Common Technical Characteristics of Some Major Stock Market

In this blog, I will be covering some charts based on the
technical data of three major stock market corrections: the Crash
of 1987, Dot-com Correction of 2000, and the Great Crash of 1929.
By technical data, I mean that the charts are produced using stock
market prices.  More than a month ago, I wrote about opening my
first stock trading account in 1987; this is the same year I was
hired for my first regular full-time job.  I worked as a store
clerk on Bay Street, which is arguably the Canadian equivalent of
Wall Street.  I considered it a reasonably enjoyable existence.
 But then came the stock market crash . . .

I recently introduced a group of risk-based market metrics that
support a trading method that I call "Skipjack" - named after the
tuna.  A chart of a good skipjack might actually resemble a tuna.
 While the simulations running on an application that I call
Simtactics suggest that the methodology is often and perhaps
generally applicable, sometimes it is quite challenging to beat the
market on a total return basis.  This caused me to use the system
on large amounts of market data to ascertain the reasons.  However,
I needed a market player more consistent than myself in order to
remove irregularities extending from my unique personal qualities.
 I worked on an autopilot feature for Simtactics that eventually
led to the Levi-Tate Group of Market Metrics.  Yes, I own the
domain names.

The Levi-Tate metrics make it possible to produce a type of
topographical or algomorphological chart that I call a Thunderbird
Chart.  A form of this chart that I call an Advantage Chart follows
the extent to which the autopilot is able to beat the market.  I
found that the chart changes significantly over time.  I therefore
decided to follow the advantage levels over different time slices
about equidistant in terms of their periodicity.  At this point, it
became possible to study how the array or gradient of algorithmic
combinations respond to stock market conditions over time.  This
leads me to the special charts on this blog that allow me to
examine the progression of the algomorphology in a truly
developmental sense.  At this level, I confess the charts are a bit
complicated both to explain and also to present.  A 12x12 grid
containing 144 combinations from 1 to 56 results in 144 rows per
column of time slice.  Rather than dwell on difficulties
visualizing the exact details, please consider taking my word for
it in relation to the general details that I am about to

On the chart below, there is a 0 on the y-axis.  Above the 0,
the Simtactics trigger package was able to successfully beat market
using a particular combination.  Below 0, the application's
apparatus failed to beat market.  The chart below makes use of data
from the Dow Jones Industrial Average one year before the Crash of
1987.  Notice that a fair number of algorithmic pairs are above 0,
which means that they successfully beat market.  As I mentioned
earlier, it isn't unusual for these metrics to beat the market.  We
know however that quite a horrific crash occurred in 1987.  I will
therefore check out the chart immediately before the crash.

This being a method of technical analysis, it is strictly based
on pricing.  I do quite a lot with the pricing, of course.
 (Technical analysis also allows for the use of the volume, too.)
 Below is the chart immediately before the crash.  Sometimes when a
crash occurs, the exact dates are unclear.  I know in a sense it
should be clear - like an automobile crash.  But in the case of the
stock market, the crash might develop over a period of time.  In
any event, many people associate October 16, 1987 with the date of
the crash.  The chart below stops at October 15, 1987: this is
because my objective is to study the technical characteristics
before the crash to determine if they can predict what is about to
occur.  Notice how this chart is quite different from 1986: most of
the combinations led to outcomes below the 0.  I point out also
that the pattern began to splinter after October 5, 1987 (before
the crash).  The takeaway here is that before a major correction,
the algorithmic outcomes generally fall below 0.  More than 90
percent of the algorithmic pairs failed to beat market.  Many of
Simtactics' algorithmic traders were noticeably more successful by
October 15, 1987.

I added the next chart just to simplify my message since I
realize that the one immediately above presents information in a
fairly complicated manner.  The bar chart below shows that the
algorithmic failure rate was extremely high for some time before
the Crash of 1987.  But just before the crash, the artificial
traders were becoming much more successful beating the market.  I
suggest actually that a "normal" condition of risk diversity was
being restored to an otherwise highly polarized market.  There are
cases of humans remaining in dangerous, toxic, and unhealthy
conditions by rationalizing out the perceived risks.  After all,
this type of insulation is possible using alcohol or drugs.  But
certainly there are social mechanisms that can lead people to hold
beyond their natural risk tolerance levels.  Another idea that I
think has some merit is how survivorship might reward the risk
tolerant or condition those whose tolerance levels are adaptive or
elastic.  Many people invest by proxy these days - that is to say,
through professional portfolio managers who for their part might
make use of algorithmic traders.  There is therefore a level of
disassociation built into the system these days separating many
investors from their investments.  But as I mentioned in my
previous blog, this simply means that human stress perceptions have
been replaced by algorithmic risk perceptions.  I believe that
humans are capable of much more insulated thinking than
professionally designed algorithms.

This next chart is from the dot-com correction.  For those that
do not recall what caused this correction, essentially many
internet companies had unproven business models; nonetheless,
investors kept trading up the value of the stocks.  It is difficult
to determine the extent to which a stock might be overpriced in a
new market.  The chart below shows that for the most part, my
algorithmic pairs could not beat the market - until of course the
market collapsed in a drunken stupor.  The algorithms started to
gain steam all at the same time to the right of the chart.  So
there is some evidence that a widespread failure rate is itself an
indication of a market that seems more likely to encounter a large
correction.  However, within this context of failure as the chart
below shows, there are noticeable systematic developments that
appear to almost suggest a reversal in performance en route to the

Again I provide a bar chart as simplification.  It shows a very
high failure rate persisting before the market buckled.  By the
way, for those interested, skipjacking remains possible even if the
autopilot appears to be failing.  But I would say that there are
fewer clear opportunities for the player to win in a relative
sense; certainly it is much more difficult to beat the market.  On
the other hand, for those that assume the market position, they are
destined to obtain the market return both positive and

No study of stock market crashes would be complete without
consider the crash associated with the Great Depression.  I had my
fingers crossed - and there it is again!  Nearly all of the
algorithmic pairs performed below market immediately before the
crash.  The Crash of 1929 is one of those slow-motion horror
movies.  The dates on the x-axis indicate my purely technical
perspective on when the crash occurred.  Those that know their data
really well might point out that the my algorithmic pairs seem to
start advancing well before the crash.  Exactly.  The sudden
increase in effectiveness seems to provide some warning.

This leads me to my final chart which isn't about a crash -
given that most of the lines are above 0.  It is actually from
trading activity last Friday for a particular ETF.  While most of
the lines are beating market, consider the suspicious downward
pattern to the right of the chart.  By the way, this downward
motion does not necessarily mean that the price is declining.  It
means that the algorithms are becoming less successful than market.
 This poses an interesting question: would it be wise to take a
buy-and-hold strategy?  What I can say is that if a buy-and-hold
strategy is considered, it would probably be worthwhile to watch
for that splintering behaviour that seems to precede stock market