Download MACD BASED DOLLAR COST AVERAGING STRATEGY

Economics and Finance Review Vol. 2(6) pp. 77 – 84, August, 2012
Available online at http://www.businessjournalz.org/efr
ISSN: 2047 - 0401
BASED DOLLAR COST AVERAGING STRATEGY:
Lessons from Long Term Equity Funds in Thailand
MACD
Kamphol Panyagometh, PhD, CFA, FRM, CFP
NIDA Business School
118 Seri Thai Road, Bangkapi, Bangkok 10120 Thailand
Pichaya Soonsap
NIDA Business School
118 Seri Thai Road, Bangkapi, Bangkok 10120 Thailand
ABSTRACT
This research was conducted with the objective of studying investment strategies for long-term equity funds
(LTF) in Thailand by comparing an average investment strategy applied as the technical analysis instrument
called the Moving Average Convergence Divergence (MACD based DCA) with a popular averaging strategy,
namely, Dollar Cost Averaging (DCA), including comparisons of lump-sum investment strategies by using the
SET50 Total Return Index from 3 January 2002 to 4 January 2012. According to the research findings, MACD
Based DCA strategies were found to outperform other investment strategies when measured in terms of
Dominance Frequency. If, however, investment effectiveness is measured in terms of the mean terminal value,
lump-sum (LS) investment strategies at the beginning of the year will be found to yield the highest mean
terminal value, while the LS investment strategy at year-end will be found to yield the highest return in
comparison to risk measured by the Sharpe ratio.
Keywords – MACD, Value Averaging, Dollar Cost Averaging, Lump Sum Investment
1. INTRODUCTION
In Thailand, long-term equity funds (LTF) are equity funds mainly focused on common stocks. The LTF was
established by governmental support with the objective of increasing the proportion of institutional investors to
make long-term investments in the Stock Exchange of Thailand (SET). Increasing institutional investors will
help the SET to enjoy greater stability. Hence, LTF investors who are ordinary persons will be entitled to tax
benefits as investment motivators under the terms that they are required to hold the investment units for no less
than five calendar years with no requirement for a full five accounting years (counted by calendar years, e.g.
each investment purchased during 2008 will mature from the month of January in 2012 onward, and the
investments made during 2009 will mature from the month of January in 2013 onward.)
The objective of this research was to use technical analysis as part of decision making on average purchase in
long-term investments such as LTF. It is well-known that technical analysis is used for findings signs or
opportunities for investment. Technical analysis uses data on price and buying-selling volumes in the past to
project the directions of future securities prices. Therefore, implementing the method of technical analysis as
part of decision making on LTF investment should help enable investors to find suitable investment
opportunities, have lower average costs and generate higher returns. This research will compare investment
strategies based on the SET50 Index with an investment period equal to investments in LTF, i.e. five calendar
years, by comparing lump-sum investment strategies and DCA investment strategies with average investment
strategy that apply the technical analysis instrument called the Moving Average Convergence Divergence
(MACD based DCA)) as part of decision making. The data from 3 January 2002 to 4 January 2012 will be used
to find the most effective investment strategy. Effectiveness will be measured in terms of mean terminal value,
Sharpe ratio and dominance frequency. The content will be divided as follows: Section 2 is the literature
review; Section 3 will explain about investment strategies; Section 4 discusses the data, assumption and research
methodology; Section 5 states the research findings and Section 6 summarizes the research findings.
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Economics and Finance Review Vol. 2(6) pp. 77 – 84, August, 2012
Available online at http://www.businessjournalz.org/efr
ISSN: 2047 - 0401
2. LITERATURE REVIEW
2.1. Studies Related to Investment Strategies
Studies on investment strategies began since the early twentieth century. The study of Constantanides
(1979) affirmed that DCA helps minimize investment risks when compared to lump-sum investments.
