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Interpretation of Technical Analysis in the Stock Market
Name: Brian O’ Conghaile
Student ID: 11311151
Final Year Project
National University of Ireland, Galway
Supervisor: Mr. Cian Twomey
February 2015
ii
I hereby certify that this material, which I now submit for assessment
on the programme of study leading to the award of degree is entirely
my own work and had not been taken from the work of others save
and to the extent that such work has been cited and acknowledged
within the text of my work.
Signed: ____________________________
ID no: _____________________ Date: _________________
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Contents
Page
Abstract iv
1. Introduction 1
2. An Overview of Technical Analysis 2
2.1 Technical Analysis Fundamental Belief
2.2 Discussion on Technical Analysis
2.3 Technical Indicators
3. Literature Review 6
3.1 Overview of Technical Analysis
3.2 Use of Technical Analysis
3.3 Further Analysis of Specific Indicators
4. Data/Methods 12
4.3 Methodology
4.4 Interpretation
5. Results 16
5.3 Relative Strength Index
5.4 Moving Average Convergence Divergence
5.5 Out of Sample Testing
6. Conclusion 22
7. Reference/Appendices 23
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Abstract
This paper aims to assess the profitability of using technical indicators for trading in
a financial market. The specific financial market in question will be the stock
market. Data is collected on 5 different stocks and the data is sourced from Yahoo
Finance UK. The out of sample test data based on the Nikkei 225 index is also
sourced from Yahoo Finance UK. Using the data collected various interpretations of
the relative strength index and the moving average convergence indicators were
tested. The most promising interpretations in terms of profitability were further
tested against a buy and hold strategy on a different stock market. From the testing
there is strong evidence to suggest that the Vakkur method for interpreting the
moving average convergence divergence is the most profitable method in
performing technical analysis on the stock market.
1
1. Introduction
As stated in the abstract, the aim of this paper is to use technical analysis to
determine a profitable trading strategy in the stock market. The foundation for
which this paper is built on is the book Murphy (1999). Contained in the book is a
comprehensive guide to technical indicators, which are the tools of technical
analysis and also discusses trading strategies and money management.
There is an ever growing popularity of certain financial markets such as the stock
market amongst amateur investors as it becomes easier and easier to trade because
of developments in the World Wide Web. An opportunity can arise for a savvy, well
prepared investor with a specific strategy to earn high returns on investments.
So what is technical analysis? A textbook definition of technical analysis would be
that technical analysis is a security analysis methodology for forecasting the
direction of prices through the study of past market data. So the biggest
assumption made by technical analysis is that the historical performance of stocks
and markets are indicators of future performance.
Now as stated in the opening statement the aim of the project is to use technical
analysis to determine a profitable trading strategy. This could easily be confused
with that the aim is to prove technical analysis works and is 100% accurate,
however the aim is to use technical analysis to find a worthwhile method to make
more money than you lose and to possibly make more than the market is
compensating you for the risk you are taking.
The format of the project is as follows. Firstly a more detailed discussion will be
made on technical analysis. This discussion will include the development of
technical analysis and any assumptions, limitations and contrary beliefs on
technical analysis. The next section will focus on reviewing and analysing all the
various literature available on technical analysis while relating this to the analysis
that I will perform. This leads on to the next section in which the various methods
or interpretations that will be applied to the data collected are introduced and
described. In the section after these methods are introduced and described the
results of the application of the various methods will be tabulated and analysed. A
final concluding section will recap the findings and discuss the implications of these
findings.
2
2. An Overview of Technical Analysis
2.1 Technical Analysis Fundamental Belief
The three main pillars that technical analysis is built upon are as defined in
Technical Analysis of the Financial Markets:
1. The market discounts for everything:
This implies that all factors that may affect the price such as fundamental
and economic factors and market psychology are all accounted for. Which
means that all that’s left to study is price movement’s which is simply
determined by demand and supply.
2. Prices move in trends:
It is believed that prices move in trends and that a trend is more likely to
continue than reverse. This is considered as momentum which is widely used
amongst technical indicators.
3. History repeats itself:
Finally it is also assumed that the history of price movements tends to repeat
itself. So the study of past security price patterns can lead to a prediction of
future price patterns.
2.2 Discussion on Technical Analysis
The concept of technical analysis has been around for quite a long time. Whether
realising it or not the use of technical analysis is believed by some to have even
dated back to ancient Babylonian and ancient Greek times. For example in ancient
Babylon the prices of the 6 main commodities were recorded on clay tablets for
centuries with this information being available to use for speculation on future
prices.
