CUKPF - RCD: Consumer Discretionary Dashboard For February

Summary

• Only one subsector has a good value score: durables and apparel.
• Retailing has a good quality score and is moderately overvalued.
• The less attractive subsector is auto and components.
• The equal-weight fund Invesco S&P 500 Equal Weight Consumer Discretionary ETF has outperformed in the last 2 years.
• 10 stocks cheaper than their peers in February.

This monthly article series shows a dashboard with aggregate subsector metrics in Consumer Discretionary. It is also a top-down analysis of sector exchange-traded funds ("ETFs") like the Consumer Discretionary Select Sector SPDR ETF ( XLY ) and the Invesco S&P 500 Equal Weight Consumer Discretionary ETF ( RCD ), whose holdings are used to calculate these metrics.

Shortcut

The next two paragraphs in italic describe the dashboard methodology. They are necessary for new readers to understand the metrics. If you are used to this series or if you are short of time, you can skip them and go to the charts.

Base Metrics

I calculate the median value of five fundamental ratios for each subsector : Earnings Yield ("EY"), Sales Yield ("SY"), Free Cash Flow Yield ("FY"), Return on Equity ("ROE"), Gross Margin ("GM"). The reference universe includes large companies in the U.S. stock market. The five base metrics are calculated on trailing 12 months. For all of them, higher is better. EY, SY and FY are medians of the inverse of Price/Earnings, Price/Sales and Price/Free Cash Flow. They are better for statistical studies than price-to-something ratios, which are unusable or non available when the "something" is close to zero or negative (for example, companies with negative earnings). I also look at two momentum metrics for each group: the median monthly return (RetM) and the median annual return (RetY).

I prefer medians to averages because a median splits a set in a good half and a bad half. A capital-weighted average is skewed by extreme values and the largest companies. My metrics are designed for stock-picking rather than index investing.

Value and Quality Scores

I calculate historical baselines for all metrics. They are noted respectively EYh, SYh, FYh, ROEh, GMh, and they are calculated as the averages on a look-back period of 11 years. For example, the value of EYh for retailing in the table below is the 11-year average of the median Earnings Yield in retail companies.

The Value Score ("VS") is defined as the average difference in % between the three valuation ratios (EY, SY, FY) and their baselines (EYh, SYh, FYh). The same way, the Quality Score ("QS") is the average difference between the two quality ratios (ROE, GM) and their baselines (ROEh, GMh).

The scores are in percentage points. VS may be interpreted as the percentage of undervaluation or overvaluation relative to the baseline (positive is good, negative is bad). This interpretation must be taken with caution: the baseline is an arbitrary reference, not a supposed fair value. The formula assumes that the three valuation metrics are of equal importance. A floor of -100 is set for VS and QS when the calculation goes below this value. It may happen when metrics in a sub sector are very bad.

Current data

The next table shows the metrics and scores as of last week's closing. Columns stand for all the data named and defined above.

Value and Quality chart

The next chart plots the Value and Quality Scores by subsector (higher is better).

Evolution since last month

The value score has improved in durables and apparel. Quality went down in consumer services.

Momentum

The next chart plots momentum data.

Interpretation

Durables and apparel are the most attractive subsector: it is slightly undervalued relative to 11-year averages, and its quality score is close to the historical baseline. Retailing and consumer services are moderately overvalued regarding the same metrics. A good quality score may justify it for the former. The less attractive subsector is auto and components: it is the most overpriced, and the quality score is below the baseline.

RCD fast facts

The Invesco S&P 500 Equal Weight Consumer Discretionary ETF ( RCD ) has been tracking the S&P 500 Equal Weight Consumer Discretionary Index since 11/01/2006. It has a total expense ratio of 0.40%, whereas XLY has only a 0.10% fee. The fund holds 56 stocks as of 2/15/2023. All constituents have the same weight after every rebalancing, but they may drift with price action. The next table shows the top 10 holdings with some basic ratios. These are the stocks with the highest momentum since the last rebalancing. Their aggregate weight is 20.4% of asset value. Risks related to the top holdings are much lower than in XLY, where Amazon ( AMZN ) and Tesla ( TSLA ) represent about 23% and 15% of asset value, respectively.

Ratios: Portfolio123.

RCD has lagged XLY by 2.3 percentage points in annualized return since inception. Moreover, the risk measured in drawdown and volatility (standard deviation of monthly returns) is significantly higher.

Nonetheless, RCD has beaten XLY by 12.7 percentage points in the last two years.

In summary, RYT is a good solution for investors seeking a consumer cyclical ETF with a limited exposure to the largest companies in the sector (in particular Amazon and Tesla). The equal-weight methodology shows disappointing return and risk metrics between 2006 and 2020, but it has outperformed since 2021.

Dashboard List

I use the first table to calculate value and quality scores. It may also be used in a stock-picking process to check how companies stand among their peers. For example, the EY column tells us that a retail company with an Earnings Yield above 0.0482 (or price/earnings below 20.75) is in the better half of the industry regarding this metric. A Dashboard List is sent every month to Quantitative Risk & Value subscribers with the most profitable companies standing in the better half among their peers regarding the three valuation metrics at the same time. The list below was sent to subscribers several weeks ago based on data available at this time.

It is a rotational list with a statistical bias toward excess returns on the long-term, not the result of an analysis of each stock.

For further details see: