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 and Innovation in investment research; dealing with free information

Investment bankers, fund managers, and institutional investors of all types depend upon investment research to achieve their purposes. Those institutions that combine the most complete and accurate information intelligence with the best interpretative skills can best serve clients and gain competitive advantage.

Investment research has a cost and there is a limit as to how much an institution can spend on this item.

When investment research is performed with respect to a portfolio, the limits on investment research are determined by the size of the portfolio, plausible returns, and practical ceilings to management fees that might be charged.

Competitive advantage in investment research depends upon getting the most accurate and complete information and then making decisions based on the highest interpretive skills, at the least possible cost, within economic limits set by the nature of the business and by realities of the market.

As mentioned in previous articles (see above), there is more information freely available today, than most investment institutions can afford to gather and process.

However, new technologies are available that can reduce information costs substantially, while giving practitioners a competitive advantage — at least for a number of years until these technologies become common practice.

Note: This is a rather long article, describing strategies and technologies for gaining competitive advantage in institutional investment research.

Investment strategy when mining free information

In today’s information rich environment, it costs far, far more to gather, organize, filter, summarize, and present factual investment intelligence than it costs to make a rational, informed decision based on this data.

Although this information is free and usually available to anyone that has access to the Internet, often in official sources such as SEC archives, the quantity of information is so vast and the presentation so disorganized and confounded with irrelevant, repetitious, and obvious self-serving statements, that the time-cost of digging out this data is far greater than most people can expend.

In other words, the information is there for all to see, but few have the time to find it.

The Crash of 2008, in large part an informational crisis, has opened the door to new opportunities for institutions that are able to think outside the box.

A plausible strategy that takes advantage of the current informational environment has three elements:

1. Find a way to process and turn raw investment information into actionable intelligence at far less cost than one’s competitors.
2. Take action on this intelligence (buy or sell securities, make recommendations to clients, etc.) before competitors can do so.
3. Having taken action on the intelligence, make sure that the whole world has the same information as quickly as possible. The market will only react to what it knows. If you are the only one that knows the facts about stock ABC, the price will probably not be influenced by these facts.

More »

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 proceeds.

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.

More »

In this article, I continue the discussion of the crowdsourcing of investment research.

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

Modern investment research, at least as applied in the Capital Market Wiki Project, extends far beyond the 1930 techniques described in the classic Graham and Dodd’s Security Analysis.

Because of the vastly increased complexity of investment markets, securities research now needs to encompass not only the terms and conditions of individual securities, but the laws, rules, and regulations that that govern issuers and the institutions where the securities are traded, cleared, and settled, the legal jurisdictions where issuers operate, taxation, accounting rules, operational techniques, related derivatives, and much more.

The “World in a Grain of Sand” Approach

The Capital Market Wiki Project adopts what is called the “WIAGOS Approach”, an acronym derived from William Blake’s poem,

To see a World in a Grain of Sand
And a Heaven in a Wild Flower,
Hold Infinity in the palm of your hand
And Eternity in an hour.

The WIAGOS Approach recognizes that today there is much more information available than most of us can research alone, while competitive advantage in security analysis depends upon being able to get at more relevant information than the next guy.

WIAGOS means that if you start researching one security and keep digging, you’ll sooner or later end up researching the whole market — if you live long enough and have enough people to help you.

To study a closed-end investment fund (a relatively simple security), one would need to know about the fund corporation, the common and preferred shares issued by the fund, the various types of revenue-enhancing or risk abatement techniques employed (leverage, securities lending, diversification, interest rate swaps, and so forth), the legal jurisdiction that governs the fund, the exchange where the securities are listed and the listing and trading rules, the government agencies regulating the fund, the accountant, law firm, custodian, and other supporting institutions relevant to the fund, similar details on every security held in portfolio, industry data on the various industrial classifications held in portfolio, the indices against which the fund is bench-marked, the management company, the firm’s brokers, clearing agents, and securities lending intermediaries, tax and corporate laws governing operations, and the various investment strategies and theories employed by the fund. See: NRO fund in Capital Market Wiki.

If a competing investor knows all this information, and you only know what you read in Standard & Poor’s — you’re at a disadvantage.

Obviously, with such a huge volume of information and many people trying to work together to get at it, there needs to be a method to deal with the virtually unlimited sea of data. This is where Capital Market Taxonomy comes in handy.

What is Capital Market Taxonomy?

Taxonomy is a method of classification and Capital Market Taxonomy is a method of organizing information about financial markets.

In Capital Market Wiki, the field of information covered by the term “capital markets” is defined quite broadly, thus:

‘Capital and financial markets include those areas of economic activity that are related, directly or indirectly, to short or long-term techniques, methods, and strategies for the investment of assets or savings, the instruments used in such investment, and the institutions involved in creating such instruments, either by borrowing and lending to users of funds, or by underwriting, creating, and marketing investment or other securities, or by supporting secondary trading markets in securities, derivatives, or other financial instruments, or by legislating, regulating, supervising, taxing, protecting, influencing, assisting, or governing such markets and activities, or by providing supporting professional or other services that contribute to such activities.’

Capital Market Taxonomy (CMT) is a method of parsing financial information. It has two main functions.

