ◽  
pp. 83
Author(s):  
Jumadin Lapopo
Keyword(s):  
Time Series ◽  
Developing Countries ◽  
Regression Analysis ◽  
Poverty Reduction ◽  
Time Series Data ◽  
Secondary Data ◽  
Series Data ◽  
Analysis Model ◽  
Independent Variables

<p>Poverty is being a problem in all developing countries including Indonesia. Among goverment programs, poverty has become the center offattention in policy at both of the regional and national levels. Looking at thephenomenon of poverty, Islam present with solution to reduce poverty through Zakat. This study aims to analyze the effect of ZIS and Zakat Fitrah against poverty in Indonesia in 1998 until 2010, data used in this study is secondary data and uses time series data, for the dependent variabel is poverty and for independent variables are ZIS and Zakat Fitrah. The analysis tools used in this study is to use multiple regression analysis model and the assumptions of classical test using the software Eviews-4. In this study also concluded that the ZIS variables significantly affect to the reduction of poverty in Indonesia although the effect is very small. In the variable Zakat Fitrah not significantly affect poverty reduction in Indonesia because of the nature of Zakat Fitrah is for consumption and not for long-term needs. The results of this study can be used for the management of zakat to be able to develop the management and to get a better system for distribution of zakat so that the main purpose of zakat can be achieved to reduce poverty.<br />Keywords : Poverty, Zakat Fitrah, ZIS.</p>

An improved morphological weighted dynamic similarity measurement algorithm for time series data

2018 ◽  
Vol 11 (4) ◽  
pp. 486-495
Author(s):  
Ke Yi Zhou ◽  
Shaolin Hu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Morphological Changes ◽  
Series Data ◽  
Similarity Measurement ◽  
Improved Method ◽  
Control Measurement ◽  
Content Type ◽  
Basic Work ◽  
Measurement Algorithm

Purpose The similarity measurement of time series is an important research in time series detection, which is a basic work of time series clustering, anomaly discovery, prediction and many other data mining problems. The purpose of this paper is to design a new similarity measurement algorithm to improve the performance of the original similarity measurement algorithm. The subsequence morphological information is taken into account by the proposed algorithm, and time series is represented by a pattern, so the similarity measurement algorithm is more accurate. Design/methodology/approach Following some previous researches on similarity measurement, an improved method is presented. This new method combines morphological representation and dynamic time warping (DTW) technique to measure the similarities of time series. After the segmentation of time series data into segments, three parameter values of median, point number and slope are introduced into the improved distance measurement formula. The effectiveness of the morphological weighted DTW algorithm (MW-DTW) is demonstrated by the example of momentum wheel data of an aircraft attitude control system. Findings The improved method is insensitive to the distortion and expansion of time axis and can be used to detect the morphological changes of time series data. Simulation results confirm that this method proposed in this paper has a high accuracy of similarity measurement. Practical implications This improved method has been used to solve the problem of similarity measurement in time series, which is widely emerged in different fields of science and engineering, such as the field of control, measurement, monitoring, process signal processing and economic analysis. Originality/value In the similarity measurement of time series, the distance between sequences is often used as the only detection index. The results of similarity measurement should not be affected by the longitudinal or transverse stretching and translation changes of the sequence, so it is necessary to incorporate the morphological changes of the sequence into similarity measurement. The MW-DTW is more suitable for the actual situation. At the same time, the MW-DTW algorithm reduces the computational complexity by transforming the computational object to subsequences.

Impact assessment of political administrations on the performance of the construction sector: a time series analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Najimu Saka ◽  
Abdullahi Babatunde Saka ◽  
Opeoluwa Akinradewo ◽  
Clinton O. Aigbavboa
Keyword(s):  
Time Series ◽  
Growth And Development ◽  
Time Series Data ◽  
Critical Factor ◽  
Sustainable Growth ◽  
Analysis Of Covariance ◽  
Series Data ◽  
Construction Sector ◽  
Content Type ◽  
Ancova Model

Purpose The complex interaction of politics and the economy is a critical factor for the sustainable growth and development of the construction sector (CNS). This study aims to investigate the effects of type of political administration including democracy and military on the performance of CNS using the Nigerian Construction Sector (NCS) as a case study. Design/methodology/approach A 48 year (1970–2017) time series data (TSD) on the NCS and the gross domestic product (GDP) based on 2010 constant USD were extracted from the United Nations Statistical Department database. Analysis of variance (ANOVA) and analysis of covariance (ANCOVA) models were used to analyze the TSD. The ANCOVA model includes the GDP as correlational variable or covariate. Findings The estimates of the ANOVA model indicate that democratic administration is significantly better than military administration in construction performance. However, the ANCOVA model indicates that the GDP is more important than political administration in the performance of the CNS. The study recommends for a new national construction policy, favourable fiscal and monetary policy, local content development policy and construction credit guaranty scheme for the rapid growth and development of the NCS. Originality/value Hitherto, little is known about the influence of political administration on the performance of the CNS. This study provides empirical evidence from a developing economy perspective. It presents the relationships and highlights recommendations for driving growth in the construction industry.

