Feeds:
Posts
Comments

Archive for the ‘Net Quick Stocks’ Category

Last week I wrote about the performance of one of Benjamin Graham’s simple quantitative strategies over the 37 years he since he described it (Examining Benjamin Graham’s Record: Skill Or Luck?). In the original article Graham proposed two broad approaches, the second of which we examine in Quantitative Value: A Practitioner’s Guide to Automating Intelligent Investment and Eliminating Behavioral Errors. The first approach Graham detailed in the original 1934 edition of Security Analysis (my favorite edition)—“net current asset value”:

My first, more limited, technique confines itself to the purchase of common stocks at less than their working-capital value, or net-current asset value, giving no weight to the plant and other fixed assets, and deducting all liabilities in full from the current assets. We used this approach extensively in managing investment funds, and over a 30-odd year period we must have earned an average of some 20 per cent per year from this source. For a while, however, after the mid-1950’s, this brand of buying opportunity became very scarce because of the pervasive bull market. But it has returned in quantity since the 1973–74 decline. In January 1976 we counted over 300 such issues in the Standard & Poor’s Stock Guide—about 10 per cent of the total. I consider it a foolproof method of systematic investment—once again, not on the basis of individual results but in terms of the expectable group outcome.

In 2010 I examined the performance of Graham’s net current asset value strategy with Sunil Mohanty and Jeffrey Oxman of the University of St. Thomas. The resulting paper is embedded below:

While Graham found this strategy was “almost unfailingly dependable and satisfactory,” it was “severely limited in its application” because the stocks were too small and infrequently available. This is still the case today. There are several other problems with both of Graham’s strategies. In Quantitative Value: A Practitioner’s Guide to Automating Intelligent Investment and Eliminating Behavioral Errors Wes and I discuss in detail industry and academic research into a variety of improved fundamental value investing methods, and simple quantitative value investment strategies. We independently backtest each method, and strategy, and combine the best into a sample quantitative value investment model.

The book can be ordered from Wiley FinanceAmazon, or Barnes and Noble.

[I am an Amazon Affiliate and receive a small commission for the sale of any book purchased through this site.]

About these ads

Read Full Post »

Zero Hedge has an article Buy Cash At A Discount: These Companies Have Negative Enterprise Value in which Tyler Durden argues that stock market manipulation has led to valuation dislocations, and gives as evidence the phenomenon of stocks trading with a negative enterprise value (EV):

With humans long gone from the trading arena and algorithms left solely in charge of the casino formerly known as “the stock market”, in which price discovery is purely a function of highly levered synthetic instruments such as ES and SPY or, worse, the EURUSD and not fundamentals, numerous valuation dislocations are bound to occur. Such as company equity value trading well below net cash (excluding total debt), or in other words, negative enterprise value, meaning one can buy the cash at a discount of par and assign zero value to all other corporate assets.

Just as the fact of your paranoia does not exclude the possibility that someone is following you*, you don’t need to believe in manipulation to believe that negative EV is a “valuation dislocation.” Negative EV stocks are often also Graham net nets or almost net nets, and so perform like net nets. For example, Turnkey Analyst took a look at the performance of negative EV stocks (click to enlarge):

Long story short: they ripped, but they were few (sometimes non-existent), and small (mostly micro), which means you would have been heavily concentrated in a few mostly very small stocks, and regularly carried a lot of cash. If you eliminated the tiniest (i.e. the smallest 10 or 20 percent), much of the return disappeared, and volatility spiked markedly. Says Wes:

A few key points:

  1. After you eliminate the micro-crap stocks, you end up being invested in a few names at a time (sometimes you go all-in on a single firm!)
  2. Sometimes the strategy isn’t invested.
  3. The amazing Bueffettesque returns for the “all firms” portfolio above are exclusively tied to micro-craps.

Here’s the frequency of negative EV opportunities according to Turnkey (click to enlarge):

No surprise, there were more following a crash (1987, 2001, 2009) and fewer at the peak (1986, 1999, 2007). If your universe eliminated the smallest 20 percent (the green line), you spent a lot of time in cash. If your universe was unrestricted (the red line), then you’d have had some prospects to mine most of the time. Clearly, it’s not an institutional-grade strategy, but it has worked for smaller sums.

Zero Hedge screened Russell 2000 companies finding 10 companies with negative enterprise value, and then further subdivided the screen into companies with negative, and positive free cash flow (defined here as EBITDA – Cap Ex). Here’s the list (click to enlarge):

Including short-term investments yields a bigger list (click to enlarge):

Like Graham net nets, negative EV stocks are ugly balance sheet plays. They lose money; they burn cash; the business, if they actually have one, usually needs to be taken to the woodshed (so does management, for that matter). Frankly, that’s why they’re cheap. Says Durden:

Typically negative EV companies are associated with pre-bankruptcy cases, usually involving large cash burn, in other words, where the cash may or may not be tomorrow, and which may or may not be able to satisfy all claims should the company file today, especially if it has some off balance sheet liabilities.

You can cherry-pick this screen or buy the basket. I favor the basket approach. Just for fun, I’ve formed four virtual portfolios at Tickerspy to track the performance:

  1. Zero Hedge Negative Enterprise Value Portfolio
  2. Zero Hedge Negative Enterprise Value Portfolio (Positive FCF Only)
  3. Zero Hedge Negative Enterprise Value (Inc. Short-Term Investments) Portfolio
  4. Zero Hedge Negative Enterprise Value (Inc. Short-Term Investments) Portfolio (Positive FCF Only)

I’ll check back in occasionally to see how they’re doing. My predictions for 2013:

  1. All portfolios beat the market
  2. Portfolio 1 outperforms Portfolio 2 (i.e. all negative EV stocks outperform those with positive FCF only)
  3. Portfolio 3 outperforms Portfolio 4 for the same reason that 1 outperforms 2.
  4. Portfolios 1 and 2 outperform Portfolios 3 and 4 (pure negative EV stocks outperform negative EV including short-term investments)

Take care here. The idiosyncratic risk here is huge because the portfolios are so small. Any bump to one stock leaves a huge hole in the portfolio.

* Turn around. I’m right behind you.

Read Full Post »

This is an oldie, but a goodie (via CNN). The travails of buying net nets, as told by the master’s apprentice:

Warren Buffett says Berkshire Hathaway is the “dumbest” stock he ever bought.

He calls his 1964 decision to buy the textile company a $200 billion dollar blunder, sparked by a spiteful urge to retaliate against the CEO who tried to “chisel” Buffett out of an eighth of a point on a tender deal.

Buffett tells the story in response to a question from CNBC’s Becky Quick for a Squawk Box series on the biggest self-admitted mistakes by some of the world’s most successful investors.

Buffett tells Becky that his holding company (presumably with a different name) would be “worth twice as much as it is now” — another $200 billion — if he had bought a good insurance company instead of dumping so much money into the dying textile business.

Here’s his story:

BUFFETT:  The— the dumbest stock I ever bought— was— drum roll here— Berkshire Hathaway.  And— that may require a bit of explanation.  It was early in— 1962, and I was running a small partnership, about seven million.  They call it a hedge fund now.

And here was this cheap stock, cheap by working capital standards or so.  But it was a stock in a— in a textile company that had been going downhill for years.  So it was a huge company originally, and they kept closing one mill after another.  And every time they would close a mill, they would— take the proceeds and they would buy in their stock.  And I figured they were gonna close, they only had a few mills left, but that they would close another one.  I’d buy the stock.  I’d tender it to them and make a small profit.

So I started buying the stock.  And in 1964, we had quite a bit of stock.  And I went back and visited the management,  Mr. (Seabury) Stanton.  And he looked at me and he said, ‘Mr. Buffett.  We’ve just sold some mills.  We got some excess money.  We’re gonna have a tender offer.  And at what price will you tender your stock?’

And I said, ‘11.50.’  And he said, ‘Do you promise me that you’ll tender it 11.50?’  And I said, ‘Mr. Stanton, you have my word that if you do it here in the near future, that I will sell my stock to— at 11.50.’  I went back to Omaha.  And a few weeks later, I opened the mail—

BECKY:  Oh, you have this?

BUFFETT:   And here it is:  a tender offer from Berkshire Hathaway— that’s from 1964.  And if you look carefully, you’ll see the price is—

BECKY:  11 and—

BUFFETT:   —11 and three-eighths.  He chiseled me for an eighth.  And if that letter had come through with 11 and a half, I would have tendered my stock.  But this made me mad.  So I went out and started buying the stock, and I bought control of the company, and fired Mr. Stanton.  (LAUGHTER)

Now, that sounds like a great little morality table— tale at this point.  But the truth is I had now committed a major amount of money to a terrible business.  And Berkshire Hathaway became the base for everything pretty much that I’ve done since.  So in 1967, when a good insurance company came along, I bought it for Berkshire Hathaway.  I really should— should have bought it for a new entity.

