Introduction
In an earlier post we analyzed the prominent and
often cited study on “net nets”, “Benjamin Graham’s Net Current Asset Values: A Performance Update”, conducted by Henry R. Oppenheimer from the Financial
Analysts Journal (1986). In this post we analyze the article “Graham’s
NetNets: Outdated or Outstanding?” by James Montier. The objective of the article
was to examine the performance of securities that were trading at no more than
twothirds of their “net current assets” during the 23 year period from 19852007
globally and regionally (namely, in the US, Europe and Japan[i]).
Our
objective was to analyse the article itself; determine its reliability, draw
our own conclusions and glean, if any, actionable advice for the practitioner
of the “net net” method of investing.
Summary
We simply
replicate the summary provided by the author for ease of reference before
conducting our own analysis.
Value
Investing: Tools and Techniques for Intelligent Investment, Chapter 22, p.
230/231:
Parameters
·
Title: Graham’s NetNets: Outdated or Outstanding
·
Author: James Montier
·
Source: Value Investing: Tools and Techniques for Intelligent Investment,
Chapter 22
·
Publication Year: 2009 (also 2008 Mind Matters 30 September 2008 by
The Société Générale Group)
·
Data source: unspecified
·
Period studied: 19852007
·
Years in study: 23
·
Markets studied: US, Japan, Europe
·
Exchanges: Unspecified
Key Methodology
·
Valuation metric: “…current assets minus its total liabilities…”
The author did not expressly state whether
preference shares were also deducted from Current Assets; however, we assume
the formula used was as specified by Graham:
i.e.
Net Current Asset Value per share = (Current Assets – (Total Liabilities +
Preferred Shares))/Common Shares Outstanding
“Of course, Graham wasn’t contented
with just buying firms trading on prices less than net current asset value. He
required an even greater margin of safety. He would exhort buying stocks with
prices of less than twothirds of net current asset value (further
increasing his margin of safety). This is the definition we operationalize
below.” i.e. the study
examines the returns to stocks trading at less than 2/3 of net current asset
value.
·
Weighting: “equally weighted”
·
Purchase/rebalance date: unspecified
·
Holding/rebalancing period: unspecified
Reliability
While
there are numerous biases/errors that can be made when conducting studies/back
tests, below we have analysed those we deem most likely to impact a study of
this nature:
1.
Survivorship bias
Given
the data source was not specified and no mention was made of controlling for
survivorship bias we have no way of knowing if the sample studied may have suffered
from survivorship bias.
2.
Look Ahead bias
No mention was made with regard
to the portfolio formation date, so we do not know if the back test suffered
from Look Ahead bias, or not.
3.
Time period bias
The study spans 23 years and we classify this as a “more reliable” period.
For reference:
·
< 10 years;
inadequate/unreliable
·
11 to 20 years; somewhat
reliable
·
> 20 years; more reliable
·
> 40 years; most reliable
4. Human error
Human error is always a possibility; however, we have little detail
on the backtesting protocol implemented so cannot comment on the increased or
decreased likelihood of human error entering the testing process.
5.
Journal rating/credibility^{[ii]}
Given this was essentially an article we cannot assign it with the
additional credibility afforded to publications appearing in well renowned
finance journals.
Reliability Assessment: Given the
absence of detail pertaining to the methodology and data source used to
undertake the back test we deem the article to possess a low level of
reliability. In addition, further analysis (refer below) causes us to
question the reliability of the results generally.
Results and Analysis
“An
equally weighted basket of netnets generated an average return above 35% p.a.
versus a market return of 17% p.a.”
“Not
only does a netnet strategy work at the global level, but it also works within
regions (albeit
to varying degrees). For instance, netnets outperformed the market by 18%,
15%, and 6%
in the USA, Japan and Europe, respectively.”
We summarise the results in the table below
(some of which were gleaned from Figure 22.2):
Global

USA

Japan

Europe


Annual Return (“p.a.”)

