Apprenticed Investor: The Folly of Forecasting

-No one truly knows what tomorrow will bring. Nobody. Any and all forecasts are, at best, educated guesses.

-All prognostications are instantly stale, subject to further revision. Conditions change, new data are released, events unfold. Yesterday’s prediction can be undone by tomorrow’s press release.

-In order to “become right,” some investors will stand by their predictions despite a stock or the market going the opposite way, hoping to be proven correct. Ned Davis called this the curse of “being right rather than making money.”

 

There are only two kinds of predictions that have some value to investors: One is probability-based, and the other is risk-based. As long as you apply the same rules — no one knows the future, they are subject to revision and should not be taken as gospel — then these are sometimes worth considering.

Probability assessments are typically based upon historical comparisons of prior markets with similar characteristics: The more variables that align, the higher the likelihood that a given scenario plays out in a similar fashion. They are of this variety: In the past, when X, Y and Z all happened together, then we expect that A is most likely, then B is possible, while C is the least likely.That doesn’t mean A will happen or C cannot — only that there’s a specific probability of these events occurring out of the millions of ways the future might unfold. Whether any particular scenario plays out is determined by how the countless variables interact over time.

Looking at the future in terms of various probabilities is a productive way to position assets and manage risk. Why? If your expectations for the future recognize that this is but one possible outcome, then you are more likely to consider and plan for other contingencies. It builds in an expectation that other scenarios can and will occur.

For example, one signal I use is to determine when to sell (the subject of a future column) is after a long uptrend is broken. It’s not that stocks cannot go higher after breaking their trend line — they sometimes do. However, most of the time this happens it signals a significant change in institutional behavior towards the stock. Typically, it reflects a shift from fund accumulation to distribution.

For those people who have been enjoying the ride in Google (GOOG) — especially the near vertical move since April — this is a high probability strategy. Once that trend line gets broken, say adios muchachos, take your profits and move along.

Again, a trend break is not a guarantee that the upside is finished, but it’s a fairly good probability assessment.

The second type of good prediction is the risk-based discussion. These forecasts care less about price targets — instead, they are an assessment of danger. In other words, to buyers of stocks under the present conditions, when this, that and the other are happening, you are taking on more (or less risk) than is typical. Saying the markets contain more or less risk at given times is a very different statement than: “I think the Dow is going to go to X.”

I engaged in a combination of broader market-based probability this week in Smart Money, along with future risk assessment. Given the change in character the market displayed since the April lows, I noted the high probability of a substantial rally in the second half of the year. My basis for this was part technical — the market regaining its prior trading range — and part anecdotal (all the hedge fund cash on the sidelines). This created a high probability of a move similar to what we saw over the summer of 2003.

But I also included a risk-based assessment based upon the age of this bull move, along with the decaying macroeconomic environment; in tandem, they set up an increasing risk environment as the year progresses. That’s how a top can form, and that presents an increased risk of a market correction or even collapse.

When you stop to consider all of the unforeseen actions that might occur between now and then, however, it becomes pretty apparent that all forecasting is at best a low probability activity.

Chaos Theory

Why are the markets so difficult to predict? To borrow a phrase from the physicists, the market demonstrates “unstable aperiodic behavior in deterministic nonlinear dynamism.”

This behavior is better known as Chaos Theory.

What does that mean in English? The market is called “aperiodic” because it never repeats itself precisely the same way. Weather is also aperiodic — it may be colder in the winter than in the summer, so there is a degree of cyclicality. But the day-to-day changes are never exactly the same year after year. The same dynamic applies to the markets: There are similarities from one era to another, but it’s never identical. In Mark Twain’s words, “History doesn’t repeat, but it rhymes.”

The markets also act with a surprising degree of instability. Small forces can create disproportionately large reactions. A surprising economic report, an off-the-cuff comment by a Fed official, a small change in earnings by any one of 1,000 companies; any one of these data points can roil the market. That behavior does not occur in what the scientists call “stable” systems.

Given the complexity of both the capital markets and the physical universe, we shouldn’t be that surprised that Chaos Theory is so applicable to the financial markets.

Considering how little we know about the totality of market conditions — and how incredibly intricate and complex the system is — it’s no surprise that pundit predictions are so frequently poor.