Publication activity
Articles grouped by publication year
Hover over a year or focus the chart and use arrow keys to inspect values.
2026 is year to date. 0 undated publications excluded.
Research across participating journals · All insights follow your collection filters.
Publication activity alongside incoming citations.
Articles grouped by publication year
Hover over a year or focus the chart and use arrow keys to inspect values.
2026 is year to date. 0 undated publications excluded.
OpenAlex · year of the citing work
Hover over a year or focus the chart and use arrow keys to inspect values.
2017–2026 annual-history window. 2026 is year to date. Unknown years have gaps.
Leading affiliations and themes in this collection.
Full distributions & methodology ↓Independent sources. Clear time scales.
Crossref and OpenAlex · separate totals for the same publication cohorts
Hover over a year or focus the chart and use arrow keys to inspect values.
Latest lifetime totals, grouped by the cited article’s year. These are not citations received in that year.
OpenAlex · incoming citation links grouped by citing-work year
Hover over a year or focus the chart and use arrow keys to inspect values.
2017–2026 annual-history window. 2026 is year to date. Unknown years have gaps.
Select journals to compare the same collection filters. Source totals stay separate. These measures describe research activity, not journal quality.
Articles by publication year
Latest lifetime totals · grouped by publication year
Latest lifetime totals · grouped by publication year
By year of the citing work · 2017–2026
2026 is year to date. Crossref and OpenAlex lifetime charts share a citation scale. Unknown observations appear as gaps; known zero stays zero. Annual receipts use available OpenAlex histories and may not sum to lifetime totals. 0 undated publications excluded from publication-year charts.
Crossref citation coverage: 7 / 7 publications (100.0%). Latest observation: 21 Oct 2026, 00:21 UTC. Means and percentages use records with known counts.
DOI-matched OpenAlex observations for the same selected publications. These counts are separate from Crossref and are never added together.
Observation range: 22 Oct 2026 07:38 UTC — 28 Oct 2026 21:32 UTC. Missing matches are not zero citations. Previous observations may remain if a refresh fails or a DOI no longer matches.
OpenAlex affiliations and primary-topic classification. Each publication counts once per institution or country; multi-affiliated publications appear in multiple groups, so percentages can sum above 100%. Countries describe author affiliations, not nationality. Topics use one primary topic per publication.
7 / 7 with data · 0 missing or unmatched. Percentages use all 7 selected publications.
7 / 7 with data · 0 missing or unmatched. Percentages use all 7 selected publications.
7 / 7 with data · 0 missing or unmatched. Percentages use all 7 selected publications.
Citations are the latest recorded totals for articles published in each year, not citations received during that year. Crossref and OpenAlex remain separate and share the same citation scale. 2026 is incomplete; newer articles have had less time to receive citations.
0 undated publications excluded. Unknown citation counts are shown as —, never as zero. Partial coverage can understate totals; expand each table for known / total records.
| Publication year | Journal / scope | Publications | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2015 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2016 | All selected publications | 4 | 4 | 4/4 | 4/4 |
| 2017 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2018 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2015 | All selected publications | 29 | 1 | 1/1 | 1/1 |
| 2016 | All selected publications | 2 | 4 | 4/4 | 4/4 |
| 2017 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| 2018 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | 5 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2015 | All selected publications | 68 | 1 | 1/1 | 1/1 |
| 2016 | All selected publications | 6 | 4 | 4/4 | 4/4 |
| 2017 | All selected publications | 15 | 1 | 1/1 | 1/1 |
| 2018 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | 5 | 1 | 1/1 | 1/1 |
Incoming citations to the selected publications, grouped by the year of the citing work. This is separate from lifetime citation totals grouped by the cited article’s publication year.
The publication filters above define which articles receive the citations. The citation-year range below only controls this chart and the incoming-citation comparison. Removing publication-year filters includes older articles’ incoming citations too.
Annual history available for 7 / 7 selected publications. Supported display window: 2017–2026; 2026 is incomplete. Within a successfully fetched ten-year window, omitted years mean zero. Missing histories and years beyond a saved observation’s window remain unknown (—); partial coverage can understate totals.