Similar examples of this type of trading can be seen through the ages and is up for
debate on whether technical analysis methods were used or not but the foundation
for modern day technical analysis was developed by Charles Dow’s theories in the
late 19th century.
Due to the fact that there is massive global interest in trading securities across the
world with stock exchanges such as the Nasdaq having trading volume of
approximately 2 billion shares daily there is plenty of discussion amongst
speculators on how to maximize profit or even “beat the market”.
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Technical Analysis versus Fundamental Analysis
Another main method of analysing securities in the financial markets is through
fundamental analysis. While technical analysis is the study of charts and price
movements in search of trends, fundamental analysis is the study of a company’s
financial statements to determine a securities intrinsic value and comparing this
with the actual value quoted in the financial market. Fundamental analysis also
considers the state of the economy and the specific industry relevant to the
company in determining the intrinsic value of the security. The combined use of
fundamental and technical analysis would not be beyond the realms of possibility,
with the fundamental analysis used to evaluate the security and the technical
analysis used in determining optimal entry and exit points of trades.
Efficient Market Hypothesis
The efficient market hypothesis is a theory which believes the price of a security
already incorporates all available information and that a trader will not be able to
achieve better than average returns in the market or “beat the market” as it is
often described.
Many academics such as Malkiel (1973) have written in favour of the efficient
market hypothesis stating no technical scheme could work for any length of time,
suggesting the prices of securities behave in a random manner and that no analysis
of historical prices can lead to accurate future predictions.
However in more recent years after witnessing bubbles and crashes in various
markets the efficient market hypothesis is subject to a lot more criticism with
academics such as Malkiel (2003) questioning the once unquestionable efficient
market hypothesis.
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2.3 Technical Indicators
As stated previously technical indicators are the tools of technical analysis. There
are uncountable many indicators available to use, not all of which are guaranteed
to produce a profitable strategy. There are various types of technical indicators
such as momentum indicators, trend indicators, volatility indicators and volume
indicators. The most effective way of describing and understanding the technical
indicators is through the use of graphical representations. In this brief review of
introducing some of the technical indicators the appropriate charts and graphs
were sourced from Investopedia.com and Stockcharts.com respectively. A popular
momentum indicator is the relative strength index. A graphical representation on
how the relative strength index works is the easiest way to describe how the
relative strength index works.
Figure 2.3.1 – Relative Strength Index (RSI)
In figure 2.3.1 above the share price is located on the top with the values of the RSI
below. The overbought and oversold are clearly indicated, with overbought
considered the optimal time to short the stock and oversold considered the optimal
time to purchase the stock.
A popular volume indicator is the on balance volume. Again a graphical
representation of the on balance volume is given on the following page.
5
Figure 2.3.2 – On balance Volume (OBV)
In the previous figure a graphical representation of a common interpretation of
OBV is given. When the OBV forms a higher low while simultaneously the share
price forms a lower low a bullish divergence is given and a buy is recommended
when the OBV rises to a new high above the red line indicated on the figure.
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3. Literature Review
The ever increasing growth of various financial markets across the globe in recent
times has led to a vast increase in academic study on the topic, especially regarding
methods defying the efficient market hypothesis which was once the fundamental
and only belief amongst academics in regards to trading in financial markets. In this
review of various texts, a general overview of the use of technical analysis in the
stock market will be given. The review will also delve into the real life use of
technical analysis with the likes of fund managers and will examine various
methods which provide evidence of profitability using technical analysis . An in
depth analysis will be provided on specific technical indicators from their derivation
to their interpretation.
3.1 Overview of Technical Analysis
Fang, Qin and Jacobsen (2014) aim is to test a wide variety of technical indicators in
the stock market and decide on which ones if any could produce profitable results.
The 93 indicators tested were of two types, market sentiment indicators and
market strength indicators. The market sentiment indicators analyse investor’s
behaviour often using a mixture of fundamental and technical data. They are then
split up in to various sub categories. The authors describe one such sub category of
the sentiment indicator known as contrarian indicators. They suggest as individual
traders often lack the knowledge and information required to evaluate their trading
decision they often exhibit behaviour of a herd. This herd behaviour can deviate the
price from its true intrinsic value thus backing against the herd will lead to profits.
The second type of technical indicator is the market strength indicators. The market
strength indicators analyse the specific trends in prices rather than the investor
behaviour that causes them. The market strength indicators are again split up into
various sub categories such as analysing the volume traded, the number of
increasing or decreasing stocks in the market or even assessing various stocks high
and low points over a certain period.