However, the study of Thorley (1994) found DCA to lead back to reducing expected rates of return
with additional risks compared to buy-and-hold strategies. The DCA method has been compared to
other investment strategies, such as the Value Averaging (VA) method proposed by Edleson (1988),
who regarded VA as a more effective strategy than DCA that offered a higher rate of return without
increasing the risks of investment portfolios. The study of Thorley (1994) contradicted Edleson. By
comparing the DCA and VA methods with the Buy-and-Hold method on the S&P500 Index during the
years 1926 – 1991, he found the VA strategy to underperform the other methods in terms of average
annual returns, Sharpe Ratio and Treynor Ratio. Later on, Marshall (2000) found the VA strategy to be
able to generate higher returns during period of fluctuating market conditions without increasing risks.
These findings concurred with the work of Leggio and Lien (2003) that studied the ability of each
method and classified all three methods according to asset type and performance measured by the
Sharpe Ratio, the Sortino Ratio or the Up-side Potential Ratio. The outcome did not support the benefit
of the DCA and VA methods. Puksamatanan (2008) compared lump-sum investments with investment
averaging strategies, such as Value Averaging, Constant Share Purchasing and Dollar Cost Averaging
on the index of the Stock Exchange of Thailand from 30 April 1975 to 1 January 2008 and found
investment strategies capable of generating returns and outperforming lump-sum investments without
increasing risks, while also minimizing the risks of escalating the Sharpe Ratio with the best strategy
being Constant Share Purchasing (CS).
2.2. Studies Related to Technical Analysis Investment Strategies
While numerous technical analysis methods have been created, Moving Average Convergence
Divergence (MACD) is one of the oscillators which has always been used for testing because the
strategy behind MACD is easy to understand and has been used to set laws for buying and selling,
whereas the Relative Strength Index (RSI) which is the most frequently used oscillator can imply only
when the market or securities has been overbought or oversold and not when buying or selling should
take place. Levy (1967) tested various laws for buying and selling by using the method of technical
analysis called Relative Strength, or the Portfolio Upgrading Rule. The aforementioned rule uses
previous price data on securities in a total of 68 methods. Some methods were found capable of
generating greater returns than the Buy-and-Hold strategy with statistical significance. However,
Jensen and Bennington (1970) who studied the method of Levy and tested it with other time periods
found Levy’s buy-and-sell law incapable of outperforming Buy-and-Hold. Therefore, the findings of
Levy were considered to have been influenced by selection bias. Lukac, Brorsen and Irwin (1988)
tested the buy-and-sell system established from twelve methods of technical analysis and implemented
the out-of-sample test. According to statistical analysis and measurement of returns after deduction of
transaction costs and risks, four out of twelve methods were found to be capable of generating net
returns with statistical significance. Brock, Lakonishok and LeBaron (1992) tested the Simple Moving
Average method and the Trading Range Break Out method by using the closed price of Dow Jones
Industrial Average index during 1897 – 1986 and the Bootstrapping method to avoid the limitations of
basic statistics. According to the findings, technical analysis was able to outperform returns on Buyand-Hold investments with statistical significance.
In the Thai capital market, a number of studies have confirmed that Asian markets continue to lack
efficiency, thereby enabling many Asian countries to seek profit from the buy-and-sell law.
Bessembinder and Chan (1995) used the Bootstrap Simulation method in their study and found the
buy-and-sell law to be capable of earning profits after deducting purchasing costs during 1975 – 1991,
thereby indicating that the Stock Exchange of Thailand can be anticipated in term of market direction.
