Every week, a low volatility researcher has the same epiphany:

A neat articulation of this view is from Feifei Li of Research Affiliates, who first shows that lots of people are investing in low volatility (there's another such piece here by Dangle and Kashofer from Vienna UofT). Clearly growth in low volatility is rising exponentially, and our intuition senses a Malthusian endgame that will be nasty and brutish.

That might seem scary, but to put it in perspective, there's now $80B in

To put these into perspective, the relative difference in the book-to-price ratio moving from 0.3 to 0.6 is about moving from the 15th percentile to the 45th percentile. Li suggests adding a valuation criteria to low volatility to counteract this value-creep. The basic idea is, say the book/market ratio has a linear relation with expected return, where a higher book/market is associated with a higher return. So if we take the universe of a set of low vol stocks, say the constituents of the ETF SPLV, which looks a the 100 least volatile stocks of the past year, and then take those stocks with the highest book/market ratios within that set, we simultaneously capture more of the value effect

There are two problems with this approach. First, the return to the book/market is not linear. Therefore, merely moving your average book/market ratio may may you feel better, but unless you pick the right stocks, you won't change much. Here's the average return by book/market deciles, for those stocks above the 20th percentile of the NYSE (all data here are from Ken French's excellent website, I use the 20th percentile cut-off because stocks below that aren't really investable in scale anyway, so potentially misleading).

Now, these are average monthly return premium above the market average. If we are looking at geometric returns, that sharp increase for the top decile isn't there, but forget that for now (I think the geometric average is more relevant given that in practice people don't rebalance monthly, but to each his own). The key is, this relationship over the investable universe is basically all happening at the end-deciles, not in between. Thus, average book/market decile can be misleading, because not much happens between the 30th and 90th percentiles.

Curiously, market cap is not allocated evenly across all ten book/market deciles because the cutoffs for the size and book/market sorts are constructed once a year using the NYSE. For example, currently, there's 3 times as much market cap in book/market decile 1 than book/market decile 10.

Here's the market-cap-weighted average book/market decile over time (in blue). I'm just calculating a number generated by French's data here, all the work is in this Excel spreadsheet (there's nothing proprietary going on here). So here's that average number calculated each month, and the total return on French's value factor (aka, HML, or High-Minus-Low factor portfolio proxy).

Clearly the low average decile corresponds to big increases in the HML factor returns. If I take that time series, and put the data into deciles, I get a pretty clear pattern for future HML returns:

Basically, the value (ie, HML) factor only pays off when the average book/market decile is in the bottom third of its distribution. Alas, we're not there, we are around the 70th percentile right now. So, here's the average return for the value factor, for that 50% of the time when the average book/market decile is above average (ie, now):

That line is sloping the

In sum, loading up on the value factor to improve low volatility is dangerous because 1) the relation between book/market and returns not linear, so simple portfolio averages can be misleading and 2) the value premium can be predictably predictable given the distribution of the market across book/market deciles.

In practice, the value premium to passive indices seems about 1-2% since it was popularized around 1990. The 2.8% HML premium from 1928-2013 is due a lot to

*tilt low volatility towards value.*This addresses two pressing issues simultaneously: avoiding overbought securities and adding value alpha.A neat articulation of this view is from Feifei Li of Research Affiliates, who first shows that lots of people are investing in low volatility (there's another such piece here by Dangle and Kashofer from Vienna UofT). Clearly growth in low volatility is rising exponentially, and our intuition senses a Malthusian endgame that will be nasty and brutish.

That might seem scary, but to put it in perspective, there's now $80B in

*value ETFs*alone, so this isn't anywhere close to value and size. Next, she shows some valuation metrics. Three different types of low vol portfolios are seemingly higher priced using two different value metrics, book/market and earnings yield. That is, low vol portfolios over the past 10 years used to have higher earnings yields than the market, and higher book/market ratios; now it's the reverse.To put these into perspective, the relative difference in the book-to-price ratio moving from 0.3 to 0.6 is about moving from the 15th percentile to the 45th percentile. Li suggests adding a valuation criteria to low volatility to counteract this value-creep. The basic idea is, say the book/market ratio has a linear relation with expected return, where a higher book/market is associated with a higher return. So if we take the universe of a set of low vol stocks, say the constituents of the ETF SPLV, which looks a the 100 least volatile stocks of the past year, and then take those stocks with the highest book/market ratios within that set, we simultaneously capture more of the value effect

**and**avoid overbought stocks. That seems like a win-win improvement.There are two problems with this approach. First, the return to the book/market is not linear. Therefore, merely moving your average book/market ratio may may you feel better, but unless you pick the right stocks, you won't change much. Here's the average return by book/market deciles, for those stocks above the 20th percentile of the NYSE (all data here are from Ken French's excellent website, I use the 20th percentile cut-off because stocks below that aren't really investable in scale anyway, so potentially misleading).

Now, these are average monthly return premium above the market average. If we are looking at geometric returns, that sharp increase for the top decile isn't there, but forget that for now (I think the geometric average is more relevant given that in practice people don't rebalance monthly, but to each his own). The key is, this relationship over the investable universe is basically all happening at the end-deciles, not in between. Thus, average book/market decile can be misleading, because not much happens between the 30th and 90th percentiles.

Curiously, market cap is not allocated evenly across all ten book/market deciles because the cutoffs for the size and book/market sorts are constructed once a year using the NYSE. For example, currently, there's 3 times as much market cap in book/market decile 1 than book/market decile 10.

Here's the market-cap-weighted average book/market decile over time (in blue). I'm just calculating a number generated by French's data here, all the work is in this Excel spreadsheet (there's nothing proprietary going on here). So here's that average number calculated each month, and the total return on French's value factor (aka, HML, or High-Minus-Low factor portfolio proxy).

Clearly the low average decile corresponds to big increases in the HML factor returns. If I take that time series, and put the data into deciles, I get a pretty clear pattern for future HML returns:

Basically, the value (ie, HML) factor only pays off when the average book/market decile is in the bottom third of its distribution. Alas, we're not there, we are around the 70th percentile right now. So, here's the average return for the value factor, for that 50% of the time when the average book/market decile is above average (ie, now):

That line is sloping the

*wrong way*if you are banking on a value premium.In sum, loading up on the value factor to improve low volatility is dangerous because 1) the relation between book/market and returns not linear, so simple portfolio averages can be misleading and 2) the value premium can be predictably predictable given the distribution of the market across book/market deciles.

In practice, the value premium to passive indices seems about 1-2% since it was popularized around 1990. The 2.8% HML premium from 1928-2013 is due a lot to

*shorting*low book/market stocks, a premium with dubious feasibility, so this number is not a good rule of thumb for the value of tilting towards value. Value ETFs like IWD arose fortuitously around 2000, and so their 3% annual outperformance is all from the bursting of the internet bubble--if those value ETFs went back to 1990, the return premium would be less. I would estimate there's 100 basis points in the value factor, yet, that's by itself. When you try to use value to add to other strategies, it's not obviously beneficial, and most low vol practitioners are doing this, so you really aren't thinking outside the box.
## 2 comments:

The size of value index funds is always close to the size of growth index funds. Indexers don't tilt to value (or to growth).

Excess demand for passive investing strategies may also lead to lower returns quoth TRB:

http://www.thereformedbroker.com/2013/08/11/memo-to-the-passive-investing-taliban/

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