How does the Shiller P/E (“CAPE”) perform as a stock selection tool?
On the Performance of Cyclically Adjusted Valuation Measures
- Wesley Gray (me) and Jack Vogel
- A version of the paper can be found here, or http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2329948
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We confirm the effectiveness of using cyclically-adjusted valuation metrics to identify high performing stocks. The Shiller P/E, or cyclically-adjusted price-to-earnings (CAPE) ratio, is not the optimal way to implement a cyclically-adjusted value measure. At the margin, the cyclically-adjusted book-to-market (CA-BM) is a better measure to predict returns. We find that more frequent rebalancing and momentum can enhance strategies based on cyclically-adjusted valuation metrics.
Cyclically-adjusted valuation metrics, or measures that sort stocks on 10-year average real earnings (or Book, EBITDA, FCF, GP, etc.) divided by the current real price (or total enterprise value in some cases), can be used to identify cheap securities. Annually rebalanced portfolios that purchase the top 10% cheapest securties based on these long-term valuation measures have outperformed the S&P 500 and the equal-weight S&P 500 over time.
How does the Shiller P/E stack up against other cyclically-adjusted measures?
All measures show an ability to identify cheap stocks that beat common market benchmarks over the long-term. At the margin, the cyclically-adjusted book-to-market ratio works better than the Shiller P/E, or CAPE.
- Sort all stocks on your favorite cyclically-adjusted measure.
- Buy the cheapest 10%, equal-weight.
- Rebalance annually.
- Many market commentators and participants are infatuated with cyclically-adjusted valuation metrics. There is no evidence that we are familiar with to suggest that these metrics are any better at sorting stocks than simple trailing twelve month measures.
- Adding momentum and more frequent rebalancing can enhance these ratio’s ability to identify winners and losers.
Time to dump CAPE and fire up CA-BM?
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Definitions of common statistics used in our analysis are available here (towards the bottom)