Because Berkshire Hathaway was carrying this anchor, all these textile assets.  So initially, it was all textile assets that weren’t any good.  And then, gradually, we built more things on to it.  But always, we were carrying this anchor.  And for 20 years, I fought the textile business before I gave up.  As instead of putting that money into the textile business originally, we just started out with the insurance company, Berkshire would be worth twice as much as it is now.  So—

BECKY:  Twice as much?

BUFFETT:  Yeah.  This is $200 billion.  You can— you can figure that— comes about.  Because the genius here thought he could run a textile business. (LAUGHTER)

BECKY:  Why $200 billion?

BUFFETT:  Well, because if you look at taking that same money that I put into the textile business and just putting it into the insurance business, and starting from there, we would have had a company that— because all of this money was a drag.  I mean, we had to— a net worth of $20 million.  And Berkshire Hathaway was earning nothing, year after year after year after year.  And— so there you have it, the story of— a $200 billion— incidentally, if you come back in ten years, I may have one that’s even worse.  (LAUGHTER)

Hat tip SD and David Lau.

Read Full Post »

Jon Heller at Cheap Stocks has a great post on The Downside of Net/Net Investing- Lazare Kaplan (LKI). Says Jon:

In July of 2009,we initiated a new position in the $1.15 range. The shares subsequently ran up to $2.50, but in September, trading was halted,and not a share has traded since.

The company has repeatedly delayed filing it’s financial reports with the SEC, due to:

a material uncertainty concerning (a) the collectability and recovery of certain assets, and (b) the Company’s potential obligations under certain lines of credit and a guaranty (all of which, the “Material Uncertainties”).

The NYSE AMEX granted the company several extensions to regain compliance; the latest on April 26th, which gave the company until May 31st to regain compliance with listing standards.

Pretty standard fare in net net world. Here’s where the going gets weird. LKI is a diamond vendor. It seems that it has been in a trading halt because some of its diamonds have gone missing. Quite a few of them. When the going gets weird, as Hunter S. Thompson used to say before he was shot out of a cannon, the weird turn pro: LKI is suing its insurers for $640M. From the May 20 press release:

LAZARE KAPLAN INTERNATIONAL SUES ITS INSURERS FOR $640 MILLION

New York, NY – May 20, 2010 – Lazare Kaplan International Inc. (AMEX:LKI) (“Lazare Kaplan”) announced today that in a federal lawsuit filed on Monday, May 17, 2010, it sued various Lloyds of London syndicates and European insurers for $640 million in damages arising out of the disappearance of diamonds that were insured by the defendants, including consequential damages. The lawsuit alleges that the insurers breached two “all risk” New York property insurance policies, and an Agreement for Interim Payment under which the insurers made a non-refundable interim payment of $28 million to Lazare Kaplan in January of this year. After making the $28 million payment, the insurers abruptly reversed course and refused to acknowledge coverage or to pay any covered losses under the policies. The complaint alleges, among other things, that the insurers, which also issued separate policies to Lazare Kaplan under English law, created a virtual coverage “whipsaw” by denying coverage under the English policies on the ground that Lazare Kaplan does not have an insurable interest in the largest portion of the property at issue while at the very same time asserting under the New York policies that there is no coverage because Lazare Kaplan insured the same property under the English policies. Lazare Kaplan expects to conduct broad-ranging discovery around the world in the course of the lawsuit.

Jon asks the obvious questions:

What happened to the diamonds? Why isn’t the company willing to speak with it’s shareholders on the issue? Why are the insurers unwilling to pay? And again, what happened to the diamonds?

This is why investing in net nets will always be pure Gonzo investing. Even though the situation with the missing diamonds is ugly, if LKI trades again it might be an interesting lottery ticket. With a market capitalization of $21M, success in the $640M suit represents a 30:1 payout.

Read Full Post »

Recently I’ve been discussing Michael Mauboussin’s December 2007 Mauboussin on Strategy, “Death, Taxes, and Reversion to the Mean; ROIC Patterns: Luck, Persistence, and What to Do About It,” (.pdf) about Mauboussin’s research on the tendency of return on invested capital (ROIC) to revert to the mean (See Part 1 and Part 2).

