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 01:27 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.
2 / 5 with data · 3 missing or unmatched. Percentages use all 5 selected publications.
2 / 5 with data · 3 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 |
|---|---|---|---|---|---|
| 2016 | All selected publications | 5 | 5 | 5/5 | 5/5 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2016 | All selected publications | 8 | 5 | 5/5 | 5/5 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2016 | All selected publications | 9 | 5 | 5/5 | 5/5 |
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): 2026-10-21T00:22:00.285376+00:00 — 2026-10-22T01:27:59.515318+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 | 5 | 5/5 |
| 2018 | All selected publications | 2 | 5 | 5/5 |
| 2019 | All selected publications | 2 | 5 | 5/5 |
| 2020 | All selected publications | 1 | 5 | 5/5 |
| 2021 | All selected publications | 2 | 5 | 5/5 |
| 2022 | All selected publications | 1 | 5 | 5/5 |
| 2023 | All selected publications | 0 | 5 | 5/5 |
| 2024 | All selected publications | 1 | 5 | 5/5 |
| 2025 | All selected publications | 0 | 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 |
|---|---|
| 2016 | 5 |
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 | 8 | 9 | 5/5 | 1.6 | 0 | 20.0 | 5/5 · 100.0% |
* Percentage among publications with a known citation count.
Etkin Piyasalar Hipotezi ve Davran??sal Finans, finansal literatürün kar??t argümanlar? savunan iki temel modelini olu?turmaktad?r. Bunlardan ilki yat?r?mc?lar? rasyonel olarak kabul ederken di?eri ise yat?r?mc?lar? normal olarak ifade etmektedir. Etkin Piyasa Hipotezi’ne göre piyasalar bilgisel olarak daima etkindir. Hisseler en nihayetinde denge fiyat?na ula?acakt?r. Ya?anan finansal krizler ise k?sa dönemli düzeltme hareketleridir. Davran??sal finans, alternatif bir model olarak geli?tirilmi?tir. Buna göre yat?r?mc?lar farkl? bilgilere farkl? zamanlarda ula?maktad?r ve onlar? farkl? de?erlendirmektedir. Yat?r?m kararlar? psikolojik ve duygusal faktörlerden etkilenmektedir. Yat?r?mc?lar?n makine olarak görülmesi kabul edilemez. Davran??sal finans çok daha yeni bir konumdad?r ve çok say?da yeni çal??maya ihtiyaç duymaktad?r. Bununla birlikte iki teori aras?ndaki çat??ma yeni finansal krizlerin önlenmesi aç?s?ndan çok önemlidir. Yak?n gelecekte teorilerin kar??t olmaktan ziyade tamamlay?c? olarak kullan?lmas? beklenmektedir.
I explore company characteristics which explain the difference in analysts’ recommendations for companies that were underwritten (affiliated) versus non-underwritten (unaffiliated) by analysts’ brokerage firms. Prior literature documents that analysts issue more optimistic recommendations to underwriting clients of analysts’ brokerage employers. Extant research uses regression models to find general associations between recommendations and financial qualities of companies, with or without underwriting relationship. However, regression models cannot identify the qualities that cause the most difference in recommendations between affiliated versus unaffiliated companies. I adopt uplift random forest model, a popular technique in recent marketing and healthcare research, to identify the type of companies that earn analysts’ favor. I find that companies of stable earnings in the past, higher book-to-market ratio, smaller sizes, worsened earnings, and lower forward PE ratio are likely to receive higher recommendations if they are affiliated with analysts than if they are unaffiliated with analysts. With uplift random forest model, I show that analysts pay more attention on price-related than earnings-related matrices when they value affiliated versus unaffiliated companies. This paper contributes to the literature by introducing an effective predictive model to capital market research and shedding additional light on the usefulness of analysts’ reports.