Ratner and Leal (1999) tested the buy-and-sell law with technical analysis methods in ten new Latin
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Economics and Finance Review Vol. 2(6) pp. 77 – 84, August, 2012
Available online at http://www.businessjournalz.org/efr
ISSN: 2047 - 0401
American and Asian countries and found Taiwan, Thailand and Mexico to be classified as markets
capable of profiting from technical analysis. The study of Ming-Ming and Siok-Hwa (2006) indicated
the capacity for earning profit from the data of the nine most popular Asian stock exchanges by using
the Variable Moving Averages method (VMAs) and the Fixed Moving Averages (FMAs) method. The
test results supported the VMAs method, especially regarding the stock exchanges of China, Thailand,
Taiwan, Malaysia, Singapore, Hong Kong, Korea and Indonesia. Pasiphol (2009) studied the capacity
of the MACD and the RSI on the SET 50 Index and found both the MACD and the RSI to be capable
of anticipating market directions. The RSI outperformed the MACD in rising markets, while the
MACD tended to outperform the RSI in falling markets.
3. INVESTMENT STRATEGIES
This study analyzed and compared the following three investment strategies in the SET 50 Index LTF:
3.1 Lump-Sum (LS) – Lump-sum investments are investments with all existing money on one occasion
and held the investment until its maturity.
3.2 Dollar Cost Averaging (DCA) – The Dollar Cost Average strategy was an investment strategy that
help minimizing the risks associated with lump-sum investments by setting an equal amount for each
investment period (e.g., per month, per quarter) without considering the conditions or levels of
securities prices. The aforementioned form of investment will acquire greater amounts of securities
when securities prices are low while acquiring lower amounts of securities when the prices are high.
This strategy is employed with the hope that the securities costs in investment portfolios will be
averaged to an appropriate level over the long run.
3.3 Moving Average Convergence Divergence based Dollar Cost Averaging (MACD based DCA) –
The Moving Average Convergence Divergence strategy was devised by Appel (1979), and has been
one of the most popular technical analysis methods. The MACD strategy comprises three Exponential
Moving Averages (EMAs) which are Lagging Indicators used in setting trend overturns. Lagging
Indicators are converted into Momentum Oscillators with the following equation:
(
)
(
)
The second indicator is called the Signal Line, which is used to reduce the variation of the first
indicator. The number of days used to set the Signal Line is nine days.
(
)
The EMA used in determining the aforementioned indicator is derived from the following equation:
(
)
(
)
Where
(
)
,
number of days
Most technical analysis purchase/sales systems will display the differences between the MACD and the
Signal Line in the form of a bar graph in which the graph is above the zero line if the MACD is above
the Signal Line and the graph falls below the zero line if the MACD is below the Signal Line.
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Economics and Finance Review Vol. 2(6) pp. 77 – 84, August, 2012
Available online at http://www.businessjournalz.org/efr
ISSN: 2047 - 0401
15
10
5
0
-5
-10
Figure 1 – Buying-Selling Signs from the MACD Analysis
When the MACD line cuts above the Signal Line from the lower side to the upper side and when it is
lower than the zero line, it is considered as a buying signal. In contrast, selling signals occur when the
MACD cuts the Signal Line from the upper side and is above the zero line.
4. DATA, ASSUMPTION AND METHODOLOGY
4.1 Data and Assumption
The data used in this study came from the SET50 TRI Index. The SET50 TRI Index is the index for
calculating all types of returns on investments in securities to be reflected in the index. The returns
generated by capital gain/loss, rights to warrants for share subscriptions which is the right of the former
shareholder in buying share subscriptions that usually grant rights to buy at lower prices than the
current market price at the time. Furthermore, all dividends on profits are paid to shareholders with the
additional assumption that dividends will be reinvested. The data used in the study was the daily
closing price data of the SET50 TRI Index from 3 January 2002 to 4 January 2012 from the Bloomberg
database over a total period of eleven years.
The researcher conducted this study by evaluating effectiveness from investments in the SET50 Index
using the investment framework in LTF which sets the investment period at five calendar years and the
investment amount in the beginning of the year at 500,000 baht, which is equal to the maximum
amount of money to be invested in LTF. Should any strategy have less than 500,000 baht invested
during the year, the remainder of the money will be invested on the last day of the year. Each strategy
holds investment units for a period of five calendar years and sells them on the first trading date of the
fifth calendar year. The money that has not been invested will be invested and receive returns equal to
returns from one-year government bonds.
