Sector Detector: Energy and Healthcare lead fundamental rankings

The stock market has continued its unabated upward trajectory for the month of March with nary a hint of giving anything back. After a brief pullback on Friday and Monday morning to the 8-day moving average surrounding the uncertainty of the healthcare vote in Congress, it has quickly returned to its methodical uptrend.  When things gets this exuberant, the more speculative stocks often outperform, which can temporarily hurt the performance of Sabrient’s fundamentals-based SectorCast-ETF model.

At year end, SectorCast-ETF was flashing warning signals about the market getting ahead of itself, and soon thereafter the market corrected. Then the model showed indications that the market wanted to breakout to the upside, which it has – first going through its 50-day moving average at the beginning of the month, and then recently breaking above strong resistance around 1150 on the S&P 500.

Today, SectorCast-ETF continues to reflect the uncertainty about the economy and market direction as the most economically sensitive sectors – InfoTech and Consumer Discretionary – are smack dab in the middle of the rankings. And the overall rankings are virtually unchanged from last week.

Latest rankings: For the third week in a row, the top and bottom sectors in SectorCast-ETF are holding steady. Energy (XLE) continues in to the top spot with a score of 73. Despite the turmoil in Congress over the healthcare bill, Healthcare (XLV) holds onto second place with a 68, down slightly from last week’s 71.

In third place, we again find Financials, followed by Utilities, InfoTech, Consumer Discretionary, and Consumer Staples tightly packed in the middle – reflecting uncertainty among the analysts who track these sectors.

XLE boasts the top score in projected year-over-year change in earnings across the sector and also shows the best (lowest) projected price/earnings ratio. However, it didn’t score as highly this week in the percentage of analysts’ positive revisions to earnings estimates, falling behind Consumer Discretionary, InfoTech, Industrials, and Consumer Staples in this measure of analyst sentiment.

XLV ranks second in return on equity and third in projected P/E, but in general it achieves its overall ranking by simply scoring reasonably well across all of the relevant factors in the model. Note that it, too, fell somewhat in the percentage of analysts’ positive revisions to earnings estimates.

Top-ranked stocks within XLE and XLV include Murphy Oil (NYSE: MUR), ConocoPhillips (NYSE: COP), Merck (NYSE: MRK), and Forest Labs (NYSE: FRX).

At the bottom of the rankings, we again find Telecommunications (IYZ) and Industrials (XLI). IYZ came in with a low score of 34, while XLI remains in the ninth spot with a score of 45. IYZ continues to show the most analyst downgrades to earnings estimates, and is near the bottom in return on equity. XLI still sports the worst (highest) projected P/E, but is actually showing a slight improvement in some of the other relevant factors. I’ll be curious to see over the next few weeks if Industrials improves in the relative rankings.

Low-ranked stocks within XLI and IYZ include C.H. Robinson Worldwide (Nasdaq: CHRW), Textron (NYSE: TXT), Cbeyond, Inc. (Nasdaq: CBEY), and Virgin Media (Nasdaq: VMED).

Again, these scores represent the view that Energy and Healthcare stocks may be undervalued overall, while Telecom and Industrials stocks may be overvalued.

Performance: The table below shows the performance of each of the prior four weekly portfolios as of the market close on Tuesday, 3/23/2010.

As the market continues its impressive climb, long position XLF has shown extraordinary performance while XLE is still stuck in the mud despite showing impressive valuation and analyst sentiment. At the same time, short position IYZ has taken an incredible move to the upside (which hurts our position), thumbing its nose at valuation metrics and analyst expectations for the coming year.

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.