The various technical indicators are tested on the S&P 500 index. The S&P 500
index was chosen based on the long historical data available on it and its high
correlation with the NYSE and NASDAQ indices which will allow the authors to use
information from these markets for some of their technical indicators. The 93
indicators are thoroughly examined, being tested by OLS regression, rolling window
regression, economic significance and various robustness checks.
After performing the rigorous examination of the indicators the authors conclude
no technical indicator outperforms the buy and hold strategy. Even with the results
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rejecting the use of technical indicators, the authors cannot categorically rule out
their effectiveness with a wide range of indicators still missing.
Market strength indicators are easier to use as they are based on historical prices.
It will always be difficult to come to a conclusive conclusion as there are limitations
such as data snooping and a limited amount of indicators tested. The paper is
written from a very statistical point of view and so some of the more complex
interpretations of indicators cannot be accurately tested.
3.2 Use of technical analysis
Although well disputed amongst academics, the application of technical across the
financial industry is widespread. Menkhoff (2010) discusses the importance of
technical analysis for fund managers, comparing its importance with fundamental
analysis and flows. He also discusses how they use it, finding the preferred time
outlook for using technical analysis. Then finally discussing why they use it, stating
three possible reasons. Either it is due to irrational behaviour, the high costs of
reliable fundamental information or heterogeneity where agents have either
different information or a different interpretation of the information.
Menkhoff chooses to scrutinize fund managers as they would be regarded as highly
qualified professional market participants.
Due to the fact that there would be no concrete data on the use of technical
analysis by fund managers Menkhoff decided the most appropriate way to obtain
data was through a questionnaire survey. The survey was targeted at fund
managers across five nations, USA, Germany, Switzerland, Italy and Thailand. The
survey aimed to compare the 3 main methods for deciding trade decisions,
technical analysis, fundamental analysis and flows. The results of the survey found
that fundamental analysis at 67% was the most important method for fund
managers for finding information on securities, with technical analysis having 23%
of most importance and flows having 10% most importance. However it was
revealed that 87% use some technical analysis, which is a large proportion. Also
technical analysis was the most preferred method in forecasting horizons in terms
of weeks, with fundamental analysis being preferred in the long run and flows being
preferred in the short run.
From the results of the questionnaire survey it is evident that fund managers place
at least some importance on technical analysis for trading decisions. As regards
how they use it, it appears from the results that for a short term hor izon of a
couple of weeks is the most preferred method. As for why the fund managers may
use technical analysis Menkhoff concludes that his results do not support a theory
of irrational behaviour, partially supports the theory of high costs of fundamental
information and does support the theory of heterogeneity.
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Technical analysis is highly used in shorter term outlook periods by fund managers.
This suggests for certain periods fund managers feel it to be the most accurate
method.
3.3 Further Analysis of specific indicators
Bulkowski (1998) describes his search for a technical indicator that leads to a
profitable trading strategy. Focused at the centre of the article is the relative
strength index. A basic mathematical definition is given of RSI and discu sses
potential look back periods for the indicator, suggesting that the shorter the look
back period the more volatile RSI became thus giving more signals with less
accuracy. This makes sense as the RSI is known as a momentum indicator so a large
increase in price with a short look back period will lead to a large increase in RSI
suggesting a shift in momentum. It is also important to note the RSI ranges
between 0 and 100 with 70 upwards considered as overbought and 30 downwards
considered as oversold for most conventional traders using technical analysis.
Bulkowski then went about testing the RSI with a wide variety of stocks over a 2
year period. The main task of the testing process was to determine the optimal look
back period, and the oversold and overbought levels. From testing various look
back periods and overbought and oversold levels, a look back period of 16 days, an
overbought level of 70 above and an oversold level of 30 below were proving to be
the most profitable. After determining the preferred strategy an out of sample test
was performed, trading shares over 7 months, yielding an average gain of 17%. A
certain problem that is often associated with RSI is giving signals too early.
Bulkowski considered applying a delayed reaction on the signal, waiting until RSI
moved back above from below 30 again before buying and waiting until RSI moved
back below from above 70 before selling. However when tested, the method
produced less return on average, with an average gain of 15%. Although Bulkowski
does admit not fully testing preferable look back periods, oversold and overbought
levels for this different strategy.
Some more alternative methods are discussed in interpreting RSI. One such method
has to do with divergence between RSI and the price of the security. Bulkowski
claims a divergence gives a signal of a reversal in the current trend. For example an
increasing RSI matched with a current decreasing price indicates a reversal in the
decreasing trend and a buy is recommended. The same can be seen for the
converse giving a sell recommendation. Another point Bulkowski felt worth
mentioning was the concept of failure swings with the RSI indicator. When RSI falls
into overbought or oversold areas and then begins to climb out of these areas
sometimes the RSI falls back in to these areas again before eventually climbing
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away. If the second venture into the overbought or oversold areas is not as far as
the first then the RSI should climb out of this area.
