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Factor Momentum vs Factor Valuation

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I am not a fan of most equity factors, but if any equity factor exists, it is the value factor. Graham and Dodd, Warren Buffet, Fama and French have all highlighted value as an investment strategy. Its essence is the ratio of a backward-looking accounting value vs. a forward-looking discounting of future dividends. As we are not venture capitalists, but rather, stock investors, all future projections are based on current accounting information. To the extent that a market is delusional, as in the 1999 tech bubble, that should show up as an excess deviation from the accounting or current valuation metric (eg, earnings, book value).  If there’s any firm characteristic that should capture some of the behavioral bias trends among investors, this is it. 

Alternatively, there’s the risk story. Many value companies are just down on their luck, like Apple in the 1990s, and people project recent troubles too far into the future. Thus, current accounting valuations are low, but these are anomalous and should be treated as such. Alas, most value companies are not doing poorly, they just do not offer any possibility of a 10-fold return, like Tesla or Amazon. Greedy, short-sighted investors love these stocks, and just as they buy lottery tickets with an explicit 50% premium to fair value, they are willing to pay for hope.

There are several value metrics and all tell a similar story now. As an aside, note that it’s useful to turn all your value metrics into ratios where higher means cheaper: B/M, E/P, CashFlow/Price, Operating Earnings/Book Equity. This helps your intuition as you sift through them. Secondly, E/P is better than P/E because E can go through zero into negative numbers, creating a bizarre non-monotonicity between you metric and your concept; in contrast if P goes to zero predicting its future performance is irrelevant.

If you rank all stocks by their B/M, take the average B/M of the top and bottom 30%, and put them in a ratio, you get a sense of how cheap value stocks are: (B/M, 80th percentile)/(B/M, 20th percentile). Historically all value ratios are trend stationary. Given B/M ratios generally move mainly via their market cap and not book value or earnings, this means that value stock performance is forecastable. A high ratio of B/M for the top value stocks over the bottom value stocks implies good times for value stocks, as the M of the value stocks increases relative to the M of the anti-value stocks (eg, growth). All of these value metrics are near historical highs over the past 70 years (see AlphaArchitech’s charts here).

This is pretty compelling, so much so that last November Cliff Asness at AQR decided to double down on their traditional value tilt. While there are dozens of value metrics today that scream ‘buy value now’, we have the Ur-metric–Book/Market–going back to 1927 in the US. This suggests we are not anywhere close to a top, which was much higher for most of the 1930s when value did relatively well on a beta-adjusted basis.

It’s easy to come up with a story as to why the 1930s are not relevant today, but that is throwing out one-tenth of your data just because it disagrees with you.

Yet there’s another way to time factors, momentum, whereby a factor’s relative performance tends to persist for a couple months at least, and perhaps a year. Momentum refers to relative outperformance as opposed to absolute performance, which is referred to as ‘trend following.’ Trend following works as well but applies to asset classes like stocks, bonds, and gold, while momentum refers to stocks, industries, or factors.

Year to date, iShares’ growth ETF (IWO) outperformed its value ETF (IWN) by 12%. For the past 10 years, growth has outperformed value by 100%. While iShare’s growth ETF has a slightly higher beta (1.27 vs 1.05), that does not explain more than 20% of this.  Regardless of your momentum definition–3 months, 12 months–value is not a buy based on its momentum, which is currently negative, and has been for over a decade in the US.

In 2019, AQR’s Tarun Gupta and Bryan Kelly authored ‘Factor Momentum Everywhere in the Journal of Portfolio Management. They noted that ‘persistence in factor returns is strange and ubiquitous.’ Incredibly, they found that performance persisted using 1 to 60 months of past returns. I was happy to assume factor momentum exists, but usually saw evidence at the 6 month and below horizon (eg, see Arnott et al). If they found it at 60 months, my Spidey sense tingled, maybe this is an artifact of a kitchen-sink regression where 121 Barra factors are thrown in, generating persistence in alpha? That hypothesis would take a lot of work, but at the very least I should see if value factor momentum is clear.

I created several value proxy portfolios using Ken French’s factor data:

  • HML Fama-French’s value proxy, long High B/M short Low B/M (1927-2020)
  • B/M book to market (1927-2020)
  • CF/P cashflow to price (1951-2020)
  • E/P earnings to price (1951-2020)
  • OP operating profits to book equity (1963-2020)

I applied a rolling regression against the past 36 months of market returns to remove the market beta. As the HML portfolio’s beta has gone from significantly positive to negative and back to slightly positive over time, it’s useful to make this metric beta-neutral to avoid seeing market fluctuations show up as value fluctuations. Unlike the Barra factors, removing the market factor is not prone to overfitting, and captures something most sophisticated investors do not just understand but actually use.

The non-zero beta is just one reason to hate on the HML factor. Another is that, as it contains a short position, it can be of little interest if the short position is driving the results, because for most factor investors–who have long horizons–short portfolio’s are not in your opportunity set. Most ‘bad’ stocks, following the low-vol phenomenon, are not just bad longs, but also bad shorts: returns are low, not negative, and volatility is very high. Shorting equity factors is generally a bad idea, and thus an irrelevant comparison because you should not be tempted to short these things.

The result is a set of 5 beta-neutral value proxy portfolios. I then ranked them by their past returns and looked at the subsequent returns. These returns are all relative, cross-sectional, because value-weighted, beta-adjusted returns across groupings net to zero each month by definition. By removing the market (ie, CAPM) beta, we can see the relative performance, which is the essence of momentum as applied to stocks (as defined by the seminal Jagdeesh and Titman paper).

The 12-month results were inconsistent with momentum in the value factor.

Using 6-months, momentum becomes more apparent (6M but returns annualized).

With the 1-month momentum, factor momentum is clear: past winners continue (returns are annualized).

I’m rather surprised not to find momentum at 12 months, given it shows up at that horizon in the trend-following literature, and would like to understand how Gupta and Kelly found it at 60 months. Nonetheless, it does seem factor momentum at shorter horizons is real.

If we exclude the US 1930s, valuations of value are at an extreme, if we include them they are not. Meanwhile, over the next several months, value’s past performance suggests continuation of the trend.  Given the big moves in value tend to last for over a year (eg, the run-up and run-down in the 2000 technology bubble), it seems prudent to accept missing out on the first quarter of this regime change and wait until value starts outperforming the market before doubling down.


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