1. Database organization: CMT is a formally defined series of tags or classifications that allow information related to capital markets to be stored digitally in a way as to be available for efficient retrieval when needed. With CMT (as implemented on Capital Market Wiki) you can retrieve information by asking questions like, “On which exchanges are derivatives base on Black Pepper traded and in what currency are such contracts settled?” or “Make a list of preferred shares under Japanese or Hong Kong law that are issued by companies in the hotel or resort business”.
2. Avoiding redundancy in crowdsourcing: If many people are to cooperate in financial research, it is useful to have a way to sort information into neat “compartments” so that each type of information has a location where researchers may work together, avoiding redundancy, allowing researchers to work on the same task knowing that someone else is not repeating the same task elsewhere. The idea is to break up a “blob” of information and separate it into categories, so that each category of knowledge has its place and can be quickly found by others working on the project.

In April 2009, Capital Market Taxonomy as implemented on Capital Market Wiki consisted of 4,848 formally defined namespaces, categories, attributes, and relations — far more than anyone (other than one suffering from the savant syndrome) could possibly remember.

However, CMT technology makes it possible for analysts to deal with the complexities of financial information effectively by remembering only four superclasses — most information classification is done by the software through standard input forms.

The four Capital Market Taxonomy super-classes

Capital Market Taxonomy is quite easy to use and understand.

All information relevant to capital markets is recorded as “articles” in a wiki encyclopedia. Each “article” deals with a topic that falls into one of four classifications.

These are the four super-classes:

1. Markets: Articles about legal jurisdictions that govern some aspect of capital markets. These can be areas of authority like nation-states, sub-national entities or monetary unions. For example, there might be articles on Nepal, United Kingdom, the State of Maryland, or the City of Naples, Florida.
2. Institutions: Articles about institutions that play some role in capital markets, such as issuers, investment banks, financial regulators, courts, auditing firms, and so forth. For example, there might be articles on the New York Stock Exchange, Neuberger Berman Real Estate Securities Income Fund, the Financial Services Authority (UK), or Kliring Penjaminan Efek Indonesia.
3. Instruments: Articles about a specific security or type of security, such as equities, bonds, preferred stocks, options or futures. This category also included “referentials“, which are the underlying values on which securities may be based, such as commodities, currencies, indices, or interest rates. For example, there might be articles about a specific common stock, a specific futures contract, or articles about soybeans, or LIBOR which are “referentials” for derivatives.
4. Operations: Articles about methods, procedures, behavioral constraints, and theories that explain how capital and financial markets function. This includes laws, regulations, and every type of operational procedure. For example, there might be an article about the Investment Company Act of 1940 (US), Auction Market Preferred Share Rate Determination, the Efficient Market Hypothesis, or Regulation of Insider Trading (Austria).

The practical effect of Capital Market Taxonomy is to break up a topic into different articles in an orderly fashion. For example, an article about a municipal bond issued by the city of Naples, Florida might have one article on the bond (in the Instruments category), and another about the city (in the Markets category). The content of each article is, to a certain degree, standardized, based on recommended formats. Therefore, the article on the city “Naples, Florida”, can be expected to contain information about municipal finances, whereas the article about the specific bond can be expected to describe the terms and conditions of that particular bond.

Avoiding redundancy and fostering specialization

The purpose of Capital Market Taxonomy is to permit efficient crowdsourcing of financial research.

In the example about a municipal bond issued by the city of Naples, Florida, we would have at least one article about the bond and another article about the city. If Naples, Florida has 20 different issues of bonds, there still would be only one article on the city itself. There might also be an article in the “Operations” category describing US tax law regarding municipal securities, but this one article would serve not only all 20 bonds issued by Naples, Florida but also all other US municipalities.

By linking articles together with “Infoboxes”, and by using “Capital Market Taxonomy”, readers can find relevant facts needed to understand the context of any article. It is not necessary, for example, to repeat the technical details of US tax treatment of municipal bonds in every article describing a specific instance of a municipal bond.

Linked compartmentalization of information makes it easier to focus specialized skills on certain topics. In the example about Naples municipal bonds, we might have an article about US tax treatment of municipal securities, written by tax experts, with another article on Naples, Florida, written by an expert in municipal finance.

Automation of Capital Market Taxonomy

As mentioned above, Capital Market Taxonomy involves over 4,800 semantic namespaces, categories, attributes, and relations, each with a formal definition. However, a researcher needs to remember only the four super-categories into which information is classified: Markets, Instruments, Institutions, and Operations.

How are the other 4,800 or so classifications applied?

The method of classification is actually quite simple. For each type of article, a form with check-boxes and automatic fields is presented by the wiki software. After spending a few minutes going through the form, the information is automatically classified in the semantic database.

Much of the classification is programed to go on behind the scenes. For example, by indicating that a bond is under the jurisdiction of the laws of Indonesia, the article will automatically be classified being in the categories Southeast Asia, and Asia and the Pacific.

The automated form also creates an “Infobox” for each article that allows readers to quickly find related articles or surf the encyclopedia, using semantic searching.

I’ll go into how OSINT techniques for investment analysis may be used by fund managers and investment bankers in a future article.