Screening for the top few percent

Stocks A to Z
#6

Greenblatt’s funds performance, e.g. value fund, aren’t eye-popping either
https://www.gothamfunds.com/default.aspx#

Philosophical question: what defines a ‘quant method’?
Buffet’s early returns were certainly great.
But his later returns, as the fund grew larger were less great, generating much debate on the BRK board of whether it beat the S&P (a start/end point dependent question).
Is WEB using a ‘quant method’?
He’s certainly thinking, i.e. presumably he’s not throwing darts at a dartboard.
But we apparently don’t know, and perhaps he doesn’t know, how to reduce his thoughts to a relatively simple algorithm (I qualified that sentence because presumably one could cook up something complicated that matched his returns year for year, but odds are it wouldn’t extrapolate)

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#7

Philosophical question: what defines a ‘quant method’?

Doesn’t need much philosophical debate.
A quant investment method is one that picks securities deterministically using only available data, with no human gut feel assessment or filtering.

Ironically it doesn’t remove the human judgment, it generally just displaces it.
All the work that would have gone into assessing the quality of a security for investment is moved to assessing the quality of the model to be used.
The work you might put into assessing a stock for 10% of your portfolio might go into assessing a model to put 1/2% of your portfolio into each of 20 stocks it picks.
In some ways assessing a model is much harder. The past lies.

Jim

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#8

A quant investment method is one that picks securities deterministically using only available data, with no human gut feel assessment or filtering.

So far has quant investment as a group outperformed the market index?

#9

So far has quant investment as a group outperformed the market index?

It’ s like asking whether individual stock selection has beat the index.
Whose?
There are thousands of different quant metrics and strategies, and practitioners.
As with individual stock selection, many don’t work, some do.

We can say that some quant strategies have worked very well for a very long time. And you don’t have to be Mr Simons.
A random example:
One fellow on the MI board has been following pretty much exactly the same quant strategy for over 22 years.
He runs that strategy in a separate account, making the results easy to track.
The account has returned 16.7%/year compounded in that time, as of late last year. The S&P 500 returned about 7.5%/year in the same stretch.
The returns on the individual positions for this particular strategy are wildly variable, so he targets 2/3 cash.
The 16.7% return is on the account as a whole, counting the cash drag. The balance in the account is up by about a factor of 30.
He trades once a month, generally replacing four of the 12 existing positions with four new ones, each position held three months.
Other than (I believe) one stretch during the credit crunch that he eased towards cash by not
opening new positions for a while, I think he has done pretty much the exact same thing the whole time.

One advantage of quant techniques is that two people doing the same approach in the same time frame will get the same returns.
It’s not like you’ll match Mr Buffett’s stock picking results by using the same strategies he does.
If you wanted to do what this quant fellow has been doing, you could.

Jim

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#10

Greenblatt’s funds performance, e.g. value fund, aren’t eye-popping either

True, though it’s been a tough few years to be a value investor. Buffett hasn’t shot the lights out either.

Some of Greenblatt’s funds have done OK, the Index plus (GINDX) and Enhanced S&P 500 Index Fund (GSPFX) have done slightly better than Berkshire over 5 years ending June 30. The Gotham funds not based on the S&P500 index haven’t done well.

I got the Longleaf Partners report last night. Used to have money with them, dumped them years ago.
Longleaf Partners Fund LLPFX 5 year CAGR 2.9% Ouch! Berkshire 10%, GINDX 10.7%, GSPFX 12%, SPY 11.2%

I like Greenblatt. He talks sense. Disappointing about the Magic Formula. Has anyone ever got a straight answer from him about not being able to replicate his results?

#11

Not knocking Greenblatt specifically, he makes sense, I like him.

It occurred to me that many of the people/funds that I like and think make sense actually have mediocre performance.

Greenblatt https://www.gothamfunds.com/default.aspx
Performance - Meh

Pabrai https://pabraifunds.com/
Performance - Meh

AlphaArhcitects https://www.gothamfunds.com/default.aspx
Performance - Meh

Mebane Faber https://cambriafunds.com/
Performance - Meh

For individual screens, I liked the BlueCheaps screen, it made sense.
Performance (last time I was able to easily check) - Meh

Not sure what to make of that. Perhaps start/end point effects?

