13

Technical Analysis II: Tools

By examining the historical pattern of the two most important measures, namely, the market price trend and volume of trading, an investor tries to estimate the future market price of a share. In the narrowest sense, technical analysis is based on the assumption that market price fluctuations reflect the logical and emotional forces prevailing in the secondary market.

Technical analysis can be broken down into three essential parts: sentiment, flow-of-funds, and market structure indicators. Sentiment or expectation indicators monitor the actions of different market participants, ie, brokers, mutual funds, institutional investors, odd lot dealers etc. The basis for examining these groups is that different groups of investors are consistent in their actions at major market turning points. For instance, insiders tend to be correct at market turning points while financial analysts are often wrong at market turning points. Thus, to gain from the market, in these situations, investors are expected to take a stand opposite from that of financial analysts.

Abarbanell J., and Bernard V., (1992) show that analysts under react to recent earnings changes and conclude that security analysts’ behaviour is only a partial explanation for share price under reaction to earnings, and may be unrelated to share price overreactions. Ausubel L.M., (1990) proves the opposite in an explanation regarding insider trading reaction. Other studies identifying the sentiment of the market are Mozier P., and Arnold J., (1984), Lee T.A., and Tweedie D.P. (1981), Bhaskar K., and Morris R., (1984), Patz D., (1989), O’Brien P.C., and Bhushan K., (1990), and O’Hanlon J., and Whiddett R., (1991).

FLOW OF FUNDS

The flow of funds analyses the financial position of various investor groups in an attempt to measure their potential capacity for buying or selling shares. Flow of funds is concerned with trends in mutual fund cash positions, insurance companies, foreign investors, bank trusts etc, which are normally a source of cash on the demand side; and new equity offerings, secondary offerings, and margin debt on the supply side. If at a given time there is a preponderance of buyers over sellers, it follows that the actual price will have to rise to bring about a balance between buyers and sellers.

Investment results of institutional money management—as pointed out by Friend I., Blume J., and Crockett J. (1970), Friend I., and DeCani J., (1966), and Schlarbaum G., (1974) have, in almost every instance, provided little indication of better performance than that attainable from a simple passive strategy of buying and holding a randomly selected, well diversified portfolio of securities.

MARKET STRUCTURE

Market structure indicators monitor the various price indexes, market breadth, cycles, and volume in order to evaluate the position of the markets. If these indicators show a strong position, it implies that prices are expected to support the position.

Since the technical approach is based on the theory that the price is a reflection of mass psychology (the crowd) in action, it attempts to forecast future price movements. This is based on the assumption that crowd psychology moves between panic, fear, and pessimism on the one hand, to confidence, greed, and optimism on the other. The art of technical analysis is to identify these changes at an early phase, since these swings in emotion take time to accomplish.

Price movements may be primary, intermediate, and short term. Major movements are identified in a period of 1 to 3 years and are a reflection of investors’ attitudes towards the business cycle. Intermediate movements usually develop over a period of 3 weeks to a year. Short term movements last for less than 3 weeks and tend to be random in nature.

In all these movements, technical analysis examines the four dimensions stated earlier, namely, price, volume, time, and breadth. Changes in price reflect changes in investor attitude, and price, the first dimension, indicates the attitude level of investors. In examining the influence of the market on share prices in general, technical analysts observe certain signals or price indicators such as price advances versus declines, new highs and lows, and the price pattern of shares compared to the market index.

Volume, the second dimension, reflects the intensity of changes in investor attitudes. The level of enthusiasm implied by a price rise on low volume, ie, small number of traded shares, is not nearly as strong as that implied by a similar price advance accompanied by very high volumes.

Epps T., and Epps M.C., (1976), Rogalski R., (1978), Tauchen G., and Pitts M., (1983), Karpoff J.M. (1987), and Smirlock M., and Starks L., (1985) have documented the importance and usefulness of price and volume changes. Barua S.K., and Sharma J.L., and Kennedy R.E. (1977), have also tested the predictability of price change patterns and conclude that market price is partly explained by the past behaviour of prices.

Time, the third dimension, measures the length of cycles in investor psychology. Change in confidence goes through distinct cycles, some long and some short, as investors’ swing from excessive optimism towards deep pessimism. The degree of price movement in the market is usually a function of the time element. The longer it takes for investors to move from a bullish to a bearish element, the greater the ensuing price change is likely to be.

French, and Gibbons and Hess have identified specific day effects on share prices. Moore G.H. (1975), Smith E.C., (1972), Ayres L.P. (1967), and Arnott R.D., and Copeland W.A., (1985) have investigated into the business cycle effects on share returns. Dewey E.R., (1971), Bressert W., (1991), Dewey E.R., and Dakin E.F., (1947), and Jacobs B.I. and Levy K.N., (1989) have elaborated investment cycles. Granville J., (1960) and Hurst J.N. (1970) have researched the timing of stock market transactions.

The fourth dimension, breadth, measures the extent of the emotion. This is important for as long as a large number of shares are advancing on the price changes, the trend indicates favourable emotion as investors have disbursed their investments in a number of shares and have a widely favourable attitude towards the share market. When the interest of investors is narrowed down to a few blue chip company shares, it means that the quality of the trend has deteriorated and a general fall in the market price of many shares is expected in the near future.

MARKET INDICATORS

All the technical analysis charts discussed earlier were analysed using a share data (eg, high, low, close, volume, etc). There is another group of technical tools designed to help an investor gauge changes in all shares within a specific market. These indicators are usually referred to as market indicators because they gauge an entire market, not just an individual share. Market indicators typically analyse the stock market, although they can be used for other markets (eg, futures, and commodities).

While the data fields available for an individual share are limited to its open, high, low, close, volume and published financial reports, there are numerous data items available for the overall stock market. For example, the number of shares that made new highs for the day, the number of shares that increased in price, the volume associated with the shares that increased in price, etc. Market indicators cannot be calculated for an individual share because the required data may not be available.

Market indicators add significant depth to technical analysis, because they contain much more information than price and volume. A typical approach is to use market indicators to determine where the overall market is headed and then use price/volume indicators to determine when to buy or sell an individual share.

Categories of Market Indicators

Market indicators fall into three categories: monetary, sentiment, and momentum.

• The external monetary conditions affecting share prices tells us how share prices could behave.
• The sentiment of various sectors of the investment community tells us how investors expect prices to behave.
• The current momentum of the market tells us how prices are actually behaving.

Monetary indicators concentrate on economic data such as interest rates. They help an investor to determine the economic environment in which businesses operate. These external forces directly affect a business’ profitability and share price. Examples of monetary indicators are interest rates, the money supply, consumer and corporate debt, and inflation.

Sentiment indicators focus on investor expectations, often before those expectations are discernible in prices. With an individual share, the price is usually the only measure of investor sentiment available. However, for a large market such as the Bombay Stock Exchange, many more sentiment indicators are available. These include the number of odd lot sales (ie, the small (individual) investors’ preferences in the market), the put/call ratio (ie, the short and long futures position in the market held by the investors), the premium on stock index futures, the ratio of bullish versus bearish investment advisors, etc.

“Contrarians” are investors who use sentiment indicators to determine how the majority of investors expect prices to more; they then do the opposite. The rationale of these investors is that if everybody agrees that prices will rise, then there are probably not enough investors left to push prices much higher. This concept is well proven as almost everyone is bullish at market tops (when they should be selling) and bearish at market bottoms (when they should be buying).

The third category of market indicators, momentum, show how prices are actually moving, but do so by looking deeper than price. Examples of momentum indicators include all the price/volume indicators applied to the various market indices (eg, the MACD of the BSE 100) and the number of shares that made new highs versus the number of shares making new lows. The relationship between the number of shares that advanced in price versus the number that declined, the comparison of the volume associated with increased price with the volume associated with decreased price etc, are all momentum indicators.

An indicator is a mathematical calculation that can be applied to a share’s price and/or volume data. The result is a value that is used to anticipate future changes in prices. A market indicator may be categorised as leading indicators or lagging indicators.

Leading indicators typically work by measuring how “overbought” or “oversold” a security is. This is done on the assumption that a security that is “oversold” will bounce back. These indicators help the buyer profit by predicting what prices will do next. Leading indicators provide greater rewards at the expense of increased risk. They perform best in sideways “trading” markets. Lagging indicators average out the past price behaviour. They are useful in establishing the trend, and in using this trend identification an investor can work on anticipating future changes in prices.

The use of indicators will depend on the behaviour of prices, ie, whether they are trending prices or trading prices. Several trading systems and indicators have been developed to determine if prices are trending or trading. The approach usually followed by technical analysis is to use lagging indicators during trending markets and leading indicators during trading markets. While it is relatively easy to determine if prices were trending or trading, it is extremely difficult to know if prices will trend or trade in the future.

