CiteNScope — Scholarly Index & Analytics
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Research across participating journals · All insights follow your collection filters.

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OpenAlex coverage 100.0% · 4/4 recordsLast OpenAlex observation 22 Oct 2026, 02:42 UTC
PUBLICATIONS4In the selected collection
CROSSREF CITATIONS291/4 with known counts
OPENALEX CITATIONS684/4 with known counts
OPENALEX MEAN17.0Per article with a known count
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topic: Credit Risk and Financial Regulations Remove ×
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Output and reach

Publication activity alongside incoming citations.

Publication activity

Articles grouped by publication year

Publications

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2026 is year to date. 0 undated publications excluded.

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Citations received

OpenAlex · year of the citing work

OpenAlex

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2017–2026 annual-history window. 2026 is year to date. Unknown years have gaps.

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Research landscape

Leading affiliations and themes in this collection.

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Institutions

University of Central Florida1Menlo College1Istanbul Medipol University14/4 records with data

Countries

United States2Tunisia1Turkey14/4 records with data

Primary topics

Credit Risk and Financial Regulations44/4 records with data
Two perspectives on citations

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Independent sources. Clear time scales.

Lifetime citations by publication year

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.

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Citations received each year

OpenAlex · incoming citation links grouped by citing-work year

OpenAlex

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.

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Annual history: 4/4 articles. Latest history observation: 2026-10-22T02:42:50.904211+00:00. Source totals are never added together.
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International Journal of Finance & Banking Studies

Publications

Articles by publication year

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Crossref citations

Latest lifetime totals · grouped by publication year

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OpenAlex citations

Latest lifetime totals · grouped by publication year

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OpenAlex · Citations received

By year of the citing work · 2017–2026

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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.

Full analytics report All distributions, publications, comparisons & data provenance
4 MATCHING PUBLICATIONSExplore results →Export CSV ↓
INDEXED PUBLICATIONS4
CROSSREF CITATIONS29
CROSSREF · CITED PUBLICATIONS100.0%
CROSSREF · MEAN CITATIONS29.0

Crossref citation coverage: 1 / 4 publications (25.0%). Latest observation: 21 Oct 2026, 00:21 UTC. Means and percentages use records with known counts.

OpenAlex / Separate citation source

A broader view of this research

DOI-matched OpenAlex observations for the same selected publications. These counts are separate from Crossref and are never added together.

OPENALEX CITATIONS68
DOI MATCH COVERAGE100.0%4 / 4 publications
MEAN · KNOWN COUNTS ONLY17.04 known · 0 unknown
MEDIAN · KNOWN COUNTS ONLY0.0

Observation range: 22 Oct 2026 00:44 UTC — 22 Oct 2026 02:42 UTC. Missing matches are not zero citations. Previous observations may remain if a refresh fails or a DOI no longer matches.

OpenAlex citation distributionNo citations31–506–20021+1Unknown0

Institutions, countries & topics

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.

Institutions

4 / 4 with data · 0 missing or unmatched. Percentages use all 4 selected publications.

Countries

4 / 4 with data · 0 missing or unmatched. Percentages use all 4 selected publications.

Primary topics

4 / 4 with data · 0 missing or unmatched. Percentages use all 4 selected publications.

Citation year / OpenAlex

Citations received over time

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 4 / 4 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): 2026-10-22T00:44:39.232511+00:00 — 2026-10-22T02:42:50.904211+00:00. Older observations may be retained after failed or incomplete refreshes.

OpenAlex · Citations received by year

168.00 2017 · All selected publications · OpenAlex · Citations received by year: 7 · Annual history coverage 4/42018 · All selected publications · OpenAlex · Citations received by year: 9 · Annual history coverage 4/42019 · All selected publications · OpenAlex · Citations received by year: 16 · Annual history coverage 4/42020 · All selected publications · OpenAlex · Citations received by year: 7 · Annual history coverage 4/42021 · All selected publications · OpenAlex · Citations received by year: 10 · Annual history coverage 4/42022 · All selected publications · OpenAlex · Citations received by year: 7 · Annual history coverage 4/42023 · All selected publications · OpenAlex · Citations received by year: 6 · Annual history coverage 4/42024 · All selected publications · OpenAlex · Citations received by year: 0 · Annual history coverage 4/42025 · All selected publications · OpenAlex · Citations received by year: 3 · Annual history coverage 4/42026 · All selected publications · OpenAlex · Citations received by year: 0 · Annual history coverage 4/4 2017201820192020202120222023202420252026
View openalex · citations received by year data & coverage
Citation yearJournal / scopeOpenAlex · Citations received by yearPublicationsAnnual history known
2017All selected publications744/4
2018All selected publications944/4
2019All selected publications1644/4
2020All selected publications744/4
2021All selected publications1044/4
2022All selected publications744/4
2023All selected publications644/4
2024All selected publications044/4
2025All selected publications344/4
2026All selected publications044/4

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 ↗

Crossref citation distribution

Latest Crossref totals for the selected publications; not citations received in the selected years.

