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This paper sets the background for the Special Issue of the Journal of Empirical Finance on the European Sovereign Debt Crisis. It identifies the channel through which risks in the financial industry leaked into the public sector. It discusses the role of the bank rescues in igniting the sovereign debt crisis and reviews approaches to detect early warning signals to anticipate the buildup of crises. It concludes with a discussion of potential implications of sovereign distress for financial markets.
We study the relevance of signaling and marketing as explanations for the discount control mechanisms that a closed-end fund may choose to adopt in its prospectus. These policies are designed to narrow the potential gap between share price and net asset value, measured by the fund’s discount. The two most common discount control mechanisms are explicit discretion to repurchase shares based on the magnitude of the fund discount and mandatory continuation votes that provide shareholders the opportunity to liquidate the fund. We find very limited evidence that a discount control mechanism serves as costly signal of information. Funds with mandatory voting are not more likely to delist than the rest of the CEFs in general or whenever the fund discount is large. Similarly, funds that explicitly discuss share repurchases as a potential response do not subsequently buy back shares more often when discounts do increase. Instead, the existence of these policies is more consistent with marketing explanations because the policies are associated with an increased probability of issuing more equity in subsequent periods.
The discount control mechanisms that closed-end funds often choose to adopt before IPO are supposedly implemented to narrow the difference between share price and net asset value. We find evidence that non-discretionary discount control mechanisms such as mandatory continuation votes serve as costly signals of information to reveal higher fund quality to investors. Rents of the skill signaled through the announcement of such policies accrue to managers rather than investors as differences in skill are revealed through growing assets under management rather than risk- adjusted performance.
This study examines the recent literature on the expectations, beliefs and perceptions of investors who incorporate Environmental, Social, Governance (ESG) considerations in investment decisions with the aim to generate superior performance and also make a societal impact. Through the lens of equilibrium models of agents with heterogeneous tastes for ESG investments, green assets are expected to generate lower returns in the long run than their non- ESG counterparts. However, at the short run, ESG investment can outperform non-ESG investment through various channels. Empirically, results of ESG outperformance are mixed. We find consensus in the literature that some investors have ESG preference and that their actions can generate positive social impact. The shift towards more sustainable policies in firms is motivated by the increased market values and the lower cost of capital of green firms driven by investors’ choices.
We examine whether the uncertainty related to environmental, social, and governance (ESG) regulation developments is reflected in asset prices. We proxy the sensitivity of firms to ESG regulation uncertainty by the disparity across the components of their ESG ratings. Firms with high ESG disparity have a higher option-implied cost of protection against downside tail risk. The impact of the misalignment across the different dimensions of the ESG score is distinct from that of ESG score level itself. Aggregate downside risk bears a negative price for firms with low ESG disparity.
Non-standard errors
(2021)
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.