Capital markets have witnessed the development of numerous market indicators. In many instances these market indicators are computed on the stock market on a whole rather than on a specific share. Certain market indicators are amenable to individual share evaluation as well as market evaluation. Listed below are a few indicators:

• Moving Averages
• Line Studies
• Bollinger Bands
• Absolute Breadth Index
• Arms Index
• Relative Strength Index
• Accumulation Swing Index
• Commodity Channel Index
• Chaikin Oscillator
• Detrended Price Oscillator
• Stochastic Oscillator
• McClellan Oscillator
• Dynamic momentum Oscillator
• Williams’%R
• Perfomance Indicator
• R-Squared Indicator
• Chande Momentum Oscillator
• Parabolic Stop and Reverse
• Volume Oscillator
• Triple Exponential Average

Moving Averages

Moving averages are used to help identify the trend of prices. By creating an average of prices that “moves” with the addition of new data, the price action on the security being analysed is “smoothed”. In other words, by calculating the average value of a share or indicator, day to day fluctuations are reduced in importance and what remains is a stronger indication of the trend of prices over the period being analysed. The term “moving” refers to the method of calculation that takes the average value over a fixed period of time and adds the latest period data to the calculation of the average while dropping the first period of the calculation. This ensures that the average continues to be calculated by the same number of periods but moves with each new period of data that occurs. Thus, the average “moves” along with price and changes in value as price data is generated. An 18 day moving average represents the trend in prices over a period of 18 days. A longer 50 day moving average is smoothed more than an 18 day moving average, with each new day’s data making less impact on the calculation of the moving average value than a shorter term moving average such as the 18 day moving average. A longer term moving average such as the 200 day moving average is plotted to identify long-term trends in price.

A basic approach to using moving averages is to use an appropriate period of moving average by identifying which term of price trend the investor wants to track down. In this approach, when price is above the moving average it is an indication of bullish behaviour. When price is below the moving average it is an indication of bearish behaviour in relation to the trend length being viewed. When price falls from above the moving average to below the moving average, it is a warning that the price trend being viewed may be weakening. When price rises from below the moving average to above the moving average, it is a bullish indication of the price trend. The shorter the term of a moving average, the more susceptible these signals are to noise in share prices.

A moving average is a lagging indicator of price trend. There is a trade off between the timeliness of a signal to a change in the price trend and the reduction of the possibility of noise trading.

A second approach is to plot two or more moving averages and look for crossover points to help identify periods of significant change in a share. When a shorter term moving average crosses from below a longer period moving average that is above it is a sign of bullish trend. The long term view of the market is overtaken by the short term average price, hence indicating a bullish signal. When a shorter term moving average crosses from above a longer period moving average it is a sign that a bearish trend is present in price movement.

Different types of moving averages have been developed in the study of trends, for use in investment decisions. A moving average can be arithmetic, which is the sum of the closing prices over a certain number of time periods divided by the number of time periods to get an average price of the share for that period.

An exponential moving average (EMA) is calculated by adding a percentage of yesterday’s moving average to a percentage of today’s closing value. In this way an investor can put more emphasis on more recent data and less weight on past data in the calculation of the moving average.

Other types of moving average calculations include time series moving average, triangular moving average, variable moving average, volume adjusted moving average, and weighted moving average. In addition to the variations in calculations, investors can also shift a moving average horizontally or vertically on a graph and base the calculation on the open, close, high, low, or average price rather than the close.

The horizontal shift of the moving average displays the computed moving average ‘n’ days before or after its actual representative date. The value entered here is related to the type of units describing the data. It does not affect the way the moving average is calculated, it only affects the way the average is displayed on the graph. Normally, the moving average is plotted on the day for which the calculation is based and is represented by a value of 0.

The vertical shift is a percentage of the computed amount, either upward or downward. Since the value entered here is a percentage of the calculated value of the moving average itself, a percentage higher than 100 will place the moving average higher than the correct place. All percentages lower than 100 will place the moving average below its correct line. Here also the computation of the moving average is not changed and only the way the moving average is displayed on the graph changes. Such shifts might sometimes be used to form an upper or lower indicator of price movements.

The most commonly used moving average are the arithmetic moving average and the exponential moving average based on closing prices without any shift. Moving average signals may influence a larger group of investors since they are very easy to compute and plot along with price movements.

The basis of interpretation is to buy when the share price moves above its moving average and to sell when the price moves below its moving average. Different lengths of averages are meant to identify different trends. Short trends are often best identified by a 5 to 20 day moving average. Minor intermediate trends are roughly 20 to 50 days. Intermediate trends are from 50 days to 100 days and long term trends are greater than 100 days. The length of the moving average chosen should match the cycle or trends estimated from the price movements.

Using a longer period of calculation can reduce the noise prevalent in price data. But this also results in delayed signals since moving averages are lagging indicators. There is thus a trade off between timeliness and following daily price fluctuations too closely. Investors using moving averages to help identify the trend of a tradable share should determine an appropriate trade off that reduces noise but also minimises the delay of signals received.

Moving averages can also be used on indicators such as the Stochastic, Relative Strength Index, and the Rate of Change in order to smooth daily fluctuations and reduce potential noise. Moving averages on other technical indicators also provide tradable signals for investors.

In order to reduce the lag in simple moving averages, technical tool users use exponential moving averages, or exponentially weighted moving averages. Exponential moving averages reduce the lag by applying more weight to recent prices relative to older prices. The weightage applied to the most recent price depends on the length of the moving average. The shorter the exponential moving average is, the more weight that will be applied to the most recent price. For example, a 10-period exponential moving average could weigh the most recent price 18.18 per cent and a 20-period exponential moving average could weigh the most recent price 9.52 per cent. Exponential moving average puts more weight on recent prices. As such, it will react quicker to recent price changes than a simple moving average.

Arithmetic and Exponential Moving Average (EMA) Calculation

The formula for an arithmetic moving average is:

Where

MAn = (n) period moving average;

Pi = price of the share;

n = time period for computing the moving average;

x = the initial time for the moving average calculation, which takes the series formation 1,2,3,4, ……t . ‘t’ being the final time series data.

 

The formula for an exponential moving average is:

Where

X = Current EMA

C = Current Price

PEMA = Previous period's EMA*

K = Smoothing constant

 

(*The first simple moving average itself is used for the first period’s calculation)

The smoothing constant applies the appropriate weighting to the most recent price relative to the previous exponential moving average. The formula for the smoothing constant is:

Where

N = Number of periods for EMA

For a 5-period EMA, the smoothing constant would be 0.3333.

The EMA formula works by weighting the difference between the current period’s price and the previous period’s EMA and adding the result to the previous period’s EMA. The difference between the current period’s price and PEMA could be either positive or negative.

If the current price (C) is higher than the previous period’s EMA (PEMA), the difference will be positive (C - PEMA). The positive difference is weighted by multiplying it by the constant [(C - PEMA) x K] and the answer is added to the previous period’s EMA, resulting in a new EMA that is higher: [(C - PEMA) x K] + PEMA.

If the current price is lower than the previous period’s EMA, the difference will be negative (C - PEMA). The negative difference is weighted by multiplying it by the constant [(C - PEMA) x K] and the final result is added to the previous period’s EMA, resulting in a new EMA that is lower: [(C - PEMA) x K] + PEMA.

Table 13.1 contains the formulae and results (from Microsoft Excel) of an arithmetic and exponential moving average calculation for Hindustan Lever Limited. For the first period’s exponential moving average, the simple moving average was used as the previous period’s exponential moving average. From period 6 onwards, the previous period’s EMA was used. A sample of the select calculation is as follows:

 

When plotted it may appear that the difference between an exponential moving average and an arithmetic moving average is minimal. For a moving average that uses only 5 trading days, the difference is minimal. Table 13.2 shows the absolute difference between actual price and respective moving average price for 12 traded days (d1, d2, …d12). Overall, the exponential moving average is consistently closer to the actual price. On an average, it can be hypothesised that the exponential moving average is closer to the actual price than the arithmetic moving average.

 

 

There are many uses for any type of moving average, but most important uses are:

• Trend identification/confirmation
• Support and resistance level identification/confirmation
• Trading points

The type of moving average an investor wants to use will depend on the trading and investing style and preferences of the investors. The simple moving average obviously has a lag, but the exponential moving average may be prone to quicker breaks. Some investors prefer to use exponential moving averages for shorter time periods to capture changes quicker. Some investors prefer long time period’s arithmetic moving averages to identify long term trend changes. Moving average type and length of time will also depend greatly on individual share and how it has reacted in the past.

Greater sensitivity and quicker signals are bound to be beneficial to the technical analyst. This is not always true and brings up a great dilemma for the technical analyst, ie, the trade off between sensitivity and reliability. The more sensitive an indicator is, the more signals that will be given. These signals may prove timely, but with increased sensitivity comes an increase in false signals. The less sensitive an indicator is, the fewer signals it will give. However, less sensitivity leads to fewer and more reliable signals. Sometimes these signals can be late as well.

Shorter moving averages will be more sensitive and generate more signals. The exponential moving average is generally more sensitive than the arithmetic moving average and, hence, will also be likely to generate more signals. However, there will also be an increase in the number of false signals and noise. Longer moving averages will move slower and generate fewer signals. These signals will prove to be more reliable, but they also may come late. See Figure 13.4.

Because moving averages follow the trend, they work best when a share is trending and are ineffective when a security moves in a trading range. With this in mind, investors should first identify shares that display some trending characteristics before attempting to analyse with moving averages. Normally a simple visual assessment of the price chart can identify if a share exhibits characteristics of trending or trading.