Mauboussin’s report has significant implications for modelling in general, and also several insights that are particularly useful to Graham net net investors. These implications are as follows:

  • Models are often too optimistic and don’t take into account the “large and robust reference class” about ROIC performance. Mauboussin says:

We know a small subset of companies generate persistently attractive ROICs—levels that cannot be attributed solely to chance—but we are not clear about the underlying causal factors. Our sense is most models assume financial performance that is unduly favorable given the forces of chance and competition.

  • Models often contain errors due to “hidden assumptions.” Mauboussin has identified errors in two distinct areas:

First, analysts frequently project growth, driven by sales and operating profit margins, independent of the investment needs necessary to support that growth. As a result, both incremental and aggregate ROICs are too high. A simple way to check for this error is to add an ROIC line to the model. An appreciation of the degree of serial correlations in ROICs provides perspective on how much ROICs are likely to improve or deteriorate.

The second error is with the continuing, or terminal, value in a discounted cash flow (DCF) model. The continuing value component of a DCF captures the firm’s value for the time beyond the explicit forecast period. Common estimates for continuing value include multiples (often of earnings before interest, taxes, depreciation, and amortization—EBITDA) and growth in perpetuity. In both cases, unpacking the underlying assumptions shows impossibly high future ROICs. 23

  • Models often underestimate the difficulty in sustaining high growth and returns. Few companies sustain rapid growth rates, and predicting which companies will succeed in doing so is very challenging:

Exhibit 12 illustrates this point. The distribution on the left is the actual 10-year sales growth rate for a large sample of companies with base year revenues of $500 million, which has a mean of about six percent. The distribution on the right is the three-year earnings forecast, which has a 13 percent mean and no negative growth rates. While earnings growth does tend to exceed sales growth by a modest amount over time, these expected growth rates are vastly higher than what is likely to appear. Further, as we saw earlier, there is greater persistence in sales growth rates than in earnings growth rates.

  • Models should be constructed “probabilistically.”

One powerful benefit to the outside view is guidance on how to think about probabilities. The data in Exhibit 5 offer an excellent starting point by showing where companies in each of the ROIC quintiles end up. At the extremes, for instance, we can see it is rare for really bad companies to become really good, or for great companies to plunge to the depths, over a decade.

For me, the following Exhibit is the most important chart of the entire paper. It’s Mauboussin’s visualization of the probabilities. He writes:

Assume you randomly draw a company from the highest ROIC quintile in 1997, where the median ROIC less cost of capital spread is in excess of 20 percent. Where will that company end up in a decade? Exhibit 13 shows the picture: while a handful of companies earn higher economic profit spreads in the future, the center of the distribution shifts closer to zero spreads, with a small group slipping to negative.

  • Crucial for net net investors is the need to understand the chances of a turnaround. Mauboussin says the chances are extremely low:

Investors often perceive companies generating subpar ROICs as attractive because of the prospects for unpriced improvements. The challenge to this strategy comes on two fronts. First, research shows low-performing companies get higher premiums than average-performing companies, suggesting the market anticipates change for the better. 24 Second, companies don’t often sustain recoveries.

Defining a sustained recovery as three years of above-cost-of-capital returns following two years of below-cost returns, Credit Suisse research found that only about 30 percent of the sample population was able to engineer a recovery. Roughly one-quarter of the companies produced a non-sustained recovery, and the balance—just under half of the population—either saw no turnaround or disappeared. Exhibit 14 shows these results for nearly 1,200 companies in the technology and retail sectors.


Mauboussin concludes with the important point that the objective of active investors is to “find mispriced securities or situations where the expectations implied by the stock price don’t accurately reflect the fundamental outlook:”

A company with great fundamental performance may earn a market rate of return if the stock price already reflects the fundamentals. You don’t get paid for picking winners; you get paid for unearthing mispricings. Failure to distinguish between fundamentals and expectations is common in the investment business.

Read Full Post »

Yesterday I ran a post on Dr. Michael Burry, the value investor who was one of the first, if not the first, to figure out how to short sub-prime mortgage bonds in his fund, Scion Capital. In The Big Short, Michael Lewis discusses Burry’s entry into value investing:

Late one night in November 1996, while on a cardiology rotation at Saint Thomas Hospital, in Nashville, Tennessee, he logged on to a hospital computer and went to a message board called techstocks.com. There he created a thread called “value investing.” Having read everything there was to read about investing, he decided to learn a bit more about “investing in the real world.” A mania for Internet stocks gripped the market. A site for the Silicon Valley investor, circa 1996, was not a natural home for a sober-minded value investor. Still, many came, all with opinions. A few people grumbled about the very idea of a doctor having anything useful to say about investments, but over time he came to dominate the discussion. Dr. Mike Burry—as he always signed himself—sensed that other people on the thread were taking his advice and making money with it.

