SPYV: An Unperfect Model Of Value (NYSEARCA:SPYV)

SPYV strategy and portfolio

The SPDR Portfolio S&P 500 Value ETF (SPYV) has been tracking the S&P 500 Value Index since 9/25/2000. It has 450 holdings, a distribution yield of 2.17% and an expense ratio of 0.04% (cheaper than SPY).

As described on the sponsor’s website,

The Index consists of those stocks in the S&P 500 Index exhibiting the strongest value characteristics based on: (i) book value to price ratio; (ii) earnings to price ratio; and (iii) sales to price ratio.

It is weighted by market capitalization and rebalanced annually.

SPYV invests almost exclusively in U.S. based companies (98.7% of asset value). Large companies represent 77.6% of the portfolio and mid-caps 22.2%.

As expected, SPYV is significantly cheaper than the S&P 500 (SPY) regarding usual valuation ratios, reported in the table below.

The top 10 holdings, listed in the next table with valuation ratios, represent 18% of asset value. The heaviest one weighs 3.25%, so the risk related to individual stocks is low.

The top 2 sectors, financials and healthcare, are almost in equal weight with about 16% of asset value. They are followed by industrials (12.7%), technology (11.6%) and consumer staples (10.9%). Other sectors are below 7%. Compared with SPY, SPYV underweights technology, communication services, consumer discretionary and overweights other sectors. This results in a better balance across all sectors.

Performance

Since inception (9/25/2000), return and risk metrics of SPYV and SPY are similar. SPYV has underperformed by a short margin of about 30 bps in annualized total return.

Data calculated with Portfolio123

The next chart plots the equity value of $100 invested in SPYV and SPY since SPYV inception.

SPYV has alternatively outperformed the broad index (2000-2007, 2009-2015), and underperformed it (2007-2008, since 2015).

Comparing SPYV with my Dashboard List model

The Dashboard List is a list of 80 stocks in the S&P 1500 index (occasionally less), updated every month based on a simple quantitative methodology. All stocks in the Dashboard List are cheaper than their respective industry median in Price/Earnings, Price/Sales and Price/Free Cash Flow. An exception in Utilities: the Price/Free Cash Flow is not taken into account to avoid some inconsistencies. After this filter, the 10 companies with the highest Return on Equity in every sector are kept in the list. Some sectors are grouped together: Energy with Materials, Telecom with Technology. Real Estate is excluded because these valuation metrics don’t work well in this sector. I have been updating the Dashboard List every month on Seeking Alpha since December 2015, first in free-access articles, then in Quantitative Risk & Value.

The next table compares SPYV performance since inception with the Dashboard List model, with a tweak: here the Dashboard List is reconstituted only once a year to make it comparable with a passive index.

Past performance is not a guarantee of future returns. Data Source: Portfolio123

The Dashboard List beats SPYV by a wide margin in return and risk-adjusted performance (Sharpe ratio). The ETF performance is real, whereas the model performance is hypothetical.

SPYV fundamental shortcomings

The two models are similar in complexity, so why is the difference so big? SPYV’s strategy has two shortcomings in my opinion. The first one is to classify all stocks on the same criteria. It means the valuation ratios are considered comparable across sectors. Obviously, they are not: you can read my monthly dashboard here to go a bit deeper into this topic. A consequence is to privilege sectors where valuation ratios are naturally cheaper, especially financials. Some other sectors are disadvantaged: those with large intangible assets like technology, consumer discretionary and communication. To make things simple, companies with large intangible assets are those with a business model based driven by R&D, or a strong branding, or large user databases, or operating in a field where competition is limited by an expensive entry ticket. All these elements are not correctly reflected by valuation ratios.

The second flaw is using the price/book ratio (P/B), which amplifies the intangible asset issue, and also adds some risk in the strategy. Intuitively, we can guess a large group of companies with low P/B contains a higher percentage of value traps than a same-size group with low price/earnings, price/sales or price/free cash flow. Statistically, such a group will also have a higher volatility and deeper drawdowns in price. The next table shows the return and risk metrics of the cheapest quarter of the S&P 500 (i.e.125 stocks) measured in price/book, price/earnings, price/sales and price/free cash flow. The sets are reconstituted annually between 1/1/1999 and 1/1/2022 with elements in equal weight.

Data calculated with Portfolio123

This explains my choice of using P/FCF and not P/B in the Dashboard List model.

Takeaway

SPYV follows a systematic strategy based on a ranking system using three valuation metrics. It is more balanced than SPY across sectors and has cheaper management fees, but it has slightly underperformed the benchmark since its inception two decades ago. The reason it failed in bringing added value to its parent index is in two little flaws in its underlying index: it ranks stocks regardless of their sectors, and one of the three metrics is the riskiest of value ratios. An efficient value model should compare stocks in comparable sets (sector, industry), like I have been doing in the Dashboard List since 2015. My model also uses three valuation metrics, but prefers price/free cash flow to price/book. Moreover, a basic profitability rule helps filter out some value traps and normalize the number of components.