Research

ESG, Market Efficiency, Information Asymmetry, Corporate Finance, Behavioral Finance

"ESG-Related Risk and Costs of Debt Capital" (link)

• Job Market Paper

Abstract: Using a comprehensive dataset from RepRisk over the past 15 years, this study provides evidence on the relations between the ESG risk and borrowing costs. Specifically, corporate bonds issued by firms with greater ESG risk have significantly higher yield spreads. The results are robust to a battery of tests incorporating other aspects, such as political affiliations, financial constraints, the Paris Agreement, green-bond designations, decomposition of the ESG risk, and industrial differences. Our results have important implications for corporate managers and investors, suggesting that the ESG risk is an important determinant of the costs of debt capital.

"Insider Trading, Liquidity Risk, and Cost of Capital" (with Sahn-Wook Huh and Inho Suk), 2023

Abstract: This study investigates the relationship between insider trading and liquidity risk in the stock market, motivated by the debate on the benefits and costs of insider trading and the need for regulation. We hypothesize that insider trading lowers liquidity risk, which in turn reduces the cost of capital. We present evidence that insider trading has a negative association with liquidity risk, specifically reducing the sensitivity of the liquidity factor on expected returns. This relationship is not affected by the time intervals used to construct the insider trading variable or the choice of liquidity measures. We also find that transactions after the SOX show more salient effects. Furthermore, this effect is more pronounced with the transactions under the Rule 10b5-1, non-routine traders, or directors and officers. Our study contributes to the literature on the impact of insider trading on liquidity, changes in liquidity risk, and the ongoing debate around regulating insider trading.

"Detecting Jumps amidst Prevalent Zero Returns: Evidence from the U.S. Treasury Securities" (with Seung-Oh Han and Sahn-Wook Huh) Journal of Empirical Finance 70, 2023, pp. 276-307 (link)

Abstract: We examine the performance of conventional jump-detection methods for the U.S. Treasury notes. We first document how the Treasury market is different from the stock market: each day the Treasury notes have a large proportion of zero returns, because the vast majority of trades are executed at the best ask/bid quotes and spreads are mostly set close to the minimum tick. Moreover, the proportions of zero returns rather capture liquidity in the Treasury market. Given the distinctive feature (frequent zero returns) in the U.S. Treasury market, we find that conventional jump-detection methods are vulnerable to biases, leading to falsely identifying jumps. We propose a low-cost solution to the biases, and empirically support the arguments by using the actual data on the Treasury notes and macro-economic news announcements.