Michael Burry’s blog, http://www.valuestocks.net, seems to be lost to the sands of time, but Burry’s techstocks.com “Value Investing” thread (now Silicon Investor) still exists. The original post in the thread hints at the content to come:

Started: 11/16/1996 11:01:00 PM

Ok, how about a value investing thread?

What we are looking for are value plays. Obscene value plays. In the Graham tradition.

This week’s Barron’s lists a tech stock named Premenos, which trades at 9 and has 5 1/2 bucks in cash. The business is valued at 3 1/2, and it has a lot of potential. Interesting.

We want to stay away from the obscenely high PE’s and look at net working capital models, etc. Schooling in the art of fundamental analysis is also appropriate here.

Good luck to all. Hope this thread survives.

Mike

Hat tip Toby.

Read Full Post »

Jon Heller of the superb Cheap Stocks, one of the inspirations for this site, has published the results of his two year net net index experiment in Winding Down The Cheap Stocks 21 Net Net Index; Outperforms Russell Microcap by 1371 bps, S&P 500 by 2537 bps.

The “CS 21 Net/Net Index” was “the first index designed to track net/net performance.” It was a simply constructed, capitalization-weighted index comprising the 21 largest net nets by market capitalization at inception on February 15, 2008. Jon had a few other restrictions on inclusion in the index, described in his introductory post:

  • Market Cap is below net current asset value, defined as: Current Assets – Current Liabilities – all other long term liabilities (including preferred stock, and minority interest where applicable)
  • Stock Price above $1.00 per share
  • Companies have an operating business; acquisition companies were excluded
  • Minimum average 100 day volume of at least 5000 shares (light we know, but welcome to the wonderful world of net/nets)
  • Index constituents were selected by market cap. The index is comprised of the “largest” companies meeting the above criteria.

The Index is naïve in construction in that:

  • It will be rebalanced annually, and companies no longer meeting the net/net criteria will remain in the index until annual rebalancing.
  • Only bankruptcies, de-listings, or acquisitions will result in replacement
  • Does not discriminate by industry weighting—some industries may have heavy weights.

If a company was acquired, it was not replaced and the proceeds were simply held in cash. Further, stocks were not replaced if they ceased being net nets.

Says Jon of the CS 21 Net/Net Index performance:

This was simply an experiment in order to see how net/nets at a given time would perform over the subsequent two years.

The results are in, and while it was not what we’d originally hoped for, it does lend credence to the long-held notion that net/nets can outperform the broader markets.

The Cheap Stocks 21 Net Net Index finished the two year period relatively flat, gaining 5.1%. During the same period, The Russell Microcap Index was down 8.61%, while the Russell Microcap Index was down 9.9%. During the same period, the S&P 500 was down 20.27%.

Here are the components, including the weightings and returns of each:

Adaptec Inc (ADPT)
Weight: 18.72%
Computer Systems
+7.86%
Audiovox Corp (VOXX)
Weight: 12.20%
Electronics
-29.28%
Trans World Entertainment (TWMC)
Weight:7.58%
Retail-Music and Video
-69.55%
Finish Line Inc (FINL)
Weight:6.30%
Retail-Apparel
+350.83%
Nu Horizons Electronics (NUHC)
Weight:5.76%
Electronics Wholesale
-25.09%
Richardson Electronics (RELL)
Weight:5.09%
Electronics Wholesale
+43.27%
Pomeroy IT Solutions (PMRY)
Weight:4.61%
Acquired
-3.8%
Ditech Networks (DITC)
Weight:4.31%
Communication Equip
-56.67%
Parlux Fragrances (PARL)
Weight:3.92%
Personal Products
-51.39%
InFocus Corp (INFS)
Weight:3.81%
Computer Peripherals
Acquired
Renovis Inc (RNVS)
Weight:3.80%
Biotech
Acquired
Leadis Technology Inc (LDIS)
Weight:3.47%
Semiconductor-Integrated Circuits
-92.05%
Replidyne Inc (RDYN) became Cardiovascular Systems (CSII)
Weight:3.31%
Biotech
[Edit: +126.36%]
Tandy Brands Accessories Inc (TBAC)
Weight:2.94%
Apparel, Footwear, Accessories
-57.79%
FSI International Inc (FSII)
Weight:2.87%
Semiconductor Equip
+66.47%
Anadys Pharmaceuticals Inc (ANDS)
Weight:2.49%
Biotech
+43.75%
MediciNova Inc (MNOV)
Weight:2.33%
Biotech
+100%
Emerson Radio Corp (MSN)
Weight:1.71%
Electronics
+118.19%
Handleman Co (HDL)
Weight:1.66%
Music- Wholesale
-88.67%
Chromcraft Revington Inc (CRC)
Weight:1.62%
Furniture
-54.58%
Charles & Colvard Ltd (CTHR)
Weight:1.50%
Jewel Wholesale
-7.41%