In its simplest form, a share’s price can be doing only one of three things: trending up, trending down, or trading in a range. An up trend is established when a security forms a series of higher highs and higher lows. A down trend is established when a security forms a series of lower lows and lower highs. A trading range is established if a security cannot establish an up trend or down trend. If a security is in a trading range, an up trend is started when the upper boundary of the range is broken and a down trend begins when the lower boundary is broken.

The graph in Figure 13.5 shows a peak and trough, along with a rising trend, a flat trade and a declining trend and indications of when a technical analyst would ideally want to trade.

The graph begins with the up-trending market that finishes in a peak flowed by a trading market. The trading market then leads to a downward trend that forms a trough. Confirmation that the trend has reversed would be a buy or sell signal.

As long as the share price stayed in a rising trend, the chartist would hold the share(s) for the upward ride. Ideally, the investor would want to sell at the peak of the cycle, but the investor has to wait to identify a peak through a reversal for a downtrend.

Moving Average Convergence Divergence (MACD)

The MACD is calculated by subtracting a 26-day moving average of a security’s price from a 12-day moving average of its price. The result is an indicator that oscillates above and below zero.

When the MACD is above zero, it means the 12-day moving average is higher than the 26-day moving average. This is bullish as it shows that current expectations (ie, the 12-day moving average) are more bullish than previous expectations (ie, the 26-day average). This implies a bullish, or upward, shift in the price lines. When the MACD falls below zero, it means that the 12-day moving average is less than the 26-day moving average, implying a bearish shift in the price lines.

The price movements of German Remedies and its MACD are shown in Figure 13.6. The share is bullish when the MACD is above zero and bearish when it is below zero.

A 9-day moving average of the MACD is usually plotted on top of the MACD indicator. This line is referred to as the “signal” line. The signal line anticipates the convergence of the two moving averages. This indicates the movement of the MACD toward the zero line.

Figure 13.7 shows the MACD and its signal line of German Remedies. The “buy” arrow is drawn when the MACD rises above its signal line and “sell” arrow is drawn when the MACD falls below its signal line.

Since the MACD is the difference between two moving averages of price, when the shorter term moving average rises above the long-term moving average, resulting in a positive MACD value, it means that investor expectations are becoming more bullish. This is an indicator that there has been an upward shift in the price line. By plotting a 9-day moving average of the MACD we can see the changing of expectations as they occur. This indicates the time when there is a shift in the preferences of investors ie, the shift in price line. Moving averages and the MACD are examples of trend following, or “lagging, ” indicators. In Figure 13.8, the longer moving average (1 month) is able to show the late entry and exit points. The 120-day, 35-week moving averages further shift the crossovers on the actual price line.

The trend following indicators do not work well in sideways (horizontal) markets. The sideways market is a trading market and hence moving averages will not be able to indicate the best entry or exit points. Figure 13.9 confirms the limited use of lagging indicators, such as moving averages, in a sideways market.

Moving averages can be used on other economic data such as bank rate. Figure 13.10 shows the call rate along with its 10-month moving average. “Buy” arrows are drawn when the call rate crossed below its moving average (falling interest rates) and “sell” arrows are drawn when the call rate crosses above its moving average (rising interest rates). Here, the relationship between fundamental indicator movement and the stock market can be established and, thus, investors can gain from a macro change.

Another example of moving average application is shown in Figure 13.11. It shows a three-month moving average of the Put/Call Ratio (a sentiment indicator). “Buy” arrows are labelled in the figure when the moving average rose above the put/call ratio line each time. This is the level where investors are expected to be bearish and expect prices to decline. ‘Sell’ arrows are indicated when the moving average fell below the put call ratio line.

Figure 13.12 shows a 50-week moving average (a momentum indicator) of the BSE Sensex. “Buy” arrows are drawn when the index rises above its 50-week moving average. “Sell” arrows are drawn when the BSE falls below its moving average. This type of momentum indicator can easily catch every market movement and identifying these market movements over a long duration would help in establishing the time zones in which there is a cyclical activity of the stock market.

Line Studies

Line studies are technical analysis tools that consist of lines drawn on top of a share’s price and/or indicator. These help in identifying the support, resistance, and trend line concepts already discussed.

Fibonacci Studies

Leonardo Fibonacci was a mathematician who was born in Italy around the year 1170. Fibonacci while studying the Great Pyramid of Gizeh in Egypt discovered the relationship between numbers that are now referred to as Fibonacci numbers. Fibonacci numbers are a sequence of numbers in which each successive number is the sum of the two previous numbers:

These numbers possess an intriguing number of interrelationships, such as the fact that any given number is approximately 1.618 times the preceding number and any given number is approximately 0.618 times the following number.

There are four popular Fibonacci studies: arcs, fans, retracements, and time zones. The interpretation of these studies involves anticipating changes in trends as prices near the lines created by the Fibonacci studies. Figure 13.13 illustrates cetain line studies conducted on Fibonacci numbers.

Fibonacci Arcs

Fibonacci arcs are displayed by first drawing a trendline between two extreme points, for example, a trough and an opposing peak. Three arcs are then drawn, centered on the second extreme point, so they intersect the trendline at the Fibonacci levels of 38.2 per cent, 50.0 per cent, and 61.8 per cent. The interpretation of Fibonacci arcs involves anticipating support and resistance as prices approach the arcs. A common technique is to display both Fibonacci arcs and Fibonacci fan lines and to anticipate support/ resistance at the points where the Fibonacci studies cross.

The points where the arcs cross the price data will vary depending on the scaling of the chart, because the arcs are drawn so they are circular, relative to the chart. Figure 13.14 illustrates how the arcs can provide support and resistance (points “A”, “B”, and “C”).

Fibonacci Fans

Fibonacci fan lines are displayed by drawing a trendline between two extreme points, ie, a trough and an opposing peak. Then an “invisible” vertical line is drawn through the second extreme point. Three trendlines are then drawn from the first extreme point so they pass through the invisible vertical line at the Fibonacci levels of 38.2 per cent, 50.0 per cent, and 61.8 per cent. The chart of Glaxo in Figure 13.15 shows how prices support at the fan lines.

The fan lines indicate points that are not pierced by the price line. When prices encountered the middle fan line (point “A”), they were unable to penetrate the line for several days. When prices did penetrate this line, they dropped quickly to the bottom fan line (points “C” and “E”) before finding support. Also note that when prices bounced off the fan line (point “C”), they rose freely to the top line (point “D”) where they again met resistance, fell to the bottom line (point “E”) and rebounded.

Retracements

Fibonacci retracements are displayed by first drawing a trendline between two extreme points, for example, a trough and an opposing peak. A series of nine horizontal lines are drawn intersecting the trendline at the Fibonacci levels of 0.0 per cent, 23.6 per cent, 38.2 per cent, 50 per cent, 61.8 per cent, 100 per cent, 161.8 per cent, 261.8 per cent, and 423.6 per cent. After a significant price move (either up or down), prices are often expected to retrace a significant portion of the original move. As prices retrace, support and resistance levels often occur at or near the Fibonacci retracement levels.

In the chart of Hero Honda shown in Figure 13.16, Fibonacci retracement lines were drawn between a major trough and peak. In most instances, the support and resistance levels occur near the Fibonacci levels of 23 and 38 per cent or between the Fibonacci levels 38 per cent and 100 per cent. Near the levels of 23,38,50, and 61.8 the price levels tend to pause, and upon touching these levels there is a downward or upward movement of the prices. These retracement lines can help in identifying the price movements without any supportive evidence from the volume of shares. For instance, the rally of the Hero Honda prices, upon touching the 100 per cent retracement line, has shot up without any volume support.

Time Zones

Fibonacci time zones are a series of vertical lines. They are spaced at the Fibonacci intervals of 1,2,3,5,8,13,21,34, etc. The interpretation of Fibonacci time zones involves looking for significant changes in price near the vertical lines. A significant step is the determination of the start of the time zones. In Figure 13.17 Fibonacci time zones were drawn on the BSE Sensex beginning at the market bottom in 1994. Significant changes in the Sensex occurred on or near the time zone lines. The starting point can thus be at a market bottom or at the market top. The price movements touching upon the time zones drawn immediately after the initial market bottom indicate a new bottom.

There are arguments against Fibonacci numbers, which stress on the Fibonacci effect being purely coincidental. The logical explanation for significant Fibonacci relationships with prices is because investors expect them to be significant. Technical analysts often look at Fibonacci retracement levels after a large advance or decline. As more and more investors expect the market to be congested following a large shift in trend, Fibonacci line studies will be more and more significant. Investors may keep the 38.2 per cent and 61.8 per cent levels when they plan their trades. Individual shares and the market indexes have been found often to react at these levels, hence it is useful to monitor Fibonacci line studies.

Bollinger Bands

Bollinger bands are similar to moving average envelopes. The difference between Bollinger bands and envelopes is envelopes are plotted at a fixed percentage above and below a moving average, whereas Bollinger bands are plotted at standard deviation levels above and below a moving average. Since standard deviation is a measure of volatility, the bands are self adjusting—widening during volatile markets and contracting during passive periods. Bollinger bands were introduced by John Bollinger.