As regards using RSI, Bulkowski uses RSI as a step in longer process of deciding on a
trade. Other steps include using fundamental analysis and other technical
indicators such as MACD and CCI as well as others. Bulkowski then moves on to
discussing not putting blind faith in the indicators using an anecdote with a long
term investment where he considered upside and downside risks before making a
decision.
Most importantly there are several points from the article which can be taken away
and used or further analysed, such as, calculating the indicator from us ing the
formula yourself gives you a better understanding of the indicator. Testing which
look back period works best as regards accuracy and profit .Commissions are next
to negligible. That there could be a more suitable look back period, overbought and
oversold levels for the delayed strategy. Different strategies can be tested like
divergence and failure swings. RSI can be combined with other indicators to create
a more accurate trading system.
Vakkur (1997) the main concept is using and interpreting the moving average
convergence divergence indicator for the purpose of trading. Vakkur has two
concerns with the MACD. He states as the MACD is a momentum indicator it often
is lagged and misses the early part of major price moves and it also can indicate
exiting a position too early when momentum slows down. Vakkur aims to modify
the indicator in such a way to address both problems.
When calculating the MACD Vakkur uses weekly values as he believes a weekly
interpretation of MACD yields better results. He uses 12 week and 9 week moving
averages in the calculations and presents MACD as a proportion, with increasing
positive MACD indicating positive momentum and decreasing negative MACD
indicating negative momentum. Vakkur then adds some rules to abide by whe n
trading with MACD. He states that a trader should go long when the MACD is higher
than the MACD of two weeks ago and the price of the asset is greater than the
weekly high of the previous week. Vakkur then describes his method using an
example depicting how his rules work and how they can yield profits. An important
point made by Vakkur about his method is that in terms of accuracy it leaves a lot
to be desired but it is quick to cut losses and rides its profitable trades for longer.
This leads to profits in the long run.
Vakkur found that over a 15 year period between 1980 and 1995 his made a net
profit of $28,653, with a total rate of return of 285%. Vakkur looks to compare this
return relative to some other possible strategy because without comparison the
return has no relative value. He compares his MACD strategy to a buy and hold
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strategy which had 97.5% total rate of return. He then went and compared their
annual return. A formula for calculating annual return is given by
This formula accounts for the amount of time each strategy is exposed to market
risk. This formula concluded the MACD had annual return of 19.6%, four times
larger than the buy and hold annual return of 4.8%.
Vakkur makes a few observations on the tests he carried out. He found some stocks
tended to perform better than others with regards to the MACD system. He states
that smaller-cap stocks that are more actively traded are more likely to form strong
sustained trends, which is what the MACD indicator thrives on. Vakkur then makes
sure any stock being traded with the MACD strategy must be tested on past prices
and produce profits which are not overly reliant on one or two great trades.
Vakkur goes on to discuss a specific example of a stock that met his testing criteria,
Micron Technology. In this example, despite the fact that the buy and hold strategy
performed well, Vakkur shows the value of the MACD trading strategy. Despite the
overall gains of the buy and hold strategy being greater, due to more time exposed
in the market the MACD strategy outperformed it based on annual return. It is also
worth noting that within this period of testing Micron Technology experienced a
major crash, which would have depleted buy and hold traders funds, tempting them
to cash out while they still could and a far reduced price. The MACD strategy does
not experience such volatility and would have been far less affected during this
crash period.
Vakkur then discusses an example of using the MACD for a mutual fund. He
recommends using a 12 week and 26 week combination because the volatility in a
mutual fund should be smaller than in a stock individually, therefor being less risky,
so the less sensitive readings would allow the trader to stay in the trades longer. He
describes using the MACD strategy in two mutual funds, T. Rowe new price horizons
and T. Rowe price Asia. Both examples again came to similar conclusions as
previously with higher total annual returns than the buy and hold strategy. The
latter of the mutual funds was chosen to address one possible criticism which is
that all the indices or stocks chosen were highly correlated and is thus why they all
succeeded together. Vakkur’s solution was to choose a mutual fund which was to
choose a mutual fund with little correlation to the US stock market. Success in this
mutual fund with the MACD strategy disproves the correlation effect.
