LONG TERM TO SHORT TERM CHARTS

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LONG TERM TO SHORT TERM CHARTS

It’s especially important to appreciate the order in which price charts should be studied in performing a thorough trend analysis. The proper order to follow in chart analysis is to begin with the long range and gradually work to the near term. The reason for this should become apparent as one works with the different time dimensions. If the analyst begins with only the near term

picture, he or she is forced to constantly revise conclusions as more price data is considered. A thorough analysis of a daily chart may have to be completely redone after looking at the long range charts. By starting with the big picture, going back as far as 20 years, all data to be considered are already included in the chart and a proper perspective is achieved. Once the analyst knows where the market is from a longer range perspective, he or she gradually “zeros in” on the shorter term.

The first chart to be considered is the 20 year monthly chart. The analyst looks for the more obvious chart patterns, major trendlines, or the proximity of major support or resistance levels. He or she then consults the most recent five years on the weekly chart, repeating the same process. Having done that, the analyst narrows his or her focus to the last six to nine months of market action on the daily bar chart, thus going from the “macro” to the “micro” approach. If the trader wants to proceed further, intraday charts can then be consulted for an even more microscopic study of recent action.

WHY SHOULD LONG RANGE CHARTS BE ADJUSTED FOR INFLATION?

A question often raised concerning long term charts is whether or not historic price levels seen on the charts should be adjusted for inflation. After all, the argument goes, do these long range peaks and troughs have any validity if not adjusted to reflect the changes in the value of the U.S. dollar? This is a point of some controversy among analysts.

I do not believe that any adjustment is necessary on these long range charts for a number of reasons. The main reason is my belief that the markets themselves have already made the necessary adjustments. A currency declining in value causes commodities quoted in that currency to increase in value. The declining value of the dollar, therefore, would contribute to rising commodity prices. A rising dollar would cause the price of most commodities to fall.

The tremendous price gains in commodity markets during the 1970s and declining prices in the 1980s and 1990s are classic examples of inflation at work. To have suggested during the 1970s that commodity price levels that had doubled and tripled in price should then be adjusted to reflect rising inflation would make no sense at all. The rising commodity markets already were a manifestation of that inflation. Declining commodity markets since the 1980s reflect a long period of disinflation. Should we take the price of gold, which is now worth less than half of its value in 1980, and adjust it to reflect the lower inflation rate? The market has already taken care of that.

The final point in this debate goes to the heart of the technical theory, which states that price action discounts everything, even inflation. All financial markets adjust to periods of inflation and deflation and to changes in currency values. The real answer to whether long range charts should be adjusted for inflation lies in the charts themselves. Many markets fail at historic resistance levels set several years earlier and then bounce off support levels not seen in several years. It’s also clear that falling inflation since the early 1980s has helped support bull markets in bonds and stocks. It would seem that those markets have already made their own inflation adjustment. (See Figure 8.1.)

Figure 8.1 The gold price peak in 1980 ushered in two decades of low inflation. Low inflation normally causes falling gold prices and rising stock prices as this chart shows. Why adjust the charts again for inflation? It’s already been done.

LONG TERM CHARTS NOT INTENDED FOR TRADING PURPOSES

Long term charts are not meant for trading purposes. A distinction has to be made between market analysis for forecasting purposes and the timing of market commitments. Long term charts are useful in the analytical process to help determine the major trend and price objectives. They are not suitable, however, for the timing of entry and exit points and should not be used for

that purpose. For that more sensitive task, daily and intraday charts should be utilized.

EXAMPLES OF LONG TERM CHARTS

The following pages contain examples of long term weekly and monthly charts (Figures 8.2–8.12). The drawings on the charts are limited to long term support and resistance levels, trendlines, percentage retracements, weekly reversals, and an occasional price pattern. Be aware, however, that anything that can be done on a daily chart can also be done on a weekly or monthly. We’ll show you later in the book how the application of various technical indicators to these long term charts is accomplished, and how signals on weekly charts become valuable filters for shorter term timing decisions. Remember also that semilog chart scaling becomes more valuable when studying long range price trends.

