Publications Database

Welcome to the new Schulich Peer-Reviewed Publication Database!

The database is currently in beta-testing and will be updated with more features as time goes on. In the meantime, stakeholders are free to explore our faculty’s numerous works. The left-hand panel affords the ability to search by the following:

  • Faculty Member’s Name;
  • Area of Expertise;
  • Whether the Publication is Open-Access (free for public download);
  • Journal Name; and
  • Date Range.

At present, the database covers publications from 2012 to 2020, but will extend further back in the future. In addition to listing publications, the database includes two types of impact metrics: Altmetrics and Plum. The database will be updated annually with most recent publications from our faculty.

If you have any questions or input, please don’t hesitate to get in touch.

 

Search Results

Lei Zhang and Kiridaran Kanagaretnam (2024). "Climate Disasters and Analysts’ Earnings Forecasts: Evidence from the United States", European Accounting Review, 1–28.

View Paper

Keywords
Abstract We examine the relationship between climate disasters and analysts’ earnings forecasts in the United States. We find that climate disasters are associated with deteriorated analyst forecast properties proxied by forecast errors and forecast dispersion. We reason that the volatility of return on assets and of cash flows, and lower financial statement comparability, are three potential channels through which climate disasters influence analyst forecast properties. We also find that this relationship is more pronounced for firms in climate-vulnerable industries. Results from the market reaction tests further support our main findings by showing that the stock market responds less strongly to positive earnings surprises during periods of high climate disasters. Our results are robust to a battery of sensitivity tests, including a two-stage least squares approach and a difference-in-differences specification. Overall, the results shed light on the association between climate disasters and analysts’ earnings forecasts, which has significant implications for academics, investors, and standard setters.