{"id":61041,"date":"2023-09-14T16:42:19","date_gmt":"2023-09-14T20:42:19","guid":{"rendered":"https:\/\/ifintechworld.com\/news\/vaw-materials-dashboard-for-september-nysearcavaw\/"},"modified":"2023-09-14T16:42:24","modified_gmt":"2023-09-14T20:42:24","slug":"vaw-materials-dashboard-for-september-nysearcavaw","status":"publish","type":"post","link":"https:\/\/ifintechworld.com\/?p=61041","title":{"rendered":"VAW: Materials Dashboard For September (NYSEARCA:VAW)"},"content":{"rendered":"
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This monthly article series shows a dashboard with aggregate industry metrics in materials. It is also a review of sector exchange-traded funds, or ETFs, like Materials Select Sector SPDR ETF (XLB) and Vanguard Materials Index Fund ETF Shares<\/span><\/strong> (<\/span>NYSEARCA:VAW<\/span>), whose largest holdings are used to calculate these metrics.<\/span><\/p>\n

Shortcut<\/strong><\/h2>\n

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.<\/p>\n

Base Metrics<\/strong><\/h3>\n

I calculate the median value of five fundamental ratios for each industry: 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).<\/span><\/em><\/p>\n

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.<\/em><\/p>\n

Value and Quality Scores<\/strong><\/h3>\n

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 <\/em>packaging <\/em>in the table below is the 11-year average of the median Earnings Yield in <\/em>packaging <\/em>companies.<\/em><\/p>\n

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).<\/em><\/p>\n

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.<\/em><\/p>\n

Current Data<\/strong><\/h3>\n

The next table shows the metrics and scores as of the last trading day’s closing. Columns stand for all the data named and defined above.<\/p>\n

<\/span><\/p>\n\n\n\n\n\n\n
<\/td>\n\n

VS<\/p>\n<\/td>\n

\n

QS<\/p>\n<\/td>\n

\n

EY<\/p>\n<\/td>\n

\n

SY<\/p>\n<\/td>\n

\n

FY<\/p>\n<\/td>\n

\n

ROE<\/p>\n<\/td>\n

\n

GM<\/p>\n<\/td>\n

\n

EYh<\/p>\n<\/td>\n

\n

SYh<\/p>\n<\/td>\n

\n

FYh<\/p>\n<\/td>\n

\n

ROEh<\/p>\n<\/td>\n

\n

GMh<\/p>\n<\/td>\n

\n

RetM<\/p>\n<\/td>\n

\n

RetY<\/p>\n<\/td>\n<\/tr>\n

\n

Chemicals<\/p>\n<\/td>\n

\n

2.22<\/p>\n<\/td>\n

\n

-9.63<\/p>\n<\/td>\n

\n

0.0431<\/p>\n<\/td>\n

\n

0.5449<\/p>\n<\/td>\n

\n

0.0173<\/p>\n<\/td>\n

\n

15.86<\/p>\n<\/td>\n

\n

39.14<\/p>\n<\/td>\n

\n

0.0431<\/p>\n<\/td>\n

\n

0.4487<\/p>\n<\/td>\n

\n

0.0203<\/p>\n<\/td>\n

\n

17.90<\/p>\n<\/td>\n

\n

42.46<\/p>\n<\/td>\n

\n

-3.40%<\/p>\n<\/td>\n

\n

-7.79%<\/p>\n<\/td>\n<\/tr>\n

\n

Constr. Materials<\/p>\n<\/td>\n

\n

36.23<\/p>\n<\/td>\n

\n

33.10<\/p>\n<\/td>\n

\n

0.0547<\/p>\n<\/td>\n

\n

0.7952<\/p>\n<\/td>\n

\n

0.0623<\/p>\n<\/td>\n

\n

19.75<\/p>\n<\/td>\n

\n

31.86<\/p>\n<\/td>\n

\n

0.0387<\/p>\n<\/td>\n

\n

0.8350<\/p>\n<\/td>\n

\n

0.0362<\/p>\n<\/td>\n

\n

12.52<\/p>\n<\/td>\n

\n

29.39<\/p>\n<\/td>\n

\n

-4.66%<\/p>\n<\/td>\n

\n

18.79%<\/p>\n<\/td>\n<\/tr>\n

\n

Packaging<\/p>\n<\/td>\n

\n

-1.22<\/p>\n<\/td>\n

\n

13.60<\/p>\n<\/td>\n

\n

0.0656<\/p>\n<\/td>\n

\n

1.2186<\/p>\n<\/td>\n

\n

0.0173<\/p>\n<\/td>\n

\n

21.92<\/p>\n<\/td>\n

\n

26.44<\/p>\n<\/td>\n

\n

0.0490<\/p>\n<\/td>\n

\n

1.0556<\/p>\n<\/td>\n

\n

0.0368<\/p>\n<\/td>\n

\n

18.03<\/p>\n<\/td>\n

\n

25.04<\/p>\n<\/td>\n

\n

-3.31%<\/p>\n<\/td>\n

\n

-8.99%<\/p>\n<\/td>\n<\/tr>\n

\n

Mining\/Metals<\/p>\n<\/td>\n

\n

-38.76<\/p>\n<\/td>\n

\n

1.54<\/p>\n<\/td>\n

\n

0.0369<\/p>\n<\/td>\n

\n

1.1174<\/p>\n<\/td>\n

\n

0.0007<\/p>\n<\/td>\n

\n

8.68<\/p>\n<\/td>\n

\n

23.35<\/p>\n<\/td>\n

\n

0.0427<\/p>\n<\/td>\n

\n

1.1835<\/p>\n<\/td>\n

\n

0.0243<\/p>\n<\/td>\n

\n

9.42<\/p>\n<\/td>\n

\n

21.06<\/p>\n<\/td>\n

\n

-4.61%<\/p>\n<\/td>\n

\n

1.06%<\/p>\n<\/td>\n<\/tr>\n<\/table>\n

<\/span><\/span>