Figure 8.2 This chart of semiconductor stocks shows the valuable perspective of a weekly chart. The late 1997 price fall stopped right at the 62% retracement level and bounced off chart support formed the previous spring (see circle).

Figure 8.3 The early 1998 bottom in General Motors began right at the trendline drawn along the 1995-1996 lows. That’s why it’s a good idea to track weekly charts.

Figure 8.4 This monthly chart shows the 1997 rally in Burlington Resources stopping right at the same level that stopped the 1989 and 1993 rallies. The 1995 bottom was at the same level as the 1991 bottom. Who says charts don’t have a memory?

Figure 8.5 An investor in Inco Ltd. during the 1997 rally could have benefited from the knowledge that the 1989, 1991, and 1995 tops occurred right at 38.

Figure 8.6 Do long term charts matter? The 1993 bottom in IBM was at the same level as the bottom formed 20 years earlier in 1974. The break of an 8 year down trendline (see box) in 1995 confirmed the new major uptrend.

Figure 8.7 Helmerich & Payne finally broke out above 19 in 1996 after failing in 1987, 1990, and 1993. The late 1996 pullback at 28 occurred near the 1980 peak.

Figure 8.8 This monthly chart of Dow Jones shows a head and shoulders bottom forming for 10 years from 1988 to 1997. The right shoulder also has the shape of a bullish ascending triangle. The breakout over the neckline at 42 completed the bottom.

Figure 8.9 A bullish symmetrical triangle was easy to spot on the monthly chart of Southwest Airlines. But you probably wouldn’t have spotted it on a daily chart.

Figure 8.10 The 1994 bottom in the Dow Utilities bounced off a trendline lasting 20 years. There are those who claim that past price action has no bearing on the future. If you still believe that, go back and look at these long term charts again.

Figure 8.11 On this linear-scaled chart of the Japanese stock market, the long term up trendline (line 1) drawn under the 1982 and 1984 lows was broken in early 1992 (see circle) near 22,000. That was two years after the actual peak.

Figure 8.12 The same Japanese chart from Figure 8.11 using log scaling. Line 1 is the trendline from the previous figure. The steeper line 2 was broken in mid-1990 (see box) at 30,000. Up trendlines on log charts are broken sooner than linear up trendlines.

INTRODUCTION

The moving average is one of the most versatile and widely used of all technical indicators. Because of the way it is constructed and the fact that it can be so easily quantified and tested, it is the basis for many mechanical trend-following systems in use today.

Chart analysis is largely subjective and difficult to test. As a result, chart analysis does not lend itself that well to computerization. Moving average rules, by contrast, can easily be programmed into a computer, which then generates specific buy and sell signals. While two technicians may disagree as to whether a given price pattern is a triangle or a wedge, or whether the volume pattern favors the bull or bear side, moving average trend signals are precise and not open to debate.

Let’s begin by defining what a moving average is. As the second word implies, it is an average of a certain body of data. For example, if a 10 day average of closing prices is desired, the prices for the last 10 days are added up and the total is divided by 10. The term moving is used because only the latest 10 days’ prices are used in the calculation. Therefore, the body of data to be averaged (the last 10 closing prices) moves forward with each new trading day. The most common way to calculate the moving average is to work from the total of the last 10 days’ closing prices. Each day the new close is added to the total and the close 11 days back is subtracted. The new total is then divided by the number of days (10). (See Figure 9.1a.)

The above example deals with a simple 10 day moving average of closing prices. There are, however, other types of moving averages that are not simple. There are also many questions as to the best way to employ the moving average. For example, how many days should be averaged? Should a short term or a long term average be used? Is there a best moving average for

all markets or for each individual market? Is the closing price the best price to average? Would it be better to use more than one average?

Figure 9.1a A 10 day moving average applied to a daily bar chart of the S&P 500. Prices crossed the average line several times (see arrows) before finally turning higher. Prices stayed above the average during the subsequent rally.

Which type of average works better—a simple, linearly weighted or exponentially smoothed? Are there times when moving averages work better than others?

