The
“Mousetrap” is a method for merging technical and fundamental indicators into a
single model. The concept for each
aspect of the model is mean reversion.
On the technical side I measure long term volume-breadth against
price-strength in a sector or industry.
On the fundamental side I measure long term growth in cash flow, book
value, and earnings against total return and total debt.
If the
debt is low and the price return is cheap compared to long term growth, we can
find a relatively safe stock within an industry that is technically primed for
a short term reversion toward the mean.
At a
point in which price begins to balance against both technical and fundamental
indicators, the stock will become attractive to momentum traders, and I will
sell just as demand begins to catch up to supply.
In this
manner I buy stocks when no one wants them, and sell them when everyone wants
them.
The
returns for this method are better than 20% annualized, and I have been
enjoying this return rate in live trading since 05/31/2011, when I launched the
model.
In
April of 2012 I began publishing the trades at www.market-mousetrap.blogspot.com
– advertising each trade the night before it was to occur. There is no ambiguity or hindsight bias in
these trades. I post them before they happen, and publish the
running returns, both good and bad.
During
this period I have experimented with some of the fundamental features, and
tightened up the technical parameters, but the general trading concept has been
the same: buy stocks in low demand sectors and sell when demand begins to pick
up. I sell them well before the top, and
in most cases only enjoy the first third of a price move. But the first third of a price move allows me
to unload them with ease (and to hold for longer, if scalability ever required
it).
Measurements
are long term in order to avoid the effect of High Frequency Trading (HFT)
algorithms on the model. With a long
enough gauge, the HFT effect becomes background noise, and the model will not degrade
in the future. Humans tend to think in
terms of weeks and months, logarithmically discounting longer time frames. HFTs are designed to take advantage of human
behavior, and in order to compete with each other they work in ever shortening
time frames. Their goal is a 60% return.
My goal
is a consistent 20% return, leaving me out of their time frames and target
return rates. I don’t compete with them,
but I’m happy to pick up the debris they shake loose in their activity.
Live
trading has been consistent with back-tested results:
By the numbers:
S&P
|
Sector Model
|
Days
|
|
Total
|
63.23%
|
1954.80%
|
5785
|
Annualized
|
3.14%
|
21.03%
|
That
is, during the 12/23/1998 to present period of combined back-test and live
trading, the S&P has given an annualized rate of return of 3.14%, while the
Sector Model has returned 21.03%.
No
back-test is possible for the Full Model, but the annualized return rate for
the live trade period of 5/31/2011 to the present has produced 21.89%
annualized returns, which is consistent with the Sector Model.
Note
that the primary goal of the Full Model is not out-performance per se, but
rather scalability. Containing only 10 stocks with an average
holding period of 70 days allows me as a small investor to perform one trade a
week. As the portfolio increases in
size, I can increase to as many as 10 trades a day, with 500 logical positions,
without even needing to resort to intraday measures.
In
other words, the Sector
Model is fine for a small portfolio, but the Full Model can function with a
nearly unlimited portfolio base.
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