STOCK TREND ANALYSIS
(Bar charts with moving averages)
A easiest way of finding a trend of a stock is using a
moving average. It is mechanical aid say a line line drawn representing
the average value of stock prices. One can conclude as long as the
prices are above moving average then the stock is bullish or uptrend.
Once when the stock cuts down the moving average and maintains below
then the trend is assumed as bearish or down trend.
1.
Moving averages:
Moving averages are a commonly used technical indicator. It is a
mechanical aid derived from average prices. For example take 12 days
moving average. It is the factor derived by addition of 12-day prices
and then divided by 12 for plotting continuously add the latest 12 days
and divide by 12 or add the next day and subtract the preceding 13th
day, then divide by 12 moving average is plotted as a line connecting
the average of closing price generally over a look back period. Moving
average may be used as a trend indicator and to identify support and
resistance levels. When the price cuts above the moving average it is a
buy signal. Similarly when the prices cuts below the moving average it
is a sell signal.
In day-to-day market the share prices fluctuate daily and forms a
lot of whipsaws also. To avoid the confusion and major errors we can
consider the moving average to judge the primary market trend direction.
Say one can buy a company / security when the prices cuts above 50 days
moving average and hold on up to the time till the prices move above 50
days moving average and sell when it cuts below the moving average.
Further the interception of moving averages will give still a clear
picture of the trend. Say if we take two moving averages 12 days, 50
days then we conclude that when the 12-day moving average cuts the
50-day moving average upward then it is buy signal.
The major problem of moving average in the perception of
investors is they are lagging in trend changes and lead to late signals.
The analysis and prediction of moving averages will become simple and
easier if the following factors are considered.
- Using
exponential moving averages (a formula adding exp. Factors)
- Using
weighted moving average
- Using
multiple moving averages
- Use
market indictors like Price rate of change, Relative strength Index,
MACD, Ultimate Oscillator, and volume trends in conjunction with
moving averages.
Elliott
Wave Consultancy wish to give some guidelines for investors for usage of
moving averages: -
For using
Single
Moving Average use
- 25 days, 30 days,
30 weeks
Multiple
Moving Averages use - 12day and 50 day, 10 week and 30 week
Long
Term Bullish check use – 150 days, 200 days, 30 weeks. Long Term
Bullish check use – 150 days, 200 days, 30 weeks.
Exponential
moving average will give a better perception than simple moving average.
How to
calculate moving average: -
A
moving average is a method of calculating the average value of a
security’s price or indicator over a period of time. The term moving
average implies and rightly so the average changes or moves. When
calculating a moving average, a mathematical analysis of the
security’s average value, over a predetermined time period is made. As
the security’s price changes over the time its average price moves up
or down.
-
Simple
Moving Average: -
A
simple moving average is calculated by adding the closing prices of
security for a number of time periods. (e.g. 12 days) and then dividing
the total by the number of time periods. The result is the average price
of the security over the time period.
To
calculate 12 day moving average of a company we add up the company’s
closing prices for 12 days. By next we divide them by 12; thus we get
the average of the company over preceding 12 days. We would plot the
12-day average on the chart. The next day (following day) we would do
the same calculation. Add up the previous / preceding 12 days closing
prices, divide by two and plot the result.
In
a nutshell “the running total is constructed by adding in the new
point and subtracting the drop point. The total is divided by ‘N’
(12 day) the span.
2. Exponential
moving average: -
An
exponential moving average is calculated by applying a percentage of
today’s closing price to yesterday’s moving average value. A leading
company Equis Intl. used some calculations, which may help investors to
get a better value.
For
example to calculate 9% exponential moving average of a company, first
we would take today’s closing price and multiply by it by 9%. We would
then add this product to the value of yesterday’s moving average
multiplied by 91% (100% - 9% = 9%).
M.A.
= (today’s close * 0.9) + [(yesterday’s moving average) * 0.91]
Because
most investors feel more comfortable working with time periods rather
than with percentages, it is better to convert the days into an
exponential percentage. For example if a 12 day exponential moving
average is requested a 9% of moving average is calculated.
The
formula for converting days to exponential percentage is as follows.
Exponential
percentage = 2 / time periods
For
example to calculate 10 day exponential moving average you would use
0.18.
0.18
= 2/ (10+1)
To
convert the exponential percentage into time periods you would use the
following formula.
Time
periods = (2/ percentage)
–1
Using our previous example we can check to see that a 0.18 exponential
moving average is actually a 10-day moving average.
10
= (2/0.18) –1
The
method used to calculate an exponential moving average puts more weight
towards recent data and less weight towards past data than does the
simple moving average method. This method is called exponentially
weighted.
3.
Weighted Moving Average:
A weighted moving average is also designed to put more weight on
recent data and less weight on past data. A weighted average is
calculated by multiplying each of the previous day’s data by a weight.
5
day weighted moving average
|
Day
No.
|
Weight
|
Price
|
Weighted
price
|
|
1
|
1
|
1*25
|
25
|
|
2
|
2
|
2*26
|
52
|
|
3
|
3
|
3*28
|
84
|
|
4
|
4
|
4*25
|
100
|
|
5
|
5
|
5*29
|
145
|
|
Total
|
|
|
|
|
15
|
15
|
15*133
|
406/15=
27.067
|
Interpretation
of moving averages: -
Example:
Simple,
exponential, weighted moving averages for 50 days.