4.2 Methodology
Under the aforementioned investment terms, each investment strategy has been set for investments as
follows:
4.2.1 Lump-Sum (LS) Strategy
The study is divided into two cases by investing 500,000 baht on the first trading day of the
year and investing the entire amount on the last trading day of the year.
4.2.2 Dollar Cost Averaging (DCA) Strategy
The investments are divided into three methods by investing at the beginning of the month, in
the middle of the month and at the end of the month. Five hundred thousand baht is invested
during the year. Therefore, 41,666.67 baht is invested each month.
4.2.3 Moving Average Convergence Divergence Based DCA (MACD based DCA) Strategy
In this study, the maximum number investment times was set at twelve times per year by
investing 41,666.67 baht per time for a total of 500,000 baht. If there are more than twelve
purchase signals during the year, investments will be made for only the first twelve signals.
However, if there are less than twelve signals during the year, the remaining amount of money
will be invested at the closing price on the last trading day of the year.
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Economics and Finance Review Vol. 2(6) pp. 77 – 84, August, 2012
Available online at http://www.businessjournalz.org/efr
ISSN: 2047 - 0401
The researcher divided the study of the MACD into six cases with differences in the time of
and
. The time of the Signal Line was equal to nine days
or
(
). The following popular times will be used for testing are
MACD (5, 8, 9), MACD (6, 19, 9), MACD (7, 13, 9), MACD (8, 17, 9), MACD (12, 26, 9)
and MACD (19, 39, 9).
4.3 Evaluation Criteria
4.3.1 Mean terminal value
The mean terminal value can be calculated from the following equation:
Where
is the mean terminal value.
is the terminal value at the end of investment time “i” .
4.3.2 Sharpe Ratio
The Sharpe Ratio compares the rates of return exceeding the risk-free rates of return with the
overall risks evaluated by standard deviation. Higher values of the Sharpe Ratio indicate high
returns from those securities when compared with risks, which can be calculated with the
following formula:
Where
is the Sharpe ratio.
is the average rate of return of the investment fund.
is the risk-free rate.
is the standard deviation of the investment fund.
4.3.3 Dominance Frequency
The Dominance Frequency measures frequency at the terminal value of a strategy which is
higher than another strategy.
∑ (
Where
(
)
)
is the terminal value of strategy X at time j.
is the terminal value of strategy Y at time j.
equals to 1 when TVXj is more than TVYj and equals to 0
when TVXj is equal to or less than TVYj.
4.4 Statistical Testing
Because the sample group size was small (lower than 30) and the type of population distribution was
unknown, a nonparametric test method was used to compare the differences in the interested
characteristics of the two paired populations by using the Wilcoxon Matched Pairs Signed Ranks Test
5. EMPIRICAL RESULTS
5.1 Mean Terminal Value
Panel A in Table 1 displays terminal values at the expiration of investments for each strategy. The
strategy with the highest mean terminal value was the LS strategy at the beginning of the year, which
had a mean terminal value on the final day equal to 860,013.74 baht. The strategy with the lowest mean
terminal value was the MACD based DCA strategy with MACD (5, 8, 9) which was valued at
786,672.78 baht. When compared to the DCA and LS strategies for investing at year’s end, the mean
81
Economics and Finance Review Vol. 2(6) pp. 77 – 84, August, 2012
Available online at http://www.businessjournalz.org/efr
ISSN: 2047 - 0401
terminal values of the DCA invested at the beginning of the month, in the middle of the month and at
the end of the month were higher than the LS invested at the end of the year. When tested with
statistical methods according to Table 1, Panel B, no strategies were found to be able to significantly
outperform another strategies.
