If you seek to tilt your US stocks exposure toward particular sectors for whatever your motivations, these data will help you determine which are more attractive for that purpose.
There is no right answer to which is "best," because of different investor facts and circumstances and portfolio goals, limits or mandates.
For example a long options speculator would probably seek volatility, while a retiree selling shares each month to support lifestyle would probably seek stability. For our own purposes with sectors, we tend to seek lower valuation and GARP relative to the S&P 500 index in most instances.
This article is about the sectors primarily, but also includes data about ETFs that are reasonable proxies for the sectors. Those ETF symbols are: XLY, XLP, XLE, XLF, XLI, XLV, VGT, XLB, VOX, XLU and XLK. They are references in association with names sectors in the figures that follow. You would very likely consider these ETFs to accomplish a portfolio "tilt," unless you used individual stocks from the sectors.
Figure 1: Bottom-Up Summary of S&P Capital IQ Quality, Fair Value and Year Ahead Stars Ratings
For each stock in each sector, we used the weight of the stock in the sector and the ratings assigned by S&P Capital IQ to arrive at a bottom-up composite rating for each sector. To keep all data numeric, we converted the alphabetic quality rating to an 8 point numeric scale (8=A+ and 1 = D).
S&P uses a 5 point scale for Fair Value and Stars (5 is best and 1 is worst).
For comparison, we also looked up the S&P Capital IQ recommendation for ETFs that are actual or reasonable proxies to see how they related to our bottom-up analysis. We did not use XLK, the SPDR technology ETF, because it includes both info tech and telecommunications - instead we used VGT and from Vanguard which track MSCI indexes. The designation "MW" means "market weight" and the designation "OW" means "overweight."
This data suggests that industrials and info tech are the most attractive when Fair Value is considered; with energy, financials and healthcare strong, but only about average in Fair Value. Utilities are overpriced according to Fair Value.
Figure 2: PEG Ratios
This data is straight from the index earnings spreadsheet downloadable at Standard and Poor's. We added the color coding.
Consumer discretionary and info tech have the most attractive PEG ratios. Utilities and telecommunications have the least attractive PEG ratios, with the utilities PEG significantly unattractive.
Figure 3: 2014 P/E Ratios
This data is straight from the index earnings spreadsheet downloadable at Standard & Poor's. We added the color coding.
All sectors show a lower P/E for 2014 versus 2013. Materials and telecommunications show the biggest multiple change, down 4.3 and 3.0 multiples respectively. The next three most significant multiple reductions are for consumer discretionary (down 2.6 multiples), and healthcare and info tech (each down 2.1 multiples).
Figure 4: Some Fundamentals from Morningstar
Best yields are from higher to lower: utilities, telecommunications, consumer staples and basic materials.
These sectors yield less than the S&P 500 index: info tech, consumer discretionary, healthcare and financials. Industrials yield at the index level.
The most expensive on a price to cash flow basis are healthcare, consumer staples and industrials. The least expensive on a price to cash flow basis are financials and telecommunications.
Above index 5-year forecast earnings growth is attributed to consumer discretionary, info tech, basic materials and industrials.
Figure 5: Capture Ratio Spreads, Sharpe and Beta
"Capture Ratio Spread" is the upside capture ratio less the downside capture ratio. The capture ratios are measured monthly versus the S&P 500 index (e.g. if the S&P 500 goes up 1%, but the sector goes up 0.9%, then the upside capture ratio is 90; and if the index then goes down 1% and the sector goes down 0.9%, the downside capture ratio is 90; and in this simple two month example, the capture ratio spread would be zero).
The sectors with the best capture ratio spreads over 1, 3, 5 and 10 years are consumer discretionary, healthcare and industrials. The worst capture spreads are for materials and financials (whether the financials will have a future remotely similar to the past is highly debated).
The best risk/reward ratios as measured by the Sharpe (volatility versus return in excess of 3-month Treasury rates) are for healthcare, consumer discretionary and consumer staples. The worst are for materials, financials and energy.
The most volatile relative to the S&P 500 index (Beta) are energy, financials and materials. The least relative volatility is seen in utilities, consumer staples, telecommunications and healthcare.
Dynamic Return Charts
Our main website provides a variety of dynamic security return tables that are automatically updated each Saturday in the early hours to reflect results through the previous close. The three tables below are screen-shots from those tables for the S&P 500 sectors and the index itself. You can access the updated tables each week at this link.
(Note that we labeled XLY "cyclicals" in our tables here and "discretionary" in other tables in this report. The terms seem to be essentially interchangeable for these purposes)
Figure 6: Short-Term Returns
Figure 7: 1-year Rolling Periods
Figure 8: Static Calendar Year Returns
Figure 9: Some Technical Condition Observations
"200-d Z-score" is the number of standard deviations distance from the price to the 200-day moving average. Values outside of +2 or -2 tend to represent overbought or oversold short-term conditions.
"P/200-d SMA" is the ratio of the price to the 200-day simple moving average.
"% Off 252-d Hi" is the percentage difference between the most recent close and the 252-day (1-year) trailing high price.
"$Vol/Min" is the number of dollars of the security traded per minute on average over the past 3-months.
The most overbought according to the Z-score are info tech and basic materials. The only sector below its 200-day average is utilities. The sectors that are the greatest percentage above their 200-day average are consumer cyclicals, healthcare and industrials. The sectors at their 1-year high are energy, industrials and basic materials. The sector in correction territory (more than 10% below its 1-year high) is utilities.
Dividend Payment Patterns
The following charts plot the actual dividend payments by each sector proxy. The data is from an internal QVM plotting tool, and the data is supplied by a commercial database service.
Figure 10: XLY
Figure 11: XLV
Figure 12: XLU
Figure 13: XLP
Figure 14: XLK
Figure 15: XLI
Figure 16: XLE
Figure 18: VOX
Figure 19: VGT
Figure 20: XLF
Short-Term Price Chart Overlays
The next three charts overlay the price of several sector ETFs on the price of the SPY (the S&P 500 ETF, shown in black) over the past three months.
Figure 21: XLV, XLY, VGT, XLB, XLI
All five ETFs outperformed the S&P 500 index.
Figure 22: XLE, XLK, VOX
XLE outperformed the index, while XLK (the info tech + telecommunications ETF) and telecommunications underperformed, although XLK came close in the end, but negatively diverged significantly in July.
Figure 23: XLF, XLU, XLP
All three of XLF, XLU and XLP underperformed the index, although XLF came close.
We own several sector ETFs and or holding an overweight of individual stocks in particular sectors, but most recently have added to healthcare, industrials and info tech.
Disclosure: QVM has positions in several of the sector ETFs mentioned in this article as of the creation date of this article (September 13, 2013). We certify that except as cited herein, this is our work product. We received no compensation or other inducement from any party to produce this article, but are compensated retroactively by Seeking Alpha based on readership of this specific article.
General Disclaimer: This article provides opinions and information, but does not contain recommendations or personal investment advice to any specific person for any particular purpose. Do your own research or obtain suitable personal advice. You are responsible for your own investment decisions. This article is presented subject to our full disclaimer found on the QVM site available here.