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.
Top 5 in the selected collection · Ranked by Crossref lifetime citations. Only articles with at least one known citation are included; source totals are never combined.
2016 · Veli Akel · Talip Torun · Barıs Aksoy
2016 · Ali Görener · Hasan Dincer · Umit Hacioglu
2016 · Yusuf Kaderli · Umut Tolga Gümüş · Emre Danışman
2016 · Ihsan Isik · Larissa Kyj · Ihsan Kulali
2016 · Senol Emir · Hasan Dincer · Umit Hacioglu
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: 13 / 13 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: 23 Oct 2026 07:29 UTC — 29 Oct 2026 16:17 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.
13 / 13 with data · 0 missing or unmatched. Percentages use all 13 selected publications.
13 / 13 with data · 0 missing or unmatched. Percentages use all 13 selected publications.
13 / 13 with data · 0 missing or unmatched. Percentages use all 13 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 | 13 | 13 | 13/13 | 13/13 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2016 | All selected publications | 32 | 13 | 13/13 | 13/13 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2016 | All selected publications | 75 | 13 | 13/13 | 13/13 |
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 13 / 13 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): 2026-10-23T07:29:59.716015+00:00 — 2026-10-29T16:17:06.161311+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 | 13 | 13/13 |
| 2018 | All selected publications | 16 | 13 | 13/13 |
| 2019 | All selected publications | 11 | 13 | 13/13 |
| 2020 | All selected publications | 11 | 13 | 13/13 |
| 2021 | All selected publications | 7 | 13 | 13/13 |
| 2022 | All selected publications | 3 | 13 | 13/13 |
| 2023 | All selected publications | 4 | 13 | 13/13 |
| 2024 | All selected publications | 6 | 13 | 13/13 |
| 2025 | All selected publications | 4 | 13 | 13/13 |
| 2026 | All selected publications | 2 | 13 | 13/13 |
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 | 13 |
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 | 13 | 32 | 75 | 13/13 | 2.46 | 1 | 61.5 | 13/13 · 100.0% |
* Percentage among publications with a known citation count.
Bu çal??man?n amac?, Türkiye’de hayat d??? sigortac?l?k sektöründe aktif karl?l??? ile firmaya özgü faktörler aras?ndaki ili?kiyi tespit ederek aktif kârl?l???n?n etkinlikten mi yoksa yo?unla?madan m? kaynakland???n? belirlemektir. Bu amaçla, Türk sigorta sektöründe faaliyet gösteren 36 hayat d??? sigorta ?irketi aras?nda 2015 y?l? verilerine göre pazar?n toplam %83.38’ini olu?turan 15 sigorta ?irketinin 2010-2015 dönemine ait mali verileri ile performans ili?kisine yönelik Period SUR panel veri analizi yap?lm??t?r. Analiz sonuçlar?na göre, aktif büyüklü?ü, likidite ve pazar pay? de?i?kenleri ile aktif karl?l??? aras?nda pozitif ve istatistiki olarak anlaml? bir ili?ki tespit edilmi?ken kald?raç, hasar/prim oran? ve firma ya?? de?i?kenleri aras?nda ise negatif ve istatistiki olarak anlaml? bir ili?ki tespit edilmi?tir. Analiz dönemi boyunca Herfindahl–Hirschman Endeksi de?erleri 1.500’ün alt?nda oldu?u için Türkiye’de hayat d??? sigortac?l?k sektörünün yo?un olmayan bir piyasa görünümünde oldu?u tespit edilmi?tir. Bu nedenle, hayat d??? sigortac?l?k sektöründe aktif kârl?l??? yo?unla?madan daha ziyade faaliyetlerdeki etkinlikten kaynaklanmaktad?r.
