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INDIVIDUAL STOCK VALUATION
INDIVIDUAL STOCK VALUATION
How Morningstar’s Quantitative Equity Research Team value 28,000 company shares from around the world It is common knowledge that individual stock valuation can be tremendously valuable and perhaps even indispensible for stock selection and portfolio construction. At the same time, however, it is equally well understood that individual stock valuation done right tends to be a time-consuming process for even the most keen and fastidious analysts. Given the constraint of time, it has not been a scalable business historically. Typically, a firm will employ a group of analysts to analyse a specific coverage list which is much smaller than the whole investable universe and consequently, many names are left uncovered and unnoticed. Investment decision makers, such as advisers or portfolio managers, are then left to fill in the holes themselves. 16 WMA JOURNAL
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t Morningstar, we’ve been wrestling with this problem for some time and we think we’ve made a significant step in the direction of progress. Currently, our analysts cover approximately 1,700 equities globally providing both fair value estimates and economic moat ratings. To complement our analysts’ work, we developed a series of quantitative equity ratings that are philosophically analogous to the ratings our analyst staff produces. In essence, we tuned a scalable, statistical model to the specific preferences our analysts have exhibited in the past and built it in such a way to incorporate any changes to their preferences in the future.
How can this be done? First, market-based and fundamental factors are paired with our proprietary analystdriven ratings into a massive dataset. Some of the individual factors included in the
dataset are market capitalisation, earnings yield, book value yield, enterprise value, and return on assets. With the dataset compiled, a machine-learning algorithm known as a random forest is employed to look across this high-dimensional dataset and identify the sets of factors that most highly correlate with different ratings. Once these sets of factors are learned, the model can be easily applied to any new equity list and their factors. Using this approach, we achieved an extremely high degree of accuracy in matching our analyst ratings to the equities they cover. We aren’t the first to find usefulness from these types of models as random forests have been adopted in systems like the Microsoft Kinect and the Netflix recommendation engine. They are also one of the models of choice for genetics researchers attempting to correlate certain genes with instances of disease, like multiple sclerosis and early-onset coronary heart disease. www.thewma.co.uk
Chart 1: Performance (Since Live Inception) – Full Universe
Chart 2: One = Most overvalued quintile, Five = Most undervalued quintile www.thewma.co.uk
With this methodology, Morningstar is now able to deploy this statistical model on all global equities and effectively have an opinion, driven by our analysts, on approximately 28,000 equities around the world (14x increase) and 5,000 equities in the UK and Europe (13x increase). Given the breadth of coverage, individual quantitative equity valuations and moat ratings can now be aggregated up to the fund level, sector level, country level, or any level in between to provide macro insights. Another benefit of the quantitative ratings is their level of granularity. Given the nature of the computation, the quantitative ratings can be displayed numerically on a continuum and thus offer a richer picture of the equity universe than our analyst ratings have provided historically. For example, for the Moat Rating, Morningstar analysts ascribe a rating of No Moat, Narrow Moat, or Wide Moat. However, the quantitative moat rating is displayed numerically allowing investors to see the level of “moatiness” a company has (i.e. widest Wide Moat stock), as well as how it’s trended over time. Of course, none of this would be worthwhile if it didn’t actually add value to investors via better risk-adjusted performance. One metric that can be used to determine whether or not the model is adding value is by looking at cumulative alpha following a quantitative rating valuation. For stocks who fall in the most undervalued decile, cumulative ex-post alpha reaches 6% on average 500 days subsequent to a valuation. Comparatively, the most overvalued decile delivers -4% cumulative ex-post alpha on average over the same time frame. Furthermore, since the inception of these ratings in June 2012, the most undervalued quintile has seen cumulative returns of approximately 30% compared to just over 15% for the most overvalued quintile with performance being nearly monotonic along the quintiles. Morningstar’s quantitative rating systems cannot replace the work done by analysts; indeed, in order to function as desired it requires constant analyst input. However, these systems provide a solution to a vexing question of how to overcome the time and resource constraints implicit in fundamental analysis as well as offer a way to apply a consistent decision framework to a broader universe of investments. Lee Davidson Quantitative Analyst, Morningstar WMA JOURNAL 17
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