This is the seventh article in a series about Post Modern Security Analysis.

Creating a Research Community

The essential element in Post Modern Security Analysis is emphasis on the gathering and selection of open source investment information and on the preparation of factual, encyclopedia-style articles based on this research.

Because of the time required to develop factual research of this quality and because the utility of such research is enhanced by research on comparable securities and input from researchers with special expertise, the solitary analyst must now look to joining a research community to receive collaborative assistance from other researchers.

Post Modern Security Analysis dates from the Crash of 2008 and the decline of acceptance of the Efficient Market Hypothesis, along with criticism regarding the quality of work produced by traditional profit-based publishers of investment statistics.

So, in this new field of endeavor the question is, “How does a researcher create a Collaborative Investment Research Community?” and “Do any such communities already exist?”

Essential elements of collaborative investment research

When the goal of collaborative investment research is to produce a factual, encyclopedia-style “article” about a certain topic, the following conditions must exist:

1. Purpose and content: The purpose and content of the “article” must be defined and agreed upon by the collaborators.
2. Consensus: The collaborators must have reached a consensus regarding the standards to which the article must be held.
3. System and Rules: There must be a system by which collaborators at different locations may work on the same article in harmony, with rules for resolving inevitable differences of opinion regarding content or fact.
4. Enforcement: There must be a way for enforcing the rules and systems of collaborative research, protecting the work against disruptive elements.

The Internet offers the least expensive communication system to bring together collaborative researchers throughout the world. It is not free, but costs are small relative to benefits.

The rise of the Internet and personal computer systems has spawned many collaborative software systems. Of all these systems, the “wiki” concept is the most appropriate for collaborative investment research.

A wiki is a website that uses wiki software, allowing the easy creation and editing of any number of interlinked Web pages, using a simplified markup language or a WYSIWYG text editor, within the browser.

Wikipedia

Most wiki software is free (open-source) and MediaWiki, the software used in the most successful wiki (Wikipedia), can be easily setup by anyone with access to a server and basic computing skills.

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In this article, I continue discussing new directions in investment research.

See: Crowdsourcing investment research: opportunities in OSINT and Free information and the Efficient Market Hypothesis and Crowdsourcing investment research: Capital Market Taxonomy

There are two basic tasks in security analysis.

The first is gathering facts about the security.

With the facts in hand, the next job is to analyze the data critically and answer the question being asked.

Current information technology and the costs of data determine how the research should proceed.

Note: This is a rather long article, describing changes in investment information over 100 years.

Facts: Complete and accurate

The most common way to judge the effectiveness of an analyst depends upon ex-post results relative to the analyst’s recommendation, compared to results of competing analysts.

Reasoning skills being equal, the analyst with the most complete and accurate information should have the best relative performance.

Analysts can not compensate for incomplete or inaccurate information — and it costs time and/or money to obtain quality input. As Bernard Baruch, the famous Wall Street speculator of the early 20th century put it:

“Every man has a right to his opinion, but no man has a right to be wrong in his facts.”
“If you get all the facts, your judgment can be right; if you don’t get all the facts, it can’t be right.”

The larger and more varied the investment market, relative to the analyst population, and to the level of technology applied to securities research, the higher the cost of gathering facts and the greater the chances that superior opportunities may be overlooked.

In other words, complex markets that have more information than can effectively be handled by the average analyst are inefficient. Inefficient markets have more opportunities waiting to be discovered than efficient markets.

Investment research in Ben Graham’s time

In 1934, when Benjamin Graham, the legendary security analyst and teacher of Warren Buffet, published the investment classic, Security Analysis, the US SEC had recently been created and the first rules on disclosure were being issued.

Most information about securities was not free and, compared to today, was difficult to obtain.

Registration statements and offering documents submitted to the SEC were available to the public only in SEC reading rooms in selected cities. To get this information, the analyst had to go to the SEC, find the document, request a photocopy, and pay copying costs per page.

