MACD  ANALYSIS OF
WEAKNESSES OF THE MOST POWERFUL TECHNICAL ANALYSIS TOOL
Sanel Halilbegovic
International Burch University, Bosnia and Herzegovina
Email: sanel.halilbegovic@ibu.edu.ba
Submission: 09/12/2015
Accept: 19/12/2015
ABSTRACT
Due to the huge popularization of the stock trading amongst youth, in the
recent years more and more of trading and brokerage houses are trying to find a
one ‘easy to understand’ tool for the novice traders. Moving average convergence divergence seems
to be the main pick and unfortunately inexperienced traders are relying on this
one tool for analysis and trading of various securities. In this paper, I will investigate the
validity of MACD as the ‘magic wand’ when solely used in investment trading
decision making. The main limitation of
this study is that it could be used more widely across industries and various
sizes of companies, funds, and other trading instruments.
Keywords: Moving average
convergence divergence (MACD), Technical Analysis, Signal and Profit relation,
Stock Market Trading, Profitability
1. INTRODUCTION
Is it because technical analysis
doesn’t have good coverage in academia or because most of the traders follow
fundamental analysis superstars such as Warren Buffet, but technical analysis
is getting very little attention. In
recent years, fueled with nonfundamental base commodities trading, technical
analysis picks up on popularity.
In certain markets technical
analysis can provide much more input than fundamental analysis, and it is
usually said that where fundamental stops, technical analysis continues as it
is based purely on the relationship between supply and demand and not so much
on the financial character of the traded instrument.
2. LITERATURE REVIEW
What technicians (popular title of
technical analysis masters) claim is that indicating ratios, dividend growth
model, Black and Scholes model and other popular financial models mean nothing
if there is no supply and demand (STOFT, 2002; EDWARDS; MAGEE, 2007).
One of the many available tools of
technical trading is something popularly called MACD or Moving Average
Convergence Divergence. MACD is known as the most valuable tool in a
technician’s toolbox and without it most of technical analyses would be
impossible.
MACD was developed by a legendary
technician mr Gerard Appel who turned his obsession for so called momentum
indicators into a MACD as we know it today (Murphy, 2004). MACD actually
consists of two lines that are comprised of three moving averages. First one is
called MACD line (usually color blue) and the other one is called ‘the signal
line’, usually red color (APPEL, 1979). MACD line is derived as follows:
MACD line =12day
Exponential Price average – 26day Exponential Price average (1)
12day exponential price average,
minus, 26day exponential price average,
SIGNAL line = 9day exponential average of MACD
line (2)
while the “signal” line is derived
as a 9day exponential average of MACD line.
This 12/26/9 is a standard and at the same time ‘original recipe’
combination of periods, some technicians decide to modify this original
combination and make their own, customized combinations.
The 12/26/9 setting got set during
1970s when the standard work week was 6 days, so in effect 12 represents 2
weeks, 26 represents a full month and 9 represents week and a half therefore
covering the entire months length. With
the arrival of the new customizable and interactive tools, one can easily
change the original 12/26/9 structure into something that is a better ‘fit’ for
the investment instrument being analyzed (SHANNON, 2008).
Since the formula is dealing with
the 12, 26, and 9 day exponential average it is important to say that
exponential moving average is a type of weighted average or an average where
some periods, or in this case days, carry more ‘weight’ than others. For example when a 9 day exponential moving
average is calculated, the most recent day is carrying most weight while the
day before the most recent day is carrying little bit less weight and so on, to
the 9th day that is carrying the least weight therefore placing more importance
on the most recent days. (PRADIPBHAI, 2013; NISON, 2001). Exponential average is calculated as:
EP(t) = α * P(t1) + (1 – α)*EP(t1) (3)
where α is a coefficient of the
division of the weight, P(t1) is the price of the stock at the trading close
of the previous day, and EP(t1) is an exponential average calculated for the
previous day. Two of the MACD lines
cross over each other and the important events are when two lines cross above
or below the central or so called ‘zero’ line.
MACD became highly valued among
traders because it becomes extremely powerful especially when for example a
strong buy signal is coupled with an increase in the trading volume
(DHARAMVEER, 2014; PRING, 1995). On the
following chart we see a number of crossovers between MACD and signal lines and
each of those crossovers can give a buy signal.
The signal strength (in this
research I measure it from 1 to 10, from weakest to strongest, respectively)
depends on the position where the crossover happens. Deeper below the zero line the crossover
happens, the stronger the buy signal it is.
So, if the crossover happens on or
around the zero line, the signal strength is 1 and if the crossover happens on
or around the 6 line the signal strength is 10. For example the first circled crossover
happened at the level ‘3’ so the signal strength would be a ‘6’, while the
second circled crossover happened at the level ‘1’ so the signal strength
would be a ‘2’.
Graph 1: MACD and crossovers
3. HYPOTHESIS
The research title alone dictates
the logical hypothesis. Key point is to
prove or disprove that by using the MACD it is possible to generate investment
profit. From this the main hypothesis is
derived:
“…With MACD usage as a stock
investment indicator, it is not possible to generate a consistent, considerable
and sustainable profit…”
This basically means that if the
relation between generated profit yield and a signal strength derived by MACD,
is not in direct proportion, it means that MACD when used as a standalone
investment decision maker does not produce consistent or sizeable profit.
The validity of the hypothesis will
be determined by the correlation coefficient (‘r’) or Pearson’s ‘r’, as well as
‘r2’ which is the coefficient of determination. If it proofs that the abovementioned
coefficients are rather high or much closer to 1 than to 0, then we can
conclude that the hypothesis ‘stands’, while if opposite happens, the
hypothesis ‘falls’.
4. METHODOLOGY
The research methods used in this
research basically include tracking of the trading history of the chosen firms
in the timeframe from September 2008 to September 2013, as well as uncovering
the relationship between the signal strength of MACD and the profitability in
case the investor reacts on the given signal. This research will dissect
collected 5year data about the three companies and try to prove or disprove the
hypothesis.
The percent profit yield will be
calculated based on the signal of the MACD and the yield will be compared to
the strength of the signal given by MACD, so in effect the analysis will
portray yield as a dependable variable while MACD signal strength will be
depicted as an undependable variable and in the end the connection between those
variables will be analyzed by statistical regression.
4.1.
Data
Analysis and Discussion
As mentioned before for each
generated MACD signal, a strength level will be assigned from 110 (weakest –
strongest). On the graph 2, shown below
we have the depiction of the price movements of Amazon Inc., and below the
price movements, MACD indicator is plotted out. In the given period of 5 years (from
20082013), Amazon’s MACD generated a total of 17 signals that are shown on the
graph 2. The price movement generally
has a positive slope with the tendency to continue rising.
There have been some periods of
retraction especially in September/October 2011 and after that MACD confirmed a
good opportunity to go long with its strong buy signal.
Signal strengths are of a solid
distribution ranging from 210.

