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#12

It’ s like asking whether individual stock selection has beat the index.
Whose?
There are thousands of different quant metrics and strategies, and practitioners.
As with individual stock selection, many don’t work, some do.

Right. I should rephrase it as “Has the well-known quant funds as group outperformed the market index?”

#13

Oops, alphaarchitects link is https://etfsite.alphaarchitect.com/

#14

I’m a long time MI board and general amateur follower of investing. My take is there’s quite a bit of difference between quant sold to the masses and quant you can do by yourself. What an individual can do easily and quite profitably is also likely to not be scalable for commercial purposes. However, the individual most likely has to be able to deal with a higher level of volatility, sometimes significantly so. When trying to implement something commercially there tends to be way more stocks held than what an individual can chose to hold which of course mathematically is going to drive the big guys to be more market like in their returns.

In practical terms as has been mentioned, you just do not have an absolute assurance that something that worked or backtested well in the past is going to work in the future. Accordingly, one needs to have a system in place that can monitor the system(s) going forward to see if its edge is being maintained and sufficiently robust that one can distinguish performance in different market types. Bad results from a good system in a bad market != failure of the system. Finally, one can find a really excellent system that is insanely volatile which can be tamed to taste with a hedging strategy.

It’s a lot of work though with no guarantees…but if you can find something that does work it can definitely be worth the effort.

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#15

What an individual can do easily and quite profitably is also likely to not be scalable for commercial purposes.

This is true of the much discussed equal-weighting.

Equal weighting the S&P500 has a practical limit of about $67bn (if I did my math right, assuming no more than 10% owned of one company).

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#16

In practical terms as has been mentioned, you just do not have an absolute assurance that something that worked or backtested well in the past is going to work in the future.

That’s very hard to endure during bear market without knowing the true worth of the holding.

Jim Simons in the interview video said that they mainly spot and act on abnormalities. That makes more sense to me, because abnormalities do occur from time to time.

#17

For individual screens, I liked the BlueCheaps screen, it made sense.
Performance (last time I was able to easily check) - Meh
Not sure what to make of that. Perhaps start/end point effects?

My guess: Badly designed screen. (by me). A poor predictive model as it lacked out-of-sample validation.
The ever-present danger of trusting a backtest too much…it is FAR too easy to pick something that happens to match the quirks of history.
For real money, it is better to use something that has worked for at least a few years AFTER somebody invented it.

But even though that helps, it isn’t bullet proof.
It’s true that certain styles of stocks go in and out of fashion.
Since most quant screens have a focus on a certain style, they too can go in and out of fashion.
(Outright failure is probably more common!)
On of my long term favourite screens, based almost entirely on earnings yield and dividend yield, beat the market by 10.6%/year in the first 10.6 years after it was published.
That makes for a screen that seems to deserve a whole lot of confidence, especially since it made intuitive sense.
What would one guess would have happened in the next 6.25 years? Underperformance by 20%/year,
mainly because dividend stocks went WAY out of fashion and screening in that pool magnified the effect.
In the last 2.25 years it has returned 43.6%/yr (2.25x the money), outperforming the S&P by 24.2%/year, having nothing but 10 high yield low P/E stocks.
So… which stretch was the fluke? What will the future hold?
I would expect, at a guess, it will do OK during periods that dividends and earnings aren’t considered trash concepts.

Jim

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#18

For individual screens, I liked the BlueCheaps screen, it made sense.
Performance (last time I was able to easily check) - Meh

BlueCheaps is such a wonderful idea and I would love to use it, but the performance doesn’t make it worth it.

#19

That’s very hard to endure during bear market without knowing the true worth of the holding.

There are many methods to quant by. A bear market is a completely normal market state which generally means no matter what you are holding you are losing money assuming a long only strategy. The issue for your system isn’t losing money; it’s whether or not it’s departing “normality” from how it lost money in the past.

