This is the second article in a series of tutorials about post-modern security analysis.

Classic “Intrinsic Value”

The central concept of classical security analysis is “intrinsic value”.

This term is defined as follows in the first chapter of “Security Analysis (1940 Edition)” by Benjamin Graham and David Dodd:

	"Intrinsic Value" is a fuzzy concept

… intrinsic value is an elusive concept. In general terms, it is understood to be that value which is justified by the facts, e.g., the assets, earnings, dividends, definite prospects, as distinct, let us say, from market quotations established by artificial manipulation or distorted by psychological excesses. But it is a mistake to imagine that intrinsic value is a definite and as determinable as is the market price.

Our notion of intrinsic value may be more or less distinct, depending on the particular case. The degree of indistinctness may be expressed by a very hypothetical “range of approximate value”, which would grow wider as the uncertainty of the picture increased … It would follow that even a very indefinite idea of the intrinsic value may still justify a conclusion if the current price falls far outside the maximum or minimum appraisal.

The “facts” on which “intrinsic value” was to be based were considered to be relatively simple and easily acquired at the time Graham & Dodd published the first edition of “Security Analysis”, which stated:

	1910 ad for "Standard" stock index cards

Descriptive analysis consists of marshalling the important facts related to an issue and presenting them in a coherent, readily intelligible manner. This function is adequately performed for the entire range of marketable corporate securities by the various manuals, the Standard Statistics and Fitch services, and others.

Because of this assumption (reasonable at the time), almost the entire volume of “Security Analysis” (often considered to be the Bible of “fundamental analysis”) was devoted to the analysis of data from these standard, easily obtained secondary sources. Little attention was devoted to the job of collating and researching data from original sources (OSINT). More »

This article is the first in a series of tutorials about the techniques of post-modern securities analysis. Click the RSS button in the sidebar to get a free subscription to the whole set.

Definition of “Security Analysis”

Security Analysis is the study of facts about negotiable instruments for the purpose of determining whether a particular instrument is appropriate for a specific investor at a particular time and the intrinsic value of the security compared to its market price, if any.

	Observe closely and get the facts ...

This analysis is usually conducted by gathering facts about the legal jurisdiction governing the security, the terms and conditions of the issue, data on the organization issuing the security, and information on operations, laws, rules, and other factors related to the instrument or the issuer.

The determination of the appropriateness of a security is made by a critical evaluation of a wide range of facts about the instrument in terms of the current price and the needs of a specific investor. More »

In August 1952, the famous Benjamin Graham published an article in “The Analysts Journal” entitled, “Toward a Science of Security Analysis”. The article was sufficiently important to be republished in “The Financial Analysts Journal” in January 1995.

	Predicting the future is often not scientific

Ben Graham definitely did not say the security analysis was a “science”, but his comments implied that he would like to have the discipline move in that direction. He specifically made comparisons to the use of mathematical techniques in “actuarial science” and suggested that the practice of bond ratings was somehow scientific in nature.

By the time of the Crash of 2008, the emanations of “science” had cast a cloak of statistical pretense over the profession of securities analysis, as this abstract of the article, “The Statistics of Sharpe Ratios” (Andrew W. Lo, Financial Analysts Journal, July 2002), indicates:

The building blocks of the Sharpe ratio—expected returns and volatilities—are unknown quantities that must be estimated statistically and are, therefore, subject to estimation error. This raises the natural question: How accurately are Sharpe ratios measured? To address this question, I derive explicit expressions for the statistical distribution of the Sharpe ratio using standard asymptotic theory under several sets of assumptions for the return-generating process—independently and identically distributed returns, stationary returns, and with time aggregation. I show that monthly Sharpe ratios cannot be annualized by multiplying by √12 except under very special circumstances, and I derive the correct method of conversion in the general case of stationary returns. In an illustrative empirical example of mutual funds and hedge funds, I find that the annual Sharpe ratio for a hedge fund can be overstated by as much as 65 percent because of the presence of serial correlation in monthly returns, and once this serial correlation is properly taken into account, the rankings of hedge funds based on Sharpe ratios can change dramatically.

Now this sounds very “scientific”, especially if one has no knowledge of the scientific method and tends to believe in astrology. However, on examining the underlying assumptions of Modern Portfolio Theory (see: Fallacies of the Nobel Gods), under the harsh prism of the scientific method, problems with this approach become evident.

The above abstract seems to criticize the use of the Sharpe ratio, as a serious subject, but would a real scientific journal waste time evaluating the tenants of Medieval Astrology?

Many non-scientific practices are useful

We take our automobiles to the repair shop when they break down and are thankful for the skill and knowledge of an honest, professional mechanic who can get the machine running again. However, few would consider the auto mechanic to be a scientist.

	Medieval minds linked science to God

Actuaries calculate the life expectancy of classes of individuals, based on mortality tables, but would not attempt to predict how long a specific individual will live (unless the subject is already falling from an airplane).

Much of security analysis has the purpose of determining whether the value of a security in the future will be higher or lower than the current value. A honest, competent security analysts will make such estimates based on careful and laborious study of relevant facts, and present conclusions properly hedged as to the general impossibility of predicting the future (in a non-scientific field of endeavor).

This is a valuable and useful service in itself and does not need to be dressed up in the pretense of science to become more useful.

In fact, scientific pretense can actually mislead investors (and analysts themselves).

Where pseudo-science harms investors

Leading up to the Great Crash of 2008, millions of hours of analyst time were wasted on drawing up tables with betas, Sharpe ratios, and other mathematical gimmicks that did not actually contribute to the understanding of an issue, but instead gave investors (and analysts) a false sense of security.

I would argue that this time (and many more hours) could have been better served digging up the facts about specific issues and examining operational and legal details under the cold light of common sense.

For example, would billions have been lost on auction rate securities if analysts had spent more time carefully researching the details of these issues and asked hard “what if” questions about the actual functioning of the Dutch Auctions on which their liquidity was based?

The principal problem with the pseudo-scientific approach to security analysis is that the methods often give analysts an excuse to avoid the much harder research work of going back to original sources of information and digging and digging for more facts until all relevant issues have been examined.

See: A security analysts greatest challenge: Laziness

Illustrations: Wiki Commons: The first illustration is from the Aurora consurgens, an illuminated manuscript of the 15th century in the Zurich Zentralbibliothek (MS. Rhenoviensis 172) — a medieval alchemical treatise that contains thirty-eight miniatures in watercolor.