Yat?r?m f?rsatlar?n?n de?erlendirilmesi süreci beklenen getiri ve riskin ölçümüne ba?l?d?r. Finansal Varl?klar? Fiyatlama Modeli (FVFM), çok uzun y?llard?r modern finans teorisinin temel ta?lar?ndan bir tanesini olu?turmaktad?r. Model, varl?klar?n beklenen getirisi ve sistematik riski aras?ndaki basit do?rusal ili?kiyi ortaya koymaktad?r. Model halen, sermaye maliyetinin hesaplanmas?, portföy yönetiminin performans?n?n ölçülmesi ve yat?r?mlar?n de?erlendirilmesi amac?yla kullan?lmaktad?r. FVFM’in çekicili?i, beklenen getiri ve risk aras?ndaki ili?kinin ölçümlenmesi konusundaki güçlü tahmin yetene?inden gelmektedir. Bununla birlikte modelin bu yetene?i 30 y?l? a?k?n bir süredir akademisyenler ve uygulamac?lar taraf?ndan sorgulanmaktad?r. Tart??malar büyük ölçüde ampirik düzeyde gerçekle?tirilmektedir. FVFM’in ampirik düzeydeki problemleri, çok say?da basitle?tirilmi? varsay?m? içermesi nedeniyle teorik hatalard?r. Çok say?daki gerçekçi olmayan varsay?m modeli pratik olarak kullan??s?z hale getirmektedir. Model ile ilgili temel ele?tiriler ise risksiz faiz oran?, pazar portföyü ve beta katsay? üzerinde yo?unla?maktad?r. Bu çal??mada, modelin varsay?mlar? ve temel bulgular?n?n yan? s?ra uygulamas?na yönelik yap?lan ele?tiriler ele al?nacakt?r.
Over the past fifty years, economic growth in emerging markets has been supported by investments in capital and technology from the developed world. The benefit of this development for the emerging markets, as measured by growth in income, employment, and wealth, is immediately apparent. There have also been significant advantages for the developed world through opportunities for higher risk adjusted returns from investments in emerging markets. This study explores the benefits of the diversification of global government bond portfolio, and provides complete performance evaluations of DMs with or without South Africa emerging market (SAEM) bonds. The study examines the benefits of inclusion of SAEM bonds in DMs, the degrees of financial integration among the research markets, the relative bond returns of dynamic factor models with time-varying coefficients and the robust tests of bond portfolio performance between DMs with SAEM and bond index. The results of this study provide important implications for global investors by identifying diversification gains in SAEM.
<p>The paper concentrates on value and size effects in country portfolios. It contributes to academic literature threefold. First, I provide fresh evidence that the value and size effects may be useful in explaining the cross-sectional variation in country returns. The computations are based on a broad sample of 66 countries in years 2000-2013. Second, I document that the country-level value and size effects are indifferent to currency conversions. Finally, I introduce an alternative macro-level Fama-French model, which, contrary to its prototype, employs country-based factors. I show that applying this modification makes the model more successful in evaluation of funds with global investment mandate than the standard CAPM and FF models.</p>
I explore company characteristics which explain the difference in analysts’ recommendations for companies that were underwritten (affiliated) versus non-underwritten (unaffiliated) by analysts’ brokerage firms. Prior literature documents that analysts issue more optimistic recommendations to underwriting clients of analysts’ brokerage employers. Extant research uses regression models to find general associations between recommendations and financial qualities of companies, with or without underwriting relationship. However, regression models cannot identify the qualities that cause the most difference in recommendations between affiliated versus unaffiliated companies. I adopt uplift random forest model, a popular technique in recent marketing and healthcare research, to identify the type of companies that earn analysts’ favor. I find that companies of stable earnings in the past, higher book-to-market ratio, smaller sizes, worsened earnings, and lower forward PE ratio are likely to receive higher recommendations if they are affiliated with analysts than if they are unaffiliated with analysts. With uplift random forest model, I show that analysts pay more attention on price-related than earnings-related matrices when they value affiliated versus unaffiliated companies. This paper contributes to the literature by introducing an effective predictive model to capital market research and shedding additional light on the usefulness of analysts’ reports.