Mean29.0
Median29
No citations01–506–20021+1Unknown3

Unknown counts are excluded from the mean, median and cited-publication rate.

Journal comparison

Same filters, source and counting rules across journals. Descriptive statistics, not a quality ranking.

Journal / publisherPublicationsCrossref citationsOpenAlex citationsOpenAlex coverageCrossref meanCrossref medianCrossref cited %*Crossref coverage
International Journal of Finance & Banking Studies
SSBFNET
429684/429.029100.01/4 · 25.0%

* Percentage among publications with a known citation count.

Explore publications

View all 4 →

Most cited · Crossref

JOURNAL ARTICLE

CAMELS-based Determinants for the Credit Rating of Turkish Deposit Banks

Serhat Yuksel · Hasan Dincer · Umit Hacioglu

International Journal of Finance & Banking Studies · 2015

Read abstract

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.

DOI 10.20525/ijfbs.v4i4.3529 Crossref citations68 OpenAlex citations
JOURNAL ARTICLE

The Sinking Fund Provision: Reinvestment Risk, Credit Improvement, and Growth Options

Tewari, Manish · Ramanlal, Pradipkumar

International Journal of Finance & Banking Studies · 2026

Read abstract

We study the sinking fund provision in 1,533 nonconvertible corporate bonds issued from 1980 to 1996 with the year 1996 being the last year of inclusion for these provisions in fixed coupon non-convertible corporate bonds. The period 1980 to 1996 is classified as the period of high interest rates (Tewari, Byrd, and Ramanlal, 2015). We find that the provision addresses agency issues due to reinvestment risk, credit improvement, and growth options. When the interest rates are high, investment grade issues with sinking fund have longer maturities (> 20 years) by a ratio exceeding 4:1, while non-sinking fund issues have shorter maturities (≤ 20 years) by a ratio of about 2:1. High yield issues with sinking fund experience credit risk improvement, while non-sinking fund issues experience credit risk deterioration. When the rates are low and expected to rise, the provision is likely to present accumulation threat to the firm thereby, deterrent to its use by the firms. Also, firms use the sinking fund as a call option to capture the benefits of growth. These results suggest that the sinking fund provision has the potential to make a comeback in the corporate debt in a high-interest rate environment and when the rates are likely to fall. The current environment of persistent high rates presents conditions ripe for inclusion of the sinking fund provision in the corporate bonds.

DOI 10.20525/ijfbs.v15i2.4949N/A Crossref citations0 OpenAlex citations
JOURNAL ARTICLE

Annual Report Readability and the Information Content of Bond Rating Downgrades

May, Anthony

International Journal of Finance & Banking Studies · 2025

Read abstract

Using a large sample of bond rating downgrades of US firms announced by the three largest rating agencies during 1997-2023, I find that cumulative abnormal stock returns (CAR) spanning the day of and day following downgrade announcements are significantly and negatively related to annual report readability, indicating that downgrades are more informative among firms with more readable annual reports. My baseline regression results indicate that a one standard deviation improvement in annual report readability corresponds to a reduction in the announcement CAR of approximately forty-three basis points, which is economically meaningful given that it constitutes roughly 27% of the sample average announcement CAR of -1.62%. Furthermore, additional analysis reveals that the informativeness enhancing effect that better annual report readability has on rating downgrades is stronger among firms that should be more difficult to monitor and evaluate, including lower rated firms, firms with high stock return volatility, and younger firms. Overall, the results strongly support the hypothesis that well-written annual reports facilitate more timely and informative bond rating downgrades, especially among more informationally problematic firms.

DOI 10.20525/ijfbs.v14i4.4793N/A Crossref citations0 OpenAlex citations
JOURNAL ARTICLE

Calibrating the LMN Model based on the J-law to Implement the Credit Risk in the Economic Scenarios Generator

Chaouch, Leila · Jerbi, Yacin

International Journal of Finance & Banking Studies · 2026

Read abstract

The aim of this paper is to calibrate the Longstaff, Mithal and Neis (2005) model (LMN model) based on the J-law (see Jerbi (2015) to implement the credit risk in the economic scenarios generator. The LMN model, based on the assumptions of the normality of the risks is used to establish a link between the spread, the probability of default and the liquidity risk. The related assumptions seem unrealistic given the depth of the distribution that characterizes the financial and economic data. In this context, we develop an LMN model based on the J-law taking into account the effects of Skewness and Kurtosis related to the two series of the CDS premium and the corporate bond prices. We use a simulated data that are from January 2009 to December 2016. We estimate the model parameters. The LMN model based on the J-law fit better with the reality than the one based on the normal Law.      

DOI 10.20525/ijfbs.v14i4.4773N/A Crossref citations0 OpenAlex citations

Selected results

JOURNAL ARTICLE

The Sinking Fund Provision: Reinvestment Risk, Credit Improvement, and Growth Options

Tewari, Manish · Ramanlal, Pradipkumar

International Journal of Finance & Banking Studies · 2026

Read abstract

We study the sinking fund provision in 1,533 nonconvertible corporate bonds issued from 1980 to 1996 with the year 1996 being the last year of inclusion for these provisions in fixed coupon non-convertible corporate bonds. The period 1980 to 1996 is classified as the period of high interest rates (Tewari, Byrd, and Ramanlal, 2015). We find that the provision addresses agency issues due to reinvestment risk, credit improvement, and growth options. When the interest rates are high, investment grade issues with sinking fund have longer maturities (> 20 years) by a ratio exceeding 4:1, while non-sinking fund issues have shorter maturities (≤ 20 years) by a ratio of about 2:1. High yield issues with sinking fund experience credit risk improvement, while non-sinking fund issues experience credit risk deterioration. When the rates are low and expected to rise, the provision is likely to present accumulation threat to the firm thereby, deterrent to its use by the firms. Also, firms use the sinking fund as a call option to capture the benefits of growth. These results suggest that the sinking fund provision has the potential to make a comeback in the corporate debt in a high-interest rate environment and when the rates are likely to fall. The current environment of persistent high rates presents conditions ripe for inclusion of the sinking fund provision in the corporate bonds.

DOI 10.20525/ijfbs.v15i2.4949N/A Crossref citations0 OpenAlex citations
JOURNAL ARTICLE

Calibrating the LMN Model based on the J-law to Implement the Credit Risk in the Economic Scenarios Generator

Chaouch, Leila · Jerbi, Yacin

International Journal of Finance & Banking Studies · 2026

Read abstract

The aim of this paper is to calibrate the Longstaff, Mithal and Neis (2005) model (LMN model) based on the J-law (see Jerbi (2015) to implement the credit risk in the economic scenarios generator. The LMN model, based on the assumptions of the normality of the risks is used to establish a link between the spread, the probability of default and the liquidity risk. The related assumptions seem unrealistic given the depth of the distribution that characterizes the financial and economic data. In this context, we develop an LMN model based on the J-law taking into account the effects of Skewness and Kurtosis related to the two series of the CDS premium and the corporate bond prices. We use a simulated data that are from January 2009 to December 2016. We estimate the model parameters. The LMN model based on the J-law fit better with the reality than the one based on the normal Law.      

DOI 10.20525/ijfbs.v14i4.4773N/A Crossref citations0 OpenAlex citations
JOURNAL ARTICLE

Annual Report Readability and the Information Content of Bond Rating Downgrades

May, Anthony

International Journal of Finance & Banking Studies · 2025

Read abstract

Using a large sample of bond rating downgrades of US firms announced by the three largest rating agencies during 1997-2023, I find that cumulative abnormal stock returns (CAR) spanning the day of and day following downgrade announcements are significantly and negatively related to annual report readability, indicating that downgrades are more informative among firms with more readable annual reports. My baseline regression results indicate that a one standard deviation improvement in annual report readability corresponds to a reduction in the announcement CAR of approximately forty-three basis points, which is economically meaningful given that it constitutes roughly 27% of the sample average announcement CAR of -1.62%. Furthermore, additional analysis reveals that the informativeness enhancing effect that better annual report readability has on rating downgrades is stronger among firms that should be more difficult to monitor and evaluate, including lower rated firms, firms with high stock return volatility, and younger firms. Overall, the results strongly support the hypothesis that well-written annual reports facilitate more timely and informative bond rating downgrades, especially among more informationally problematic firms.

DOI 10.20525/ijfbs.v14i4.4793N/A Crossref citations0 OpenAlex citations
JOURNAL ARTICLE

CAMELS-based Determinants for the Credit Rating of Turkish Deposit Banks

Serhat Yuksel · Hasan Dincer · Umit Hacioglu

International Journal of Finance & Banking Studies · 2015

Read abstract

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.

DOI 10.20525/ijfbs.v4i4.3529 Crossref citations68 OpenAlex citations

Data coverage & freshness

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

SourceMost recent successful journal syncLatest attempt
crossref21 Oct 2026 00:21 UTC · ijfbscomplete · ijfbs · 21 Oct 2026 00:21 UTC
ojs-rest21 Oct 2026 00:21 UTC · ijfbscomplete · ijfbs · 21 Oct 2026 00:14 UTC
oai-pmh20 Oct 2026 19:56 UTC · ijfbscomplete · ijfbs · 20 Oct 2026 19:55 UTC
openalex-enrichment21 Oct 2026 00:22 UTC · ijfbscomplete · ijfbs · 21 Oct 2026 00:21 UTC
Full import history & methodology →