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 12×12 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 present.
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 crash.
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 negative.
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 crashes.