Graph 2:
Amazon Inc daily trading over 5 years including MACD
On the graph 3, shown below we have
the depiction of the price movements of Apple Corp, and below the price
movements, MACD indicator is plotted out.
In the given period of 5 years (from 20082013), Apple’s MACD generated
a total of 19 signals that are shown on the graph 3.
The price movement generally has a
positive slope with the tendency to correct itself downwards. What is very interesting is the fact that
Apple has touchtested a psychological ceiling at $100 and as soon as it
touched it the selloff began.
Signal
strengths are of a solid distribution ranging from 210.



















Graph 3:
Apple Corp daily trading over 5 years with MACD
On the graph 4, shown below we have
the depiction of the price movements of IBM Corporation, and below the price
movements, MACD indicator is plotted out.
In the given period of 5 years (from 20082013), IBM’s MACD generated a
total of 16 signals that are shown on the graph 4.
The price movement generally has a
positive slope with the tendency to generally continue rising. There is a very good chance that the price
will hit a very hard and psychological ceiling at $200 per share as this
resistance has been tested at least 5 times in the past 6 months.
If the price penetrates the given
ceiling and since it was tested on so many occasions, if the price goes north
of $200, than $200 level will become a super strong floor which would represent
a good opportunity for a long term investment.
Signal strengths are of a solid
distribution ranging from 310.
