5.2 Sharpe Ratio
The terminal values at the expiration dates of investments were calculated for rates of return and
modified with risk-free rates of return and standard deviation throughout the investment period to
determine the Sharpe Ratio for each strategy. The Sharpe Ratio obtained was then calculated for
average values. The LS investment at the end of the year was found to have the highest Sharpe Ratio at
0.4784, while the LS investment at the beginning of the year had the lowest average Sharpe Ratio at
0.3248. According to the analysis of statistical significance in Table 2, Panel B, MACD (8, 17, 9) was
found to have a higher Sharpe Ratio than MACD (12, 26, 9) and MACD (12, 26, 9) had a higher
Sharpe Ratio than MACD (19, 39, 9) with statistical significance at 0.1 level.
5.3 Dominance Frequency
Frequency and chances that the terminal value of a strategy would outperform the terminal value of
another strategy were compared. According to Table 3, the terminal value of MACD (8, 17, 9) was the
only strategy with a chance to outperform all other strategies by more than 50%. The LS investment at
the end of the year had the least number of times when its terminal value outperformed other strategies
at less than 50% as compared to other strategies.
Table 1: Performance Comparisons Based on Terminal Values
Panel A. Terminal Values
Lump-sum
Dollar Cost Average (DCA)
Value Average (VA)
MACD (Fast EMA, Slow EMA, Signal Line)
Beg. Year End. Year Beg. Mth. Mid. Mth. End. Mth. Beg. Mth. Mid. Mth. End. Mth.
5,8,9
6,19,9
7,13,9
8,17,9
12,26,9
19,39,9
2002-2006
1,444,963 1,275,066 1,243,784 1,226,284 1,230,646 1,253,983 1,288,694 1,239,809 1,192,026 1,187,487 1,187,846 1,147,328 1,195,077 1,255,367
2003-2007
1,209,576 519,617 935,522 906,000 886,087 890,232 854,106 802,079 903,355 775,060 720,908 722,683 673,173 675,500
2004-2008
694,763 757,339 796,095 797,094 800,818 799,094 734,877 800,915 835,778 840,656 848,058 988,838 827,953 825,818
2005-2009
419,238 401,030 414,456 413,319 415,094 415,874 414,549 416,820 455,270 456,624 455,183 477,515 454,907 443,297
2006-2010
606,049 649,629 608,373 612,070 614,214 578,109 584,089 585,448 666,084 669,295 669,295 672,170 676,140 659,598
2007-2011
944,580 688,731 801,623 795,759 783,025 796,904 786,419 778,304 964,498 942,515 942,515 867,002 818,747 810,226
2008-2012
700,927 1,326,535 897,623 922,086 955,264 813,456 861,920 883,334 755,602 780,160 781,120 826,991 823,029 881,778
Avg.
860,014 802,564 813,925 810,373 812,164 792,522 789,236 786,673 824,659 807,400 800,704 814,647 781,290 793,083
Panel B. Difference in Terminal Values (p-value)
Lump-sum
Dollar Cost Average (DCA)
Value Average (VA)
MACD (Fast EMA, Slow EMA, Signal Line)
Beg. Year End. Year Beg. Mth. Mid. Mth. End. Mth. Beg. Mth. Mid. Mth. End. Mth.