35%

42%

21%

17%

Market

17%

24%

6%

11%

Outperformance (absolute)

18%

18%

15%

6%

Outperformance (relative)

51%

75%

250%

55%

The author specified that the returns were
“p.a.” i.e. per annum. It is not clear, to us, if “p.a.” represents the
arithmetic mean or geometric mean i.e. the compound annual growth rate. This,
in our opinion is of critical importance, especially given the length of the
study and given it incorporated Japan, a market which experienced, to our knowledge,
the greatest stock market bubble in recorded history during the period
examined. But how could we find out?
Interestingly the author refers to the
Oppenheimer study with which we are intimately familiar.
“In
1986, Henry Oppenheimer published a paper in the Financial Analysts Journal
examining the returns on buying stocks at or below 66% of their net current asset value
during the period 1970–1983.
The holding period was one year. Over its life, the portfolio contained a
minimum of
18 stocks and a maximum of 89 stocks. The mean return from the strategy was 29%
p.a. against
a market return of 11.5% p.a.”
Table V
from the Oppenheimer study is reproduced below for ease of reference:
Using
the above information, we can deduce the following with reasonable
confidence:
The
author derived “29%” by multiplying the monthly portfolio return of 2.45% by 12
i.e. 2.45% * 12 = 29.4% i.e. “29%”.
The author
derived “11.5%” by multiplying the monthly market return of 0.96% by 12 i.e.
0.96% * 12 = 11.52% i.e. “11.5%”.
We know
the compound annual growth rate in the Oppenheimer study for the full sample
period was 28.2% (refer table IV, not reproduced here). Compounding 2.45% for
12 months yields 33.7% ((1+ 2.45%)^121). Therefore, we determine that the
results in table V of the Oppenheimer study represent the arithmetic
mean and not the geometric mean referred to in Table IV. Hence, based on the
comparative statistics referenced by the author, we deduce, with a strong
likelihood, that the author has calculated the arithmetic mean return.
The
implication of calculating the arithmetic mean return vs the geometric mean return
is stark. The arithmetic mean would, on the balance of probability, have
materially overstated returns. Of particular concern are the returns stipulated
for Japan which experienced, to our knowledge, the largest equity market bubble
in recorded history in
the late 1980’s which formed part of the period studied (i.e. 1985 to 2007). To
illustrate the misleading nature of an arithmetic mean we refer to the “Summary
Edition Credit Suisse Global Investment Returns Yearbook 2019” Table 1:
There
is a material difference between the arithmetic and geometric mean achieved for
all equity markets examined. For Japan the arithmetic mean is more than double
the geometric mean! While the returns quoted are “real” returns and the time
period different, our point remains, an arithmetic mean has, and does,
overstate the actual returns achieved.
Moreover,
a cursory glance at Figure 22.2 illustrates returns of 24% p.a. to the “USA
Market” from 19852007; this also indicates an arithmetic mean has been used
because, to our knowledge, the US market simply did not achieve a
compound annual growth rate of 24% from 1985 to 2007!
To take
our point to a theoretical extreme let’s examine a short series of returns
calculating both the arithmetic and geometric mean:
Remarkably,
it is theoretically possible to achieve an arithmetic mean twice that of
the market (16.00% vs 8.00%; 2x); and simultaneously attain only a fraction of
that return based on the geometric mean (4.99% vs 0.95%; 0.19x).
Conclusions and Practical
Implementation
Given the relative dearth of studies examining the returns of net
nets, especially those in Japan, we are grateful for this article. Nonetheless,
due to the silence on the data source, details pertaining to the research
methodology and the strong likelihood that the returns specified were
calculated using the arithmetic mean, thus overstating returns, little
can be relied on from this article.
While it may be disheartening to be left with something of a “null
hypothesis”, it does provide us with the opportunity to be clichéd and conclude
with a quote from none other than Charles T. Munger:
“Any year that passes in which you don’t destroy one of your best loved ideas is a wasted year.”
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