Graph 4:
IBM daily trading over 5 years including MACD
A tabular representation of MACD
signals for all three companies, their strengths and corresponding profit
margins are shown in the table 1. This table shows all the details such as the
date of the signal, price when signal was generated, the actual signal strength
and the profit margin that corresponds to the given signal.
Table 1: tabular depiction of signal
strengths and trades for Amazon, Apple and IBM

Date 
Stock Price 
MACD 

Date 
Stock Price 
MACD 

Date 
Stock Price 
MACD 
AMZN 
12/22/2008 
49.84 
9 
AAPL 
10/15/2008 
97.95 
10 
IBM 
10/29/2008 
88.20 
10 

6/29/2009 
83.03 
67% 

4/20/2009 
120.5 
23.02% 

2/17/2009 
90.67 
2.80% 

10/12/2009 
93.6 
5 

6/1/2009 
139.35 
3 

3/13/2009 
90.36 
5 

1/11/2010 
130.31 
39% 

6/11/2009 
139.95 
0.43% 

4/15/2009 
98.85 
9.40% 

8/9/2010 
128.83 
5 

7/16/2009 
147.52 
2 

7/17/2009 
115.42 
6 

11/23/2010 
168.2 
3 

8/11/2009 
162.83 
10.38% 

8/10/2009 
118.70 
2.84% 

12/7/2010 
176.77 
5% 

9/14/2009 
173.72 
2 

11/6/2009 
123.49 
5 

1/24/2011 
176.85 
37% 

10/1/2009 
180.86 
4.11% 

11/27/2009 
125.70 
1.79% 

2/8/2011 
183.06 
5 

11/10/2009 
202.98 
2 

2/16/2010 
125.23 
6 

2/23/2011 
176.68 
3% 

11/19/2009 
200.51 
1.22% 

3/29/2010 
128.59 
2.68% 

3/24/2011 
171.1 
8 

12/21/2009 
198.23 
3 

5/11/2010 
126.89 
5 

4/18/2011 
178.34 
4% 

1/13/2010 
210.65 
6.27% 

5/20/2010 
123.80 
2.44% 

6/21/2011 
194.23 
6 

2/12/2010 
200.38 
6 

6/11/2010 
128.45 
6 

7/15/2011 
212.87 
10% 

3/24/2010 
229.37 
14.47% 

6/24/2010 
128.19 
0.20% 

8/26/2011 
199.27 
10 

6/14/2010 
254.28 
4 

7/8/2010 
127.97 
5 

9/26/2011 
229.85 
15% 

6/29/2010 
256.17 
0.74% 

7/20/2010 
126.55 
1.11% 

10/12/2011 
236.81 
2 

7/22/2010 
259.02 
5 

3/22/2011 
158.00 
5 

10/21/2011 
234.78 
1% 

8/6/2010 
260.09 
0.41% 

5/6/2011 
168.89 
6.89% 

12/5/2011 
196.24 
7 

4/14/2011 
332.42 
3 

6/17/2011 
164.44 
6 

2/2/2012 
181.72 
7% 

5/4/2011 
349.57 
5.16% 

8/1/2011 
180.75 
9.92% 

3/22/2012 
192.4 
2 

5/25/2011 
336.78 
2 

8/25/2011 
165.58 
8 

4/5/2012 
194.39 
1% 

6/3/2011 
343.44 
1.98% 

10/18/2011 
178.90 
8.04% 

4/26/2012 
195.99 
5 

6/27/2011 
332.04 
6 

1/20/2012 
188.52 
6 

5/15/2012 
224.39 
14% 

8/4/2011 
377.37 
13.65% 

2/14/2012 
192.22 
1.96% 

7/30/2012 
236.09 
4 

10/12/2011 
402.19 
3 

4/26/2012 
205.58 
3 

9/21/2012 
257.47 
9% 

10/21/2011 
392.87 
2.32% 

5/9/2012 
201.23 
2.12% 

11/19/2012 
229.71 
8 

11/30/2011 
382.2 
7 

6/8/2012 
195.14 
7 

12/17/2012 
253.86 
11% 

3/8/2012 
541.99 
41.81% 

8/23/2012 
195.70 
0.29% 

4/1/2013 
261.61 
3 

5/25/2012 
562.29 
8 

11/19/2012 
190.35 
8 

4/19/2013 
260.32 
0.49% 

7/24/2012 
600.92 
6.87% 

2/8/2013 
201.68 
5.95% 

5/8/2013 
258.68 
7 

11/16/2012 
527.68 
10 

5/3/2013 
204.51 
8 

6/21/2013 
273.36 
6% 

12/10/2012 
529.82 
0.41% 

6/5/2013 
202.74 
0.87% 

8/29/2013 
283.98 
10 

2/13/2013 
467.01 
8 



9.31.2013 
319.04 
12.35% 

4/3/2013 
431.99 
7.50% 




4/29/2013 
430.12 
6 




6/6/2013 
438.46 
1.94% 




7/11/2013 
427.29 
6 







8/27/2013 
488.59 
14.35% 




A
