Green Investing, Information Asymmetry, and Capital Structure
Shasha Li, Biao Yang
IWH Discussion Papers,
No. 20,
2023
Abstract
We investigate how optimal attention allocation of green-motivated investors changes information asymmetry in financial markets and thus affects firms‘ financing costs. To guide our empirical analysis, we propose a model where investors with heterogeneous green preferences endogenously allocate limited attention to learn market-level or firm-specific fundamental shocks. We find that a higher fraction of green investors in the market leads to higher aggregate attention to green firms. This reduces the information asymmetry of green firms, leading to higher price informativeness and lower leverage. Moreover, the information asymmetry of brown firms and the market increases with the share of green investors. Therefore, greater green attention is associated with less market efficiency. We provide empirical evidence to support our model predictions using U.S. data. Our paper shows how the growing demand for sustainable investing shifts investors‘ attention and benefits eco-friendly firms.
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Regulation and Information Costs of Sovereign Distress: Evidence from Corporate Lending Markets
Iftekhar Hasan, Suk-Joong Kim, Panagiotis Politsidis, Eliza Wu
Journal of Corporate Finance,
October
2023
Abstract
We examine the effect of sovereign credit impairments on the pricing of syndicated loans following rating downgrades in the borrowing firms' countries of domicile. We find that the sovereign ceiling policies used by credit rating agencies create a disproportionately adverse impact on the bounded firms' borrowing costs relative to other domestic firms following their sovereign's rating downgrade. Rating-based regulatory frictions partially explain our results. On the supply-side, loans carry a higher spread when granted from low-capital banks, non-bank lenders, and banks with high market power. We further document an operating demand-side channel, contingent on borrowers' size, financial constraints, and global diversification. Our results can be attributed to the relative bargaining power between lenders and borrowers: relationship borrowers and non-bank dependent borrowers with alternative financing sources are much less affected.
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"Let Me Get Back to You" — A Machine Learning Approach to Measuring NonAnswers
Andreas Barth, Sasan Mansouri, Fabian Wöbbeking
Management Science,
No. 10,
2023
Abstract
Using a supervised machine learning framework on a large training set of questions and answers, we identify 1,364 trigrams that signal nonanswers in earnings call questions and answers (Q&A). We show that this glossary has economic relevance by applying it to contemporaneous stock market reactions after earnings calls. Our findings suggest that obstructing the flow of information leads to significantly lower cumulative abnormal stock returns and higher implied volatility. As both our method and glossary are free of financial context, we believe that the measure is applicable to other fields with a Q&A setup outside the contextual domain of financial earnings conference calls.
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Centre for Evidence-based Policy Advice
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IWH-DPE Call for Applications – Fall 2024 Intake
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Charts
Info Graphs Sometimes pictures say more than a thousand words. Therefore, we selected a few graphs to present our main topics visually. If you should have any questions or would…
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