VIOV: An ETF With Little Added Value (NYSEARCA:VIOV)

VIOV strategy and portfolio

The Vanguard S&P Small-Cap 600 Value ETF (NYSEARCA:VIOV) has been tracking the S&P SmallCap 600 Value Index since September 2010. It holds 462 stocks, has a SEC Yield of 1.54%, and the total expense ratio is 0.15%. It is a direct competitor of the SPDR S&P 600 SmallCap Value ETF (SLYV) and the iShares S&P SmallCap 600 Value ETF (IJS), which track the same underlying index and have similar expense ratios. SLYV and IJS are older and have more assets under management.

As described in the prospectus by S&P Dow Jones Indices, S&P 600 constituents are ranked in Value and Growth styles using three valuation ratios and three growth metrics. The valuation ratios are book value to price, earnings to price and sales to price. By construction, 33% of the parent index constituents exclusively belongs to each style, and 34% belongs to both styles. The Value subset serves as S&P 600 Value Index and is rebalanced annually. It is capital-weighted, with an adjustment for constituents belonging to both styles. For example, a company with a value rank better than its growth rank is given a larger weight in the Value Index than in the Growth Index.

As expected, aggregate valuation ratios are cheaper than for the iShares Core S&P Small-Cap ETF (IJR), which tracks the S&P SmallCap 600 index. However, the difference is not so large:

Source: Fidelity

VIOV currently holds 462 stocks. The top 10 holdings represent less than 7% of the portfolio value, and the largest one weighs less than 1%, so the risk related to any individual stock is very low. The next table reports their valuation ratios.

Ratios: Portfolio123

The heaviest sectors are financials (20.3% of asset value) and industrials (18.3%). Other sectors are below 12%. Compared to the small-cap benchmark, VIOV significantly overweights financials, industrials and materials. It underweights technology and healthcare.

Since VIOV inception (09/07/2010), VIOV and IJS have similar annualized return (11.98% vs 11.86%). I will use IJS to assess the underlying index on a longer period since 07/24/2000. IJS is very close to the parent index S&P SmallCap 600 in performance and risk metrics.

Data calculated with Portfolio123

However, VIOV has outperformed by 3.7 percentage points in the last 12 months (-10.8% vs -13.5% for IJR).

Comparison with my Dashboard List model

The Dashboard List is a list of 80 stocks in the S&P 1500 index, 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. 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, communication 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 VIOV underlying index since inception with the Dashboard List model, with a tweak: the list is reconstituted annually instead of once a month to make it comparable to a passive index.

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

The Dashboard List outperforms the S&P 600 Value Index by 2.5 percentage points in annualized return and has slightly better risk metrics (drawdown and volatility). However, IJS price history is real and the model performance is hypothetical.

Price to Book: a risky concept of value

I like the idea of mixing various ratios to rank value stocks like VIOV does. However, the underlying index has two shortcomings in my opinion. The first one is to classify all stocks using the same criteria. It means the valuation ratios are considered comparable across sectors. Obviously they are not: you can read my monthly dashboard here for more details about this topic. A consequence is to privilege sectors where valuation ratios are naturally cheaper, especially financials. Sectors with large intangible assets like technology are disadvantaged. To make things simple, companies with large intangible assets are those with a business model based on massive 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 shortcoming comes from the price/book ratio (P/B), which adds some risk in the strategy. Speaking probabilities, 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 also explains my choice of not using P/B in my Dashboard List model (more info at the end of this post).

Takeaway

VIOV follows a systematic process based on various value and growth metrics to classify S&P 600 stocks in value and growth categories, and invests in the value subset. VIOV has the same underlying index as IJS and SLYV. These three funds are similar for long-term investment. IJS is a better trading instrument thanks to a higher liquidity: the bid-ask spread and the risk of slippage are smaller. Historical return and risk metrics of VIOV index are very close to those of the S&P SmallCap 600.

VIOV is not a bad product: it may be part of a tactical allocation strategy switching between value and growth depending on market conditions. However, it doesn’t meet expectations of bringing added value to its parent index. In my opinion, there are two flaws in its strategy: it ranks stocks regardless of their sectors, and using price/book as a value factor increases the risk of catching value traps. An efficient value model should compare stocks in comparable sets (sector, industry), like I do in the Dashboard List since 2015. This model also uses three valuation metrics, excluding price/book. Moreover, a simple ROE rule helps filter out some value traps and normalize the number of components.