Past methods that work don’t always guarantee success in the future. W eekly
analysis of MACD may be better than daily analysis. The MACD will need to be
tested for short selling because Vakkur believes the MACD is not as effective for
short selling. Commissions and dividends were ignored. It might be best to wait for
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double bottoms to let the stock reset to a bottom. Check for correlation between
the choices of stocks.
In a more recent study on specific technical indicators Lachwani and Khodiyar
(2013) regarding the relative strength index, the moving average convergence
divergence and the moving averages strategies. Their strategies are tested in the
S&P CNX NIFTY which is the benchmark index for the Indian stock exchange over a
period from 2001-2010.
Firstly considering the MACD, Lachwani and Khodiyar test three different
interpretations, beginning with the signal line crossover. Effectively the signal line
crossover suggests going long when the MACD rises above the signal line and
suggests going short when the MACD falls below the signal line. The next
interpretation tested was the centreline crossover. A bullish signal is given when
the MACD turns positive and a bearish signal is given when the MACD turns
negative. The final interpretation tested was the divergence, specifically the
divergence between the MACD and the price. When price hits a lower low and the
MACD hits a higher low then a bullish divergence is formed, prices are believed to
have bottomed and downward momentum is slowing down. The opposite can be
applied to a bearish divergence when price hits a higheár high and MACD hits a
lower high.
In relation to the RSI Lachwani and Khodiyar offered two interpretations, firstly the
overbought and oversold indication. They determined the overbought level as an
RSI of 70 above and an oversold level as 30 below. They also tested the RSI
divergence which similarly to the MACD indicates a bullish divergence when prices
hits a lower low and RSI hits a higher low and with the opposite of price hitting a
higher high and RSI hitting a lower high for bearish divergence.
In terms of the results the two indicators and their interpretations were tested
against moving averages and buy and hold strategies over different periods. Over
different periods different methods produced the best results. Overall the best
strategy appears to be the RSI overbought and oversold level indicator producing
high results particularly the daily RSI over the 10 year period. It is also worth noting
both MACD and RSI divergence interpretations were based on how accurate they
were at predicting trends. The methods were 70 percent and 60 percent accurate
respectively, suggesting both are reliable tools to be utilised.
The more recent paper confirms with even more empirical evidence the profitability
of certain strategies using the MACD and RSI. Different moving averages can be
tested and may be more suitable for the RSI.
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4. Data/Methods
The data necessary for analysis was collected over a 2 year period from January
2013 to January 2015. The only required data for the initial analysis was the
historical share prices over the specified period of 5 randomly chosen well known
stocks on the Nasdaq 100. The stocks chosen were Facebook (FB), Netflix (NFLX),
Amazon (AMZN), Apple (AAPL) and Zynga (ZNGA). For each individual stock the
relative strength index (RSI) and the moving average convergence divergence
(MACD) were calculated. From the readings reviewed in the previous section the
RSI and MACD were deemed to have the potential to produce profitable returns
and were therefore chosen for testing. A small sample of stocks was chosen due to
the restricted timeframe for this paper, however more shares could be added
without major difficulties on a future date. A derivation of both the relative
strength index and the moving average convergence divergence are to foll ow.
However before the derivations a useful thought to keep in the back of one’s mind
when trading and coming up with a method is the following properties.
Designing a trading system
Any effective and thus profitable trading system would need to satisfy th e following
properties.
1) Price Forecasting: Which is effectively the utilisation of the technical
indicators. Using the technical indicators to predict, with a certain degree of
accuracy, the future price movement of the security.
2) Trading tactics: Following on from the first property trading tactics is the
next crucial piece in the complex profitable trading puzzle and without it, the
first property, which may be accurate, may not be effective. It has to do with
the timing of entry and exit of a trade, which can be mastered by adjusting
the time gap between the analyses of each security or perfecting the
interpretation of the data given by the technical indicators.
3) Money Management: Finally a trader needs to use their funds wisely, a well -
diversified portfolio is generally recommended. Aggressive and conservative
trading plans will be put into use depending on the security and/or the
market.
4.1 Methodology
Before any analysis and interpretations can be thought about the technical
indicators must be derived. Both indicators were formulated and discussed in great
detail in Murphy (1999). The first indicator to be derived is the relative strength
index (RSI). The formula for calculating the relative strength index is,
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Where
The second indicator to be derived is the moving average convergence divergence
(MACD). The formula for the Moving average convergence divergence is,
Where
As can be seen from both formulae the RSI and MACD are already open to
interpretation. Firstly the RSI depends on what n period is chosen. Secondly the
MACD is dependent on what period of moving averages are chosen, the signal line
is then dependent on what moving average of the MACD is chosen, with both then
affecting the MACD Histogram.
