Fundamental securities analysis and open source financial intelligence

Fundamental securities analysis will be advantageous for investors in the wake of the Crash of 2008. (See: Finding investment opportunity in the post stock-buyback era.)

By “fundamental securities analysis”, I don’t mean just financial statement analysis as in Graham and Dodd, but rather a combination of such techniques with data mining for relevant facts, using open source financial intelligence techniques (OSINT).

Open source financial intelligence

The main profit opportunity in fundamental securities analysis lies in the fact that only a tiny portion of available financial information is actually published by the leading commercial sources such as Standard & Poor’s, Moody’s, and Thomson’s. (See: Commercial sources of information.)

Much of the information omitted by the commercial services is available free on the Internet, but the time and effort required to find the facts, discard the irrelevant, and analyze meaning is a greater burden than most of us, even sophisticated investors, are willing or able to expend.

Many investors  share the tunnel vision of commercial information sources
Many investors share the tunnel vision of commercial information sources

Open source intelligence (OSINT) is a technique used by intelligence agencies throughout the world to gather and analyze free, non-secret, raw public information such as in public government files (like the SEC), newspapers, websites, and miscellaneous printed ephemera. (See: Open source financial intelligence.)

Open source intelligence is used by UK MI5

The US Central Intelligence Agency and the UK MI-5 are typical users of open source intelligence techniques.

Techniques and the trade craft of open source intelligence are taught in universities such as John Hopkins, Maryland, Trinity Washington, American Military University, and Mercyhurst College, but not in most business schools. (See: Open source tradecraft.)

The fact that most MBAs are not trained in open source intelligence, but rather the opposite — the Harvard Business School case method — gives a competitive advantage to those analysts who know how to mine the vast bog of free information for those facts that are important in security selection.

Teaching open source financial intelligence techniques

Security analysis is much more than financial statement analysis. The analyst must also know the terms and conditions of each class of security and the laws that constrain the operations of a particular venture.

A company may be limited by off-balance sheet liabilities on pension plans, contracts with suppliers, restricted licensing agreements, and much more. Only when you have this essential information, can you decide what financial ratios are appropriate.

In fact, the skillful analyst will devise ratios and ways to measure an enterprise that are tailor-made for specific circumstances.

To get information that you need, you must know how to wade through the vast marshes of open source information, hunting for clues as to what is relevant and what is not.

Somewhere in this marsh of information, there is vital data ...

Most open source information is useless. Garbage to be discarded. Puffery, opinion, irrelevancies, and non-facts make up much of what you must wade thorough.

SEC documents are laced with disclaimers, legal boilerplate, and required irrelevant “facts” that waste your time, but must be sorted through. Much is repetitious.

But now and then, you come across that pearl of wisdom … an “ah ha” moment … that makes it all worthwhile.

Of course, competing analysts who are too busy for such drudgery, will never have your insights and will march off in lockstep to the tune of the very limited informational ditty served up by the same easy, convenient, commercial sources.

Fundamental analysis has never been easy — which is the reason that Warren Buffet and Benjamin Graham were able to make money.

Fundamental analysis that is relevant to the 21st century and the over-abundance of free information, must go beyond the 1930 techniques of Graham, Dodd, and Buffet.

Capital Market Wiki provides templates for teaching open source financial research in universities. This wiki also has formats, guidelines, and help files that allow you to learn on your own, by doing.

Non-standardized information

The challenge of open source financial intelligence is that the information marsh through which you must wade is unorganized and uncharted.

Like Forrest Gump’s mother’s “box of chocolates”, you never know what you’ll get.

OSINT is like a box of chocolates ... you never know what you'll get.

Things are even more complicated by the fact that capital markets are now international. Information available in Indonesia is not the same as in the United Kingdom. Not only are there language barriers, but there are differences in culture, laws, and commercial customs.

Extremely important information can easily be over-looked. For example, the fact that Bernard Madoff was, according to SEC documents freely available on the Internet, holding in custody billions of dollars of client assets, with full managerial discretion, and audited only by a tiny one-man firm in a strip mall in a New York suburb, should have been sufficient to set alarms bells ringing. But this information was available only to open-source analysts who were looking very closely, with a skeptical eye.

Technology and economics for open source financial research

Internet technology and search engines such as Google have brought open source intelligence techniques within the reach of anyone.

Among the new technologies that are useful for open source financial research are collaborative research thorough wiki systems, semantic databases, capital market taxonomy, and open source economics.

I’ll get into these issues in a future article.

Or you can find more information on the Capital Market Wiki introduction page.

Photo credit: “Tunnel vision”: nikpawlak (flickr)

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