by Tim Gaumer.
During a prolonged period of low market volatility, global market participants were fortunate to experience an upward drift in equity markets, with the rally driven by both earnings upgrades and valuation multiple expansion. Until very recently, markets were in “risk on” mode. The complacency that had settled over markets was shaken on February 2 when the U.S. Bureau of Labor Statistics reported nonfarm payrolls grew by 200,000 in January and wages saw their largest increase since the end of the last recession. The specter of inflation saw volatility return with a vengeance. If volatility has reemerged, then portfolio managers and research teams may want to focus more on the risk of earnings disappointments, given heady market expectations. In this environment, companies that miss earnings expectations or experience downward analyst revisions may see their stock prices severely punished. In this paper, we’ll describe how StarMine® SmartEstimates® can be used to help predict and avoid these securities.
SmartEstimates are our proprietary estimates designed to provide more accurate forecasts of financial measures than analyst consensus. They are constructed by placing more weight on the more recent forecasts from the most accurate analysts – based on each analyst’s historical track record and the age of the estimate.
The StarMine SmartEstimate algorithm was finalized in 1999 and is a heavily utilized data point within the Thomson Reuters desktop, Eikon, in a data feed for quants and as an input into various StarMine factor models, including Analyst Revisions Model (ARM), Intrinsic Valuation (IV) and Relative Valuation (RV). The Predicted Surprise® is a Thomson Reuters StarMine-branded analytic that measures the percentage difference between the I/B/E/S® Mean, or consensus estimate, and the StarMine SmartEstimate. The Predicted Surprise percent is predictive of the direction of both future analyst revisions and earnings surprises. Our 2009 SmartEstimate and Predicted Surprise white paper found that when the magnitude of the Predicted Surprise percent was greater than +2% or -2%, it correctly predicted the direction of earnings surprises with about a 70% accuracy rate, and even better in the presence of corroborating revisions.1
While many market pundits agree that global equity markets are expensive (and, as the CAPE P/E ratio shown in Exhibit 1 illustrates, especially so in the United States), we acknowledge that most portfolio managers are not paid to make market timing decisions; they’re paid to provide alpha generative performance for a given mandate. As such, they need to navigate the challenges of all market conditions. Given that institutional asset managers need to maintain their market exposure, a more productive and possibly profitable approach during what may be the late stage of a prolonged bull market is to shift some research time from trying to pick the next winner to avoiding losers. You can generate alpha by picking winning stocks. Avoiding big losers is another, and often overlooked, source of alpha and avoiding an earnings miss in a high-expectation security is likely to provide both improved portfolio performance alongside a reduction in portfolio volatility.
The StarMine Predicted Surprise can play a valuable role in both idea generation and risk mitigation, as it provides institutional investors with a systematic forecast of likely beats and misses on a global universe of approximately 16,000 equities, updated daily. A market environment that appears to be one of both elevated market expectations and rising volatility leaves no room for earnings disappointments. For that reason, this study will focus on the performance of the Predicted Surprise in predicting negative earnings surprises.
We reexamined a large global universe of equities against those with a Predicted Surprise of -2% or greater. This universe of 9,000 securities consists of the top 3,000 by market cap in the U.S., 500 in Canada, 2,000 in Developed Europe, 1,000 each in Developed Asia ex-Japan and Japan, and 1,500 in Emerging Markets. We utilized I/B/E/S data of analyst estimates and reported actuals at monthly frequency over a 19-year period from January 1998 to November 2017. Because the availability of historical quarterly estimates is poor in many countries outside the U.S., we conducted our analysis utilizing only fiscal year estimates for the then-current fiscal period (FY1). We examine the Predicted Surprise at every month-end throughout the entire fiscal year in our analysis. In this study, we found that the Predicted Surprise accurately forecasted negative earnings surprises nearly 73% of the time. We then broke the sample into two subsets: early years (Jan-98 – Nov-08) and later years (Dec-08 – Nov-17). The predictive performance was essentially equal between the two periods (73.05% and 71.97%, respectively). This study thus confirms the persistence in the efficacy of the SmartEstimate and Predicted Surprise, despite being commercially available for many years.
Against those results, we examined negative earnings surprises of the constituents of our global universe. That result is shown in Exhibit 2, together with its U.S. and ex-U.S. subsets. Over the time frame of our study, 48.0% of all companies missed their earlier consensus estimate. Put another way, if you were to try and predict if one of the companies in our global universe was going to report an earnings miss, mere guesswork would give you a hit rate of 48.0%. In contrast, if you were to base your prediction on those companies with a StarMine Predicted Surprise of -2% or worse, you’d be right 72.6% of the time. The Predicted Surprise improves the prediction accuracy by 24.6 percentage points compared to random chance.
The StarMine Predicted Surprise is a unique, valuable input for effective portfolio risk management, allowing the investment team to be aware of firms with a heightened risk of disappointing results during earnings season.
This approach can assist with:
In this Research Note, we’ll explore in more detail the validity of the adage “flight ...
At the recent Lipper Fund Selector Forum 2018, Jake Moeller, Head of Lipper UK & ...
At the recent Lipper Fund Selector Forum 2018, Detlef Glow, Head of Lipper EMEA Research, ...
Authored by Brenda Zhang. With the explosive growth in passive exchange-traded ...