- A buy
signal when the security’s price up above the moving average
- A sell
signal when the security’s price fall below the moving average
- When the
prices are far away (say 30 to 50%) from the moving average then
security seems overbought if prices are above the M.A. and oversold
if prices are below the M.A. and a correction is likely to happen
shortly.
- Flattening
of moving average over a medium to long term moving average signal a
buy. Say a buy signal when 12 day moving averages cuts the 50-day
moving average on upper side.
- Downward
penetration of short term moving average over medium / long term
moving average give a sell signal.
4. Time
series moving averages: -
The time series moving
average is calculated using linear regression techniques. Rather than
plotting a straight linear regression line a time series moving average
points the last point of the line. It does this using the specified
number of periods for each day. The individual points are then connected
together with a line to form a time series moving average.
This
moving average is sometime referred to as a moving linear regression
study or a regression oscillator.
For
information on calculating linear regression using the least squares
method (the basis behind series moving averages) refer to any basis
statistics book.
5.
Triangular Moving Averages: -
A
triangular moving average is similar to exponential or weighted moving
average assign the majority of weight to the most recent data. Simple
moving averages assign the weight equally across the data. Wit a
triangular moving average the majority of the weight is assigned to the
middle portion of the data.
smoothed
simple moving average. To calculate a 9 day period (similar for all odd
periods) triangular moving average
1.
Divide 9 by 2 and get 4.5
2.
Round 4.5 up to 5
3.
Triangular moving average (odd periods) = (mov(c, 5,s) ,5,s)
A
12-day period (similar to all even periods) is calculated as follows
1.
Divide 12 by 2 to get 6
2.
Add 1 to 6 to get 7
3.
Triangular moving average (even periods0 = (mov (mov(c,6,s),7,s)
The
rule is to take the length divided by 2 as one moving average and that
number plus 1 as the second.
6.
Variable Moving Averages: -
A variable moving average is an exponential moving average that
automatically adjusts smoothing constant based on the volatility on the
data series. The more the volatile the data the larger the smoothing
constant used I the moving average calculation. The larger the smoothing
constant, the more weight given to the current data. The opposite is
true for less volatile data.
Traders often associate high volatility with strongly trending markets.
However this is mistake. Strong trending markets often less volatile
because of the consistency of the day-to day price changes. Its when
prices are erratic I their day to day movements (i.e. Down a lot, up a
little, up a lot, down a little etc.) That volatility increases. This
can occur up trending, down trending or sideways markets.
Typical moving averages suffer from the inability to compensate for
changes in volatility. During volatile markets, you want a moving
average to increase the sensitivity, so that you will quickly be one of
the correct side of any wild gyrations. By automatically adjusting the
smoothing constant, a variable moving is able to adjust its sensitivity,
allowing performing better in both high and low volatile markets.
VMA=
(0.78* (volatility index)*close) + (1-0.078*(volatility index)*
yesterday’s VMA.
The
absolute value of a 9 period Chande momentum oscillator is used for
volatile index. The higher this index the more volatile the market,
thereby increasing the sensitivity of the moving averages.
Tushar
Chande in the march 1992 issue of “Technical Analysis of Stock and
Commodity” magazine presented this method of calculating a variable
moving average.
7.
Volume Adjusted Moving averages: -
Dick
Arms, a well known as the developer of the Arms index and the equivolume
charting method has developed a unique method for calculating moving
averages. In keeping with this prior work , the calculation method
incorporates volume and is appropriately called a volume adjusted moving
average.
The
calculation for a volume adjusted moving average is somewhat complex.
However it is conceptually easy to understand.
All moving averages (even volume adjusted) use some type of
weighting scheme to average the data. Exponential and weighted moving
averages assign the majority of weight to the most recent data. Simple
moving average assign the weight equally across all the data. Volume
moving averages assign the majority of weight to the most volatile data.
And as its name implies volume adjusted moving averages assign the
majority of weight to the day’s with the most volume.
A volume
adjusted moving average is calculated as follows: -
1.
Calculate the average volume using every time period in the
chart.
2.
Calculate volume increment by multiplying the average volume by
0.67
3.
Calculate each period’s volume ratio by dividing each
period’s actual volume by the volume increment.
4.
Starting at the most recent time period and working backwards
multiply each period’s price ratio and cumulatively sum these values
until the user specified number of volume increments is reached. Note
that only a fraction of the last period’s volume will likely be used.
8.
Weighted Moving Averages: -
A weighted moving average is also designed to put more weights on recent
data and less weight on past data. A weighted moving average is
calculated by multiplying each of the previous days by a weight. The
following table shows now a 5-day weighted moving average is calculated.
Day
No - weight * price
= weighted price
1
1
25
25
2
2
26
52
3
3
28
84
4
4
25
100
5
5
20
125
Total
15
133
27.067
Note
how the 5 day weighted average gives five times more weight to today’s
price. (ie. 5*29) than to the price 5 days ago.
In
a nutshell there are various moving averages are developed. A simple
average is enough to access the movement. An exponential moving average
will give a better perception than simple moving average. A volume
adjusted moving average is helpful for traders.
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