Yat?r?m f?rsatlar?n?n de?erlendirilmesi süreci beklenen getiri ve riskin ölçümüne ba?l?d?r. Finansal Varl?klar? Fiyatlama Modeli (FVFM), çok uzun y?llard?r modern finans teorisinin temel ta?lar?ndan bir tanesini olu?turmaktad?r. Model, varl?klar?n beklenen getirisi ve sistematik riski aras?ndaki basit do?rusal ili?kiyi ortaya koymaktad?r. Model halen, sermaye maliyetinin hesaplanmas?, portföy yönetiminin performans?n?n ölçülmesi ve yat?r?mlar?n de?erlendirilmesi amac?yla kullan?lmaktad?r. FVFM’in çekicili?i, beklenen getiri ve risk aras?ndaki ili?kinin ölçümlenmesi konusundaki güçlü tahmin yetene?inden gelmektedir. Bununla birlikte modelin bu yetene?i 30 y?l? a?k?n bir süredir akademisyenler ve uygulamac?lar taraf?ndan sorgulanmaktad?r. Tart??malar büyük ölçüde ampirik düzeyde gerçekle?tirilmektedir. FVFM’in ampirik düzeydeki problemleri, çok say?da basitle?tirilmi? varsay?m? içermesi nedeniyle teorik hatalard?r. Çok say?daki gerçekçi olmayan varsay?m modeli pratik olarak kullan??s?z hale getirmektedir. Model ile ilgili temel ele?tiriler ise risksiz faiz oran?, pazar portföyü ve beta katsay? üzerinde yo?unla?maktad?r. Bu çal??mada, modelin varsay?mlar? ve temel bulgular?n?n yan? s?ra uygulamas?na yönelik yap?lan ele?tiriler ele al?nacakt?r.
Etkin Piyasalar Hipotezi ve Davran??sal Finans, finansal literatürün kar??t argümanlar? savunan iki temel modelini olu?turmaktad?r. Bunlardan ilki yat?r?mc?lar? rasyonel olarak kabul ederken di?eri ise yat?r?mc?lar? normal olarak ifade etmektedir. Etkin Piyasa Hipotezi’ne göre piyasalar bilgisel olarak daima etkindir. Hisseler en nihayetinde denge fiyat?na ula?acakt?r. Ya?anan finansal krizler ise k?sa dönemli düzeltme hareketleridir. Davran??sal finans, alternatif bir model olarak geli?tirilmi?tir. Buna göre yat?r?mc?lar farkl? bilgilere farkl? zamanlarda ula?maktad?r ve onlar? farkl? de?erlendirmektedir. Yat?r?m kararlar? psikolojik ve duygusal faktörlerden etkilenmektedir. Yat?r?mc?lar?n makine olarak görülmesi kabul edilemez. Davran??sal finans çok daha yeni bir konumdad?r ve çok say?da yeni çal??maya ihtiyaç duymaktad?r. Bununla birlikte iki teori aras?ndaki çat??ma yeni finansal krizlerin önlenmesi aç?s?ndan çok önemlidir. Yak?n gelecekte teorilerin kar??t olmaktan ziyade tamamlay?c? olarak kullan?lmas? beklenmektedir.
Over the past fifty years, economic growth in emerging markets has been supported by investments in capital and technology from the developed world. The benefit of this development for the emerging markets, as measured by growth in income, employment, and wealth, is immediately apparent. There have also been significant advantages for the developed world through opportunities for higher risk adjusted returns from investments in emerging markets. This study explores the benefits of the diversification of global government bond portfolio, and provides complete performance evaluations of DMs with or without South Africa emerging market (SAEM) bonds. The study examines the benefits of inclusion of SAEM bonds in DMs, the degrees of financial integration among the research markets, the relative bond returns of dynamic factor models with time-varying coefficients and the robust tests of bond portfolio performance between DMs with SAEM and bond index. The results of this study provide important implications for global investors by identifying diversification gains in SAEM.
<p>The paper concentrates on value and size effects in country portfolios. It contributes to academic literature threefold. First, I provide fresh evidence that the value and size effects may be useful in explaining the cross-sectional variation in country returns. The computations are based on a broad sample of 66 countries in years 2000-2013. Second, I document that the country-level value and size effects are indifferent to currency conversions. Finally, I introduce an alternative macro-level Fama-French model, which, contrary to its prototype, employs country-based factors. I show that applying this modification makes the model more successful in evaluation of funds with global investment mandate than the standard CAPM and FF models.</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 |