Volume as a Third Dimension: A Brief Example

THE TRADER’S MANTRA: VOLUME PRECEDES PRICE

The mantra that volume precedes price has served many a trader well and remains as much a fundamental truth today as it was when I first heard it more than 35 years ago. Price movements occur daily in the financial markets. Using Volume Analysis, we turn our focus on the volume action behind these movements and look for insight into the strength and intensity of the crowd that fueled the move. Although not all market participants are equivalent, the volume numbers continually reveal their collective sentiment.

Market price trends do not happen in a vacuum; rather, it is the behavioral or programmed responses of traders and managers that result in the volume shifts that precede a price move. As the crowd mobilizes, as reflected in the volume numbers, its size and conviction will determine the direction and strength of the price movement. As the conviction of the crowd falters and the volume numbers pull back and diminish, so too will this impact the timing and direction of the trend. This chapter displays some visual examples of this truth at work.

Viewing the Market in Three Dimensions

One of the major reasons for using Volume Analysis is to capture volume’s ability to signal changes in trading sentiment to traders, which ultimately cause changes in trend or price direction. It helps to think of Volume Analysis as a multidimensional map of the trading environment. While price action looks at two dimensions, the inclusion of volume gives a visual look behind price movement, providing greater detail as to what the driving forces are and how they can be anticipated to behave.

One way to think of this combination is to consider the situation of a mountain biker traveling through rugged terrain. Our biker can pack a simple flat map or one showing topographical features. A biker with a map of the technical features of the course will have considerably more information and be able to make qualitatively different decisions from a rider using a flat map. Our biker relying on a topographical map will be able to gauge the level of difficulty of the incline, the ruggedness of the course, and ultimately the time frame for completion. He will be able to access the amount of energy he needs to reserve for certain portions of the course. Sure, our biker might get from point A to point B with a flat map, but lacking information about the technical terrain could be both costly and life threatening.

Volume as a Third Dimension: A Brief Example

The incorporation of a volume component into your trading strategy can be illustrated by a simple exercise. While sitting down, focus your eyes on a point in front of you in the room. Now cover one of your eyes. You should notice two things. First, your field of vision is cut in half, as the peripheral vision on one side is completely gone. Second, covering one eye compromises depth perception. You've just seen how adding volume gives you a broader read on market activity (peripheral vision) while also allowing you to see intensity and conviction behind price movements (depth perception). Now let's consider the example of the iShares Dow Jones Real Estate Trust ETF (IYR) shown in Chart 2.1. The top frame shows a flat, two-dimensional price momentum analysis. The bottom frame shows the Relative Strength Index, or RSI, a price-based momentum oscillator that is essentially a calculated figure representing the cumulative number of incrementally higher and lower closes in a given period. Even though the RSI is quite a sophisticated tool, the only input for its calculation is price data.

Notice how, as price moves into the vicinity of its previous low, the 14-period RSI breaks down to a new low, which could be interpreted as an indication that the downside price action will continue.

Now, lets examine the same price plot of IYR, only this time with volume bars plotted beneath price. The goal with this type of chart is to concentrate on the synergy between volume and price. In Chart 2.2, note how, as the iShares Dow Jones Real Estate Trust ETF (IYR) approached the same price point shown in Chart 2.1, the volume action, which heretofore had been low,

 

Chart 2.1 Price and Relative Strength Index, DJ Real Estate Trust ETF

Chart 2.2 Price Movement with Volume, DJ Real Estate Trust

noticeably increased. Using Volume Analysis, a sharp-eyed trader would translate this as a battle for near-term direction. This type of increased volume action would be recognized as a signal that a change in direction was due.

Rather than confirming the trend using RSI (erroneously, as it turns out), this multidimensional focus on both price trend and volume action suggests more correctly that a change in price direction was in the offing. It was only with the addition of volume that we were able to uncover the intensity and conviction of buying and selling pressure, and ultimately the money flowing into or out of the IYR index. This sort of multidimensional view reveals the power of Volume Analysis —and the potential limitations of relying on price-based technical analysis alone.

The Three-Dimensional View: A Second Example

This second example displays the S&P Select SPDR Financial Trust ETF (XLF) in its January to February 2010 decline. Equities were getting hit with a spirited pullback, but the buildup in volume on XLF as it made its January and February lows showed heightened activity that alerted traders that a change in trend was coming.

First, in Chart 2.3, a price plot of XLF is shown using a one-dimensional price momentum study. The chart shows the price of XLF plotted

 Chart 2.3 Price and 20-Period Rate of Change, S&P Financial SPDR (XLF)

Chart 2.4 Price with Daily Volume, S&P Financial SPDR (XLF)

with its 20-period Price Rate of Change, measuring the momentum, or strength of price movement. Note how the price indicator tumbled to new lows without signaling a potential reversal in trend.