#20

For individual screens, I liked the BlueCheaps screen, it made sense.
Performance (last time I was able to easily check) - Meh
…
BlueCheaps is such a wonderful idea and I would love to use it, but the performance doesn’t make it worth it.

Yes, it is a disappointment. Certainly a high earnings yield has not been the key to riches in recent years.
Maybe that’s the reason for the dreary returns, or more likely the good returns prior to that were more like a fluke.

I think the later attempt is much better, the “LargeCapCash” screen, introduced here https://discussion.fool.com/a-spy-alternative-screen-34516863.as…
It’s intended as an alternative to SPY.
“The goal is a screen which is as safe as the S&P 500 but with the hope of somewhat higher returns over the long run.”

This has indeed beat the S&P by a small amount since its introduction 2.2 years ago, with a high correlation.
So, in that sense it’s meeting its goals: so far so good, anyway.

General description: An equally weighted slate of 40 large cap dividend payers with high ROE and lots of cash on hand.

There are two variations mentioned in the kickoff post, with and without dividends.
Perhaps to my surprise, the one which requires dividends from all picks has always tested just a little better, and this remains the case.
There are more elaborations later in the thread, but the version I track is one of the simplest:

Start from the Value Line 1700 set of stocks.
Eliminate stocks with no “Timeliness” rating (just a sanity check to skip those just listed or in the middle of M&A).
Eliminate any stocks that don’t pay a dividend
Of those remaining with a reported ROE, take the top 30% or 32% sorted on ROE.
For each stock remaining, calculate its cash balance in excess of long term debt and sort on that.
(Note: this uses largest cash balances in absolute terms, not largest cash balances as fraction of market cap…that’s why it’s a large cap screen)
Buy equal dollar amounts of the top 40 sorted on [cash-debt], hold two months, repeat.
Since the criteria don’t change often, there isn’t much trading.
Rebalance all positions to equal weight annually.

It has had a very unpleasant stretch since around the start of November, but still market beating overall so far by 2.91%/year after trading costs.
Of course 2.2 years isn’t enough to draw any firm conclusions, but at least it has not taken the opportunity to blow up.
The backtest January 1989 to April 2000 showed outperformance of 5.15%/year, which is as always probably too optimistic.

Top picks this year have generally included Microsoft, TSMC, Cisco, Nvidia, Accenture, Costco.
Microsoft has been the top pick every month since 2009.
But, as this is equally weighted, it doesn’t really matter: unlike an index fund, there is no concentration in the top pick.

Jim

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#21

“I think the later attempt is much better, the “LargeCapCash” screen”

Another nice screen. Thank you, Jim.

My point at the beginning of the thread was that, as everybody knows, the returns get higher near the top percentiles of the screening metric. A 3rd order polynomial nicely fits a graph of return versus percentile rank. Here are some results using Jamie Gritton’s MI Backtester (date range 2003-2021, holding period 3 months):

screen… CAGR… S&P 500
market cap top 1700 and ROE-5-year average top 20%… 13%… 11%
market cap top 1700 and ROE-5-year average top 10%… 14%… 11%
market cap top 1700 and ROE-5-year average top 5%… 15%… 11%
market cap top 1700 and ROE-5-year average top 2.5%… 16%… 11%

I think that one could put together a nice portfolio simply by choosing 25 of the top 42 stocks (2.5% of 1700) as ranked by 5-year average ROE (or 5-year average ROA, as ROA gives a similar list of stocks). I chose 25 stocks instead of all 42 in order to allow one to eliminate companies that one doesn’t like, maybe a tobacco company.

#22

… as everybody knows, the returns get higher near the top percentiles of the screening metric.

This is true, but also underappreciated. The key bit is “near the top”.
Anomalous and predictive returns tend to come at the extremes of a given metric or pair of metrics, and frequently only there.
There are almost no metrics which have a direct correlation with returns through the deciles.
Nor is there any reason to suppose there would be: The interesting spots are in corners of the space.
Any linear effect would be painfully easy to compete away.