Bollinger bands are usually displayed on top of share prices, but they can be displayed on any other indicator also. As with moving average envelopes, the basic interpretation of Bollinger bands is that prices tend to stay within the upper and lower band. The distinctive characteristic of Bollinger bands is that the spacing between the bands varies based on the volatility of the prices. During periods of extreme price changes (ie, high volatility), the bands widen to become freer. During periods of stagnant pricing (ie, low volatility), the bands narrow to contain prices.

Trading bands are one of the most powerful concepts available to the technically based investor, but they do not, give absolute buy and sell signals based on price touching the bands. Rather they indicate whether prices are high or low on a relative basis. An intelligent investor can make buy and sell decisions by using indicators to confirm price action.

Some of the interpretations that can be drawn out of Bollinger bands are that:

• Sharp price changes tend to occur after the bands tighten, as volatility lessens.
• When prices move outside the bands, a continuation of the current trend is implied.
• Bottoms and tops made outside the bands followed by bottoms and tops made inside the bands indicate reversals in the trend.
• A move that originates at one band tends to go all the way to the other band. This observation is useful when projecting price targets.

Calculation of Bollinger bands

Bollinger bands are displayed as three bands. The middle band is a normal moving average. In the following formula, “n” is the number of time periods in the moving average (this is usually 20 days).

The upper band is similar to the middle band. The middle band is shifted up by the number of standard deviations (eg, two deviations). In the following formula, “D” is the number (1 or 2) of standard deviations.

The lower band similarly is the moving average shifted down by the same number of standard deviations (ie, D). The formula for computing the lower band is given below:

Figure 13.18 shows Bollinger bands on Colgate’s prices. The bands are calculated using a 20 day simple moving average and are spaced two deviations apart. The bands are at their widest when prices were volatile during October 1999. They narrowed when prices entered a consolidation period later, in July 2000. The narrowing of the bands increases the probability of a sharp breakout in prices. The longer prices remain within the narrow bands the more likely a price breakout will occur. High volumes support the volatile market in October 1999, and hence the decline in prices immediately. This is followed by the consolidation period. The narrow bands have continued till December 2001.

Absolute Breadth Index

The Absolute Breadth Index (ABI) is a market momentum indicator that was developed by Norman G Fosback. The ABI shows how much activity, volatility, and change is taking place in the stock market. This specifically ignores the direction in which prices are moving. The ABI is an “activity index”. High values indicate market activity and change, while low values indicate lack of change. Usually a high value of the moving average is accompanied by strong rallies in the market. For interpretation, the moving average of this absolute breadth index can also be computed (Figure 13.19).

The Absolute Breadth Index is calculated by taking the absolute value of the difference between the stock market’s advancing issues and declining lssues.

In Fosback’s book, Stock Market Logic, he indicates that historically, high values typically lead to higher prices three to twelve months later. Fosback found that a highly reliable variation of the ABI is to divide the weekly ABI by the total issues traded. A 10-week moving average of this value is then calculated. Values above 40 per cent are considered very bullish and values below 15 per cent are looked upon as bearish indications.

The variation of this formula is the ten week moving average of ABI. ABI in this case is computed as follows:

Figure 13.20 shows the Nifty and a three-month moving average of the ABI. The Nifty is in a bearish market since the ABI is below its 15 per cent mark thorough out. The moving averages of this ABI also indicate the buy and sell point, similar to the earlier chart.

Arms Index

The Arms Index is a market indicator that shows the relationship between the number of shares that increase or decrease in price (advancing/declining issues) and the volume associated with shares that increase or decrease in price (advancing/declining volume). It is calculated by dividing the advance/ decline ratio by the upside/downside ratio.

Richard Arms developed the Arms Index in 1967. Over the years, the index has been referred to by a number of different names. When Barron’s published the first article on the indicator in 1967, they called it the Short term Trading Index. It has also been known as TRIN (TRading INdex).

The Arms Index is primarily a short term trading tool. The index shows whether volume is flowing into advancing or declining shares. If more volume is associated with advancing shares than declining shares, the Arms Index will be less than 1.0; if more volume is associated with declining shares. The Arms Index is considered bullish when it is below 1.0 and bearish when it is above 1.0. The index can also be used effectively as an overbought/oversold indicator. When the indicator drops to extremely overbought levels, it is forecasting a selling opportunity. When it rises to extremely oversold levels, a buying opportunity is approaching. The determination of an extremely overbought or oversold level depends on the length of the moving average used to smooth the indicator and on market conditions.

The index is usually smoothed with a moving average. A weekly or 7-day moving average can be computed for short term analysis, a 20-day moving average can be used to interpret intermediate-term price movements, and a 55-day moving average can be used for long term analysis.

The Arms Index is calculated by dividing the number of shares that advanced in price by the number of shares that declined in price. This is referred to as the advance/decline ratio. Next, the volume of advancing shares is divided by the volume of declining shares. This ratio is called as the upside/downside ratio. Finally, the advance/decdline ratio is divided by the upside/downside ratio to compute the Arms index.

Relative Strength Index

The Relative Strength Index (RSI) was first introduced by Welles Wilder in an article in the Commodities (now known as Futures) magazine in June, 1978. Subsequently, calculations and interpretations of the RSI were provided in his book, New Concepts in Technical Trading Systems.

The RSI is a price following oscillator that ranges between 0 and 100. It measures the internal strength of a share by monitoring changes in its closing prices. The RSI usually tops above 70 and bottoms below 30. It usually forms these tops and bottoms before the price chart. A popular method of analysing the RSI is to look for a divergence in which the security is making a new high, but the RSI is failing to surpass its previous high. This divergence is an indication of an impending reversal. When the RSI then turns down and falls below its most recent trough, it is said to have completed a “failure swing.” The failure swing is considered a confirmation of the impending reversal.

Divergences occur when the price makes a new high (or low), but, this is not confirmed by a new high (or low) in the RSI. Prices usually correct and move in the direction of the RSI. The most significant signal is generated on “bullish” or “bearish” divergences between the RSI and the price of the share.

The RSI is a simple formula. Numerous variations of the same formula have been used in the computation of the RSI. The basic formula is:

where

RS = average of upward price change over a select number of days/average of downward price change over the same number of days.

When Wilder introduced the RSI, he recommended using a 14-day RSI. Since then, the 9-day and 25-day RSIs have also been used. When less number of days are used to calculate the RSI, the indicator is subject to more volatility.

The other variation of computing RSI is as follows:

where

D = an average of downward price change;

U = an average of upward price change.

 

RSI fluctuates between 0 and 100. RSI peaks indicate overbought levels and suggest price tops, while RSI troughs denote oversold levels and share price bottoms. Absolute levels can vary in meaning from share to share and in different market environments. Two horizontal reference lines are normally placed at 30 (indicating an oversold area) and 70 (indicating an overbought area). These reference lines can be adjusted depending on the market environment. Sometimes these lines can be moved to 40 and 80 in bull markets and lower them to 20 and 60 in bear markets. The RSI can stay overbought in bull markets and oversold in bear markets for prolonged periods.

Figure 13.21 has the share price movement and the 10-day RSI of Ranbaxy. The arrows below the price lines in the chart indicate long or buy signals. The arrows above the price line indicate the short or sell positions for the share. The circled trough in the RSI value indicates the decline that moved below the lower reference line. A higher trough in the RSI is almost always accompanied by a subsequent low in the share price. A bullish divergence occurred during January and April 2000 as prices were falling while the RSI was rising. Prices have subsequently corrected and trended upward.

Accumulation Swing Index

The Accumulation Swing Index (ASI) is another momentum indicator. The accumulation swing index is a variation of Welles Wilder’s RS index. It plots a running total of the swing index value of each bar. The Swing Index seeks to isolate the “real” price of a share by comparing relationships between the current prices (ie, open, high, low, and close) and the previous period’s prices. The Swing Index is a value from 0 to 100 for an up bar and 0 to-100 for a down bar. The swing index is calculated by using the current bar’s open, high, low, and close, as well as the previous bar’s open, high, low, and close.

Where

c1 = previous close	c2 = current close	r1 = absolute value of (h2 – c1)
11 = previous low	l2 = current low	r2 = absloute value of (12 – c1)

h1 = previous high	h2 = current high	r3 = absolute value of (h2 – 12)
o1 = previous open	o2 = current open	r4 = absolute value of (c1 – o1)

        r = r1 − r2/2 + r4/4 + (if r1 >= Max (r2, r3)

        r = r2 − r1/2 + r4/4 (else if r2 >= Max (r1, r3)

        r = r3 + r4/4 (else)

        k = Max (r1, r2)

        limit = 10,000 or some extreme value if the share does not have limit move

The Accumulative Swing Index is used to gain a better long term picture than using the plain Swing Index, which uses data from only two bars. If the long term trend is up, the accumulative Swing Index is a positive value. Conversely, if the long term trend is down, the accumulative Swing Index is a negative value. If the long term trend is sideways (non-trending), the Accumulative Swing Index fluctuates between positive and negative values.

The ASI will give the chartist numerical price swings that are value quantified, and it will also show short term trend turnarounds. A breakout is indicated when the Accumulative Swing Index exceeds its value on the day when a previous significant high swing point was made. A downside breakout is indicated when the value of the accumulative swing index drops below its value on a day when a previous significant low swing point was made.