Contrast Chart 2.3 of XLF with a chart with its volume plotted below price, as shown in Chart 2.4. This added frame now shows that volume increased as price made its January 2010 low, rallied briefly, and then made its final February low. This volume action displayed a tug-of-war going on for near-term price direction. As the chart reveals, buyers eventually won the battle as price pushed higher into the spring.

Conviction Is What Counts

Taking this discussion a step further, two-dimensional price momentum indicators can show overbought and oversold levels, but where they fall short is in their ability to display the conviction of the traders that pushed price to these levels. In classic technical analysis, one of the most commonly used price momentum tools is the Stochastic Oscillator developed by George Lane. The Stochastic is an excellent and much-relied-upon banded oscillator that works well with volume indicators. However, many traders attempt to perform their complete trading analysis based upon its relationship to price movement. The following example shows how adding volume to this banded oscillator gives a trader a multidimensional and more accurate representation of a market trend.

Chart 2.5, the Nasdaq 100 Trust ETF (QQQQ), shows the Stochastic Oscillator and its behavior during the 2008 market meltdown. It reached the oversold level many times throughout the decline, but it really did not show the conviction behind each push into oversold territory. With a two- dimensional price oscillator, it is difficult to see any difference from one oversold instance to another. Analyzing the market using price alone did not provide essential trading information as to the sentiment behind these overbought or oversold market conditions. This is where volume comes into play, improving a trader s ability to interpret those conditions and ultimately to trade more efficiently and profitably.

When volume and a volume-based indicator are added to the chart, it gives a visual display of the conviction behind the selling pressure during the 2008 price decline (see Chart 2.6 for QQQQ); we added the Force Index, a volume-based oscillator developed by Dr. Alexander Elder that is discussed in greater detail in Chapter 10.

Chart 2.5 Stochastic Oscillator without Volume Component, Nasdaq 100 Trust ETF

Chart 2.6 Stochastic Oscillator with Force Index (Volume-Based), Nasdaq 100 Trust ETF

The Force Index uses volume data in its calculation and is able to provide a more accurate assessment of the trading terrain. While the Stochastic indicator bottomed in the same range each time, the Force Index plunged to a deep low in September 2008 (in the middle of the chart), which showed the intensity or conviction behind the selling pressure. The increase in volume showed that more crowd participants were contributing to the intensity of this decline.

The depth of the September 2008 plunge in the Force Index could have been used as a comparison for determining that the second sell-off in March 2009 had less conviction. This provided an important piece of information for interpreting the March 2009 low. It signaled that sellers had exhausted themselves as compared to what happened during September’s push lower. The fact that the Force Index made a higher low (and volume itself was much lower) while price bottomed at the same level (November 2008 and March 2009) showed that a change in trend should have been anticipated. The price-based Stochastic Oscillator indicated that the ETF was oversold. It was the added volume component, however, that revealed the crowds lagging conviction and a strong probability of a reversal.

Flash Crash: A Preliminary Look at Volume Analysis

Here’s another look at how Volume Analysis and a volume-based oscillator could have alerted traders that a change of trend was on the way. This example uses both price- and volume-based oscillators leading up to the May 6, 2010 “flash crash.” (See Chapter 6 for more details.) Chart 2.7 once again displays the Stochastic Oscillator plotted just below price. This time the volume-based Klinger Oscillator (see Chapter 10) is added to a plot of daily volume in the bottom frame. This oscillator is often used to spot long-term trends in money flow as well as short-term reversals. Note how the Stochastic indicator topped at over 80 (the overbought zone) several times, signaling that a short-term pullback could be expected. However, the Stochastic indicator alone was unable to gauge the conviction of traders behind the move.

The Klinger Oscillator, on the other hand, painted a much different picture. While the Stochastic Oscillator was topping in the same area over 80 as price made its final top on April 26, 2010, the Klinger Oscillator made a much lower peak. This served as a red flag that there was very little conviction behind the push to the new price high. This situation is

Chart 2.7 Stochastic with Klinger Oscillator, May 2010 Flash Crash, Nasdaq 100 Trust ETF

known as a negative divergence between price and volume, a condition alerting a trader that a change in trend may be near. Such signals tell a trader at the very least to lighten up on equity exposure, as a pullback in price is expected. Also note the lighter volume in the bottom frame of Chart 2.7, confirming a diminished conviction behind the push into the April 26, 2010, high. This negative divergence displayed by the Klinger Oscillator served as an early warning signal as price collapsed in the wake of the May 6, 2010, flash crash less than two weeks later.

We’ve just seen how adding volume serves to complement more traditional price-based indicators and analysis. Volume Analysis works well in a layered format; we will cover such “overlays” throughout the book and, in particular, in Chapter 11.

Summary

• Volume Analysis respects the relationship between volume and price.
• Adding volume is equivalent to adding a third dimension to your trading strategy. By using volume, we gain insight into the topography of the trend and the sentiment and conviction behind the price movement.
• Volume Analysis can enhance trading profits substantially when utilized to assess trend strength, direction, and timing. The omission of volume can cause oversights and formidable errors.