Annual-history observations (UTC): 2026-10-22T07:38:18.273817+00:00 — 2026-10-28T21:32:48.914486+00:00. Older observations may be retained after failed or incomplete refreshes.
| Citation year | Journal / scope | OpenAlex · Citations received by year | Publications | Annual history known |
|---|---|---|---|---|
| 2017 | All selected publications | 7 | 7 | 7/7 |
| 2018 | All selected publications | 11 | 7 | 7/7 |
| 2019 | All selected publications | 24 | 7 | 7/7 |
| 2020 | All selected publications | 12 | 7 | 7/7 |
| 2021 | All selected publications | 13 | 7 | 7/7 |
| 2022 | All selected publications | 11 | 7 | 7/7 |
| 2023 | All selected publications | 8 | 7 | 7/7 |
| 2024 | All selected publications | 1 | 7 | 7/7 |
| 2025 | All selected publications | 4 | 7 | 7/7 |
| 2026 | All selected publications | 0 | 7 | 7/7 |
Counts represent citation links, not unique citing papers across a journal: one paper citing two selected articles contributes two links. Older citations outside this window are excluded, so annual sums need not equal lifetime totals. OpenAlex methodology ↗
Publications by publication year. 2026 is an incomplete year.
| Period | Publications |
|---|---|
| 2015 | 1 |
| 2016 | 4 |
| 2017 | 1 |
| 2019 | 1 |
Latest Crossref totals for the selected publications; not citations received in the selected years.
Unknown counts are excluded from the mean, median and cited-publication rate.
Same filters, source and counting rules across journals. Descriptive statistics, not a quality ranking.
| Journal / publisher | Publications | Crossref citations | OpenAlex citations | OpenAlex coverage | Crossref mean | Crossref median | Crossref cited %* | Crossref coverage |
|---|---|---|---|---|---|---|---|---|
| International Journal of Finance & Banking Studies SSBFNET | 7 | 38 | 94 | 7/7 | 5.43 | 2 | 57.1 | 7/7 · 100.0% |
* Percentage among publications with a known citation count.
This paper demonstrates the relationship between CAMELS ratios and credit ratings of deposit banks in Turkey. Annual data was used for the period between 2004 and 2014 in this study. Moreover, 20 deposit banks of Turkey were analyzed and 21 different ratios of CAMELS components were used. In addition to that, credit ratings of these banks were provided from Moody’s corporation or annual activity reports of the banks. After that, we created multi nominal logistic regression analysis in order to illustrate the relationship. The major finding in this study is that three components (Asset Quality, Management Quality, and Sensitivity to Market Risk) of CAMELS have effects on credit ratings whereas the ratios related to Capital Adequacy and Earnings are not effective. As a result, it was recommended that Turkish deposit banks should concentrate on the percentage of fixed assets and interest income to have a better rating. Moreover, having high market share with respect to total assets and lower interest expense are also other important points for this purpose. On the other hand, Turkish deposit banks should control the proportion of financial assets and increase the amount of FX liquid assets to prevent credit ratings to decrease. Additionally, market share of banks for loans should not reach at high level for this objective.
The purpose of this study is to determine major indicators of the Greek crisis that started in 2009 and the effects of which can still be observed. In this regard, 8 independent variables were applied so as to fulfill the objective. Besides, the annual data between the years 1984 and 2016 was analyzed with Probit model. As a consequence of this study, it was concluded that inflation and gross savings are the leading meters of Greek crisis based on probit method. On the other hand, according to the MARS results, 3 different variables are identified as the indicators of the debt crisis in Greece. It is concluded that there is a negative relation between financial crisis with saving ratio and current account balance. Additionally, it is also identified that high unemployment ratio leads to financial crisis. While comparisng the results of these two approaches, it is concluded that MARS is much more successful than the probit method to predict the debt crisis in Greece. It is strongly recommended that saving ratio should be increased in Greece. For this purpose, governments should take some actions in order to increase this ratio more than 15.5%. Within this framework, media channels can be used by the government to tell the people about the importance of the savings to have sustainable economic development.
This study aims to determine the influencing factors of the banks to join corporate social responsibility activities. Within this scope, annual data of 23 deposit banks in Turkey for the periods between 2005 and 2015 was taken into the consideration. In addition to this situation, panel probit model was used in the analysis so as to achieve this objective. According to the results of the analysis, it was determined that there is a negative relationship between CSR activities and nonperforming loans ratio. This situation shows that banks do not prefer to make social responsibility activities in case of higher financial losses. In addition to this situation, it was also identified that there is a positive relationship between return on asset and corporate social responsibility activities of the banks. In other words, it can be understood that Turkish deposit banks, which have higher profitability, joint more CSR activities in comparison with others.
The purpose of this study is to explore the importance and ranking of technical analysis variables in Turkish banking sector. Random Forest method is used for determining importance scores of inputs for eight banks in Borsa Istanbul. Then two predictive models utilizing Random Forest (RF) and Artificial Neural Networks (ANN) are built for predicting BIST-100 index and bank closing prices. Results of the models are compared by three metrics namely Mean Absolute Error (MAE), Mean Square Error (MSE), Median Absolute Error (MedAE). Findings show that moving average (MAV-100) is the most important variable for both BIST -100 index and bank closing prices. Therefore, investors should follow this technical indicator with respect to Turkish banks. In addition ANN shows better performance for all metrics.