Trendline breakouts can be confirmed by comparing trendlines on the ASI to trendlines on the price chart. A false breakout is indicated when a trendline drawn on the price chart is crossed but a similar trendline drawn on the accumulative swing index is not crossed. In the chart of the NSE Nifty Index in Figure 13.22, the trendline initially drawn confirms the short term trend witnessed through the ASI chart. The horizontal pattern that developed later is also confirmed in the ASI chart, indicating a not so profitable buy/sell signal.

Commodity Channel Index

The Commodity Channel Index (CCI) measures the variation of a share’s price from its statistical mean. High index values show that prices are unusually high compared to average prices whereas low index values indicate that prices are unusually low. Donald Lambert developed the CCI. Though first used in commodities trading, it is now applied in the technical analysis of share prices also. The Commodity Channel Index, CCI, is designed to detect beginning and ending market trends. The computational procedure standardises market prices much like a standard score in statistics. The final index attempts to measure the deviation from normal or major changes in the market’s trend.

There are two basic methods of interpreting the CCI. The first is to look for divergences and secondly it is used as an overbought/oversold indicator. A divergence occurs when the share’s prices are making new highs while the CCI is failing. This divergence is usually followed by a correction in the share’s price. To use the CCI as an overbought/oversold indicator, values above +100 imply an overbought condition (and a pending price correction) while values below -100 imply an oversold condition (and a pending rally). Any value less than -100, eg, -125, suggests a short position, while a rise to-85 indicates a position to liquidate the short position.

The following are the basic steps involved in the calculation of the CCI.

1. Add each period’s high, low, and close and divide this sum by 3. This is the average price.
2. Calculate an n period simple moving average of the average prices computed in Step 1.
3. Computation of mean deviations: For each of the prior n periods, subtract today’s Step 2 value from Step 1’s value n days ago. For example, if a 5 day CCI were calculated, five subtractions using today’s Step 2 value will be computed.
4. Calculate an n period simple moving average of the absolute values of each of the results in Step 3.
5. Multiply the value in Step 4 by 0.015 (a constant). [Standardisation procedure]
6. Subtract the value in Step 2 from the value in Step 1.
7. Divide the value in Step 6 by the value in Step 5.

These steps can be illustrated through a worksheet (Table 13.3). The prices of ABB are entered in columns C, D, E, F as Open, High, Low, and Close. The columns are indicated in brackets in the following figure. The steps are given in their formula mode as well as in the result mode.

Figure 13.23 shows the price chart of ABB and its 5 day CCI. A bearish divergence occurred at point “A” (prices were increasing as the CCI was declining). Prices subsequently fell down. A correction of prices accordingly took place and the share prices fell, as indicated by the CCI. A bullish rally occurred at point “B” (prices were advancing while the CCI was not showing any divergence). It can also be noted that the break in the value of 300 led to the divergence between the share price and the CCI indicator.

Technical indicators that give values within a pre-defined range are called oscillators. Oscillators work well in trading range periods. Oscillators are useful for identifying short term movements. They determine turning points, which are useful in identifying buy/sell opportunities. For example, Welles Wilder’s Relative Strength Index (RSI) is derived from a formula that compares upward and downward moves, and only gives values between 0 and 100. A high value is defined as “overbought” and a low value is “oversold”. Oscillators are thus powerful tools to identify buy/sell points.

Chaikin Oscillator

The Chaikin Oscillator is a moving average oscillator based on the accumulation/distribution indicator. The accumulation/distribution indicator considers the relevance of traded volume in an investment decision. Volume analysis helps in identifying internal strengths and weaknesses that exist under the cover of price action. In many instances, volume divergences versus price movement itself can successfully predict a major market reversal. Technical analysis always includes monitoring volume movements along with price movements. This relationship has been strengthened by the Granville OB V (on balance volume) concept that views the total volume on an up day as accumulation and the total volume on a down day as distribution. When an OB V line gives a divergence signal versus a price line, it can be a valuable technical signal and usually triggers a reversal in price.

OBV usually produces a curving line on the price chart. This line can be used either to confirm the current price trend or warn of an impending reversal, by diverging from the price action. The total volume for each day is assigned a plus or minus value, depending on whether prices close higher or lower for that day. A higher close causes the volume for that day to be given a plus value, while a lower close counts for negative volume. A running cumulative total is then maintained by adding or subtracting each day’s volume, based on the direction of the market close. It is the direction of the OBV line (its trend) that is important and not the actual numbers.

 

Table 13.3

Step 1 (K)	Step 2 (L)	Step 3,4 (M)
= AVERAGE (D2F2)
= AVERAGE (D3F3)
= AVERAGE (D4F4)
= AVERAGE (D5F5)
= AVERAGE (D6F6)	= AVERAGE (K2:K6)
= AVERAGE (D7F7)	= AVERAGE (K3:K7)	=(ABS(K2-L7)+ABS(K3-L7)+ABS(K4-L7)+ABS(K5-L7)+ABS(K6-L7)/5
= AVERAGE (D8F8)	= AVERAGE (K4:K8)	=(ABS(K3-L8)+ABS(K4-L8)+ABS(K5-L8)+ABS(K6-L8)+ABS(K7-L8)/5
= AVERAGE (D9F9)	= AVERAGE (K5:K9)	=(ABS(K4-L9)+ABS(K5-L9)+ABS(K6-L9)+ABS(K7-L9)+ABS(K8-L9)/5
= AVERAGE (D10F10)	= AVERAGE (K6:K10)	=(ABS(K5-L10)+ABS(K6-L10)+ABS(K7-L10)+ABS(K8-L10)+ABS(K9-L10)/5
= AVERAGE (D11F11)	= AVERAGE (K7:K11)	=(ABS(K6-L11)+ABS(K7-L11)+ABS(K8-L11)+ABS(K9-L11)+ABS(K1O-L11)/5
Step 5(N)	Step 6(0)	Step 7(P)
=(M7*0.015)	=(K7-L7)	=(O7/N7)
=(M8*0.015) =(M9*0.015)	=(K8-L8) =(K9-L9)	=(O8/N8) =(O9/N9)
=(M10*0.015)	=(K10-L10)	=(O10/N10)
=(Ml 1*0.015)	=(K11-L711)	=(O11/N11)

If prices are trending lower, so should the OB V line. It’s when the volume line fails to move in the same direction as prices that a divergence exists and warns of a possible trend reversal.

In order to determine whether there was accumulation or distribution in the market on an individual stock on a given day, Granville compared the closing price to the previous close. Larry Williams, when determining the accumulation/distribution points, compared the closing price to the opening price. Adding a percentage of total volume to the line if the close was higher than the opening and, subtracting a percentage of the total volume if the close was lower than its opening price resulted in a cumulative line. This cumulative accumulation/distribution line was able to predict price reversals better than the classic OBV approach to volume divergences.

The Chaikin Oscillator is computed using this cumulative accumulation/distribution line, but by substituting the average price ([high + low]/2) of the day for the opening price. The Chaikin Oscillator is an excellent tool for generating buy and sell signals when its action is compared to price movement. The premise behind the oscillator is three-fold.

• The first premise is that if a share or market average closes above its midpoint for the day (as defined by [high + low]/2), then there was accumulation on that day. The closer a share or average closes to its high, the more accumulation there was. Conversely, if a share closes below its midpoint for the day, there was distribution on that day. The closer a share closes to its low, the more distribution there was.
• The second premise is that rising volume and a strong volume accumulation accompanies a price advance. Since volume is the crucial determinant of strength of a rally, it follows that lagging volume on rallies is a sign of less strength to move shares still higher. Conversely, declines are usually accompanied by low volume, but may end with panic-like share disposals by investors.
• The third premise is that the Chaikin Oscillator can monitor the flow of volume into and out of the market. Comparing this flow to price action can help identify tops and bottoms, both short term and intermediate term.

The Chaikin Oscillator is created by subtracting a 10 period exponential moving average of the accumulation/distribution line from a 3 period exponential moving average of the accumulation/distribution line.

Where

EMA = Exponential Moving Average

A/D line = Cumulative Accumulation/Distribution line

 

The most important signal generated by the Chaikin Oscillator occurs when prices reach a new high or new low for a swing, particularly at an overbought or oversold level, and the oscillator fails to exceed its previous extreme value and then reverses direction. Signals, through the `Chaikin Oscillator, in the direction of the intermediate-term trend are more reliable than those against the trend.

A second way to use the Chaikin Oscillator is to view a change of direction in the oscillator as a buy or sell signal, but only in the direction of the trend. For example, if a share is in an uptrend, then an upturn of the oscillator while in negative area would constitute a buy signal only if the share were above its 90 day moving average. A downturn of the oscillator while in positive area (above zero) would be a sell signal if the share were below its 90 day moving average of closing prices.

Figure 13.24 shows Zee Telefilms and its Chaikin Oscillator. Bearish divergences (where prices increased to new highs while the oscillator was falling) occurred at point “A” and bullish divergences (prices declined while the oscillator showed an increase) occurred at point “B”. These divergences were indicators of the sell and buy points.

Detrended Price Oscillator

The Detrended Price Oscillator (DPO) attempts to eliminate the trend in prices. Detrended prices allow the technical analyst to easily identify trend cycles and overbought/oversold levels. Long term cycles are made up of a series of short term cycles. Analysing these short term components of long term cycles can be helpful in identifying major turning points in the long term cycle. The DPO helps the technical analyst to separate the long term cycles.