0.3060
LS Beg >
5,8,9
6,19,9
7,13,9
8,17,9
12,26,9
19,39,9
0.1990
0.3060
0.3060
0.1184
0.1990
0.1552
0.4329
0.5000
0.5000
0.3677
0.3060
0.3060
0.4329
0.4329
0.4329
0.4329
0.5000
0.4329
0.3060
0.3060
0.2495
0.2495
0.3060
0.1990
0.2495
0.4329
0.1184
0.0881* 0.0455**
0.3060
0.4329
0.4329
0.3677
0.3060
0.2495
0.3060
0.3060
0.1552
0.0881*
0.1990
0.3677
0.4329
0.4329
0.3677
0.3677
0.4329
0.1184
0.1184
0.2495
0.4329
0.4329
0.5000
0.4329
0.4329
0.3677
0.3677
0.1990
0.3060
0.3677
0.3060
0.3677
0.1184
0.3677
0.1990
0.3060
0.3677
0.3060
0.3677
0.1552
0.1990
0.3060
0.4329
0.2495
0.3677
0.1552
0.4329
0.3677
0.4329
0.3060
0.1990
0.4464
0.5000
0.2495
0.2495
0.2495
0.2495
0.3060
0.0881*
0.2495
LS End >
0.6940
DCA Beg >
0.8010
0.5671
DCA Mid >
0.6940
0.5671
0.7505
DCA End >
0.6940
0.5671
0.5671
0.6940
VA Beg >
0.8816
0.5671
0.8816
0.6940
0.5671
VA Mid >
0.8010
0.5000
0.9119
0.8448
0.8816
0.6323
VA End >
0.8448
0.5671
0.9545
0.9119
0.8816
0.6323
0.5000
MACD(5,8,9) >
0.5671
0.6940
0.6940
0.8010
0.7505
0.8010
0.7505
0.8010
MACD(6,19,9) >
0.5000
0.6940
0.5671
0.6323
0.5671
0.6940
0.7505
0.6940
0.5671
MACD(7,13,9) >
0.5000
0.7505
0.5671
0.5671
0.5671
0.6323
0.6323
0.5671
0.6323
0.5536
MACD(8,17,9) >
0.6323
0.7505
0.6323
0.5671
0.5000
0.6940
0.6323
0.7505
0.5671
0.5000
0.7505
MACD(12.26,9) >
0.6940
0.6940
0.6940
0.6323
0.5671
0.6323
0.5671
0.6323
0.6940
0.7505
0.7505
0.9119
MACD(19,39,9) >
0.6940
0.8010
0.7505
0.6323
0.5671
0.8816
0.8448
0.8448
0.8010
0.7505
0.6940
0.7505
0.4329
0.5671
** and * indicates statistical significance at the 0.05 level and 0.10 level, respectively
82
Economics and Finance Review Vol. 2(6) pp. 77 – 84, August, 2012
Available online at http://www.businessjournalz.org/efr
ISSN: 2047 - 0401
Table 2: Performance Comparisons Based on Sharpe Ratios
Panel A. Sharpe Ratios
Lump-sum
Dollar Cost Average (DCA)
Value Average (VA)
MACD (Fast EMA, Slow EMA, Signal Line)
Beg. Year End. Year Beg. Mth. Mid. Mth. End. Mth. Beg. Mth. Mid. Mth. End. Mth.
5,8,9
6,19,9
7,13,9
8,17,9
12,26,9
19,39,9
2002-2006
1.0184
1.3301
1.0967
1.0956
1.1263
1.1143
1.1689
1.1338
1.0108
1.0604
1.0604
1.0064
1.0711
1.3337
2003-2007
0.8025
-0.0453
0.6529
0.6231
0.6073
0.6276
0.5815
0.5086
0.6163
0.4673
0.3686
0.3841
0.2973
0.2877
2004-2008
0.2130
0.4139
0.4595
0.4695
0.4861
0.4631
0.4809
0.4868
0.4618
0.4891
0.5044
0.7177
0.4915
0.5632
2005-2009
-0.2333
-0.3377
-0.2689
-0.2734
-0.2722
-0.2665
-0.2711
-0.2692
-0.1982
-0.2098
-0.2111
-0.1507
-0.2124
-0.2326
2006-2010
-0.0066
0.0831
0.0150
0.0220
0.0277
-0.0293
-0.0189
-0.0155
0.0735
0.0822
0.0820
0.0984
0.0774
0.0886
2007-2011
0.3510
0.4875
0.2846
0.2833
0.2745
0.2929
0.2998
0.2816
0.4272
0.4155
0.4204
0.3426
0.3185
0.3248
2008-2012
0.1283
1.9476
0.4528
0.4975
0.5087
0.3161
0.3869
0.3914
0.2443
0.2603
0.2614
0.3165
0.3242
0.4273
Avg.
