The decline of quant value explained - Jeremy Grantham

Super-investor Jeremy Grantham was recently interviewed on Pat O’Shaughnessy’s “Invest Like the Best” podcast.

He sums up the decline of traditional quant value in quite possibly the most succinct way I have heard or read. The fact that this is also coming from an investor who started investing before most investors today were even born gives him credit, in my books at least.

(Podcast episode and transcript here:
http://investorfieldguide.com/jeremy-grantham-an-uncertain-crisis-invest-like-the-best-ep-177/ )

Transcript Excerpt:

We were lucky when we came in, in that some [value] parameters that somewhat reflected a value had worked
pretty well as contrarian indicators for 80 years or so when we started 40, 50 years ago. And those were
things like price to book, PE, price to cash flow, price to sales. What I have long thought of as dopey
value. What they are really is just expressions of the market’s disgust and the cheapest price to book
are really the assets, which dollar for dollar, the market thinks are the least useful. And the lowest PE
are the earnings the market least believes will be sustainable and the highest yield are the most likely to
be cut or not sustained.
Jeremy Grantham:
There’s no reason why those things should work and indeed for 20 years now, they haven’t been
working. But in the old days they worked because the market loved comfort so much that it was
constantly overpaying a little bit for the Proctor and Gambles and underpaying for those nasty cyclicals
that kept getting excess production and getting crushed. And we came in, my first firm, I started in 1969
and we applied those standard Graham and Dodd techniques. And they worked beautifully. Life was
simple. They didn’t work every year. And you occasionally had a string, a painful string, of two, three or
even four years where growth stocks were trash and upset your clients.

But they came back, made up for the lost ground. And so if they gave up four points one year, they
would make it back and deliver the usual four points a year the following year. And so life was easy. And
I think the general caliber of competition back in those days was very weak. And therefore, if you did
decent analysis and looked for value, you could find it.

Jeremy Grantham:
So we were able to build simple mechanistic models, giving points for cheap book and so on and have a
win on a very broad basis. So we could manage a lot of money. And we were winning two out of three
years and adding a few points on average a year. And that era perhaps started to end around 2000 and
too many machines were picking it up; too many quants, too much money.

Jeremy Grantham:
And pretty soon the historical aversion to cheap stocks had disappeared because they acquired the
reputation for having won. The quants made it clear. They understood that for 80, 100 years into the
midst of time, these were factors that worked. And indeed academics wrote it up and got a lot of credit
for it, such as simple minded idea.
Jeremy Grantham:
And anyway, that was the past. And those early pickings have gone leaving, as I said, old fashioned labor
intensive, stock by stock analysis; not only labor intensive, but risk intensive, because you have to bet
that the things will change and you have to bet that the market is wrong. And that gives a lot of people,
not surprisingly a lot of trouble.

I just want to say thanks, Ryan. I found the interview very interesting indeed.

Glad you enjoyed it Yuval, as did I.

I enjoyed the podcast. I think I’m a little contrarian on this explanation of why value stopped working. It’s like the Yogi Berra saying “Nobody goes to that restaurant anymore because it’s too crowded”. I’m more in line with Clifford Assess skepticism of this argument (he was also recently on Tobias Carlisle’s podcast). We’ve see what it looks like when a factor gets too crowded and arbitraged away because it happened to the accrual anomaly … everyone crowds into it, it shoots straight up for a while and then steadily decays over time. That’s not what happened to traditional value metrics. No one is crowding into them, in fact they’re completely unloved. I tend to think it can be explained more in the Robert Shiller’s Narrative Economics line of thought … value isn’t working because the story on value is it doesn’t work anymore and therefore no one wants to own it. Eventually the narrative with change, maybe when we finally get a rising interest rate environment, but it might be a long time yet. In the meantime, I’m not going to beat my head against the wall arguing against what the market is telling me.

All good points and good ideas.

But I believe most people are saying that value stopped working as well as it did. Or at worst it stopped working completely. Saying that–like the image below before 2018–it didn’t much matter whether you put money into the SPY benchmark or into small-cap value (SLYV).

I think they are saying any advantage to value was arbitraged away. I certainly do not disagree with this.

But is there still a need to explain why the factors are inverted and in fact are harmful since 2018?

I agree that many can do better than SLYV. I agree that people here can do better than SLYV with their own value factors.

But still, why does SLYV do about as well as SPY until 2018 and then begin to dramatically underperform? Is this separate from or in-addition-to the above discussion?

Does this need a separate explanation?

Also would the CAPM say we should be rewarded for risk now? Isn’t the spread between high-yield corporate bonds and government bonds up? This spread has been an explanation for outperformance of small-caps by academics (reward for increased risk). Looks like FAMA and French are wrong about both size and value now too.

