By Anthony Trongone, Ph.D., CFP, CTA*
Most day traders want to achieve high returns but are unaware of their exposure to downside loss. How vulnerable are your assets to a single-day market correction? Unfortunately, if your assessment of impending risk is misleading, your overall performance will suffer.
A reliable assessment of downside risk depends on the accuracy of your indicators. Traditionally, this begins when you look at standard deviations; however, this does not always provide us with a dependable measurement of future loss.
For instance, the chart shown below displays every 100-point loss in the Dow Jones Industrial Average prior to the 416-point meltdown on February 27, 2007.
In the column chart, we are tracking the 59 days with a triple-digit loss leading up to this one-day 416-point correction. Was the past a good indicator of this painful correction?
Although there were many (59) one-day declines greater than 100 points, prior to this 416-point collapse, the days with triple-digit declines were becoming less frequent and the spacing between them, further apart. This left investors with a false sense of security.
And, when investors perceive less risk, they are likely to increase their exposure to risky investments. However, once stocks begin plummeting, the psychological reaction of the "crowd" is always extremely difficult to quantify because investors begin selling the pain.
A Textbook Assessment of Risk
In a statistics class, we learn to quantify the probability of risk by applying the =normdist function in Microsoft Excel. This probability function returns the normal distribution for the specified mean score together with its standard deviation. It is generally applied to compute the probability of the risk of future loss.
For instance, in the 792 trading days, the average score for the DJIA was 2.75 points, with a standard deviation of 68.5; therefore, the probability (=normdist) of achieving a 416-point loss was .000. Although highly improbable, a 416-point loss did actually occur on the following trading day -- proving that the market has no statistical boundaries.
In the data entry box shown below, we can see the =normdist function in action, using a loss of 100 points, its mean, standard deviation and its cumulative setting:
According to the normal probability distribution theory, the probability of experiencing a 100-point decline = 6.68%. Experiment by plugging in various losses to produce your results. Because there were 59 triple-digit losses in 792 trading days, the percentage of failure is 792 / 59 = 13.42%.
Trading between Triple-Digit Losses
In the following yearly chart, we are examining the upside strength of this popular index. The blue lines represent the closing price of the DJIA. It is enjoying a spectacular run -- gaining 1,344 points in 207 trading days.
Remarkably, if you look at the days between extreme losses (i.e., the days inside the three black lines with black arrows), this is where the majority of profits reside (2,646 points). In these 82 trading days, the average daily profit was 32 points. The trading environment was less profitable if you were trading outside these black lines. Within the red vertical lines, the average loss was 14 points per day.
A study of the spacing between the red downward trading days is instructive. During the 207 days, the point gain was 1,344 points. Remarkably, 2,646 came when there were long breaks between a 100-point loss (that is, during the days indicated by the three black arrows).
The table shown below indicates the results of trading when there is no excessive volatility:
The next table reports the differences between the two trading environments:
The eSignal chart illustrates the performance of trading within the days of triple-digit losses. It clearly demonstrates how important it is to restrict your trading to a setting with less downward volatility.
The 40-day rally, starting on March 14, 2007, was worth 1,286 points. More recently, a 980-point gain in 25 trading days came after the initial impact of the subprime crisis began to recede.
Most technicians would become cautious over the inability of a stock to sustain support. After falling to the important 12,500 psychological level, the Dow came roaring back to life.
In the 19 trading days from July 20 - August 15, 2007, this barometer of market vitality fell 60 points per day. Furthermore, during this flurry of volatility, the daily price change was 163 points, resulting in 6 days with a 200+ point decline. Nevertheless, on the next day, after falling to 12,518, it came roaring back for a long rally to 14,000 points
Note: Apply the =ABS function. This returns the daily price change without a sign.
Naturally, two primary questions emerge from these findings:
- How can you avoid being in a long position when the market begins to encounter a nasty correction -- in which you encounter a maelstrom of extreme losses?
- Sometimes, there is a long string of extreme losses, so how can you determine when these 100-point meltdowns are winding down? Once they do, they certainly give you the opportunity to begin trading in an upward environment.
In a future article, I will discuss the disadvantages of traditional indicators and supply you with more powerful indicators to better forecast impending risk. In a follow-up article, I will discuss how to avoid being in an environment of extreme loss, as well as offer some suggestions on how to determine -- after navigating through financially rough waters -- when the tide is about to turn.
*Reprinted (and modified) with permission from Anthony Trongone, Ph.D., CFP, CTA
