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: 3 / 3 publications (100.0%). Latest observation: 21 Oct 2026, 07:22 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 01:07 UTC — 22 Oct 2026 01:47 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.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 selected publications.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 selected publications.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 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 |
|---|---|---|---|---|---|
| 2016 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2017 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2018 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2020 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2023 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2016 | All selected publications | 10 | 1 | 1/1 | 1/1 |
| 2017 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2018 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2020 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2023 | All selected publications | 5 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2016 | All selected publications | 17 | 1 | 1/1 | 1/1 |
| 2017 | All selected publications | 3 | 1 | 1/1 | 1/1 |
| 2018 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2019 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2020 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2023 | All selected publications | 6 | 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 3 / 3 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): 2026-10-22T01:07:40.119601+00:00 — 2026-10-22T01:47:07.034181+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 | 0 | 3 | 3/3 |
| 2018 | All selected publications | 3 | 3 | 3/3 |
| 2019 | All selected publications | 7 | 3 | 3/3 |
| 2020 | All selected publications | 0 | 3 | 3/3 |
| 2021 | All selected publications | 7 | 3 | 3/3 |
| 2022 | All selected publications | 3 | 3 | 3/3 |
| 2023 | All selected publications | 0 | 3 | 3/3 |
| 2024 | All selected publications | 2 | 3 | 3/3 |
| 2025 | All selected publications | 3 | 3 | 3/3 |
| 2026 | All selected publications | 1 | 3 | 3/3 |
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 |
|---|---|
| 2016 | 1 |
| 2017 | 1 |
| 2023 | 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 | 2 | 5 | 9 | 2/2 | 2.5 | 2.5 | 50.0 | 2/2 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 1 | 10 | 17 | 1/1 | 10.0 | 10 | 100.0 | 1/1 · 100.0% |
* Percentage among publications with a known citation count.
In this study, we aimed to explain the influencing factors of interest rate risk in Turkish banking sector. Within this scope, 20 deposit banks in Turkey were evaluated. Furthermore, annual data for the periods between 2006 and 2015 was analyzed by using panel logit method. According to the results of this analysis, it was identified that 2 explanatory variables affect interest rate risk of Turkish banks. First of all, it was determined that banks, which have higher amount of deposits, are exposed to higher amount of interest rate risk. In addition to this situation, it was also defined that there is a negative relationship between the amount of capital and interest rate risk. As a result of this issue, it was recommended that Turkish banks should increase capital amount in order to manage interest rate risk more effectively.
The banking system’s role as an intermediary between depositors and borrowers makes its stability a crucial element of the economy. Individual bank stability impacts the stability of the financial sector and, consequently, the real sector and economic growth. This study is a detailed investigation of bank stability scores for the Turkish banking sector. Turkey experienced a major banking crisis from 2000 to 2001. However, with an improved regulatory environment and structural reforms, its resilience to shocks increased significantly in subsequent years. The present study contributes to the literature in three areas. First, it applies eight variations of the Z-score to the Turkish banking sector to test the importance of time and risk variation. Second, it investigates the impact on stability scores of several factors–the implementation of new capital regulations and Basel III liquidity rules, the Turkish lira crisis and the effects of COVID-19). Finally, it measures the contribution to systemic risk of each Turkish bank using the leave-one-out (LOO) Z-score method; this is the first study to use this method to analyse the contribution of different bank types to the overall stability of the sector.