0.3248
0.4784
0.3847
0.3882
0.3941
0.3597
0.3754
0.3596
0.3765
0.3664
0.3551
0.3878
0.3382
0.3990
Panel B. Difference in Sharpe Ratios (p-value)
Lump-sum
Dollar Cost Average (DCA)
Value Average (VA)
MACD (Fast EMA, Slow EMA, Signal Line)
Beg. Year End. Year Beg. Mth. Mid. Mth. End. Mth. Beg. Mth. Mid. Mth. End. Mth.
0.1552
LS Beg >
5,8,9
6,19,9
7,13,9
8,17,9
12,26,9
19,39,9
0.2495
0.2495
0.2495
0.3060
0.3060
0.4329
0.1184
0.1184
0.1184
0.1990
0.1990
0.1990
0.2495
0.2495
0.3060
0.1990
0.1990
0.1990
0.3677
0.4329
0.4329
0.5000
0.3060
0.3677
0.3677
0.1990
0.2495
0.3677
0.1552
0.5000
0.4329
0.5000
0.4329
0.5000
0.1552
0.1552
0.4329
0.3677
0.2495
0.4329
0.4329
0.4329
0.5000
0.4329
0.3060
0.1990
0.2495
0.5000
0.3677
0.3060
0.5000
0.3060
0.3677
0.0881*
0.2495
0.4329
0.3677
0.3677
0.1990
0.3060
0.1184
0.2495
0.4329
0.3677
0.3677
0.1990
0.3060
0.1184
0.4329
0.3677
0.4329
0.4329
0.3677
0.1184
0.3675
0.3060
0.3675
0.4330
0.4330
0.4583
0.4329
0.2495
0.3677
0.1552
0.3060
0.0881*
0.3677
LS End >
0.8448
DCA Beg >
0.7505
0.7505
DCA Mid >
0.7505
0.7505
0.6323
DCA End >
0.7505
0.6940
0.8010
0.8448
VA Beg >
0.6940
0.8010
0.7505
0.5671
0.8448
VA Mid >
0.6940
0.8010
0.6323
0.6323
0.8010
0.9119
VA End >
0.5671
0.8010
0.8448
0.7505
0.7505
0.7505
0.7505
MACD(5,8,9) >
0.8816
0.6323
0.5000
0.5671
0.5000
0.5671
0.5671
0.5671
MACD(6,19,9) >
0.8816
0.5671
0.5671
0.5671
0.6323
0.6323
0.6323
0.6323
0.6325
MACD(7,13,9) >
0.8816
0.5671
0.5000
0.5671
0.6940
0.6323
0.6323
0.5671
0.6940
0.5417
MACD(8,17,9) >
0.8010
0.5000
0.5671
0.5000
0.5000
0.8010
0.8010
0.5671
0.6325
0.5671
0.8010
MACD(12.26,9) >
0.8010
0.6940
0.5000
0.5671
0.6940
0.6940
0.6940
0.6323
0.5670
0.7505
0.8448
0.9119
MACD(19,39,9) >
0.8010
0.6323
0.8448
0.6940
0.6323
0.8816
0.8816
0.8816
0.5670
0.6323
0.6940
0.6323
0.1552
0.1990
0.0881*
0.9119
** and * indicates statistical significance at the 0.05 level and 0.10 level, respectively
Table 3: Performance Comparisons Based on Dominance Frequencies
Dominance Frequency
LS Beg >
Lump-sum
Dollar Cost Average (DCA)
Value Average (VA)
Beg. Year End. Year Beg. Mth. Mid. Mth. End. Mth. Beg. Mth. Mid. Mth. End. Mth.
