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

Kecskés, A., Michaely, R. and Womack, K. (2017). "Do Earnings Estimates Add Value to Sell-Side Analysts’ Investment Recommendations?", Management Science, 63(6), 1855-1871.

View Paper

Keywords
Abstract Sell-side analysts change their stock recommendations when their valuations differ from the market’s. These valuation differences can arise from either differences in earnings estimates or the nonearnings components of valuation methodologies. We find that recommendation changes motivated by earnings estimate revisions have a greater initial price reaction than the same recommendation changes without earnings estimate revisions: about +1.3% (−2.8%) greater for upgrades (downgrades). Nevertheless, the postrecommendation drift is also greater, suggesting that investors underreact to earnings-based recommendation changes. Implemented as a trading strategy, earnings-based recommendation changes earn risk-adjusted returns of 3% per month, considerably more than non-earnings-based recommendation changes. Evidence from variation in firms’ information environment and analysts’ regulatory environment suggests that recommendation changes with earnings estimate revisions are less affected by analysts’ cognitive and incentive biases.