4.2 Interpretation
As mentioned previously the technical indicators are subject to interpretation from
an endogenous point of view, which is the inputs into the formula. They are also
open to interpretation from an exogenous point of view, which is the output of the
formula. Both types of interpretation for both technical indicators will be discussed
in this section.
4.2.1 Relative strength Index
1. Endogenous
Firstly regarding the relative strength index the objective is to test and come to a
conclusion on the most appropriate period to use for the average up closes and
average down closes. In the article by Bulkowski the optimal average up closes and
average down closes referred to as the optimal look back period was a period of 16
days for daily trading, which would be a medium/long look back period. With that
in mind three different look back periods were tested, one short, 3 days, one
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medium, 14 days, and one long, 20 days. With the three look back periods so far
apart a diverse range of results and interpretations should be expected.
2. Exogenous
With the relative strength index being one of the most popular and discussed
technical indicators it is not surprising that many traders and academics alike have
various interpretations on the relative strength index. Based on the readings
studied the following interpretations were tested.
a) Overbought/Oversold
The overbought/oversold is a very popular and very simple interpretation of
the relative strength index. It simply states if the value of the relative
strength index increases to a value above 70 the stock is considered
overbought and the trader should short or sell the stock. Conversely if the
relative strength index falls to a value below 30 then the stock is considered
oversold and the trader should go long or buy the stock.
b) Alternative Overbought/Oversold
The alternative overbought/oversold interpretation uses the exact same
signals of overbought and oversold as the previous interpretation. The only
difference being in the exit point, a long trade is exited when the relative
strength index rises above 50 and a short trade is exited when the relative
strength index falls below 50.
c) Delayed Signal
The delayed signal is a further modification of the overbought/oversold
interpretation. When the relative strength index rises above 70 and then
subsequently falls below 70 a short/sell signal is given. On the other side
when the relative strength index falls below 30 and then subsequently
increases above 30 a long/buy signal is given.
d) Alternative Delayed Signal
The alternative delayed signal is the same as the delayed signal method in
terms of initial signals and applies the exact same changes to the delayed
signal as the alternative overbought/oversold method applied to the
overbought /oversold method.
4.2.2 Moving Average Convergence Divergence
1. Endogenous
As stated when deriving the formula the moving average convergence divergence
indicator, the output that the indicator will give can be varied based on the choices
of inputs for certain values, namely the length of the periods for the moving
averages in calculating the MACD and the length of the moving average in
calculating the signal line. In the paper by Vakkur the short and long moving
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averages for calculating the MACD were 12 period and 26 period averages
respectively, while the moving average for calculating the signal line was 8 periods.
As varying the periods for calculating the MACD will not change the MACD much as
it always requires one short moving average and one long moving average the
endogenous analysis will be based on varying the moving average for calculating
the signal line. Two different moving averages were tested, an 8 period moving
average and a 3 period moving average.
2. Exogenous
a. Vakkur Method
In his paper Vakkur claimed great success with his method and so with this in
mind the first interpretation to be tested on the MACD abides by the rules
set out by Vakkur. Vakkur’s rules were from a buy perspective only as he
found the short selling perspective to be unprofitable so this method only
tests from a buy point of view. As regards the rules set out by Vakkur if the
MACD histogram was greater than the MACD histogram of 2 periods previous
and the current price was greater than the high of the previous period, a buy
signal was given. As for the exit point, if the MACD histogram was to
decrease, the low of that period was taken as a marker and unless the MACD
started to increase again this marker would indicate the exit point when it
was hit again.
b. Centre Line Crossover Buy
The second interpretation of the moving average convergence divergence is
a more commonly used method known as the centre line crossover. As this
method only deals with buy actions a bullish signal is given when the MACD
moves above zero and turns positive. A deliberate edition was performed on
the exit point which can often be difficult to judge using such methods. The
exit point is found by using the same logic as the previous Vakkur method
except using the MACD instead of the MACD Histogram.
c. Centre Line Crossover Sell
The final interpretation is the short selling version of the previous method.
When the MACD falls below zero a bearish signal is given and the stock
should be shorted. As for the exit point the logic of Vakkur’s method is
applied, if the MACD was to increase, the high of that period was taken as a
marker and unless the MACD began to decrease again this marker would
indicate the exit point when it was hit again. The buy and sell centre line
crossovers were tested separately due to claims from Vakkur of poor
performance when shorting with his method.