To calculate the DPO, first, a time period is specified for which the trend is to be removed. Cycles longer than this time period are removed from prices, thus leaving the data with only short term cycles.

To calculate the Detrended Price Oscillator, first an n period simple moving average (where n is the number of periods in the moving average) is created.

Next, from the closing price, the moving average “(n/2) +1” days ago is subtracted. The result is the DPO.

The chart of ITC in Figure 13.25 shows the 20-day Detrended Price Oscillator. As can be seen from the chart, peaks in the DPO highlight the minor peaks in ITC’s price chart. The circled parts of the DPO match the arrows marked in the price chart. But the long term price trend during June 2000 is not reflected in the DPO. This is because the 20 day DPO removes cycles of more than 20 days. DPO helps in highlighting the minor buy and sell points. It is most useful to speculative traders who do not want to wait for the confirmation of a price movement and want to trade on the short term price movements to make a profit.

Stochastic Oscillator

The stochastic process has an infinite progression of jointly distributed random variables. The Stochastic Oscillator compares where a share’s price closed, relative to its trading range over the last n-time periods.

Basically, this is an overbought/oversold technical indicator. If a share or index is identified as “oversold”, there exists the possibility that buyers will enter the market, driving the price upward. On the other hand, if a share is “overbought”, the sellers will overpower buyers to drive the price lower.

In trending markets, stochastic oscillator can stay overbought or oversold. In a strong uptrending market, the stochastic oscillator can stay overbought, while in a strong downtrending market the measure can maintain its oversold condition. From a timing perspective, stochastic oscillator works best in a non-trending or consolidating market.

The Stochastic Oscillator compares a share’s closing price level to its range over a specified period of time. Generally speaking, prices tend to close near their highs in up trending markets and near their lows in down trending markets. As an upward trend becomes exhausted, these closing values tend to drift lower away from the highs. Conversely, as a down trending market starts to stabilise, closing prices gradually move away from the lows.

The indicator attempts to determine when prices start to consolidate or “cluster” around their low levels of the day in an up trending market or around their high levels of the day in a down trending market. These conditions indicate that a trend reversal is imminent.

The stochastic indicator is plotted as two lines: the “Percent D” %D line and the “Percent K” %K line. The %K is the more sensitive of the two oscillators, but it is the %D line that carries greater weight and gives major signals. Both these values range from zero to 100. Values above 80 are considered strong and suggest prices are closing near their highs. Values below 20 indicate prices are closing near their lows and are indicative of weakness.

It is usually estimated that %K will change direction before %D. However, when the %D line changes directions prior to the %K line, a slow and steady reversal is often indicated. When both %K and %D lines change direction, and the faster %K changes direction to retest a crossing of the %D line, but does not cross it, a conformation of the stability of the prior reversal is made.

The formula for the %K parameter of the stochastic or “raw stochastic” is:

This formula can be restated as follows:

Where

CL = the current day’s close

Ln = the lowest point over the past n days

Hn = the highest point over the past n days

n = the number of days, typically five or more

 

A moving average of %K is then calculated using the number of time periods (n days) used in the %K computations. This moving average is called %D. %D represents a smoothing of %K and is a n-day moving average of %K.

The formula for %D sometimes can be computed as follows:

where

L = the lowest low for the n-day period

H = the highest high for the same n-day period

 

The fast stochastic (%K) and (%D) are plotted on the same chart.

The slow stochastic, a less sensitive indicator, goes a step further in the smoothing process. The %D of the fast stochastic becomes the new %K, which is then smoothed once again using a n-day moving average to obtain the new “slow” %D. The slow stochastic is preferred for filtering out market noise and is less prone to violent price movements.

For example, to calculate a 10 day %K, First, the security’s highest high and lowest low over the last 10 days are identified. The lowest low over the past 10 days is then subtracted from the current closing price to arrive at the numerator. The denominator shows the difference between the highest high and the lowest low. This K parameter is then stated in terms of a percentage by multiplying the parameter by 100.

For this example, let’s assume that during the past 10 days the highest high for Cipla’s share was 1210 and the lowest low was 1125, the denominator, is (1210–1125) 85 points. If the current closing price is 1194.10, the numerator will be (1194.10–1125), ie, 69.10 points. %K then would be calculated as (69.10/ 85)* 100 = 81.29 per cent.

The 0.8129 shows that current day’s close was at the level of 81.29 per cent (ie, almost near the highest point) relative to the security’s trading range over the last 10 days. If current day’s close is on the other hand 1160, the Stochastic Oscillator would be (50/85)* 100 = 58.82 per cent. This percentage shows that the share currently closed almost at the mid-point of its 10 day trading range.

The Stochastic Oscillator always ranges between 0 per cent and 100 per cent. A value of 0 per cent shows that the security’s close was the lowest price that the security has traded during the preceding n time periods. A reading of 100 per cent shows that the security’s close was the highest price that the security has traded during the preceding n time periods.

Stochastic Oscillators can be used as both short and intermediate term trading, depending on the number of time periods used when calculating the oscillator.

There are several ways to interpret a Stochastic Oscillator. Three popular methods include:

Overbought/Oversold Indicator

As an overbought/oversold indicator, the shares are bought when the oscillator (either %K or %D) falls below a specific level (eg, 20) and then rises above that level; and are sold when the oscillator rises above a specific level (eg, 80) and then falls below that level. As with the interpretation of other oscillators, the stochastic oscillator should be applied in a trending market rather than in a trading market. If a trending market is suggested, the stochastic oscillator can be used to enter trades in the direction of the trend.

When %K reaches 100 per cent, it does not mean that prices cannot move higher. Conversely, if %K moves to zero per cent, prices can still continue lower. These extremes can denote strengths at the 100 per cent level or weakness at the zero per cent level. When the move in the share is sustained, the indicator can remain at extreme levels for extended periods of time. This is also highlighted in Figure 13.26 representing the share price movement of Cipla from June onwards.

The stochastic to a share that has been facing a long term consolidation period, ie, not trending would not be a suitable tool for investment analysis. In such non-trending periods, the stochastic indicator, even though it indicates the overbought or oversold situations, may not result in best profitable trades for the investor.

The stochastic method is based on the assumption that prices tend to close near the upper part of the trading range during an up trend and near the lower part during a down trend. This range refers to the trading period under consideration. For example, daily data would have a trading range for a day, weekly data for the week, etc.

As the trend approaches a turning point, the price closes further away from its extreme. In other words, it closes away from the daily high in a rising market and away from the daily low in a declining one. The objective of the stochastic formula is to identify these points in an advancing market when the closes are clustered nearer to the lows than to the highs, since this indicates that a trend reversal is at hand. For down markets the process is reversed.

When the Stochastic Oscillator reach extremes it warns investors that the probabilities of a change in trend is growing. This is especially true when the Stochastic Oscillator gets in the high 90 per cent level. At those levels, a downtrend is likely to develop soon. Conversely, when the stochastic oscillator gets down to extremely oversold levels (below 10 per cent) a trend reversal should be expected. This tool is very useful in determining market risk and provides a unique perspective of the trending market.

Buy/Sell Signals

Using the Stochastic Oscillator, buy signals are shown when the %K rises above the %D and sell signals are perceived when the %K line falls below the %D line. The arrows above the price line represent the sell points and the arrows below the price line represent the buy points in the Cipla figure. Again, in a trading market, the buy and sell points are very distinct.

The Stochastic Oscillator gives a unique perspecting to the market, especially when different time periods are considered. The time frame can be made suitable to a specific trading style. For example, the 20/80 buy/sell zones can be narrowed down to 30/70 buy/sell zones to suit the volatility of the shares. Stochastic Oscillators can also be drawn by shrinking the prior period to less than 10 or slow them down by selecting a 50 day prior period.

There are small cycles within large cycles, and so forth. Daily measurement of the Stochastic Oscillator gives a short cycle or 10 to 14 day periods. The weekly or intermediate cycle measures the four to five month cycle. The long term cycle measures the 40 month cycle and closely tracks the 3 yearly cycle. The decade cycle tracks the 10-year period. Rather than just look at one market time, it is best to view the market’s behaviour from the perspective of a cycle within a cycle. That way an investor can understand the behaviour of a sub-cycle as well as larger cycles that dominate smaller cycles.

The Cipla share data using a slow Stochastic Oscillator marks the buy/sell points in Figure 13.27.

Divergences

Stochastic oscillators can also highlight divergence. The price may be making higher highs, while the stochastic could be making lower lows. Conversely, the price may be setting lower lows while the oscillator could be making higher highs. In such divergences, the oscillator will confirm the future price movements; in other words, the price movements will be corrected towards the oscillator’s position.

For example, the Stochastic Oscillators drawn on Bata share prices (Figure 13.28) show a divergence in the beginning of January 2001, which is confirmed latter as a bearish trend (confirming the oscillator movement) during July-September 2001. During this period (January-March) prices are making a series of new highs and the Stochastic Oscillator is failing to surpass its previous lows.

Stochastic oscillators may not always be perfect indicators. They need to be used in conjunction with other tools, even though they are one of the best tools for an investor. The slope of the market, or the natural growth rate in shares could influence stochastic oscillators. It is for this reason that there is a need to adjust the market for this slope by “de-trending” this slant before running the stochastic formulas on price. This de-trending process removes “noise” or false signals.