Corporation financial distress prediction with deep learning: analysis of public listed companies in Malaysia

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zulkifli Halim ◽  
Shuhaida Mohamed Shuhidan ◽  
Zuraidah Mohd Sanusi
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Financial Distress ◽  
Time Series Data ◽  
Series Data ◽  
Learning Models ◽  
Content Type ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Gated Recurrent Unit

PurposeIn the previous study of financial distress prediction, deep learning techniques performed better than traditional techniques over time-series data. This study investigates the performance of deep learning models: recurrent neural network, long short-term memory and gated recurrent unit for the financial distress prediction among the Malaysian public listed corporation over the time-series data. This study also compares the performance of logistic regression, support vector machine, neural network, decision tree and the deep learning models on single-year data.Design/methodology/approachThe data used are the financial data of public listed companies that been classified as PN17 status (distress) and non-PN17 (not distress) in Malaysia. This study was conducted using machine learning library of Python programming language.FindingsThe findings indicate that all deep learning models used for this study achieved 90% accuracy and above with long short-term memory (LSTM) and gated recurrent unit (GRU) getting 93% accuracy. In addition, deep learning models consistently have good performance compared to the other models over single-year data. The results show LSTM and GRU getting 90% and recurrent neural network (RNN) 88% accuracy. The results also show that LSTM and GRU get better precision and recall compared to RNN. The findings of this study show that the deep learning approach will lead to better performance in financial distress prediction studies. To be added, time-series data should be highlighted in any financial distress prediction studies since it has a big impact on credit risk assessment.Research limitations/implicationsThe first limitation of this study is the hyperparameter tuning only applied for deep learning models. Secondly, the time-series data are only used for deep learning models since the other models optimally fit on single-year data.Practical implicationsThis study proposes recommendations that deep learning is a new approach that will lead to better performance in financial distress prediction studies. Besides that, time-series data should be highlighted in any financial distress prediction studies since the data have a big impact on the assessment of credit risk.Originality/valueTo the best of authors' knowledge, this article is the first study that uses the gated recurrent unit in financial distress prediction studies based on time-series data for Malaysian public listed companies. The findings of this study can help financial institutions/investors to find a better and accurate approach for credit risk assessment.

A history of university income in the United Kingdom and Australia, 1922–2017

2020 ◽  
Vol 49 (2) ◽  
pp. 229-248
Author(s):  
Tamson Pietsch
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Content Type ◽  
The United Kingdom ◽  
History Of University ◽  
History Of ◽  
Strategic Influence ◽  
The Uk ◽  
Comparable Time

PurposeThe purpose of this paper is to create comparable time series data on university income in Australia and the UK that might be used as a resource for those seeking to understand the changing funding profile of universities in the two countries and for those seeking to investigate how such data were produced and utilised.Design/methodology/approachA statistical analysis of university income from all sources in the UK and Australia.FindingsThe article produces a new time series for Australia and a comparable time series for the UK. It suggests some of the ways these data related to broader patterns of economic change, sketches the possibility of strategic influence, and outlines some of their limitations.Originality/valueThis is the first study to systematically create a time series on Australian university income across the twentieth century and present it alongside a comparable dataset for the UK.

Influence of systemic banking crisis and currency crisis on the relationship of export and economic growth

2017 ◽  
Vol 10 (1) ◽  
pp. 82-110
Author(s):  
Syed Ali Raza ◽  
Mohd Zaini Abd Karim
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Financial Crises ◽  
Currency Crises ◽  
Time Series Data ◽  
Banking Crises ◽  
Series Data ◽  
Content Type ◽  
Relationship Of ◽  
The Relationship

Purpose This study aims to investigate the influence of systemic banking crises, currency crises and global financial crisis on the relationship between export and economic growth in China by using the annual time series data from the period of 1972 to 2014. Design/methodology/approach The Johansen and Jeuuselius’ cointegration, auto regressive distributed lag bound testing cointegration, Gregory and Hansen’s cointegration and pooled ordinary least square techniques with error correction model have been used. Findings Results indicate the positive and significant effect of export of goods and services on economic growth in both long and short run, whereas the negative influence of systemic banking crises and currency crises over economic growth is observed. It is also concluded that the impact of export of goods and service on economic growth becomes insignificant in the presence of systemic banking crises and currency crises. The currency crises effect the influence of export on economic growth to a higher extent compared to systemic banking crises. Surprisingly, the export in the period of global financial crises has a positive and significant influence over economic growth in China, which conclude that the global financial crises did not drastically affect the export-growth nexus. Originality/value This paper makes a unique contribution to the literature with reference to China, being a pioneering attempt to investigate the effects of systemic banking crises and currency crises on the relationship of export and economic growth by using long-time series data and applying more rigorous econometric techniques.

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