Host: Yin Luo and Team
Date:  Wednesday, February 28th at 12pm EST

Location: Wolfe NYC Office, 757 Third Ave, New York, NY 10017

In this seminar, we will bridge the gap between the options and stock markets by discussing a suite of tools to measure earnings uncertainty during the reporting season. Our empirical analysis suggests that the earnings uncertainty measure is highly accurate in quantifying the price fluctuations around earnings announcement news. We also test the efficacy of earnings uncertainty and other metrics (e.g., earnings dispersion and realized volatility) in screening for short candidates. We will introduce the ERIE (Earnings Risk Insight and Estimate) model, which leverages earnings growth expectations, earnings uncertainty, and a proprietary systematic earnings prediction model to identify companies with the largest, unexpected earnings risk. Yale University Professor Frank Zhang will also be presenting his research titled “Winning is Not Enough: Changing Landscapes of Earnings Surprises and the Market Reaction”? https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3853030 . We will also be joined by Joanna Wang from Nomura Securities, who will discuss “Capitalizing on Earnings Volatility.” Lastly, we will demonstrate how to incorporate earnings uncertainty into a risk model, within the portfolio construction process. Incorporating earnings uncertainty into the idiosyncratic component of a risk model can boost the accuracy of our baseline Wolfe QES Standard Risk Model considerably – particularly during earnings season, enhancing risk adjusted performance.