R-Squared Indicator

The R-Squared Indicator illustrates the relationship between prices using the linear regression trendline. The closer the two are to each other, the stronger the trend. The formula for R-square is:

where

Ŷ = Fitted Regression Value

Ȳ = Mean Value

 

If the R-Squared Indicator falls below the critical values at 95% confidence level, it would illustrate no correlation between the price and the linear regression trendline.

The graph in Figure 13.29 indicates the R-square value and the trend equation on the price chart of Gujarat Ambuja Cements. The R-square value of 0.6835 is more than the critical value of 0.03 for 120 data sets. The up trend is very strong for the share and future values of the share price can be estimated from this base equation.

CMO Indicator

The Chande Momentum Oscillator (CMO) was developed by Tushar Chande to capture momentum. The CMO is closely related to other momentum oriented indicators such as the Relative Strength Index, Stochastic oscillator, Rate-of-Change, and so on.

Regression Statistics
Multiple R	0.826757
R Square	0.683527
Adjusted R Square	0.680845
Standard Error	6.402322
Observations	120

• It uses data for both up days and down days in the numerator, thereby directly measuring momentum.
• The calculations are applied on unsmoothed data. Therefore, short term extreme movements in price are not hidden. Once calculated, smoothing can be applied to the CMO, if desired.
• The scale is restricted between +100 and -100. The restricted scale also allow convenient comparison of values across different shares.

Computation of the CMO is given below:

The CMO can be used to measure several conditions such as trendiness, overbought/oversold situations, and divergences.

Trendiness

The CMO can be used to measure the degree to which a share is trending. The higher the CMO, the stronger the trend. Low values on the CMO show a share in a sideways trading range. The CMO is helpful in establishing the entry and exit rules of a trend following system. Entering the market when the CMO is high and exit when CMO moves lower would help the investor.

Overbought/oversold

The primary method of interpreting the CMO is looking for extreme overbought and oversold conditions. As a general rule, Chande quantifies an overbought level at +50 and the oversold level at -50. At +50, up-day momentum is three times the down-day momentum. Likewise, at -50, down-day momentum is three times the up-day momentum. These levels correspond to the 70/30 levels on the RSI. Overbought/ oversold entry and exit rules can be established by plotting a moving average line on the CMO. For example, if a 20 period CMO is used, a 9 period moving average may serve as a trigger line. Buy signals are perceived when the CMO crosses above the 9 period moving average, and sell signals, when it crosses below.

Divergence

Divergence between the CMO and the price, as is often done with other momentum indicators, can also be used in technical analysis.

The chart in Figure 13.30 draws the CMO for Cipla. Overbought and oversold levels are indicated as sell and buy points on the graph. The 9 day moving average drawn over the CMO line also indicates the trigger points in the chart.

McClellan Oscillator

The McClellan Oscillator, developed by Sherman and Marian McClellan in the late 1960’s, calculates the difference between two exponential moving averages by using the advances and declines from the same time period. The two moving averages are always 19 and 39 time periods and represent 10 per cent and 5 per cent trend values respectively. The McClellan oscillator is a good short term indicator, anticipating positive and negative changes in the advance/decline line for market timing.

The McClellan Oscillator uses averages and differences to gauge market breadth. Every day that shares are traded the difference between the number of shares that closed higher (advances) and those that closed lower (declines) is called the daily breadth. To plot the McClellan Oscillator accurately, the chart must contain both advancing and the declining issues. The McClellan Oscillator uses exponential averages. If a stock market index is rallying but more issues are declining than advancing, then the rally is narrow and much of the stock market may not be participating.

where

EMA = Exponential Moving Average

 

The McClellan Oscillator is a momentum indicator. When the short term average moves above the long term average, a positive value is recorded. As with most oscillators, the McClellan Oscillator shows an overbought situation when the indicator measures in the positive 70 to 100 range and it shows an oversold issue in the minus 70 to 100 range. Buy signals are indicated when the oscillator advances from oversold levels to positive levels, and, conversely, sell signals are indicated by declines from overbought to negative territory. A rising trendline would be a positive sign to the investor.

In the chart of NSE (Nifty) in Figure 13.31 the decline in the McClellan Oscillator indicates a sell signal. This is substantiated by the 19 day and 39 day exponential moving averages that are in the below -70 region during April 2000 to April 2001, thus, indicating a sell signal for the index.

Parabolic Stop and Reverse (SAR)

The Parabolic SAR is a very useful and accurate indicator during a trending period. But during trading time periods this indicator does not serve any purpose. Welles Wilder first introduced this time/price system in his book New Concepts in Technical Trading Systems. SAR stands for stop and reverse, and the term parabolic comes from the shape of the curve (resembling a parabola) that is created on the technical chart.

Sometimes called a reversal system, the Parabolic SAR allows the investor to follow the dots in either an up or downward trend until stop position is reached and the trend is reversed. It is primarily used in trending markets and is based on the assumption of having a position in the market. The indicator may also be used to determine stop points and estimating when the trader would reverse a position and take a trade in the opposite direction.

The first entry point on the buy side occurs when the most recent high price of an issue has been broken and it is at this time that the SAR is placed at the most recent low price. As the price of the share rises, the dots will rise as well, first slowly and then picking up speed and accelerating with the trend. The SAR starts to move a little faster as the trend develops and the dots soon catch up to the price momentum of the share.

Parabolic SAR should only be employed in trending markets to identify entry and exit points. A stop loss is calculated for each day using the previous day’s data. The advantage is that the stop level can be calculated in advance of the market opening.

• A stop level below the current price indicates that the position is long. The stop will move up every day until activated (when price falls to the stop level).
• A stop level above the current price indicates that the position is short. The stop moves down every day until triggered (when price rises to the stop level).

Traders can most profitably use the stop loss orders using the development of the SAR to lock in profits that have been realised on paper in an upward trend. Also traders can use this tool effectively to determine the time to cover their short positions.

SAR Calculation

• On day 1 of a new trade (the day that the trade is entered), the Parabolic SAR is taken as the significant point from the previous trade.

  If the trade is long the significant point (SP) will be the extreme low reached in the previous trade. If the trade is short then the significant point will be the extreme high reached in the previous trade.

• To calculate Parabolic SAR for the following day:

  Take the difference between the extreme point and the SAR (on day t − 1 ) and multiply by the acceleration factor.

  If the trade is long, add the result to the SAR on day t − 1.

  If the trade is short, subtract the result from the SAR on day t − 1.

The computations of the plot points for parabolic SAR are:

where

SAR is the limit beyond which the assumed position becomes irrelevant on the market, placed to the previous HI (high) or LO (low). The HI and LO are referred as significant points (SP).

SAR(t−1) corresponds to the level of reversal calculated to time SAR -1:

Wilder’s acceleration factor (AF) is 0.02 for the initial calculation. Thereafter, the AF is increased by 0.02 in every period when a new high is made. If a new high is not made then the AF is not increased from the last SAR. This continues until the AF reaches 0.2. Once the AF reaches 0.2 it stays at that value for all future SAR calculations until the trade is stopped out. The AF is never increased above 0.2.

In the chart of Bata India Limited in Figure 13.32, during the period of January and February 2002, there is a trading movement of the chart rather than a trending movement. The month of March has highlighted the lowest low (circled L), which is a clear signal for the investor to buy the stock from the market. In the next set of highs and lows, the trend movement continues and hence the trader does not involve in these high and low positions. On the other hand, the highest high (rounded H) during July leads to a change in trend and this induces the investor to sell the shares in the market.

Volume Oscillator

Volume simply indicates enthusiasm, or lack thereof, for a share and does not deal with the price of the share. To confirm a market turnaround or trend reversal, the technical analyst most importantly must determine whether or not the measurements of price and volume momentum agree with each other. If they do not, it is a sure indicator of weakness in the trend, and may point to a trend reversal.

A volume oscillator is an indicator that measures the relationship between two moving averages. The volume oscillator calculates a fast and slow volume moving average. The difference between the two (fast volume moving average minus slow volume moving average) is then plotted as a histogram. The fast volume moving average is usually over a period of 14 days. The slow volume moving average is usually 28 days.

The formula for the volume oscillator is as follows:

The histogram, like an oscillator, fluctuates above and below a zero line. Volume can provide insight into the strength or weakness of a price trend. This indicator plots positive values above the zero line and negative values below the line. A positive value suggests there is enough market support to continue the price activity in the direction of the current trend. A negative value suggests there is a lack of support, that prices may begin to become stagnant or reverse.

If a market is rallying, the volume oscillator should rise. When the issue becomes overbought, the oscillator will reverse its direction. If the market is declining or moving in a horizontal direction, the volume should contract. It is important to note that an increasing price together with declining volume would lead to a bearish market. When the market is at the top position it will be seen as oversold in the volume chart.

The chart in Figure 13.33 shows the HLL volume oscillator along with the price chart. The sudden decline in the volume oscillator indicates a bearish market for the share. The bearish tendency continues into the future as can be seen from the price chart. The negative volume oscillator further confirms the stagnant price developments in the HLL share.