<p>Considering the impacts of the conflict on the economic parameters in terms of macroeconomics, the following factors might affect the profitability of the company: foreign capital outflows, decrease in exports, increase in the interest rates, disruption of the investment climate, increase in the exchange rates, increase in the costs of import entry etc. Due to the expectable decrease in profit shares as to the investors, the contraction in the risk appetite will cause volatility in the prices of equity securities markets based on the impacts of the conflict, and the equity securities will depreciate. In this study, the main contributions on conflict risk and related econometric models have been discussed.</p>
The purpose of this study is to determine major indicators of the Greek crisis that started in 2009 and the effects of which can still be observed. In this regard, 8 independent variables were applied so as to fulfill the objective. Besides, the annual data between the years 1984 and 2016 was analyzed with Probit model. As a consequence of this study, it was concluded that inflation and gross savings are the leading meters of Greek crisis based on probit method. On the other hand, according to the MARS results, 3 different variables are identified as the indicators of the debt crisis in Greece. It is concluded that there is a negative relation between financial crisis with saving ratio and current account balance. Additionally, it is also identified that high unemployment ratio leads to financial crisis. While comparisng the results of these two approaches, it is concluded that MARS is much more successful than the probit method to predict the debt crisis in Greece. It is strongly recommended that saving ratio should be increased in Greece. For this purpose, governments should take some actions in order to increase this ratio more than 15.5%. Within this framework, media channels can be used by the government to tell the people about the importance of the savings to have sustainable economic development.
This study aims to determine the influencing factors of the banks to join corporate social responsibility activities. Within this scope, annual data of 23 deposit banks in Turkey for the periods between 2005 and 2015 was taken into the consideration. In addition to this situation, panel probit model was used in the analysis so as to achieve this objective. According to the results of the analysis, it was determined that there is a negative relationship between CSR activities and nonperforming loans ratio. This situation shows that banks do not prefer to make social responsibility activities in case of higher financial losses. In addition to this situation, it was also identified that there is a positive relationship between return on asset and corporate social responsibility activities of the banks. In other words, it can be understood that Turkish deposit banks, which have higher profitability, joint more CSR activities in comparison with others.
<p>Considering the impacts of the conflict on the economic parameters in terms of macroeconomics, the following factors might affect the profitability of the company: foreign capital outflows, decrease in exports, increase in the interest rates, disruption of the investment climate, increase in the exchange rates, increase in the costs of import entry etc. Due to the expectable decrease in profit shares as to the investors, the contraction in the risk appetite will cause volatility in the prices of equity securities markets based on the impacts of the conflict, and the equity securities will depreciate. In this study, the main contributions on conflict risk and related econometric models have been discussed.</p>
<p>The World Bank is the most important financier for international investment. The bank opens credits mostly for investment projects in developing countries. Turkey has received various investment credits since its membership to the World Bank on March 11, 1947. The credits were used for economic and social domains. Turkey has also been granted credits from the European Investment Bank (EIB). The credits received are composed of micro credits that belong to small and medium enterprises (SMEs). A regional development bank, Islamic Development Bank, has also received credits through Eximbank and Industrial Development Bank of Turkey (TSKB) to finance Turkish SMEs. This paper deals with Turkish investment strategies in the framework of basic principles of investment – development banks.</p>
<p>Banks, one of the most significant economic means of the nations and international organizations, have occasionally become one of the institutions mostly affected by the financial crisis in the world. Especially in the economies of the developed countries, one of the most important aspects of the financial sector is measured by the size of the financial resources and assets of the banks. The size and place of the investment banks depends on the investment and existing credit reserves appropriated by its members. From this perspective, scrutinizing the Investment and Developments Banks, which is one of the economic growth criteria, is of high importance. To this end, in addition to the operation of the Investment and Development Banks, financial products of these banks in Turkey are also analyzed in this study.</p>
Source coverage refers to this result set; a publication may occur in several sources. Sync times refer to contributing journals.
Missing abstracts: 0 · Missing authors: 0 · Missing publication dates: 0 · Dates without day precision: 0
| Source | Most recent successful journal sync | Latest attempt |
|---|---|---|
| crossref | 21 Oct 2026 00:21 UTC · ijfbs | complete · ijfbs · 21 Oct 2026 00:21 UTC |
| ojs-rest | 21 Oct 2026 00:21 UTC · ijfbs | complete · ijfbs · 21 Oct 2026 00:14 UTC |
| oai-pmh | 20 Oct 2026 19:56 UTC · ijfbs | complete · ijfbs · 20 Oct 2026 19:55 UTC |
| openalex-enrichment | 21 Oct 2026 00:22 UTC · ijfbs | complete · ijfbs · 21 Oct 2026 00:21 UTC |