Introduction
Identifying items that are identical to
each other is at the base as to how we learn in the western world. As children
we are asked what images are the same as other images. Later on we learn that
the values derived from a particular equation are the same as the values
derived from another equation, and labeled an identity. Many of today’s college
and graduates students learn about investing in forty or fifty minute classes
in an academic institution rather than in the marketplace. Thus, it is no
wonder that many professional investors and so-called sophisticated investors
use identities or labels in finding investment solutions in marketplaces that
are always changing. Therefore, it is not surprising that far too many investors will continue to
suffer from simplistic, quick, applications of identity labels.
Historical
Precedent
This post is being written on the last
weekend in June, 2017. Forty-four years ago I first published the weekly Lipper Mutual Fund Performance Analysis.
At the time my brother owned the Databank and had been publishing since 1968. There was a large balloon payment due to my brother for my acquiring the Databank. What does this have to do with today’s
misapplication of identities?
Going back to the 1930s there were reports
on the performance of mutual funds. (None of those reports that were published
in those periods exist today.) The commercial purpose of these were to help
salespeople in their marketing efforts to sell funds. In the case of my brother’s
firm, it was to find outstanding managers to manage separate accounts.
These efforts created a central identity. However,
I saw something quite different. I saw first a need on the part of the
independent directors of funds to have an accurate, timely, independent source
of fund performance analysis covering multiple time periods from very short-term to quite long-term periods. The second and eventually larger user of these
analyses were the senior management of fund groups to help them manage the
portfolio managers and funds under their command.
The reason for highlighting the multiple
time periods sprang from my experience as an investor, which was based on the
thought that one never really understood an investment until one could observe its
performance in both down markets and other periods of sub-par performance.
Thus, some forty years ago I took what was a then standard identity set and
delved deeper into it to get more useful knowledge and applications.
The Current
Picture
The nexus of the academics getting
interested in the market, perhaps to augment their own income, and the rapid
development of fast computers with prodigious memory, the price actions in many
marketplaces were translated into mathematical equations. Just as the written
word, a published equation takes on the aura of an absolute truth and a sense
of inevitability. Currently there is a great deal of money invested in published
index matching vehicles. That none of these measures were ever designed to be
prudently managed portfolios (which had various liquidity, payment needs, and
regulatory constraints as well as expenses) was ignored. Little to no attention
was made to the commercial motivations of the index publisher.
This week, the Fortune 500 double issue was
published. In the US, the first index-like investment vehicle which started in
the 1930s was based on the forty largest companies by sales on the Fortune list
at that time. It was perhaps a coincidence that half of the forty were on the
Dow Jones Industrial Average and half in what evolved to be the S&P
500. No one seemed to focus on the need of Time Inc, the publisher of Fortune
to sell advertising. It was a given that the larger the company’s sales, the more
likely the larger its advertising budget.
The original Dow Jones average was to record the dollar value change of
leading stock prices or in today’s lexicon, volatility. Publishing the more volatile
prices had the greater the likelihood that their newsletter and eventually
their newspaper would get paying readers. The NASDAQ indices was designed to
focus some attention on the Over-The-Counter market which was not represented
in the DJIA. NASDAQ wanted more listings.
Except for the sales culture, professional investors increasingly found that the published indices were not as useful in the more recent
markets. This has led to the production of passive indices based on market capitalization,
products produced (energy), legal domicile, largest stock market activity,
earnings, dividends, etc. These are often called smart beta or factor based.
From my standpoint they are an improvement, but in many cases these are using
the wrong identities at the moment.
Information
Technology Sectors
Charles Schwab & Co., has addressed the concerns that the soaring tech
sector stock price performance is sending a reminder of the “dot com” peak of
2000 and subsequent collapse. The data that they show is persuasive that while
the tech group has done well it is more soundly-based than in 2000. What I
found of great investment interest in the data was that the tech companies in
the S&P500 had net profit margins of
17.8% and a price to sales ratio of 4.5x. Both the Mid-caps in the S&P 400
and the Small-caps in the S&P 600 tech sectors had margins in the 3% range
and price to sales of 1.4x. As an investor the way I look at these data points,
I wonder how much of the lager tech companies are benefiting from materially
lower tax rates due to their more global activities. If and when net tax
realizations become lower, the Mid and Small caps should rise relative to Large
caps. Perhaps more significant is the major disparity in the price to sales
ratios. With all other things being equal, which they almost never are, the
prices of Mid and Small caps are much easier for acquirers.
If one were going to select on the basis of statistical
factors alone, I would, at the moment, be more interested at tax rates paid and
price to sales ratios than market capitalizations. The Federal Reserve
Board has come up with their own factors
to approve the capital spending of large banks which could well lead to useful
factor investing which can be summarized as follows:
Credit and counterparts risk
Liquidity risk
Operational risk
Information technology risk
Trading activities market risk
Interest rate risk
Strategic risk
Model risk
Reputational, fiduciary and business
conduct risk
"Beware of Wrong Identities"
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