There are many questions to be considered when using moving averages. We’ll address many of these questions in this chapter and show examples of some of the more common usages of the moving average.

THE MOVING AVERAGE: A SMOOTHING DEVICE WITH A TIME LAG

The moving average is essentially a trend following device. Its purpose is to identify or signal that a new trend has begun or that an old trend has ended or reversed. Its purpose is to track the progress of the trend. It might be viewed as a curving trendline. It does not, however, predict market action in the same sense that standard chart analysis attempts to do. The moving average is a follower, not a leader. It never anticipates; it only reacts. The moving average follows a market and tells us that a trend has begun, but only after the fact.

The moving average is a smoothing device. By averaging the price data, a smoother line is produced, making it much easier to view the underlying trend. By its very nature, however, the moving average line also lags the market action. A shorter moving average, such as a 20 day average, would hug the price action more closely than a 200 day average. The time lag is reduced with the shorter averages, but can never be completely eliminated. Shorter term averages are more sensitive to the price action, whereas longer range averages are less sensitive. In certain types of markets, it is more advantageous to use a shorter average and, at other times, a longer and less sensitive average proves more useful. (See Figure 9.1b.)

Which Prices to Average

We have been using the closing price in all of our examples so far. However, while the closing price is considered to be the most important price of the trading day and the price most commonly used in moving average construction, the reader should be aware that some technicians prefer to use other prices. Some prefer to use a midpoint value, which is arrived at by dividing the day’s range by two.

Figure 9.1b A comparison of a 20 day and a 200 day moving average. During the sideways period from August to January, prices crossed the shorter average several times. However, they remained above the 200 day average throughout the entire period.

Others include the closing price in their calculation by adding the high,

low, and closing prices together and dividing the sum by three. Still others prefer to construct price bands by averaging the high and low prices separately. The result is two separate moving average lines that act as a sort of volatility buffer or neutral zone. Despite these variations, the closing price is still the price most commonly used for moving average analysis and is the price that we’ll be focusing most of our attention on in this chapter.

The Simple Moving Average

The simple moving average, or the arithmetic mean, is the type used by most technical analysts. But there are some who question its usefulness on two points. The first criticism is that only the period covered by the average (the last 10 days, for example) is taken into account. The second criticism is that the simple moving average gives equal weight to each day’s price. In a 10 day average, the last day receives the same weight as the first day in the calculation. Each day’s price is assigned a 10% weighting. In a 5 day average, each day would have an equal 20% weighting. Some analysts believe that a heavier weighting should be given to the more recent price action.

The Linearly Weighted Moving Average

In an attempt to correct the weighting problem, some analysts employ a linearly weighted moving average. In this calculation, the closing price of the 10th day (in the case of a 10 day average) would be multiplied by 10, the ninth day by nine, the eighth day by eight, and so on. The greater weight is therefore given to the more recent closings. The total is then divided by the sum of the multipliers (55 in the case of the 10 day average: 10 + 9 + 8 +…+ 1). However, the linearly weighted average still does not address the problem of including only the price action covered by the length of the average itself.

The Exponentially Smoothed Moving Average

This type of average addresses both of the problems associated with the simple moving average. First, the exponentially smoothed average assigns a greater weight to the more recent data. Therefore, it is a weighted moving average. But while it assigns lesser importance to past price data, it does include in its calculation all of the data in the life of the instrument. In addition, the user is able to adjust the weighting to give greater or lesser weight to the most recent day’s price. This is done by assigning a percentage value to the last day’s price, which is added to a percentage of the previous day’s value. The sum of both percentage values adds up to 100. For example, the last day’s price could be assigned a value of 10% (.10), which is added to the previous day’s value of 90% (.90). That gives the last day 10% of the total weighting. That would be the equivalent of a 20 day average. By giving the last day’s price a smaller value of 5% (.05), lesser weight is given to the last day’s data and the average is less sensitive. That would be the equivalent of a 40 day moving average. (See Figure 9.2.)

Figure 9.2 The 40 day exponential moving average (dotted line) is more sensitive than the simple arithmetic 40 day moving average (solid line).

