Sector Detector: Valuations favor Healthcare and Utilities
Keep in mind, SectorCast is designed to take a one-month forward look.
Top-ranked stocks within XLP and XLV include Cephalon (Nasdaq: CEPH), AmerisourceBergen (NYSE: ABC), Public Service Enterprise (NYSE: PEG), and TECO Energy (NYSE: TE).
At the bottom of the rankings, once again we find Materials (XLB) as the fundamentally most overvalued sector with a low score of 33. Despite its recent technical strength, it remains saddled with the highest aggregate projected P/E and poor return ratios—both return on equity and return on sales. Industrials (XLI) again finds itself solidly in the second-to-the-bottom spot with a score of 35. It is hampered by a poor projected change in year-over-year earnings across the sector.
Low-ranked stocks within these sectors include Alcoa (NYSE: AA), Vulcan Materials (NYSE: VMC), PACCAR (Nasdaq: PCAR), and Monster Worldwide (NYSE: MWW).
These scores represent the view that Healthcare and Utilities stocks may be undervalued overall, while Materials and Industrials stocks may be overvalued. It’s worth mentioning that Consumer Discretionary (XLY) saw its score jump from 39 last week to 59 this week, primarily because it enjoyed a number of analyst upward earnings revisions among its constituent stocks during the week.
Performance: The table below shows the performance of each of the prior four weekly portfolios as of the market close on Tuesday, 1/12/10.
According to our SectorCast-ETF model, the Materials sector continues to display inadequate forward-looking numbers to support current valuations. However, optimistic sentiment about future demand, inflation expectations, and a weak dollar seem to be overriding fundamentals at the moment.
No doubt, the current rankings put a definite defensive posture to the long/short portfolio. The market pulled back on Tuesday, 1/12/10, and indeed the three ETFs at the top of our rankings were the top three performers for the day (although XLI was surprisingly close behind).
Disclosure: Author has no positions in stocks or ETFs mentioned.
About SectorCast: The rankings are based on Sabrient’s SectorCast model, which builds a virtual profile of each of the 10 ETFs in the table below based on bottom-up scoring of their constituent stocks. The model employs a fundamentals-based multi-factor approach including forward valuation, earnings growth prospects, analyst revisions, and various return ratios.
SectorCast has tested to be highly predictive for identifying the best (most undervalued) and worst (most overvalued) sectors, with a 1-month forward look. Of course, each ETF has a unique set of constituent stocks, so the sectors represented will score differently depending upon which set of ETFs is used. For Sector Detector, I use 8 Select Sector SPDRs, but because the SPDRs combine InfoTech and Telecom into one ETF, I use the two iShares for those sectors rather than the SPDR Select Technology ETF.
About Trading Strategies: Sector Detector has shown how you can use this information in three ways to identify ETFs that have the potential to enhance your upside, downside, or market-neutral trading ideas. First, if you are bullish on the broad market, you can go long the SPDR Trust exchange-traded fund (SPY), which tracks the S&P 500 Index, and enhance it with long positions in SectorCast’s top-ranked sector ETFs. Conversely, if you are bearish and short (or buy puts on) the SPY, you could also consider shorting the two lowest-ranked sector ETFs to enhance your short bias.
However, if you really don't want to bet on which way the market is going, you could try a market-neutral, long/short trade—that is, go long the top-ranked ETFs and short the lowest-ranked ETFs. And here’s a more aggressive strategy to consider: You might trade some of the highest and lowest ranked stocks from within those top and bottom-ranked ETFs, such as the ones I identify above.
About Performance Tracking: I track each week’s set of ETFs as a mini-portfolio over the course of four weeks. Because SectorCast does not include any technical triggers, this will give the fundamentals-based model a chance to achieve its predicted move.