57.14% 57.14% 57.14% 57.14% 71.43% 71.43% 71.43%
42.86%
MACD (Fast EMA, Slow EMA, Signal Line)
5,8,9
6,19,9 7,13,9 8,17,9 12,26,9 19,39,9
28.57% 42.86% 42.86% 42.86% 42.86% 42.86%
LS End >
42.86%
42.86%
42.86%
42.86%
42.86%
42.86%
28.57%
28.57%
28.57%
28.57%
28.57%
28.57%
DCA Beg >
42.86%
57.14%
DCA Mid >
42.86%
57.14%
42.86%
57.14%
42.86%
57.14%
71.43%
71.43%
42.86%
42.86%
42.86%
42.86%
42.86%
28.57%
28.57%
42.86%
71.43%
57.14%
42.86%
42.86%
42.86%
42.86%
42.86%
DCA End >
42.86%
57.14%
57.14%
71.43%
28.57%
42.86%
71.43%
57.14%
28.57%
42.86%
42.86%
42.86%
42.86%
28.57%
VA Beg >
28.57%
57.14%
42.86%
57.14%
57.14%
VA Mid >
28.57%
57.14%
28.57%
28.57%
28.57%
42.86%
57.14%
42.86%
28.57%
42.86%
42.86%
28.57%
28.57%
14.29%
42.86%
28.57%
42.86%
42.86%
42.86%
42.86%
VA End >
28.57%
57.14%
28.57%
42.86%
42.86%
57.14%
57.14%
28.57%
28.57%
42.86%
42.86%
42.86%
42.86%
28.57%
MACD(5,8,9) >
71.43%
71.43%
57.14%
57.14%
71.43%
71.43%
71.43%
71.43%
MACD(6,19,9) >
57.14%
71.43%
57.14%
57.14%
57.14%
57.14%
57.14%
57.14%
57.14%
42.86%
57.14%
42.86%
57.14%
71.43%
28.57%
42.86%
57.14%
71.43%
MACD(7,13,9) >
57.14%
71.43%
57.14%
57.14%
57.14%
57.14%
57.14%
57.14%
42.86%
42.86%
MACD(8,17,9) >
57.14%
71.43%
57.14%
57.14%
57.14%
71.43%
57.14%
57.14%
57.14%
57.14%
71.43%
28.57%
57.14%
71.43%
71.43%
MACD(12.26,9) >
57.14%
71.43%
57.14%
57.14%
57.14%
71.43%
57.14%
57.14%
42.86%
42.86%
42.86%
28.57%
71.43%
MACD(19,39,9) >
57.14%
71.43%
71.43%
71.43%
71.43%
85.71%
71.43%
71.43%
28.57%
28.57%
28.57%
28.57%
57.14%
42.86%
83
Economics and Finance Review Vol. 2(6) pp. 77 – 84, August, 2012
Available online at http://www.businessjournalz.org/efr
ISSN: 2047 - 0401
6. SUMMARY AND CONCLUSION
This study was conducted with the objective of studying the investment efficiency of various investment
strategies, such as Lump-Sum investments, Dollar Cost Averaging (DCA) and implementations of the technical
analysis instrument known as MACD based DCA under investment conditions in the LTF for receipt of tax
privileges. According to the study throughout the seven investment periods, the LS investment at the beginning
of the year yielded the highest mean terminal value while the LS investment at the end of the year yielded the
highest average Sharpe Ratio. However, when statistically analyzed, no statistical significance was found in
comparison to other strategies. The LS investment at the end of the year had less than 50% chance of
outperforming the other strategies. The DCA investment strategy at the beginning of the month, in the middle of
the month and at the end of the month had higher mean terminal values than the LS investment at the end of the
year. The MACD based DCA strategy was found to have more than 50% chance of receiving a higher terminal
value than other strategies at certain times and the MACD had significantly higher efficiency at certain times
when compared to the MACD at other times.
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