Triple Exponential Average

The triple exponential average (TRIX) indicator is an oscillator used to identify oversold and overbought markets and it can also be used as a momentum indicator. Like many oscillators, the TRIX oscillates around a zero line. A positive value indicates an overbought market while a negative value indicates an oversold market. When the TRIX is used as a momentum indicator, a positive value suggests momentum is increasing while a negative value suggests momentum is decreasing. Many analysts believe that the TRIX crossing above the zero line is a buy signal, and it closing below the zero line is a sell signal. Also, divergences between price and the TRIX can indicate significant turning points in the market.

TRIX calculates a triple exponential moving average of the log of the price over the period of time.

where

E3(j) triples the moving average of the closing price of the current day

E3(j-1) triples the moving average of the closing price of the previous day

To calculate the TRIX indicator:

1. Calculate an n period exponential moving average of the closing prices.
2. Calculate an n period exponential moving average of the moving average calculated in Step #1.
3. Calculate an n period exponential moving average of the moving average calculated in Step #2.
4. Calculate the 1-period (eg, 1 day) per cent change of the moving average calculated in Step #3.

Two main advantages of the TRIX over other trend following indicators are its filtration of market noise and its tendency to be a leading rather than a lagging indicator. It filters out market noise using the triple exponential average calculation, thus eliminating minor short term cycles that indicate a change in market direction. It has the ability to lead a market because it measures the difference between the smoothed price information.

Trades should be placed when the indicator changes direction (ie, buy when it turns up and sell when it turns down). A n period (normally 9 day) moving average of the TRIX can be plotted to create a “signal” line. Using the moving average line, investors can buy when the TRIX rises above its signal, and sell when it falls below its signal. Divergences between the security and the TRIX can also help identify turning points.

The Reliance chart shown in Figure 13.34 has a TRIX indicator. The TRIX moves around the narrow range of (+/-) 4 per cent for this share. Not much of a buy/sell opportunity is seen from the graph. However, when the moving average is plotted at, there are clear cut buy and sell signals along the price chart. The chart uses a 9-day moving average, which helps in timing the buy/sell decision. The shorter the time frame, the more accurately the indicator will signal the move in the price chart.

Williams’ % R

Williams’ %R is a momentum indicator that measures overbought/oversold levels. Williams’ %R was developed by Larry Williams. The formula used to calculate Williams’ %R is similar to the Stochastic Oscillator formula:

The interpretation of Williams’ %R is very similar to that of the Stochastic Oscillator, except that %R is plotted upside down and the Stochastic Oscillator computes the difference in the numerator as (closing price n period lowest low). Values in the range of 80 to 100 per cent indicate that the share is oversold while values in the 0 to 20 per cent range suggest that it is overbought. As with all overbought/oversold indicators, it is best to wait for the share’s price to change direction before trading on the basis of the oscillator.

Selling simply because the share appears overbought may influence the investor to exit the market long before its price shows signs of lowering. The %R indicator can point out or anticipate a reversal in the underlying share’s price. The indicator almost always forms a peak and turns down a few days before the share’s price peaks and turns down. Likewise, %R usually creates a trough and turns up a few days before the share’s price turns up.

Figure 13.35 chart shows the Jagjit Industries’ price and its 10-day Williams’ %R. The buy and sell points have been plotted based on the oversold or overbought situations in the oscillator.

Dynamic Momentum Oscillator

In the July 1996 Futures magazine, E Marshall Wall introduced the Dynamic Momentum Oscillator (Dynamo). He describes the Dynamo Oscillator to be:

where

Mc= the midpoint of the oscillator

MAo= a moving average of the oscillator

O= the oscillator

This concept can be applied to any oscillator to improve its results. Applying the Dynamo Oscillator to a Stochastic Oscillator would give:

where

Mc = Stochastic Oscillator’s midpoint (The stochastic oscillator moves from 0 to 100, hence the midpoint of the oscillator will be 50).

MAo = the Moving average of the Stochastic which is represented as MAo (Stoch(n))

O = the Stochastic Oscillator, which is represented as Stoch(n)

This example, when applied to a Relative Strength Index (RSI) oscillator, is expressed as:

where

Mc = RSI’s midpoint (which is taken as 50)

MAo = the Moving average of the RSI expressed as MAo (RSI (n))

O = the RSI Oscillator which will be expressed as RSI (n)

 

Looking at the dynamo of the Stochastic Oscillator of Cipla shares (Figure 13.36), it can be seen that the buy and sell points of Cipla are projected by the dynamo. The buy and sell points have been identified whenever the dynamo touched the extreme bands of 80 and 20.

Performance Indicator (ROC)

The performance indicator (rate of change) displays a share’s price performance as a percentage.

The performance indicator displays in percentage the increase/decrease in the price over a specified time period. For example, if the performance indicator is 10, it means that the share’s price has increased 10 per cent since the prior period. Similarly, a value of-10 per cent means that the share’s price has fallen by 10 per cent since the prior period.

The performance indicator is calculated by dividing the change in prices by the first price.

Performance charts are helpful in comparing the price movements of different shares.

Figure 13.37 shows Grasim’s price chart and its performance indicator. The indicator shows that Grasim’s price has been fluctuating between +5 per cent and -5 per cent during January-March 1999.

To understand the ROC charts, a few shares’ performance can be compared and the choice can be made from among the shares by watching the movement of the ROC. The ROC of Sensex verses a Tata Steel are shown in Figure 13.38. The wide fluctuations of the Tata Steel shares verses the Sensex is most evident from the chart.

Despite the evidence that technical analysis tools are good forecasters of market prices, there is not much confidence among investors in the usage of these tools.

Weakness of Technical Analysis

Technical analysis is subject to certain weaknesses such as analyst bias, open to interpretation and mistiming in the market.

Analyst Bias: Just as with fundamental analysis, technical analysis is subjective and the investor’s personal biases can be reflected in the analysis. If the investor is always optimistic on a share performance, then a bullish bias will overshadow the analysis. On the other hand, if the investor is a pessimist, then the analysis will be weighted down by a bearish outlook.

Open to Interpretation: Technical analysis is open to interpretation. Even though there are standards, many times two investors will look at the same chart and infer two different scenarios or see different patterns. Both will be able to come up with logical support and resistance levels as well as evidence to justify their position.

Timing the Market: By the time the trend is identified, in most instances, a substantial portion of the move has already taken place in the market. After such a large move, the reward to risk ratio is not great. Even after a new trend has been identified, another important level crops up immediately. Even though the principles of technical analysis are the same, each type of share may require specific tools for interpretation.

Technical analysts consider the market to be 80 per cent psychological and 20 per cent logical. Fundamental analysts consider the market to be 20 per cent psychological and 80 per cent logical. Whether or not the market is psychological or logical, the price set by the market reflects the sum knowledge of all participants. These participants are supposed to have considered (discounted) everything that has happened and settled on a price to buy or sell. These are the forces of supply and demand at work. By examining price action to determine which force is prevailing, technical analysis focuses directly on the decision of timing the investment decisions.

SUMMARY

Technical analysis is based on published capital market data as opposed to fundamental data, such as earnings, sales, growth rates, or government regulations. Market data include the price of a share or the level of a market index, volume (number of shares traded), and other technical indicators such as the put/ call ratio.

Users of Technical analysis work on the premise that market prices adjusts to the equilibrium price and believe that the process by which prices adjust to new information is one of gradual adjustment toward a new (equilibrium) price. As the share adjusts from its old equilibrium to its new level, the price tends to move in a trend. Hence, technical analysts believe that share prices show identifiable trade situations that can be exploited by investors. They seek to identify changes in the direction of a price movement and take a position in the share to take advantage of the trend.

CONCEPTS

• Moving Averages	• Line Studies
• Bollinger Bands	• Absolute Breadth Index
• Arms Index	• Relative Strength Index
• Accumulative Swing Index	• Commodity Channel Index
• Chaikin Oscillator	• Detrended Price Oscillator
• Sochastic Oscillator	• McClellan Oscillator
• Dynamic Momentum Oscillator	• Williams’%R
• Performance Indicator	• Put/Call Ratio
• Parabolic Stop and Reverse	• Volume Oscillator

SHORT QUESTIONS

1. What is MACD?
2. What are lead indicators?
3. What are lag indicators?
4. What are Fibonacci Retracement lines?
5. What are divergences? What do they signify?
6. What is an overbought indication in a stochastic oscillator?
7. What is an oversold indicator in a stochastic oscillator?
8. What are %K, %D, %R indicators?

ESSAY QUESTIONS

1. What are oscillators? Briefly explain them.
2. How is an exponential moving average different from a moving average?
3. List a few lag and lead indicators and discuss the use of these indications in timing a market trade.
4. What are Oscillators? How are they different from moving averages? Would you recommend the usage of an Oscillator? Explain.
5. What are the weaknesses of technical analysis?

PROBLEMS

1. From the following MACD data indicate the bullish and bearish market.
   
2. Compute the upper, lower, and middle Bollinger bands from the following data (Hindalco share data).
   
3. Work out the stochastic oscillator from the following data regarding SBI share price (use 10 days as time duration).
   
4. The following volume oscillator has been plotted for ITC share data. Identify the bullish/bearish period for the share.
   
5. Compute the ROC from the following Tata Steel share price data.
   

What should Rakesh’s trading strategy be forthis share?