Donghyun Kim

Chung-Ang University

221 Heuksuk-dong

Dongjak-gu

Seoul, 156-756

Korea, Republic of (South Korea)

SCHOLARLY PAPERS

5

DOWNLOADS
Rank 43,759

SSRN RANKINGS

Top 43,759

in Total Papers Downloads

1,799

SSRN CITATIONS

2

CROSSREF CITATIONS

0

Scholarly Papers (5)

1.

Informed Trading of Out-of-the-Money Options and Market Efficiency

Number of pages: 51 Posted: 05 Jul 2016 Last Revised: 15 Mar 2021
Chang Mo Kang, Donghyun Kim, Junyong Kim and Geul Lee
Hanyang University - School of Business, Chung-Ang University, University of Wisconsin - Milwaukee - Department of Finance and Coinplug, Inc.
Downloads 1,174 (29,222)

Abstract:

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Options, return predictability, trading volume

2.

Underlying Bond Return Predictability by ETF Returns

Number of pages: 36 Posted: 01 May 2020
University of Wisconsin - Milwaukee - Sheldon B. Lubar School of Business, Chung-Ang University, Chung-Ang University - College of Business & Economics and University of Central Florida - College of Business Administration
Downloads 307 (158,965)
Citation 2

Abstract:

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ETF, bond, return predictability, liquidity

3.

Risk Management Transparency and Compensation

Number of pages: 58 Posted: 25 Jul 2014 Last Revised: 19 May 2022
Chang Mo Kang and Donghyun Kim
Hanyang University - School of Business and Chung-Ang University
Downloads 243 (201,536)

Abstract:

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managerial compensation, risk management, corporate hedging, financial officers, FAS 133

4.

Aggregation of Idiosyncratic Shocks in the Customer-Supplier Network

Number of pages: 51 Posted: 16 Sep 2020
Donghyun Kim and Yi Liu
Chung-Ang University and Sun Yat-sen University (SYSU)
Downloads 67 (532,130)

Abstract:

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aggregation, idiosyncratic return, customer-supplier, granular network

5.

News Sentiment and Bond Risk Premia

Number of pages: 41 Posted: 03 May 2023
Chang Mo Kang, Donghyun Kim and Hoyoung Park
Hanyang University - School of Business, Chung-Ang University and Hanyang University
Downloads 8 (920,947)

Abstract:

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news sentiment, bond risk premia, interest rates, factor analysis, machine learning, word-to-vector