The Acquirer’s Multiple screeners examine the universe of US-listed stocks and ADRs to find the cheapest on the acquirer’s multiple, the name for the metric used by activists, private equity firms and corporate raiders to find deeply undervalued targets.
How are the universes defined?
We exclude stocks traded over-the-counter (OTC stocks), and closed-end funds, shrinking the universe of listed stocks to about 5,000 stocks.
We divide the remaining universe of US-listed stocks into three universes: Large Cap, All Investable, and Small and Micro Cap.
The Large Cap screener is drawn from the largest universe of stocks, those with a market cap greater than $20 billion.
The All Investable screener is drawn from the largest half of all listed companies. It includes stocks in the Large Cap universe. This universe gives the best balance of returns and ease of investability.
The Small and Micro Cap screener is drawn from the universe of stocks that fall outside the All Investable universe. We define the universe as the smallest half of all listed companies.
Why use the 50th percentile as the demarcation point between the All Investable and Small and Micro Cap universes?
Why not use say the 66th or 33rd percentile?
It’s an arbitrary cut-off, but it’s chosen for practical reasons. As at January 1, 2015, the smallest company in the All Investable universe had a market capitalization of $131 million. In the 2009 edition of his wonderful book What Works on Wall Street (Fourth Edition), quantitative investing pioneer Jim O’Shaughnessy used a minimum market capitalization of about $205 million for his All Stocks universe.
O’Shaughnessy arrived at $205 million by adjusting for inflation the $150 million figure he recommended in the first edition of the book (published in 1995), which he determined by consulting traders at several large Wall Street brokerages. O’Shaughnessy’s objective in constructing his All Stocks universe was to focus only on stocks that a professional investor could buy without running into liquidity problems, which, in 1995, was $150 million.
Our objective is to find stocks that an activist might target. Activists tend to look for companies that are no smaller than approximately their assets under management (AUM). The rationale is straightforward: Activism is by definition hands on, active investing. It’s difficult to manage more than 10 positions in the ordinary course, and 20 positions would be considered the maximum number of holdings.
To influence management, a fund needs to buy 5 percent or more, at which point it is obliged to disclose its holding and intentions in a Schedule 13D filing. If a fund holds 20 positions, and invests 5 percent of its AUM in a target company to buy 5 percent of the target’s stock, the funds will be targeting companies no smaller than the fund’s AUM.
A fund may target bigger companies, but, for foregoing trading liquidity and portfolio concentration reasons, funds tend not to target smaller companies. The minimum for SEC registration is $100 million in AUM, and the nearest round-number percentile is the 50th, so we’ve used that as the minimum for the All Investable universe.
For most investors, this universe will provide the best balance of returns and ease of investability.
If you think the 50th percentile cut-off is too high for the All Investable universe, consider this: On March 6, 2009–at the stock market trough in the 2007-2009 financial crisis–the smallest company in the largest half of the market (what we now define as the All Investable universe) had a market capitalization of just $27 million. (By way of comparison, the market capitalization of the smallest company in the Russell 3000 was $2.7 million!).
Why use a percentile cut-off for the universes, rather than a fixed market capitalization?
We use a percentile cut-off for the universes: the 10th percentile and larger for the Large Cap, and the 50th percentile to demarcate the line between the All Investable and Small and Micro Cap universes.
Why don’t we use a fixed market capitalization cut-off–say $1 billion for the large capitalization stocks, and $200 million to distinguish between investable stocks and small and micro cap stocks–as many investors do? If we use a fixed market capitalization cut-off, the universes vary in size depending on the direction of the market.
If the market goes up, more stocks move will into the Large Cap and All Investable universes and those universes will grow in number. If the market goes down, more stocks will move into the Small and Micro Cap universes, and the Large Cap and All Investable universes shrink in number. This is a perverse outcome.
As value investors, all else being equal, we would prefer that our investment universes do the opposite: Shrink as the market gets more expensive and grow as the market gets cheaper. As the screeners are already focused on the cheapest 1 percent of stocks, we are satisfied if the universes stay approximately the same size in number as the market goes up and down. That’s why we use the percentile cut-off.
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18 Comments on “1. We pick the 90 Best Deep Value Stock Picks”
Hi,
I don’t think it’s mentioned, so assume they’re not, but just to be sure:
Are the universes used in the stock screeners filtered in advance by eliminating those with the highest risk of financial distress, earnings manipulation, fraud and possessing the highest short interest ratio … as described in Quantitative Value as first steps and used in your investing approach for Carbon Beach?
Thanks & Regards
Yes, they are. Z, F, M Scores, and Short Interest Ratio.
Excellent 🙂 Thanks for the quick reply
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Do all the screeners (only) show the top 30 in their category? I would for instance for shortening (counter balance) reasons be interested in the most overvalued stocks.
Yes they do. Shorting on valuation tends to be a bad idea. Short on financial distress, fraud, and manipulation.
Hello Mr. Carlisle, great book, very well put together website and screener. As you did, I have also followed and read Mr. Greenblatt’s research. I was wondering-your research defines “US companies” as those with a stock float in the US, such as FCA, while it appears Mr. Greenblatt’s definition of a US company is one headquartered in the US. Is that correct? Thanks in advance.
I don’t recall. I’d be astonished if it made any difference.
Hi Tobias,
Today, I am seeing an Argentinian company at the top of the investable st screener (CRESY) – curious whether that’s an aberration or it is a legitimate member of the Russell 3000 that passed all screen filters?
Thanks,
PK
I also wonder what has changed recently about it that brought it into scope – price has not dropped, if anything it’s been going up a bit so had to be either an unexpected earnings spike or some significant amount of cash suddenly materializing …
Yeah, seems to be an aberration. Thanks for the pointing it out.
Hi Tobias,
I think we might have another one today in the All Investable screener, EME (actually EME.L) – this is an LSE traded stock from the looks of it (and quoted in GBP). Could you please check?
Thanks,
PK
EME should be Emcor Group. For some reason we’re pulling the UK EME data and it’s messing up the screen. Thanks for the heads up.
Thanks Tobias. Looks like it was trading 10% lower than now 2 months ago, curious why the screener wasn’t picking it up until now.
Regards,
PK
Hey Toby, what is the best way to screen by deciles in a stock screener? (I’m searching AUS stocks in unclestock.com and using your screener). Is it okay to use the All Ords Index and manually calculate the deciles based on market cap? Cheers
Hey Toby,
What ranges or values do input when you screen for Z, F, M Scores?
I kill the worst 5% across the blend.
In the book, Quantitative Value, it had this line:
Shumway concluded that half of the variables proposed by Altman’s Z-score were no longer predictive of bankruptcy. In 2004, Sudheer Chava and Robert Jarrow took Shumway’s model one step further, concluding that Shumway’s results were robust and agreeing that Altman’s Z-score no longer reliably forecasted bankruptcy.
I’m wondering why the Z-score is used to eliminate the worst 5% instead of the Campbell, Hilscher, Szilagyi model that was shown to be more predictive?