<p><em>In this study, we investigate factors affecting net interest margin (NIM) of commercial banks in Turkey. Especially, our results highlight the relation between unconventional monetary policy shocks and bank margins. To this end, first, we conduct an identification analysis about which parameters of asymmetric interest corridor framework are important in explaining variations in NIM. Using industry-level data, we show that there exists a pass through from BIST interbank overnight repo/reverse repo market rate and weighted average cost of funding (WACF) to bank loan and deposit rates. As a result of reduced-form Vector Autoregression (VAR) analysis we find the existence of a transmission mechanism from BIST rate and WACF to commercial loan rate, consumer loan rate and deposit rate. Same pass through to loan and deposit rates is also shown in individual bank level with the Panel Vector Autoregression (Panel VAR) analysis in the case of 16 commercial banks in Turkey during the period 2011Q1-2016Q1. After the identification analysis, we examine the relationship between NIM and policy rates through System Generalized Method of Moments (GMM) techniques by controlling bank specific, industry related and macroeconomic factors. We find that a change in the monetary policy rate has significant and positive impact on NIM. Among bank-specific factors, equity ratio and operating expenses are found to be significantly affecting NIM during the sample period. Our empirical findings also stress the significance of lag values of NIM. Estimations conducted with standardized variables indicate that economic significance of lag values and bank specific variables are larger than that of policy.</em></p>
The banking system’s role as an intermediary between depositors and borrowers makes its stability a crucial element of the economy. Individual bank stability impacts the stability of the financial sector and, consequently, the real sector and economic growth. This study is a detailed investigation of bank stability scores for the Turkish banking sector. Turkey experienced a major banking crisis from 2000 to 2001. However, with an improved regulatory environment and structural reforms, its resilience to shocks increased significantly in subsequent years. The present study contributes to the literature in three areas. First, it applies eight variations of the Z-score to the Turkish banking sector to test the importance of time and risk variation. Second, it investigates the impact on stability scores of several factors–the implementation of new capital regulations and Basel III liquidity rules, the Turkish lira crisis and the effects of COVID-19). Finally, it measures the contribution to systemic risk of each Turkish bank using the leave-one-out (LOO) Z-score method; this is the first study to use this method to analyse the contribution of different bank types to the overall stability of the sector.
<p><em>In this study, we investigate factors affecting net interest margin (NIM) of commercial banks in Turkey. Especially, our results highlight the relation between unconventional monetary policy shocks and bank margins. To this end, first, we conduct an identification analysis about which parameters of asymmetric interest corridor framework are important in explaining variations in NIM. Using industry-level data, we show that there exists a pass through from BIST interbank overnight repo/reverse repo market rate and weighted average cost of funding (WACF) to bank loan and deposit rates. As a result of reduced-form Vector Autoregression (VAR) analysis we find the existence of a transmission mechanism from BIST rate and WACF to commercial loan rate, consumer loan rate and deposit rate. Same pass through to loan and deposit rates is also shown in individual bank level with the Panel Vector Autoregression (Panel VAR) analysis in the case of 16 commercial banks in Turkey during the period 2011Q1-2016Q1. After the identification analysis, we examine the relationship between NIM and policy rates through System Generalized Method of Moments (GMM) techniques by controlling bank specific, industry related and macroeconomic factors. We find that a change in the monetary policy rate has significant and positive impact on NIM. Among bank-specific factors, equity ratio and operating expenses are found to be significantly affecting NIM during the sample period. Our empirical findings also stress the significance of lag values of NIM. Estimations conducted with standardized variables indicate that economic significance of lag values and bank specific variables are larger than that of policy.</em></p>
In this study, we aimed to explain the influencing factors of interest rate risk in Turkish banking sector. Within this scope, 20 deposit banks in Turkey were evaluated. Furthermore, annual data for the periods between 2006 and 2015 was analyzed by using panel logit method. According to the results of this analysis, it was identified that 2 explanatory variables affect interest rate risk of Turkish banks. First of all, it was determined that banks, which have higher amount of deposits, are exposed to higher amount of interest rate risk. In addition to this situation, it was also defined that there is a negative relationship between the amount of capital and interest rate risk. As a result of this issue, it was recommended that Turkish banks should increase capital amount in order to manage interest rate risk more effectively.
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 07:22 UTC · ijrbs | complete · ijrbs · 21 Oct 2026 07:22 UTC |
| ojs-rest | 21 Oct 2026 07:22 UTC · ijrbs | complete · ijrbs · 21 Oct 2026 06:46 UTC |
| oai-pmh | 20 Oct 2026 19:56 UTC · ijfbs | complete · ijfbs · 20 Oct 2026 19:55 UTC |
| openalex-enrichment | 21 Oct 2026 07:23 UTC · ijrbs | complete · ijrbs · 21 Oct 2026 07:22 UTC |