truncated table showing only the signal strength and the corresponding profit
margin is shown in the table 2.
Table 2: Truncated table showing signal strength and corresponding profit
margins
AMZN 

AAPL 

IBM 

Signal 
Profit 
Signal 
Profit 
Signal 
Profit 
9 
67.00 
10 
23.02 
10 
2.80 
5 
39.00 
3 
0.43 
5 
9.40 
5 
37.00 
2 
10.38 
6 
2.84 
3 
5.00 
2 
4.11 
5 
1.79 
5 
3.00 
2 
1.22 
6 
2.68 
8 
4.00 
3 
6.27 
5 
2.44 
6 
10.00 
6 
14.47 
6 
0.20 
10 
15.00 
4 
0.74 
5 
1.11 
2 
1.00 
5 
0.41 
5 
6.89 
7 
7.00 
3 
5.16 
6 
9.92 
2 
1.00 
2 
1.98 
8 
8.04 
5 
14.00 
6 
13.65 
6 
1.96 
4 
9.00 
3 
2.32 
3 
2.12 
8 
11.00 
7 
41.81 
7 
0.29 
3 
0.50 
8 
6.87 
8 
5.95 
7 
6.00 
10 
0.41 
8 
0.87 
10 
12.35 
8 
7.50 



6 
1.94 




6 
14.35 


4.2.
Findings
Using the above tables, usable
dataset is created and it can be used for the statistical tests that will
numerically prove or disprove the hypothesis.
Table 3: Paired Sample Test

Paired Differences 
t 
df 
Sig. (2tailed) 

Mean 
Std. Deviation 
Std. Error Mean 
95% Confidence Interval of the Difference 

Lower 
Upper 

Pair 1 
AMZprofit  APLprofit 
.3058 
20.723 
5.98245 
13.47312 
12.86145 
.051 
11 
.960 
Pair 2 
AMZprofit  IBMprofit 
9.6109 
13.085 
3.94528 
.82027 
18.40155 
2.436 
10 
.035 
Pair 3 
APLprofit  IBMprofit 
6.1590 
15.064 
4.54201 
3.96114 
16.27933 
1.356 
10 
.205 
The
profit yields and signal strength of three chosen companies, Amazon, Apple and
IBM, were analyzed using paired sample t test.
The paired ttest is used when each
observation in one group is paired with a related observation in the other
group. In this case profit yields in relation to signal strength of one company
are paired to that of another company, e.g. Amazon profit yields are paired
with profit yields of Apple and IBM.
Table above demonstrates the results
of paired sample t test for three chosen companies. The pvalue (level of
significance) for pair 2 Amazon ProfitIBM Profit is 0,035 what is lower than
0,05, so the null hypothesis will be rejected. There exists significant
difference in profit yields in relation to signal strength, between Amazon and
IBM.
According to the table results, the
remaining two pairs, pair 1 and 3, the difference between companies
(AmazonApple and AppleIBM), is not statistically different form zero.
Besides using Paired Sample Test,
the regression tool is also used in order to explain the strength of the
relationship between independent variable (signal strength) and dependent
variable (profit margin).
The relationship between the two
variables will be mainly explained by a coefficient of determination or r2. R2
is a statistical measure of how close the data are to the fitted regression
line. Coefficient of Determination is calculated as Explained variation / Total
variation
R2 is a number that is always
between 0 and 100%:
 0% indicates that the model
explains none of the variability of the response data around its mean.
 100% indicates that the
model explains all the variability of the response data around its mean.
Table 4: Correlations

Profit 
Signal 

Pearson
Correlation 
Profit 
1.000 
.168 
Signal 
.168 
1.000 

Sig.
(1tailed) 
Profit 
. 
.117 
Signal 
.117 
. 