Cash Weight: 8.58%

Jon is putting together a new net net index, which I’ll follow if he releases it into the wild.

Read Full Post »

Jae Jun at Old School Value has updated his great post back-testing the performance of net current asset value (NCAV) against “net net working capital” (NNWC) by refining the back-test (see NCAV NNWC Backtest Refined). His new back-test increases the rebalancing period to 6 months from 4 weeks, excludes companies with daily volume below 30,000 shares, and introduces the 66% margin of safety to the NCAV stocks (I wasn’t aware that this was missing from yesterday’s back-test, and would explain why the performance of the NCAV stocks was so poor).

Jae Jun’s original back-test compared the performance of NCAV and NNWC stocks over the last three years. He calculated NNWC by discounting the current asset value of stocks in line with Graham’s liquidation value discounts, but excludes the “Fixed and miscellaneous assets” included by Graham. Here’s Jae Jun’s NNWC formula:

NNWC = Cash + (0.75 x Accounts receivables) + (0.5 x  Inventory)

Here’s Graham’s suggested discounts (extracted from Chapter XLIII of Security Analysis: The Classic 1934 Edition “Significance of the Current Asset Value”):

As I noted yesterday, excluding the “Fixed and miscellaneous assets” from the liquidating value calculation makes for an exceptionally austere valuation.

Jae Jun has refined his screening criteria as follows:

  • Volume is greater than 30k
  • NCAV margin of safety included
  • Slippage increased to 1%
  • Rebalance frequency changed to 6 months
  • Test period remains at 3 years

Here are Jae Jun’s back-test results with the new criteria:

For the period 2001 to 2004

For the period 2004 to 2007

For the period 2007 to 2010


It’s an impressive analysis by Jae Jun. Dividing the return into three periods is very helpful. While the returns overall are excellent, there were some serious smash-ups along the way, particularly the February 2007 to March 2009 period. As Klarman and Taleb have both discussed, it demonstrates that your starting date as an investor makes a big difference to your impression of the markets or whatever theory you use to invest. Compare, for example, the experiences of two different NCAV investors, one starting in February 2003 and the second starting in February 2007. The 2003 investor was up 500% in the first year, and had a good claim to possessing some investment genius. The 2007 investor was feeling very ill in March 2009, down around 75% and considering a career in truck driving. Both were following the same strategy, and so really had no basis for either conclusion. I doubt that thought consoles the trucker.

Jae Jun’s Old School Value NNWC NCAV Screen is available here (it’s free).

Read Full Post »

Jae Jun at Old School Value has a great post, NCAV NNWC Screen Strategy Backtest, comparing the performance of net current asset value stocks (NCAV) and “net net working capital” (NNWC) stocks over the last three years. To arrive at NNWC, Jae Jun discounts the current asset value of stocks in line with Graham’s liquidation value discounts, but excludes the “Fixed and miscellaneous assets” included by Graham. Here’s Jae Jun’s NNWC formula:

NNWC = Cash + (0.75 x Accounts receivables) + (0.5 x  Inventory)

Here’s Graham’s suggested discounts (extracted from Chapter XLIII of Security Analysis: The Classic 1934 Edition “Significance of the Current Asset Value”):

Excluding the “Fixed and miscellaneous assets” from the NNWC calculation provides an austere valuation indeed (it makes Graham look like a pie-eyed optimist, which is saying something). The good news is that Jae Jun’s NNWC methodology seems to have performed exceptionally well over the period analyzed.