I’m just not sure that I know what has been going on since 2018. While I think the above explanations are correct, I am not sure I have heard a complete explanation yet. I wish I knew if the more recent change was cyclical.

Best,

Jim


It’s not just VALUE that does not work. Factors are just a gimmick dreamed up by the fund providers to sell their ETFs to the unsuspecting public. Check the Vanguard factor ETFs for proof of poor performance.

The most idiotic offerings are the multi-factor funds - why not just buy a fund tracking one of the major indexes if one wants more factors to invest in.

I can assure everyone that quantitative analysis is alive and well! You simply have to look at Inspector Sector’s Cloud Computing to come to that understanding. You have to run with “what works”. I have been criticized for making this statement before but it is now clear. What is working is NOT traditional value metrics, but other factors including revenue growth, smoothness of revenue growth, sales surprises, etc.

My guess is that if you want to understand what is going on then you would have to go back a hundred years to the last industrial revolution (assembly line) and see how growth stocks performed in that environment. That is where we are at now with digital transformation.

There are a number of reasons why traditional value metrics may not be effective. The first being that earnings is a comfort metric that investors had keyed in on. The truth is that Amazon has demonstrated that earnings are essentially a meaningless construct. Companies that declare earnings pay taxes, so why do that? Why not reinvest back into the company instead? Earnings may impress investors but what do investors really get out of it (earnings)?

One thing that I recently discovered is that Stock-Based Compensation increases the common equity which (I believe) results in higher book value. Another thing that I recently discovered is that deferred revenue for X-as-a-Service companies is often considered either current or long-term liability. It tends to show up as debt on the balance sheet. So these issues probably render the P/B ratio somewhat meaningless, at least for s/w companies.

P/S ratio is also not meaningful, especially when dealing with very high-growth stocks that are expanding into greenfields. You can throw P/S out the window as well.

In any case, the “state of the art” valuation is discounted cash flow valuation which superficially “works” but unfortunately every analyst on the planet misuses it. FOr example, what is the terminal growth of Apple? This company has been around since the late '70s and if one applied the standard analyst formula of estimated GDP growth, Apple would be growing at 2% per year, which clearly isn’t the case.

This brings me back to quantitative analysis. Correctly implemented, you extract the factors that are working in the present markets and run with them. How you determine what is working is the black art that you have to master.

Steve

Should note also that Cliff Asness says AQR’s proprietary value based metrics haven’t been performing well for 2 years. And even Renaissance Technology’s Institutional Alpha Fund (note, not the Medallion Fund) is down -21% for the year. Just glancing at their F13 they’re holding a lot of big pharmaceuticals like Bristol Myers Squiq and Novo-Nordisk which are obvious value tilts. So the struggles of value are not just confined to the obvious price based metrics that any Joe Schmoe can pull up in their Yahoo Screener.

These are good insights, as usual, thanks Steve.

A question though… if we are in the middle of a new industrial revolution, wouldn’t this show up in all global markets? Instead the growth seems to be mostly rewarded in the very developed world, and the US in particular. Seems to be a mixture of techology and loose monetary policy. It will be interesting to see if emerging markets experience a wake up when they start developing monetary policies that resemble developed markets (or if US growth declines once it no longer has that relative edge over the rest of the world).

https://www.bloomberg.com/news/articles/2020-04-29/copying-rich-world-s-virus-plan-is-big-risk-for-emerging-markets

Or this could trigger actual inflation, which would buoy commodity-rich Emerging market value and impair developed market growth

Interesting times

I’m not sure I understand the question. The beneficiaries are s/w companies, regardless of where they operate. Consider MELI and BABA, certainly not in the USA. The pandemic has dramatically accelerated the shift to the cloud. It isn’t loose monetary policy although that doesn’t hurt.

Inflation will not be a factor so long as baby boomers are retiring and interest rates remain low. i.e. people won’t spend if they are not earning substantial interest on their savings.

Steve

Actually, Steve nailed it. Don’t go ga-ga over quotes from gurus (something I see often in the forums, over passages and articles that range from mediocre to absurd to obsolete). In substance Steve’s points are far superior to those of the guru quoted at the start of this thread. If you’re not looking at and understanding what is and isn’t working . . . you’re DOA no matter how beautiful a set of backtested equity curves you can produce.

Around the time I ceased to be a p123 insider, I recall having posted and discussed at a webinar the areas that were working and anyone who took those remarks seriously and factored them into their modeling should have been doing very well. (I haven’t looked at the numbers for Steve’s Cloud Computing DM but just knowing he was hitting that theme is all I need to tell me it has to be looking sweet.)