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5. Results
In the following section the results of different types of methods for various
interpretations are summarised and tabulated. The following approach was used in
excel to calculate the results. For each share the technical indicator was calculated
for each endogenous input. Then the exogenous interpretation was applied. That
left 12 different interpretations of the RSI and 4 different interpretations of the
MACD. Applying the interpretations was a long and tasking process as the data for
each share spanned over 2 years. There was no clever algorithm for computing
trades as many of the interpretations were too complex, thus all trades were
computed manually and checked over for accuracy.
5.1. Relative Strength Index
The individual results for the five various methods for the relative strength index
over 3 different look back periods described in the previous section are now
discussed in detail
5.1.1 Overbought/Oversold
The results of the Overbought/Oversold method are displayed in Table 5.1.1 below
Table 5.1.1 – Return for Overbought/Oversold Method
Method € Profit
(Loss)
Number of
Trades
€ Average
Invested
per Position
Return
(%)
Return
per trade
(%)
3 period 1638.95 506 19203.52 12.84% 0.05064%
14 period (43458.96) 86 42259.31 -103.8% -2.414%
20 period (13243.94) 52 35829.96 -53.99% -2.037%
From the testing of the overbought/oversold interpretation it is visually visible that
a look back period of 3 days well outperformed the two longer term look back
periods. The shorter look back period resulted in the relative strength index being
more volatile in nature resulting in plenty more trades but also shorter time
invested with a lot less average capital being deployed into each signal. The high
amount of capital needed to invest in the longer look back periods was defying the
17
third aim of the trading system outlined previously and was a clear concern. The
next approach aims to tackle that problem head on.
5.1.2 Alternative Overbought/Oversold
The results of the alternative overbought/oversold interpretation are displayed
below.
Table 5.1.2 – Alternative Overbought/Oversold Method
Method € Profit
(Loss)
Number of
Trades
€ Average
Invested
per
position
Return (%) Return per
trade (%)
3 period 1211.40 781 13283.78 15.23% 0.0390%
14 period 5367.41 118 21700.73 24.73% 0.4192%
20 period 1005.69 68 20724.34 9.05% 0.25867%
As regards the alternative overbought/oversold the target of decreasing the
amount of capital per position was achieved, with a vast reduction for all three look
back periods. As well as the target being achieved the method also produced better
returns, although the method sacrifices some of the upside momentum by exiting
trades earlier than the traditional overbought/oversold method it is far quicker in
cutting off unprofitable trades, which in the long run lead to higher returns.
5.1.3 Delayed Signal
The results of the delayed signal interpretation are displayed on the following page.
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Table 5.1.3 – Delayed Signal Method
Method € Profit
(Loss)
Number of
Trades
€ Average
Invested
per
position
Return (%) Return per
trade (%)
3 period 30223.43 716 18849.50 157.27% 0.4393%
14 period (53268.16) 99 45137.21 -118.01% -2.3840%
20 period (31888.28) 59 40466.51 -70.52% -2.3506%
The results for the delayed signal interpretation give the most volatile spread of
returns so far. The look back period of 3 days again greatly outper forms the two
longer term look back periods. This approach gives similar results to the traditional
overbought/oversold interpretation except with the profit or loss on returns
magnified.
5.1.4 Alternative Delayed Signal
The results of the alternative delayed signal are displayed below.
Table 5.1.4 – Alternative Delayed Signal Method
Method € Profit
(Loss)
Number of
Trades
€ Average
Invested
per
position
Return (%) Return per
trade (%)
3 period 3554.40 526 13926.06 23.54% 0.0899%
14 period 4330.98 116 22014.80 19.67% 0.3392%
20 period 565.87 70 22186.18 4.42% 0.1229%
The results of the alternative delayed signal method show the same kind of effects
as the alternative overbought/oversold method with the capital required per
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investment vastly reduced. The gigantic losses of the longer look back periods were
also reined in with profits for both methods. However the method had a
detrimental effect on the 3 period look back which was to be expected as quite a
large amount of the upside momentum was sacrif iced for this more conservative
method.
5.2 Moving Average Convergence Divergence
The individual results of the moving average convergence divergence method are
displayed and discussed below.
5.2.1 Vakkur Method
The results of the Vakkur interpretation are displayed below.
Table 5.2.1 – Vakkur method
Method € Profit
(Loss)
Number of
trades
€ Average
invested
per
position
Return (%) Return per
position
(%)
3 period 24925.72 360 9910.38 251.51% 1.40%
8 period 18576.56 220 9912.49 187.41% 1.69%
All that can be said for describing the results of the Vakkur method is that from this
analysis the claims of Vakkur are accurate with the strategy being highly profitable
and high profits for both moving averages of the signal line.