Jae Jun’s back-test methodology was to create two concentrated portfolios, one of 15 stocks and the other of 10 stocks. He rolled the positions on a four-weekly basis, which may be difficult to do in practice (as Aswath Damodaran pointed out yesterday, many a slip twixt cup and the lip renders a promising back-tested strategy useless in the real world). Here’s the performance of the 15 stock portfolio:

“NNWC Incr.” is “NNWC Increasing,” which Jae Jun describes as follows:

NNWC is positive and the latest NNWC has increased compared to the previous quarter. In this screen, NNWC doesn’t have to be less than current market price. Since the requirement is that NNWC is greater than 0, most large caps automatically fail to make the cut due to the large quantity of intangibles, goodwill and total debt.

Both the NNWC and NNWC Increasing portfolios delivered exceptional returns, up 228% and 183% respectively, while the S&P500 was off 26%. The performance of the NCAV portfolio was a surprise, eeking out just a 5% gain over the period, which is nothing to write home about, but still significantly better than the S&P500.

The 10 stock portfolio’s returns are simply astonishing:

Jae Jun writes:

An original $100 would have become

  • NCAV: $103
  • NNWC: $544
  • NNWC Incr: $503
  • S&P500: $74

That’s a gain of over 400% for NNWC stocks!

Amazing stuff. It would be interesting to see a full academic study on the performance of NNWC stocks, perhaps with holding periods in line with Oppenheimer’s Ben Graham’s Net Current Asset Values: A Performance Update for comparison. You can see Jae Jun’s Old School Value NNWC NCAV Screen here (it’s free). He’s also provided a list of the top 10 NNWC stocks and top 10 stocks with increasing NNWC in the NCAV NNWC Screen Strategy Backtest post.

Read Full Post »

Aswath Damodaran, a Professor of Finance at the Stern School of Business, has an interesting post on his blog Musings on Markets, Transaction costs and beating the market. Damodaran’s thesis is that transaction costs – broadly defined to include brokerage commissions, spread and the “price impact” of trading (which I believe is an important issue for some strategies) – foil in the real world investment strategies that beat the market in back-tests. He argues that transaction costs are also the reason why the “average active portfolio manager” underperforms the index by about 1% to 1.5%. I agree with Damodaran. The long-term, successful practical application of any investment strategy is difficult, and is made more so by all of the frictional costs that the investor encounters. That said, I see no reason why a systematic application of some value-based investment strategies should not outperform the market even after taking into account those transaction costs and taxes. That’s a bold statement, and requires in support the production of equally extraordinary evidence, which I do not possess. Regardless, here’s my take on Damodaran’s article.

First, Damodaran makes the point that even well-researched, back-tested, market-beating strategies underperform in practice:

Most of these beat-the-market approaches, and especially the well researched ones, are backed up by evidence from back testing, where the approach is tried on historical data and found to deliver “excess returns”. Ergo, a money making strategy is born.. books are written.. mutual funds are created.

The average active portfolio manager, who I assume is the primary user of these can’t-miss strategies does not beat the market and delivers about 1-1.5% less than the index. That number has remained surprisingly stable over the last four decades and has persisted through bull and bear markets. Worse, this under performance cannot be attributed to “bad” portfolio mangers who drag the average down, since there is very little consistency in performance. Winners this year are just as likely to be losers next year…

Then he explains why he believes market-beating strategies that work on paper fail in the real world. The answer? Transaction costs:

So, why do portfolios that perform so well in back testing not deliver results in real time? The biggest culprit, in my view, is transactions costs, defined to include not only the commission and brokerage costs but two more significant costs – the spread between the bid price and the ask price and the price impact you have when you trade. The strategies that seem to do best on paper also expose you the most to these costs. Consider one simple example: Stocks that have lost the most of the previous year seem to generate much better returns over the following five years than stocks have done the best. This “loser” stock strategy was first listed in the academic literature in the mid-1980s and greeted as vindication by contrarians. Later analysis showed, though, that almost all of the excess returns from this strategy come from stocks that have dropped to below a dollar (the biggest losing stocks are often susceptible to this problem). The bid-ask spread on these stocks, as a percentage of the stock price, is huge (20-25%) and the illiquidity can also cause large price changes on trading – you push the price up as you buy and the price down as you sell. Removing these stocks from your portfolio eliminated almost all of the excess returns.

In support of his thesis, Damodaran gives the example of Value Line and its mutual funds:

In perhaps the most telling example of slips between the cup and lip, Value Line, the data and investment services firm, got great press when Fischer Black, noted academic and believer in efficient markets, did a study where he indicated that buying stocks ranked 1 in the Value Line timeliness indicator would beat the market. Value Line, believing its own hype, decided to start mutual funds that would invest in its best ranking stocks. During the years that the funds have been in existence, the actual funds have underperformed the Value Line hypothetical fund (which is what it uses for its graphs) significantly.

