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: 5 / 5 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: 21 Oct 2026 00:22 UTC — 22 Oct 2026 01:39 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.
4 / 5 with data · 1 missing or unmatched. Percentages use all 5 selected publications.
4 / 5 with data · 1 missing or unmatched. Percentages use all 5 selected publications.
5 / 5 with data · 0 missing or unmatched. Percentages use all 5 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 |
|---|---|---|---|---|---|
| 2013 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2014 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2015 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2016 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2017 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 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 | 0 | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2013 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2014 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2015 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| 2016 | All selected publications | 6 | 2 | 2/2 | 2/2 |
| 2017 | All selected publications | — | 0 | 0/0 | 0/0 |
| 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 | — | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2013 | All selected publications | 5 | 1 | 1/1 | 1/1 |
| 2014 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2015 | All selected publications | 9 | 1 | 1/1 | 1/1 |
| 2016 | All selected publications | 8 | 2 | 2/2 | 2/2 |
| 2017 | All selected publications | — | 0 | 0/0 | 0/0 |
| 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 | — | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2025 | All selected publications | 0 | 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 5 / 5 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): 2026-10-21T00:22:00.296479+00:00 — 2026-10-22T01:39:28.483642+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 | 1 | 5 | 5/5 |
| 2018 | All selected publications | 3 | 5 | 5/5 |
| 2019 | All selected publications | 1 | 5 | 5/5 |
| 2020 | All selected publications | 2 | 5 | 5/5 |
| 2021 | All selected publications | 3 | 5 | 5/5 |
| 2022 | All selected publications | 2 | 5 | 5/5 |
| 2023 | All selected publications | 4 | 5 | 5/5 |
| 2024 | All selected publications | 1 | 5 | 5/5 |
| 2025 | All selected publications | 1 | 5 | 5/5 |
| 2026 | All selected publications | 0 | 5 | 5/5 |
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 |
|---|---|
| 2013 | 1 |
| 2015 | 1 |
| 2016 | 2 |
| 2025 | 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 | 5 | 9 | 22 | 5/5 | 1.8 | 1 | 60.0 | 5/5 · 100.0% |
* Percentage among publications with a known citation count.
Bu çal??mada Türkiye’de pay senetleri fiyatlar? ile döviz kurlar? aras?ndaki ili?ki, VAR metodu (vektör oto regresyon modeli) kullan?larak ara?t?r?lm??t?r. Döviz kurlar? ve pay senedi fiyatlar?ndan olu?an de?i?kenler aras?nda do?rusal bir ba??nt? olup olmad???n?n tespiti amac?yla bu model kullan?lm??t?r. Bu amaçla, döviz kurlar?, B?ST Banka ve B?ST S?nai endeksleri ele al?nm??t?r. Veriler Ocak 2011’den Aral?k 2014’e kadar olan döneme ait günlük de?i?meleri içermektedir. Türkiye’de en fazla i?lem yap?lan döviz kurlar? ABD dolar? ve Euro oldu?u için, bu kurlar tercih edilmi?tir.
The study concentrates on the benefits of passive commodity investments in the context of the phenomenon of financialization. The research investigates the implications of increase in the correlation coefficients between equity and commodity investments for investors in financial markets. The paper is composed of several parts. First, the attributes of commodity investments and their benefits in the portfolio optimization are explored. Second, the phenomenon of the financialization is described and the research hypothesis is developed. Next, an empirical analysis is performed. I simulate the mean-variance spanning tests to examine the benefits of commodity investments before and after accounting for the impact of financialization. I proceed separate analysis for pre- and post-financialization period. The empirical research is based on asset classes’ returns and other related variables from years 1991-2012. The performed investigations indicate that the market financialization may have significant implications for commodity investors. Due to increase in correlation coefficients, the inclusion of the commodity futures in the traditional stock-bond portfolio appears to be no longer reasonable.
As two important constituents of China’s macro economy, there are a variety of relationships among China’s stock market, real estate market and its macro economy. In order to investigate these relationships, in this paper, especially with the Macroeconomic Boom Index reflecting China’s macro economy, we use cointegration theory and Granger analysis to demonstrate that there are long-term equilibrium relationship and bidirectional causality between the macro economy and the securities business, also between the macro economy and the real estate market, however, this kind of long-term Equilibrium relationship and bidirectional causality appears very weak.
The pricing of six highly liquid futures commodity contracts is investigated using a Markov switching procedure. The data set spans an exceptionally turbulent period, characterized by a complex interplay of economic/financial and political shocks. Markov switching analysis exploits time series nonlinearity to identify the nature and the timing of the implicit changes of regime. Building on a HAM framework, we use the time varying parameterization of the transition probability estimates to link these shifts to exogenous variables. We provide in this way additional information on the co-movement of the time series and on their regime shifts, eventually conducive to herding pricing behaviour. The WTI oil futures price and DJIA stock index are used as proxies for global shocks as they turn out to be the main common drivers of the changes in regime of most futures commodity prices.
<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 pricing of six highly liquid futures commodity contracts is investigated using a Markov switching procedure. The data set spans an exceptionally turbulent period, characterized by a complex interplay of economic/financial and political shocks. Markov switching analysis exploits time series nonlinearity to identify the nature and the timing of the implicit changes of regime. Building on a HAM framework, we use the time varying parameterization of the transition probability estimates to link these shifts to exogenous variables. We provide in this way additional information on the co-movement of the time series and on their regime shifts, eventually conducive to herding pricing behaviour. The WTI oil futures price and DJIA stock index are used as proxies for global shocks as they turn out to be the main common drivers of the changes in regime of most futures commodity prices.
<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>
Bu çal??mada Türkiye’de pay senetleri fiyatlar? ile döviz kurlar? aras?ndaki ili?ki, VAR metodu (vektör oto regresyon modeli) kullan?larak ara?t?r?lm??t?r. Döviz kurlar? ve pay senedi fiyatlar?ndan olu?an de?i?kenler aras?nda do?rusal bir ba??nt? olup olmad???n?n tespiti amac?yla bu model kullan?lm??t?r. Bu amaçla, döviz kurlar?, B?ST Banka ve B?ST S?nai endeksleri ele al?nm??t?r. Veriler Ocak 2011’den Aral?k 2014’e kadar olan döneme ait günlük de?i?meleri içermektedir. Türkiye’de en fazla i?lem yap?lan döviz kurlar? ABD dolar? ve Euro oldu?u için, bu kurlar tercih edilmi?tir.
The study concentrates on the benefits of passive commodity investments in the context of the phenomenon of financialization. The research investigates the implications of increase in the correlation coefficients between equity and commodity investments for investors in financial markets. The paper is composed of several parts. First, the attributes of commodity investments and their benefits in the portfolio optimization are explored. Second, the phenomenon of the financialization is described and the research hypothesis is developed. Next, an empirical analysis is performed. I simulate the mean-variance spanning tests to examine the benefits of commodity investments before and after accounting for the impact of financialization. I proceed separate analysis for pre- and post-financialization period. The empirical research is based on asset classes’ returns and other related variables from years 1991-2012. The performed investigations indicate that the market financialization may have significant implications for commodity investors. Due to increase in correlation coefficients, the inclusion of the commodity futures in the traditional stock-bond portfolio appears to be no longer reasonable.
As two important constituents of China’s macro economy, there are a variety of relationships among China’s stock market, real estate market and its macro economy. In order to investigate these relationships, in this paper, especially with the Macroeconomic Boom Index reflecting China’s macro economy, we use cointegration theory and Granger analysis to demonstrate that there are long-term equilibrium relationship and bidirectional causality between the macro economy and the securities business, also between the macro economy and the real estate market, however, this kind of long-term Equilibrium relationship and bidirectional causality appears very weak.
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 |