5.2.2 Centre Line Crossover Buy
The results of the centre line crossover buy interpretation are displayed on the
following page.
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Table 5.2.2 – Centre Line Crossover Buy method
Method € Profit
(Loss)
Number of
trades
€ Average
invested
per position
Return (%) Return per
position (%)
Buy Method 17813.84 92 9920.19 179.57% 3.90%
The results of the centre line crossover buy are also very promising with high
returns over the period.
5.2.3 Centre Line Crossover Sell
The results of the centre line crossover sell interpretation are displayed below.
Table 5.2.3 – Centre Line Crossover Sell method
Method € Profit
(Loss)
Number of
trades
€ Average
invested
per position
Return (%) Return per
position (%)
Sell Method -5440.07 100 9931.55 -54.78% -1.10%
From the results of the centre line crossover sell interpretation the claims of Vakkur
appear to hold some truth regarding the unprofitability of shorting using the MACD.
5.3 Out Of Sample Testing
The out of sample test was performed using the most promising interpretations of
each technical indicator. A new set of daily data was collected this time from the
Nikkei 225 index over a one year period from January 2014 to January 2015. An
index was chosen outside the US to eliminate any possibility of high correlations
between the new data set and the previous data set. The two interpretations
chosen were the delayed signal 3 day look back period and the Vakkur method 3
day moving average. The two interpretations were also tested against the simple
buy and hold strategy over this new data set. The results of the new test are
displayed on the following page.
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Table 5.3 – Nikkei 225 Out of Sample Test
Method Profit ($) Average
Investment per
Position
Adjusted Annual
Return (%)
Delayed Signal 1294.94 14608.72 21.83%
Vakkur 1059.73 9929.437 37.59%
Buy and Hold 1285.67 9934.13 19.19%
From the initial profit results it appears that there is no difference between all
three methods. However under further scrutiny an annual return can be calculated
based on the days exposed to market risk. When this is calculated the Vakkur
method outperforms the two other methods by nearly twofold. Due to the fact the
Vakkur method only requires less than half of the time tied up in the index as the
buy and hold strategy the trader using this strategy can spread their capital
amongst different indices and stocks and is why the Vakkur method becomes the
overall best strategy.
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6. Conclusion
In this paper various technical indicator interpretations were tested on the stock
market. The results of the tests strongly suggest that the Vakkur method is the
most profitable technical method. The method, albeit producing substandard
accuracy, utilises its ability to stick to positively moving price trends, yielding in
huge returns when correctly predicted. While when the trend is wrongly predicted
the trade is quickly cancelled and limited losses are incurred. This result supports
the view that technical analysis can be used as a profitable trading mechanism and
rejects the theories of efficient market hypothesis and random walk theory. There
are of course areas within the paper where further development would be required
such as adding further stocks to the portfolio and testing other technical indicators
for profitable trading. Even with this limited study on technical analysis the
foundation has been set for further study based on the groundwork achieved in this
paper.
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7. References/Appendices
References
John J. Murphy (1999). Technical Analysis of the Financial Markets, A
comprehensive guide to trading methods and applications
Andrew W.Lo and Jasmina Hasanhodzic The Evolution of Technical Analysis
http://www.nasdaq.com [Accessed 5 December 2014]
Richard A. Brealey Principles of Corporate Finance
Burton G. Malkiel (1973). A Random Walk Down Wall Street .
Burton G. Malkiel The Efficient Market Hypothesis and Its Critics
Fang, Jacobsen and Qin (2014). Technical Market Indicators: An Overview
Menkhoff (2010). The use of technical analysis by fund managers: International
evidence. Journal of Banking and Finance.
Bulkowski (1998). Improving the Win-Loss Ratio with the Relative Strength Index
Vakkur (1997). The moving average convergence/divergence
Lachwani and Khodiyar (2013). Profitability of Technical Analysis: A Study on S&P
CNX Nifty. Quest-Journal of Management and Research.
http://www.investopedia.com [Accessed 10 January 2015]
https://uk.finance.yahoo.com [Accessed December 2014 to January 2015]
http://stockcharts.com [Accessed 10 January]
Appendix
A small sample of the data collected is shown below. This appendix is more to show
how the data was collected rather than the data itself.
24
RSI – Overbought/Oversold, Overbought/Oversold Alternative, Delayed Signal and
Alternative Delayed signal 3 period methods.
25
MACD- Vakkur 3 period, Crossover Signal Buy and Crossover Sell methods.
26
MACD – Vakkur 8 period method.