The computer makes this all very easy for you. You just have to choose the number of days you want in the moving average—10, 20, 40, etc. Then select the type of average you want—simple, weighted, or exponentially smoothed. You can also select as many averages as you want—one, two, or three.

The Use of One Moving Average

The simple moving average is the one most commonly used by technicians, and is the one that we’ll be concentrating on. Some traders use just one moving average to generate trend signals. The moving average is plotted on the bar chart in its appropriate trading day along with that day’s price action. When the closing price moves above the moving average, a buy signal is generated. A sell signal is given when prices move below the moving average. For added confirmation, some technicians also like to see the moving average line itself turn in the direction of the price crossing. (See Figure 9.3.)

If a very short term average is employed (a 5 or 10 day), the average

tracks prices very closely and several crossings occur. This action can be either good or bad. The use of a very sensitive average produces more trades (with higher commission costs) and results in many false signals (whipsaws). If the average is too sensitive, some of the short term random price movement (or “noise”) activates bad trend signals.

Figure 9.3 Prices fell below the 50 day average during October (see left circle). The sell signal is stronger when the moving average also turns down (see left arrow). The buy signal during January was confirmed when the average itself turned higher.

While the shorter average generates more false signals, it has the advantage of giving trend signals earlier in the move. It stands to reason that the more sensitive the average, the earlier the signals will be. So there is a tradeoff at work here. The trick is to find the average that is sensitive enough to generate early signals, but insensitive enough to avoid most of the random “noise.” (See Figure 9.4.)

Figure 9.4 A shorter average gives earlier signals. The longer average is slower, but more reliable. The 10 day turned up first at the bottom. But it also gave a premature buy signal during November and an untimely sell signal during February (see boxes).

Let’s carry the above comparison a step further. While the longer average performs better while the trend remains in motion, it “gives back” a lot more when the trend reverses. The very insensitivity of the longer average (the fact that it trailed the trend from a greater distance), which kept it from getting tangled up in short term corrections during the trend, works against the trader when the trend actually reverses. Therefore, we’ll add another corollary here: The longer averages work better as long as the trend remains in force, but a shorter average is better when the trend is in the process of reversing.

It becomes clearer, therefore, that the use of one moving average alone has several disadvantages. It is usually more advantageous to employ two moving averages.

How to Use Two Averages to Generate Signals

This technique is called the double crossover method. This means that a buy signal is produced when the shorter average crosses above the longer. For example, two popular combinations are the 5 and 20 day averages and the 10 and 50 day averages. In the former, a buy signal occurs when the 5 day average crosses above the 20, and a sell signal when the 5 day moves below the 20. In the latter example, the 10 day crossing above the 50 signals an

uptrend, and a downtrend takes place with the 10 slipping under the 50. This technique of using two averages together lags the market a bit more than the use of a single average but produces fewer whipsaws. (See Figures 9.5 and 9.6.)

Figure 9.5 The double crossover method uses two moving averages. The 5 and 20 day combination is popular with futures traders. The 5 day fell below the 20 day during October (see circle) and caught the entire downtrend in crude oil prices.

The Use of Three Averages, or the Triple Crossover Method

That brings us to the triple crossover method. The most widely used triple crossover system is the popular 4-9-18-day moving average combination. The 4-9-18 method is used mainly in futures trading. This concept was first mentioned by R.C. Allen in his 1972 book, How to Build a Fortune in Commodities and again later in a 1974 work by the same author, How to Use the 4-Day, 9-Day and 18-Day Moving Averages to Earn Larger Profits from Commodities. The 4-9-18-day system is a variation on the 5, 10, and 20 day moving average numbers, which are widely used in commodity circles. Many commercial chart services publish the 4-9-18-day moving averages. (Many charting software packages use the 4-9-18-day combination as their default values when plotting three averages.)

Figure 9.6 Stock traders use 10 and 50 day moving averages. The 10 day fell below the 50 day in October (left circle), giving a timely sell signal. The bullish crossover in the other direction took place during January (lower circle).