The Importance of Price Noise
Market noise is an important but elusive component of price movement. It is the up-and-down, erratic price movement that goes nowhere and often causes you to be stopped out of a trade only to see prices reverse back in your direction. Traders have no trouble recognizing noise. Most price shocks are an extreme case of noise, when the large move is followed by an equally sharp reversal the next day. A price shock is not noise if prices continue in the direction of the shock. That is most likely a structural change. The elusive part is:
- How do you tell the difference between a structural change and a price shock?
- When does a price move indicate a trend change, and when is it just noise?
- How do you take advantage of price noise?
NOISE EXPLAINED
The way we think about price noise is a day with very high volatility but a close nearly unchanged, or a day when prices closed sharply lower, then reversed nearly the entire move the next day. We also associate noise with the way prices react to our trading method. We get stopped out of a long position when prices break a key level, but that turns out to be the low of the move, and we're out at the worst price of the day.
In general, noise is a disorderly move. It doesn't need to be volatile, just erratic and unpredictable. Econometricians say that when you remove the trend, the seasonal pattern, and the cycle, the three main components of price movement, what you have left is noise. That's interesting but not very useful. We don't want to remove those three elements because the combination of everything causes price moves that can generate profits. Instead, we'll think of noise in the same way we approach the walk of a drunken sailor (no offense to sailors—it could be anyone).
If a sailor were to walk from point A to point B in a straight line, we can say that his route has no noise. If he meanders slightly off that straight path, we can see that as a small amount of noise; however, if he staggers first to the right, then sharply to the left, then backward and forward by different amounts, but ultimately heading slightly toward his goal, we would say there was a large amount of noise.
FIGURE 2.1 Noise is calculated as the net move (from A to B) divided by the sum of the individual moves (1 through 7). All values are taken as positive numbers.
Once you understand the picture, seen in
Figure 2.1, the concept should become clear:
the straighter the path, the less noise; the more erratic the path, the more noise. This pattern can be expressed as a value we call the
efficiency ratio. First, we measure the net distance gained from point A to point B, always taken as a positive number. Then we measure the actual path taken by the sailor in his journey from point A to point B. Those values are also always positive, regardless of whether he is stumbling forward or staggering backward. The efficiency of his walk is given by the ratio
Referring now to the
efficiency ratio (ER), if the sailor walked in a straight line, the ratio would be 1 because the numerator and denominator would be the same. As the sailor wobbles more, the denominator gets larger. If he wandered back and forth for a really, really long time, the denominator would get very big, and the ratio would move toward zero. Therefore, a walk with no noise will have the ratio of 1.0, and a completely directionless walk would be zero. This can be shown mathematically as
where
t represents today, P is the price, and
n is the total number of days used in the calculation. As with many financial calculations, this is done over a fixed, relatively short period. By calculating the ratio each day based on rolling time periods, we get a history of the price noise. When we average those individual ratios over a long period of time, we get a profile of the amount of noise in a specific stock, index, or commodities market.
Note that the value ER
t can be zero (or near zero) if the denominator is extremely large or if the numerator is zero, which can happen if the starting and ending prices are the same. The ratio has no sign, so that we don't know if the prices have gone up or down over the calculation period.
Because the calculation is greatly dependent on the starting and ending values, some mathematicians consider it unstable. However, averaging the values over some period of price history minimizes that problem.