N 
Profit 
52 
52 
Signal 
52 
52 
Pearson correlation measures the
strength and direction of the linear relationship between the two variables.
The Pearson correlation between profit yields and signal strength is 0.168,
which means that there is no strong correlation between profit yields and
signal strength in relation to MACD indicator.
Table 5: Model Summary
Model Summary^{b} 

Adjusted R Square 
Std. Error of the Estimate 
Change Statistics 
DurbinWatson 

R Square Change 
F Change 
df1 
df2 
Sig. F Change 

0.009 
10.181 
0.028 
1.453 
1 
50 
0.234 
1.627 
R^{2} describes the proportion of variance in the
dependent variable (profit yields)
which can be explained by the independent variables (signal strength). It
is an overall measure of the strength of association and does not reflect the
extent to which any particular independent variable is associated with the
dependent variable. In this case, R^{2} is 0.028, what means that just
2.8% of variance in profit yields can be described by the signal strength which
is related only to the MACD indicator.
Table 6  Standardized and
Unstandardized Coefficients
Model 
Unstandardized Coefficients 
Standardized Coefficients 
t 
Sig. 

B 
Std. Error 
Beta 

1 
(Constant) 
2.348 
3.654 

.643 
.523 
Signal 
.718 
.596 
.168 
1.205 
.234 

a.
Dependent Variable: Profit 
B Coefficient  These are the values for the regression
equation for predicting the dependent variable from the independent variable.
From the table above, we can see that the B coefficient for independent
variable – signal strength is 0.718, what means that for every unit of increase
in signal strength, it is predicted that the profit yield will increase for
0.718. However, the corresponding pvalue is 0.234 which is higher than 0.05,
what means that coefficient for signal strength is not significantly different
from zero.
5. CONCLUSION
Based on the above research and analysis, the conclusion
is that the hypothesis “…With MACD usage as a stock investment indicator, it is
not possible to generate a consistent, considerable and sustainable profit…”
stands, as the statistical tools have shown that the relationship between
signal strength and the generated profit is very weak.
This applies mainly to young and upcoming investors who
are lured mainly by FOREX brokers who claim that huge profits can be made using
‘a simple but proven tool called MACD’.
Investments in any kind of financial instruments must be based on
multiple indicators and even if all selected indicators ‘agree’ on the signal,
the investor should be cautious and protect themselves via stops, hedging or
other lossprevention techniques.
The limitations of this paper come mainly in the form of
the lack of width across industries and across the markets as it would be
interesting to see does the same thing happen over the World’s markets and
different industries. These limitations can serve as a ‘seed’ and as a igniter
for the upcoming graduates and master and PhD candidates who could develop and
drilldown this topic fatherly.
REFERENCES
APPEL, G.
(1979) The Moving Average Convergence
Divergence Method, Great Neck, NY: Signalert
DHARAMVEER,
D. (2014) Technical Analysis of Indian Forex Market, GEInternational Journal of Management Research, p. 46.
Edwards R.;
Magee J. (2007). Technical Analysis of
Stock Trends, AMACOM, p. 3654.
MURPHY, J.
(2004) Intermarket Analysis  Profiting
from Global Markets, Wiley Trading
NISON, S.
(2001) Japanese Candlestick Charting
Techniques, Prentice Hall, p. 81108.
PRADIPBHAI,
N. (2013) Comparison Between Exponential Moving Average Based MACD with Simple
Moving Average MACD of Technical Analysis, International
Journal of Scientific Research, p. 614.
PRING, M.
(1995) Investment Psychology Explained,
Wiley, p. 4563.
SHANNON, B.
(2008) Technical Analysis Using Multiple
Timeframes, LifeVest Publishing Inc., p. 90108
STOFT, S.
(2002) Power System Economics,
Wiley/InterScience, p. 5463.
SOURCE OF
GRAPHS: http://bigcharts.marketwatch.com
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