Pricing climate change exposure

We estimate the risk premium for firm-level climate change exposure among S&P 500 stocks and its time-series evolution between 2005 to 2020. Exposure reflects the attention paid by market participants in earnings calls to a firm’s climate-related risks and opportunities. When extracted from realized returns, the unconditional risk premium is insignificant but exhibits a period with a positive risk premium before the financial crisis and a steady increase thereafter. Forward-looking expected return proxies deliver an unconditionally positive risk premium with maximum values of 0.5%–1% p.a., depending on the proxy, between 2011 and 2014. The risk premium has been lower since 2015, especially when the expected return proxy explicitly accounts for the higher opportunities and lower crash risks that characterize high-exposure stocks. This finding arises as the priced part of the risk premium primarily originates from uncertainty about climate-related upside opportunities. In the time series, the risk premium is negatively associated with green innovation; Big Three holdings; and environmental, social, and governance fund flows and positively associated with climate change adaptation programs

Firm‐level climate change exposure

We develop a method that identifies the attention paid by earnings call participants to firms' climate change exposures. The method adapts a machine learning keyword discovery algorithm and captures exposures related to opportunity, physical, and regulatory shocks associated with climate change. The measures are available for more than 10,000 firms from 34 countries between 2002 and 2020. We show that the measures are useful in predicting important real outcomes related to the net-zero transition, in particular, job creation in disruptive green technologies and green patenting, and that they contain information that is priced in options and equity markets

De rol van accounting in stroomsgewijze produktie

De rol van accounting in stroomsgewijze produkti

De winst is absoluut belangrijk!

De winst is absoluut belangrijk

Qualitate Qua; Aansprakelijkheid en de accountant

Qualitate Qua; Aansprakelijkheid en de accountan

Mobilizing Text As Data

Textual analysis methods have become increasingly popular and powerful tools for researchers in finance and accounting to extract meaningful information from unstructured text data. This paper surveys the recent applications of these methods in various domains, such as corporate disclosures, earnings calls, investor relations, and social media. It also discusses the advantages and challenges of different textual analysis methods, such as keyword lists, pattern-based sequence classification, word embedding, and other large language models. We provide guidance on how to choose appropriate methods, validate text-based measures, and report text-based evidence effectively. We conclude by suggesting some promising directions for future research using text as data

Firm‐Level Political Risk and Credit Markets

We take advantage of a new composite measure of political risk (Hassan et al., 2019) to study the effects of firm-level political risk on private debt markets. First, we use panel data tests and exploit the redrawing of US congressional districts to uncover plausibly exogenous variation in firm-level political risk. We show that borrowers’ political risk is linked to interest rates set by lenders. Second, we test for the transmission of political risk from lenders to borrowers. We predict and find that lender-level political risk propagates to borrowers through lending relationships. Our analysis allows for endogenous matching between lenders and borrowers and indicates the presence of network effects in diffusing political risk throughout the economy. Finally, we introduce new text-based methods to analyze the distinct sources of political risk to lenders and borrowers and provide textual evidence of the transmission of political risk from lenders to borrowers