Once you had the document in hand, financial analysis consisted of copying numbers to a paper spreadsheet, summing columns by hand or with a non-electric adding machine, and calculating ratios, also by hand, or perhaps by using a mechanical calculator driven by a little crank.

Stock prices were delivered on a telegraphic printer, called a ticker, on a long band of paper. To create a graph of stock prices it was necessary to plot prices and volumes, one by one, on graph paper. Calculations of present value, bond interest rates, or annuities were done by using little volumes of financial tables.

Everything was paper-based. Newspapers clippings were annotated, pasted on backing paper, and filed. Some subscribed to clipping services.

Not only was financial information hard to get at and work with, there was far, far less of it than today. Corporations were much simpler. There were no organized exchanges for financial derivatives. Products like asset-backed securities, index funds, and swaps were not available. Most markets were local, only 5% of the population invested in equities, and far fewer companies were listed than today.

Early providers of standard statistics

In 1860, Henry Varnum Poor published a book on the “History of Railroads and Canals in the United States”. In 1906, Standard Statistics Bureau was formed to publish previously unavailable data on U.S. corporations.

In 1910, the Standard Statistic Bureau service consisted of a 600-page bound 2-volume quarterly covering U.S. and Canadian stocks and bonds. The service was also provided in the form of index cards, with updates delivered periodically.

Standard Statistic Bureau advertised the dual convenience of having hard to get information gathered into one place, but also having this information indexed and filed — what served the purpose of a database in those days.

This early service proudly proclaimed that only facts, not opinions, would be delivered:

No advice on the market, no “tips” but every bit of authentic information that can be obtained from any source about any one or any number of securities in which you may be interested.

The data was not only hard to get (requiring trips to libraries, archives, and corporate headquarters and tedious hand-copying of data and payment of copying fees), but, in terms of the market at that time, quite complete.

Of course, there just wasn’t that much information available.

A typical Standard Statistics report (on Western Union, a leading stock at the time), shown on the right, had:

• Unaudited four year income statements with five items: Gross revenues; Operating expenses; Other income; Interest on bonds; and Dividends.
• Unaudited two year balance sheets with 22 items, divided between assets and liabilities.
• High and low stock prices of the last nine years.
• Dividends paid with dates.
• Directors’ names, addresses of the company, transfer agent, etc.

The entire 1911 Western Union “Standard Statistics” report could fit into a single item on a footnote to an auditor’s report on public companies in today’s market.

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In the article, “Free information and the Efficient Market Hypothesis“, I give an example of how valuable free information relevant to investments is available on the Internet and how this is largely unexploited.

This free “open source” information calls for open source intelligence techniques (OSINT) to be useful. See: Open Source Capital Market Intelligence.

Free information has a time cost

The problem with “free” investment related information today is that there is so much of it.

Only a tiny fraction is harvested and published by commercial sources like Standard & Poor’s.

Anyone with a computer can get at this information, but because the data is raw, unfiltered, and buried in mountains of irrelevant, useless text, it takes time and skill to dig out what is worthwhile.

In other words, free information has a “time cost” that is a significant barrier.

To break through this barrier, new technologies are required.

From the assembly line to crowdsourcing

Standard & Poor’s, Moody’s, and the rest of today’s commercial providers are all rooted in 19th century industrial techniques.

Some types of financial information (like financial statements and stock prices) are amenable to industrialization.

This information can be processed with standard tools and forced into cookie-cutter patterns, producing a standard product at a predictable cost.

Take a look at a typical report published by Standard & Poor’s and you’ll see the industrial nature of the product.

However, other types of essential information (securities terms and conditions, contract details, laws, regulations) require tailor-made human intervention and are not amenable to industrial methods.

Furthermore, the amount, location, and nature of relevant information that is worth mining is not known before you start digging.

In other words, you don’t know whether you’ll find gold, iron, truffles, of King Tut’s tomb — you can’t use just one kind of tool and start digging. You don’t know how long you’ll be digging or who might be interested in what you find.

To solve this problem, we must abandon the logical solutions of the industrial age and try something completely different.

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