P123 has Business taxonomy commands. Don’t be afraid to use them. You can’t do all math all the time. And interestingly, from what I can see, the FactSet taxonomy in areas that are working, and likely to continue working for a while, are quite strong . . . easily strong enough to blow away the impact of whatever other factor availabilities or PIT concerns some might have.

As to value, the factor sometimes works and sometimes doesn’t. But value “investing” always works. Big difference. A first-grader in the top 98% of his class can tell which P/E. P/S. etc. ratios are lower than others. That HAS TO tell you there’s more to value investing than just knowing how to count. (The quant gurus y’all love to quote never did get past a first-grader level of dealing with value, which is why they’re fu**ing up now and scrambling to try to understand why. For more, see Value “Investing” Always Works Even When The Value “Factor” Falters – Acti-quant which, actually, is an expansion of material I already handed you on a silver platter in the On-Line Strategy Course. I had a mini-Twitter debate with Cliff Asness on this but he politely – which is way out of character for him – ran off when it became apparent that we were, as he put it, speaking different languages.)

Steve,

Isn’t the success of your Cloud Computing model to a significant extent a sector bet that has worked? I’m not sure what quant-related conclusions I can draw from it.

Roger

To determine what is working now, is it possible to determine factors with the strongest momentum in the previous 6 or 12 months and then use the strongest 2 or 3 factors for a simulation?

Certainly, the model benefits significantly from the sector. But the point is that the sector has characteristics that are independent of other sectors such as energy, materials, or healthcare for example. You wouldn’t apply general-purpose value metrics to cloud computing, would you?

There are many different approaches to determine “what is working”. I choose factors that have performed best over the last 5 years and combine into an optimal RS. I use the RS for the next year. Rinse and repeat.

Personally, I wouldn’t use less than 12 months history, but in any case I suggest a 4:1 or 5:1 ratio, [In-sample]:[Out-of-sample]. So if you are using 12 months optimization backseat, then don’t run your model for more than 3 months before you re-optimize.

Steve

Further to this discussion. The latest RS for Inspector Sector’s Cloud Computing was developed back in November using the previous data vendor. So it has more than 6 months OOS under its belt.

I have just run the optimizer, swapping in different RS’s with a 6-month backtest. Obviously this testing is done with FactSet instead of S&P. I have also turned off preliminary data. There will be differences between what the Design Model actually achieved and what the backtest achieves due to the differences in data vendor and data fixes.

With this in mind, the original RS developed in November with different data vendors still comes in 2nd for %Return over the last 6 months. out of 15 RS’s. See attached.

The point is that the optimization process did a pretty good job. While it would have been nice if it had come out the best in the latest 6-month period, I don’t consider that to be realistic. There will always be some degradation over time with changing markets. The fact that the RS remains near the top speaks for the process used.

Will I change to “Core: Sentiment”??? I’m not sure yet. I don’t have a good feel for how reliable the backtest results will be, especially with sentiment data. Any thoughts would be appreciated.

Steve


jsk,

I am not sure I have a good answer to your question. But you may find this article interesting: Factor Momentum and the Momentum Factor

Best,

Jim

This is why we need a proper walk forward optimizer, imo.

I kind of simplified things by declaring victory based on OOS %return. In practice, I optimize based on the monochronic increase in RS buckets. I’m not sure that this would be automatable in real life.

Steve,

Your way is a fine way to do it. Indeed, I just funded a system and I optimized it in a way that cannot be called significantly different from what you describe. Not that my system is any good but I agree with the method.

But walk-forward, the way Korr 123 describes it, really belongs in a different category than my P123 model and perhaps yours too.

It is a way to at least try to adapt to a changing market regime. It can adjust to changes in the market. It is not optimized for the full 20 years at P123. That is not to say that I think it always works. Indeed, I would not argue if someone said it is difficult to make it work.

He means to use it in a dynamic way and changing way. Like looking back 3 months, plug that data into a regression, neural net or whatever and predict the next week’s returns.

Next week look back 3 months again (advanced 1 week from the last time you did it) and predict the following week.

In other words walk it forward each time.

Actually, the majority of my money uses such a system (from another site).

That does not stop me from using a system optimized in a similar way to what you describe here at P123 (I do).

But I do not think the two can be compared. Not apples to oranges but rather monkeys to clouds. Not one better than the other, necessarily. Just different tools for different situations.

And Korr is right that it can work, imo.

Here is a link to a McFarber paper as an example of a simple walk-forward model: Relative Strength Strategies for Investing

Many have read this paper and can form a quick judgement (or may already have an opinion from this paper) of walk-forward methods on their own.

Best,

Jim