I have recently started devoting disciplined energy towards
becoming financially literate. You may wonder, "What does financial
literacy have to do with the O’Reilly audience?" Well,
after spending some time playing around with the different financial web
sites and reading a really interesting book, I started to contemplate a new web
site (destination) and web service (APIs). The book I just read is
called, “
href="http://www.amazon.com:80/exec/obidos/ASIN/0971695830/verdada-20?creative=125581&camp=2321&link_code=as1"
target="_top">The Vital Few vs. The Trivial Many,” by George Muzea, and
what is interesting about it is that it introduces a few meaningful
concepts that I hadn’t previously contemplated.
One is a market fundamental that sixty percent of all stock
price movements are related to the overall trend of the market. This
suggests, somewhat intuitively, that your ability to make money in the stock
market is in direct proportion to your ability to invest only when the
percentages are with you. It just so happens that there is a narrow
window of time that the market percentages are cyclically unfavorable, allowing
you to buy in at favorable prices, and a narrow window of time that the market
percentages are cyclically favorable, allowing you an ideal sell point (the
proverbial buy low, sell high). Since search patterns are well formed for
ferreting out such opportunities, but the requisite publicly available data
sources (that must be parsed) are dispersed over the Internet, this is an ideal
application for “information management” automation.
In a nutshell, here’s how it works. You buy beaten
down stocks in mid-October and sell them all in mid-February the following
year, making this a four-month strategy (I wish I was writing this column in
October, but that’s another story). The logic is that the market is
inherently down-pressured by the institutional tax loss selling that must be
completed by October 31 as mandated by the IRS (as opposed to tax loss selling
for individuals, which can be completed until December 31), and up-pressured by
the new money that flows into IRAs and 401Ks at the beginning of a new
year.
So how do you automate this? Step one:
start with the NYSE "New Low List" (in
href="http://www.barrons.com/">Barron’s weekly and
href="http://online.wsj.com/public/us">Wall Street Journal daily), which
essentially is a list of companies hitting 52-week lows. Step two:
subtract liabilities and long-term debt from assets on an identified company’s
balance sheet, removing entries that are a negative number (this data can be
accessed at CBS Marketwatch). Step
three: from the remaining list, search the ten-year chart of each
remaining stock (also available at CBS Marketwatch).
If the current stock price of a given stock in the list is not in the lower
third of its ten-year price history, remove it from the list. Step four:
from the remaining list, filter on institutional ownership, where the
institutional ownership value equals 30% or greater (data that is available at
href="http://moneycentral.msn.com/investor/invsub/ownership/ownership.asp">MSN
Money). Step five: filter out remaining entries that have not
at least shown a penny of profit in the last quarter’s earnings (also available
at CBS Marketwatch). The
interesting thing, and part of the reason automation is compelling for this
exercise is that from a list of 100 or so "New Lows," maybe 10 will
remain after completing this process, and these stocks will have both strong
balance sheets and current earnings, be in the bottom range of their 10 year
trading history, and down pressured by the institutional tax loss selling that
must be completed by October 31st. Once the cyclical down pressure is
replaced by the cyclical up-pressure on January 1, these stocks should rise.
The author of the book suggests that you determine your total investment
and buy equal dollars (not shares) of each of the quality stocks and
then sell them all by mid-February. The author has used this strategy
successfully in 24 out of 25 years.
Similarly, the author separates the market into what he
calls The Trivial Many, the mass investor market and so-called experts,
who you effectively want to bet against when investing, and The Vital Few, corporate
insiders, such as officers, board members and major shareholders, whose buying
and selling actions you should track like a hawk. Generally speaking,
insiders sell into price strength and buy into price weakness (again, buy low,
sell high). You want to look for new stock purchases where insiders are buying
as the stock goes up or when insiders are buying a depressed stock, and you
want to see them increasing their ownership percentages by at least 30 percent
of their holdings in the company. Also, since it is normal for insiders to buy
as their stock goes down and sell as it goes up, you particularly want to look
for divergence from this normal behavior. For example, your
eyes should be wide when you see an insider, especially the chief financial
officer who normally sells stock only when price rises suddenly break this
pattern by selling into price weakness. It usually means that the company’s
business conditions have deteriorated and that bad news is coming. On the other
hand, you should be really impressed when you see insiders buy at higher prices
than their earlier purchases. This usually means that business conditions are
at least as strong as when they first bought, and in many cases, getting
stronger. Better than expected news will more than likely surface a few months
later.
So how do you take advantage of this one technologically?
First off, insider actions are required to be registered with the SEC within
two days of their initiation (e.g., an insider sells a block of their stock
holdings), and this data is accessible within a specific database, known as
href="http://www.sec.gov/edgar/searchedgar/webusers.htm">EDGAR. While
there are premium services that show correlations between a given insider’s
action and movement of the stock, the basic data is out there gratis. The
specific type of filing to search for is known as Form 4, or “Statement of
Changes of Beneficial Ownership.”
As there is a fairly finite amount of insider activity, and
insider buys are generally more predictive than inside sales, database size
should be manageable even over a period of years. As to why insider
buying is generally more predictive than insider selling, consider that
insiders may sell for any number of non-business related reasons (such as to
buy that vacation home) but will generally only buy if they believe that the
market has under-valued their company’s forward looking business
momentum.
One way of fine tuning such a search query string is to
track all insider buying actions, in terms of adding them to the database, but
set a special flag to alert you when there are follow-on stock purchases by the
same insider in the face of an increasing stock price (i.e., an insider buys
10,000 shares when the stock is at $10, and then purchases a similarly
meaningful amount of shares when the stock hits $13 a few months later. The
logic here is that follow-on purchases in the face is rising share prices are
heavily predictive.
Again, you can tweak the model in terms of what constitutes
a meaningful amount of shares (in terms of number of shares or absolute dollars
relative to the insider’s holdings in the company). Similarly, you can
track and set your own flags for correlations between frequency of buys,
multiple buyers within the company, timing of purchases and the stock’s price.
The key point in all of this is that you can build a web
site that expresses your best stab at providing online answers to different
“what-if” financial questions built around objective models tied to predictive
data that is publicly available. Further the underlying elements of this
web site can be expressed as a web service, enabling like-minded peers to tweak
the inputs and outputs to their hearts desire. So, for example, if a
consumer of your web service loves a columnist, like
href="http://www.forbes.com/columnists/col_archive.jhtml?aname=Kenneth+L.+Fisher&author=kenneth+and+fisher">Ken
Fisher or Herb
Greenberg, or an online pub like Motley Fool,
they can build and maintain a portfolio that tracks their favorite columnists’
recommendations and cross-validates them around the insider-buying model.
Pretty cool, I think.
