VONV: An Outdated Idea Of Value (NASDAQ:VONV)

VONV Strategy And Portfolio

The Vanguard Russell 1000 Value ETF (NASDAQ:VONV) has been tracking the Russell 1000 Value Index since September 2010. It has 849 holdings, a distribution yield of 1.86% and a total expense ratio of 0.08%. It is a direct competitor of the iShares Russell 1000 Value ETF (IWD), which tracks the same underlying index and whose total expense ratio is 0.19%. IWD is an older fund: it was launched in May 2000. It is also more liquid and has a higher AUM (about $54B vs. $6.4B for VONV).

As described by FTSE Russell, the underlying index measures the performance of a large cap segment of the US stock market including companies with lower price-to-book ratios and lower expected growth. It represents about half of the total market value of the Russell 1000 Index.

VONV invests almost exclusively in U.S. companies (98.9% of asset value), mostly in the large cap segment (67.4%). As expected, VONV is cheaper than its parent index Russell 1000 (IWB) regarding the usual valuation ratios reported in the next table.

Source: Fidelity

VONV currently holds 849 stocks. The top 10 holdings represent 18.4% of the portfolio value. The next table lists their weights and valuation ratios. The heaviest position weights about 3%, so the risk related to any individual stock is quite low.

Ratios: Portfolio123

The top two sectors are financials (20.2%) and healthcare (17.0%). Compared to the Russell 1000, the fund underweights technology, communication, consumer discretionary and overweights all other sectors.

Since VONV inception (09/20/2010), VONV and IWD have almost identical annualized return (11.38% vs. 11.27%). The small difference is likely due to management fees, and possibly to minor tracking errors. I will use IWD to assess the underlying index on a longer period.

Since 05/22/2000, IWD is very close to the parent index Russell 1000 in performance and risk metrics.

Data calculated with Portfolio123

The next chart plots the equity values of $100 invested in IWD and IWB since IWD inception. The two funds have alternatively outperformed each other. The Russell 1000 Value Index (IWD/VONV) has been steadily lagging IWB from 2018 to 2021. However, it has outperformed in 2022 (-6.0% for VONV vs. -13.7% for IWB year-to-date).

Comparing VONV 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 VONV performance 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 Russell 1000 Value Index by 4.6 percentage points in annualized return. However, IWD price history is real and the model performance is hypothetical.

Price To Book: A Risky Concept Of Value

Why is the difference so large? The underlying index has two shortcomings in my opinion. The first and largest 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 for more details about 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. 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 explains my choice of using P/FCF and not P/B in the Dashboard List model.

Takeaway

VONV follows a rule-based strategy based on price/book ratio and various growth metrics to split the Russell 1000 index in Value and Growth subsets, and invests in the value side. The underlying index has failed to outperform its parent index in two decades. It is not a bad product: in fact it seems quite equivalent to the Russell 1000 index on the long-term. It may be useful in a tactical allocation strategy switching between value and growth depending on market conditions. I see two reasons why it doesn’t meet expectations of bringing added value to its parent index. First, it ranks stocks regardless of their sectors. Second, price/book is the riskiest of the usual value ratios. VONV has a 3-star rating at Morningstar, which is average. 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.