Crux of the Duplex Method
plus Case Study
of the Dow Stock Index
We live in a world full of complex and chaotic systems. A good example
concerns the stock market that stymies all manner of investors ranging
from casual amateurs to gung-ho professionals.
According to the
Efficient Market Hypothesis, the current price always reflects the
totality of information available to the investing public. As a
byproduct, no one can detect any clues for predicting the market in a
trusty fashion.
Instead, the market is deemed to move in an
utterly erratic way. In particular, a popular myth known as the Random
Walk shuffle contends that the price level shifts with equal likelihood
and to similar extent in either direction, whether to the upside or
downside.
At first glance, the image of pure randomness does ring
true in practice. For instance, the average investor is unable to beat
the market averages such as the Dow Jones index. While the lack of
success may seem like a letdown, the truth is even worse. In actuality,
the participants in the aggregate lag comfortably behind the benchmarks
of the bourse.
If we look more closely, the lousy performance of
the actors springs mostly from their frantic efforts to beat the
competition. Amid the frenzy, the demons of greed and fear prod the
antsy players into making impulsive moves that are not only groundless
and futile but actually counterproductive and harmful to their cause.
On
the bright side, though, the market displays a smattering of patterns
that can be exploited by a sober person. An example concerns the
seasonal cycle behind the monthly moves of the Dow benchmark.
To
fathom the elusive waves in a stringent fashion, we turn to the duplex
method of modeling shifty systems. The sturdy framework makes use of the
binomial test: the simplest and strongest, as well as safest and
surest, way to profile chancy events regardless of the domain.
To
this end, we first transform the conceptual models of the stock market
into a trio of precise templates. The formal blueprints are then
converted into R code: the top choice of programming language and
software platform for statistical workouts. The trenchant results serve
to debunk the fable of efficiency and confirm the existence of hardy
patterns in the marketplace.
In short, the benefits of the
seasonal model lie in simplicity and potency in sundry forms. The
drawcards include the ease of acquiring the information required, the
leanness of the dataset employed, the ubiquity of the software deployed,
the universality of the experimental setup, and the strength of the
conclusions at high levels of statistical significance.
NOTE: The full report is titled, “Basic Models of Complex Systems”. The document may be downloaded in PDF form at Smashwords or ResearchGate. Moreover, a digest of the report is available as a video at YouTube or Internet Archive.
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