<p>Location selection problem in banking is an important issue for the commercial success in competitive environment. There is a strategic fit between the location selection decision and overall performance of a new branch. Providing physical service in requested location as well as alternative distribution channels to meet profitable client needs is the current problematic to achieve the competitive advantage over the rivalry in financial system. In this paper, an integrated model has been developed to support in the decision of branch location selection for a new bank branch. Analytic Hierarchy Process (AHP) technique has been conducted to prioritize of evaluation criteria, and multi-objective optimization on the basis of ratio analysis (MOORA) method has been applied to rank location alternatives of bank branch. </p>
Küreselle?me, mal ve hizmetlerin dünya ölçe?inde serbest dola??m?d?r. Günümüzde teknolojide ya?anan geli?meler küreselle?menin h?z?n? artt?rm??t?r. Küreselle?meyle birlikte, ekonomik geli?melerde ya?anan belirsizlik ve karma??kl?k, toplumlar?n ve bireylerin sosyo- ekonomik yap?lar?n? de?i?tirmi?tir. Toplumlar?n sosyo ekonomik yap?lar? bireylerin finansal kararlar?yla ?ekillenmektedir. Bireylerin alm?? oldu?u finansal kararlar?n do?rulu?u, bireylerin finansal okuryazarl?k düzeyi ile ilgilidir. Karma??k ve belirsiz ko?ullar içindeki ekonomide toplumun en küçük yap?s? olan ailelerin ald??? finansal kararlar sadece aileyi de?il tüm toplumu etkilemektedir. Ailelerin de?i?imin h?zl? ve sürekli oldu?u ekonomik hayatta ald??? yanl?? finansal kararlar nedeniyle birlik ve düzeni bozulmaktad?r. Bunun için demografik özelli?i farkl? her bireyin sahip oldu?u finansal okuryazarl?k düzeyini yükseltecek finansal e?itim uygulamalar?n?n önemi yüksektir. Birçok ara?t?rmada, ya?l? ve e?itim düzeyi dü?ük bireylerin daha çok finansal risk içinde oldu?u vurgulanmaktad?r. Bu çal??mada, Türk Silahl? Kuvvetlerinde görev yapm?? emekli askeri personelin finansal okuryazarl?k seviyesi incelenmi?tir. Ülkenin savunma hizmetlerinde çal??an Türk Silahl? Kuvvetleri personeli, görev sahas? itibariyle finansal sistemlerde ya?anan geli?melere uzak kalabilmektedir. Bu durum, bilgiye dayal? finansal kararlar alma becerisinin önemini art?rmaktad?r. Çal??ma sonucunda, emekli askeri personelin finansal okuryazarl?k seviyesi ve finansal bilgileri tespit edilmi?tir. Bu tespitler do?rultusunda aktif olarak görev yapan askeri personelin gelece?e dönük finansal fark?ndal???n? artt?rmak amaçlanm??t?r.
By drawing on Ukrainian experience, this paper analyzes the anatomy of bank efficiency in a transitional economy. Acknowledging the vast disparities in the business technology of different size banks, in this comprehensive study, we innovatively estimate group-specific (distinct) frontiers for small, medium, and large size banks. The results from separate frontiers reveal that Ukrainian banks record 38% technical inefficiency, 26% pure technical inefficiency, and 17% scale inefficiency on average. Apparently, banks in transition waste about the two fifths of their factor inputs during the production of financial services. The cardinal source of sub-performance in transitional banks seems to be managerial inefficiencies. We also found that banks operating in areas with more political influence and more developed infrastructure outperform the banks operating in politically and economically weaker regions. The results also indicate that larger banks, enjoying public trust in a risky business climate, dominate smaller banks in all forms of efficiency. However, such bias for size causes large banks to suffer from decreasing returns to scale and small banks from idle capacity. Consequently, the policies promoting consolidation between small and large banks may alleviate the excess (idle) capacity for large (small) banks in a transitional economy.
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>
<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>
Bu çal??man?n amac?, Türkiye’de hayat d??? sigortac?l?k sektöründe aktif karl?l??? ile firmaya özgü faktörler aras?ndaki ili?kiyi tespit ederek aktif kârl?l???n?n etkinlikten mi yoksa yo?unla?madan m? kaynakland???n? belirlemektir. Bu amaçla, Türk sigorta sektöründe faaliyet gösteren 36 hayat d??? sigorta ?irketi aras?nda 2015 y?l? verilerine göre pazar?n toplam %83.38’ini olu?turan 15 sigorta ?irketinin 2010-2015 dönemine ait mali verileri ile performans ili?kisine yönelik Period SUR panel veri analizi yap?lm??t?r. Analiz sonuçlar?na göre, aktif büyüklü?ü, likidite ve pazar pay? de?i?kenleri ile aktif karl?l??? aras?nda pozitif ve istatistiki olarak anlaml? bir ili?ki tespit edilmi?ken kald?raç, hasar/prim oran? ve firma ya?? de?i?kenleri aras?nda ise negatif ve istatistiki olarak anlaml? bir ili?ki tespit edilmi?tir. Analiz dönemi boyunca Herfindahl–Hirschman Endeksi de?erleri 1.500’ün alt?nda oldu?u için Türkiye’de hayat d??? sigortac?l?k sektörünün yo?un olmayan bir piyasa görünümünde oldu?u tespit edilmi?tir. Bu nedenle, hayat d??? sigortac?l?k sektöründe aktif kârl?l??? yo?unla?madan daha ziyade faaliyetlerdeki etkinlikten kaynaklanmaktad?r.
Nowadays, one of the leading factors used in the evaluation of a country’s economic development is energy consumption. Because of economic growth, demand for energy is also increasing. In this study, the emerging European countries’ (the Czech Republic, Poland, Romania, Turkey) and the CIS countries’ (Kazakhstan, Russia, Ukraine, Uzbekistan) electricity consumption has been forecasted for five years period (2015-2019). In the study, GM(1,1) Rolling Model, which is developed in the framework of Grey System Theory is used as a mathematical model for real-time forecasting. The results of the study show that there will not be a significant change in electricity demand in this two area during the 2015-2109 period.
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 |