As an unrelated aside, just on the subject of decile testing:
Tests by market cap decile are almost all flawed, as a result of a quirk of the math.
They will always show the smaller cap outperforming, even though it might not be a real effect.
Assume some random noise in prices.
Consider the top 200 by market cap, with the set reconstituted periodically.
Ranks 198 and 202 swap places due to random price noise before the next time you take the top 200.
They swap back again the next time after that, and so forth.
Number 202 was counted as a small cap during the first interval, and did well during that interval overtaking 201, 200, and 199, so small caps get points.
Number 198 was counted as a large cap during the first interval, and did badly during that interval, so large caps lose points.
The more often you reconstitute, and the more N-tiles you consider (the more boundaries), the bigger the effect of noise pumping returns into small caps.
This distortion happens when measuring any metric that uses price as an input. Including dividend yields and P/E ratios.
Technically such tests are only flawed if you mistakenly conclude something like “small caps are perennial outperformers” as a result.
They’re not flawed if your intent is to reconstitute the portfolio as frequently as was done in the test.
The test can pump returns out of noise, but so can a portfolio.

I think that one could put together a nice portfolio simply by choosing 25 of the top 42 stocks
(2.5% of 1700) as ranked by 5-year average ROE (or 5-year average ROA, as ROA gives a similar list
of stocks). I chose 25 stocks instead of all 42 in order to allow one to eliminate companies that
one doesn’t like, maybe a tobacco company.

The first idea might have more merit than the second–
Murphy’s law for quant investing: don’t hand pick from a quant list, since the ones you skip will be the ones that do best.
I eliminate things myself myself, but I test those eliminations in the backtest as well.

Jim

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#23

“Murphy’s law for quant investing: don’t hand pick from a quant list, since the ones you skip will be the ones that do best.”

So true, in investing and in other fields, as James Montier pointed out in “Painting By Numbers: An Ode to Quant.” Filtering out tobacco stocks is probably a good example, as tobacco stocks have historically been some of the best performers. Filtering out companies with high debt or declining revenues might be a good idea, though.

#24

Screens for large cap companies with consistently high ROE read like a Who’s Who of successful companies, AAPL, GOOGL, MA, etc. Warren has told us to look for companies with high ROE, low debt and enduring competitive advantages. A basket of such stocks may not beat the S&P by 5 percentage points, but it has a good chance of beating the S&P by 2 percentage points, and with lower volatility. Deciding exactly how to select the companies, weight the holdings and periodically adjust the weightings is certainly important, but maybe not as important as buying the top few percent of stocks as ranked by profitability in the first place. One can sleep better. I’m in.

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#25

Screens for large cap companies with consistently high ROE read like a Who’s Who of successful companies, AAPL, GOOGL, MA, etc…

Screening for slightly smaller cap companies with high ROE probably works even better.
The companies picked just aren’t as widely known.

1998 to date, from within the Value Line 1700 set of firms,
40 stocks by highest ROE from within the 500 biggest by market cap: 10.72%/year
40 stocks by highest ROE from within the 500 smallest by market cap: 14.89%/year
S&P 500 same dates: 7.90%/year

No allowance for trading costs: this is just a demonstration that it’s probably not a bad hunting ground.
Assumes the 40 positions are equally weighted and rebalanced regularly.

Note, that’s not the 500 smallest firms listed in the US, just the smallest 500 covered by Value Line in their 1700-stock edition.

Interestingly, the returns from the version considering only the smaller firms performed better than a version which considers all 1700. (half a percent)
This seems to imply that at any given ROE the smaller firm is the better pick.
And that, at the margin, it’s better to go with a smaller firm with a very slightly lower ROE than a larger firm with a slightly higher ROE.
This is probably simply because big firms with great economics are obvious: lots of people consider them and buy them, so they tend not to be cheap.

If anybody is interested in seeing what “highest ROE from among 500 smallest” looks like, this is a set of picks from earlier this month
TPX CWH BCO TEN SABR BLMN NTNX SWBI HBI SBH BKE MED RCII
ENR ALSN ATKR SUN CNR MATX LAZ BCC PBI GDEN AMCX HEP ATEN
ACEL YETI VSTO RGR MXL JWN PLAY HLNE WIRE KFRC TPB ERF MLI COMM

Jim

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