Damodaran’s argument is particularly interesting to me in the context of my recent series of posts on quantitative value investing. For those new to the site, my argument is that a systematic application of the deep value methodologies like Benjamin Graham’s liquidation strategy (for example, as applied in Oppenheimer’s Ben Graham’s Net Current Asset Values: A Performance Update) or a low price-to-book strategy (as described in Lakonishok, Shleifer, and Vishny’s Contrarian Investment, Extrapolation and Risk) can lead to exceptional long-term investment returns in a fund.

When Damodaran refers to “the price impact you have when you trade” he highlights a very important reason why a strategy in practice will underperform its theoretical results. As I noted in my conclusion to Intuition and the quantitative value investor:

The challenge is making the sample mean (the portfolio return) match the population mean (the screen). As we will see, the real world application of the quantitative approach is not as straight-forward as we might initially expect because the act of buying (selling) interferes with the model.

A strategy in practice will underperform its theoretical results for two reasons:

  1. The strategy in back test doesn’t have to deal with what I call the “friction” it encounters in the real world. I define “friction” as brokerage, spread and tax, all of which take a mighty bite out of performance. These are two of Damodaran’s transaction costs and another – tax. Arguably spread is the most difficult to prospectively factor into a model. One can account for brokerage and tax in the model, but spread is always going to be unknowable before the event.
  2. The act of buying or selling interferes with the market (I think it’s a Schrodinger’s cat-like paradox, but then I don’t understand quantum superpositions). This is best illustrated at the micro end of the market. Those of us who traffic in the Graham sub-liquidation value boat trash learn to live with wide spreads and a lack of liquidity. We use limit orders and sit on the bid (ask) until we get filled. No-one is buying (selling) “at the market,” because, for the most part, there ain’t no market until we get on the bid (ask). When we do manage to consummate a transaction, we’re affecting the price. We’re doing our little part to return it to its underlying value, such is the wonderful phenomenon of value investing mean reversion in action. The back-test / paper-traded strategy doesn’t have to account for the effect its own buying or selling has on the market, and so should perform better in theory than it does in practice.

If ever the real-world application of an investment strategy should underperform its theoretical results, Graham liquidation value is where I would expect it to happen. The wide spreads and lack of liquidity mean that even a small, individual investor will likely underperform the back-test results. Note, however, that it does not necessarily follow that the Graham liquidation value strategy will underperform the market, just the model. I continue to believe that a systematic application of Graham’s strategy will beat the market in practice.

I have one small quibble with Damodaran’s otherwise well-argued piece. He writes:

The average active portfolio manager, who I assume is the primary user of these can’t-miss strategies does not beat the market and delivers about 1-1.5% less than the index.

There’s a little rhetorical sleight of hand in this statement (which I’m guilty of on occasion in my haste to get a post finished). Evidence that the “average active portfolio manager” does not beat the market is not evidence that these strategies don’t beat the market in practice. I’d argue that the “average active portfolio manager” is not using these strategies. I don’t really know what they’re doing, but I’d guess the institutional imperative calls for them to hug the index and over- or under-weight particular industries, sectors or companies on the basis of a story (“Green is the new black,” “China will consume us back to the boom,” “house prices never go down,” “the new dot com economy will destroy the old bricks-and-mortar economy” etc). Yes, most portfolio managers underperform the index in the order of 1% to 1.5%, but I think they do so because they are, in essence, buying the index and extracting from the index’s performance their own fees and other transaction costs. They are not using the various strategies identified in the academic or popular literature. That small point aside, I think the remainder of the article is excellent.

In conclusion, I agree with Damodaran’s thesis that transaction costs in the form of brokerage commissions, spread and the “price impact” of trading make many apparently successful back-tested strategies unusable in the real world. I believe that the results of any strategy’s application in practice will underperform its theoretical results because of friction and the paradox of Schrodinger’s cat’s brokerage account. That said, I still see no reason why a systematic application of Graham’s liquidation value strategy or LSV’s low price-to-book value strategy can’t outperform the market even after taking into account these frictional costs and, in particular, wide spreads.

Hat tip to the Ox.

Read Full Post »

Older Posts »

Follow

Get every new post delivered to your Inbox.

Join 3,745 other followers

%d bloggers like this: