Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Structural Models of Corporate Bond Pricing: An Empirical Analysis
2004606 citationsYoung Ho Eom, Jean Helwege et al.Review of Financial Studiesprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Young Ho Eom's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Young Ho Eom with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Young Ho Eom more than expected).
This network shows the impact of papers produced by Young Ho Eom. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Young Ho Eom. The network helps show where Young Ho Eom may publish in the future.
Co-authorship network of co-authors of Young Ho Eom
This figure shows the co-authorship network connecting the top 25 collaborators of Young Ho Eom.
A scholar is included among the top collaborators of Young Ho Eom based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Young Ho Eom. Young Ho Eom is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Eom, Young Ho, et al.. (2014). An Empirical Analysis of the Determinants of Tied South Korean Aid. 52(1). 123–144.2 indexed citations
7.
Eom, Young Ho, et al.. (2011). Empirical Investigation on the Relationship of Firm-Volatility and the Cross-section of Stock Returns. 24(1). 91–131.3 indexed citations
8.
Eom, Young Ho, et al.. (2010). An Empirical Study of KRW Interest Rate Swap Market: Focused on ‘Mispricing’ Compared to Theoretical Fair IRS Rates and Arbitrage Opportunities. Korean Journal of Financial Studies. 39(1). 59–101.
9.
Eom, Young Ho, et al.. (2009). The Cross-section of Stock Returns in Korea: An Empirical Investigation. 22(1). 1–44.12 indexed citations
10.
Eom, Young Ho, et al.. (2008). A Research on the Capital Structure of Korean Corporations: Comparison of the Trade-off Theory and the Pecking-Order Theory. 14(2). 1–60.1 indexed citations
11.
Eom, Young Ho, et al.. (2008). The Predictability of Volatility Index in KOSPI200 Option Market. 22(3). 1–33.2 indexed citations
Eom, Young Ho, Jean Helwege, & Jing‐Zhi Huang. (2004). Structural Models of Corporate Bond Pricing: An Empirical Analysis. Review of Financial Studies. 17(2). 499–544.606 indexed citations breakdown →
Subrahmanyam, Marti G., Young Ho Eom, & Jun Uno. (2000). Credit Risk and the Pricing of Japanese Yen Interest Rate Swaps. The Faculty Digital Archive (New York University).6 indexed citations
Brenner, Menachem & Young Ho Eom. (1997). No-Arbitrage Option Pricing: New Evidence on the Validity of the Martingale Property. The Faculty Digital Archive (New York University).16 indexed citations
Altman, Edward I., Young Ho Eom, & Dong-Won Kim. (1995). Failure Prediction: Evidence from Korea. Journal of International Financial Management and Accounting. 6(3). 230–249.60 indexed citations
20.
Altman, Edward I., Young Ho Eom, & Dong Won Kim. (1994). Distress Classification of Korean Firms. The Faculty Digital Archive (New York University).2 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.