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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 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. 77 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 78 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. 300 275 250 225 200 175 2/01/2545 13/02/2545 28/03/2545 16/05/2545 28/06/2545 14/08/2545 25/09/2545 7/11/2545 23/12/2545 79 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. 80 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. REFERENCE Appel, G., 1979, The Moving Average Convergence Divergence Method, Great Neck, NY: Signalert. Bessembinder, H., and Chan K., 1995, The profitability of technical trading rules in the Asian stock markets, Pacific Basin Finance Journal, (3), 257-284. Brock, W., Lakonishok, J., and LeBaron, B., 1992, Simple technical trading rules and the stochastic properties of stock returns, The Journal of Finance, (47), 1731-1764. Constantinides, G.M., 1979, A Note On The Suboptimality Of Dollar-Cost Averaging As An Investment Policy, Journal of Financial and Quantitative Analysis, Volume 14, Number 2 June, 443-450. Edleson, M.E., 1988, Value Averaging: A New Approach To Accumulation, American Association of Individual Investors Journal, Volume X, Number 7 August, 11-14. Jensen, M.C. and Benington, G.A., 1970, Random Walks and Technical Theories: Some Additional Evidence, Journal of Finance, Volume 25, Issue 2. Leggio, K. and Lien, D., 2003, Comparing Alternative Investment Strategies Using Risk-Adjusted Performance Measures, Journal of Financial Planning, Volume 16, Number 1 January, 82-86. Levy, R.A., 1967, Relative Strength as a Criterion for Investment Selection, Journal of Finance, Volume 22, Issue 4 December, 595-610. Lukac, L.P., Brorsen, B.W. and Irwin, S.H., 1988, A Test of Futures Market Disequilibrium Using Twelve Different Trading Systems, Applied Economics, 20, 623-639. Marshall, P.S., 2000, A Statistical comparison of value averaging vs. dollar cost averaging and random investment techniques, Journal of Financial and Strategic Decisions, Volume 13 Number 1 Spring. Ming-Ming, L., and Siok-Hwa, L., 2006, The profitability of the simple moving averages and trading range breakout in the Asian stock markets, Journal of Asian Economics, Volume 17, Issue 1, 144-170. Pasiphol, A., 2009, Forecasting Stock Index Derection: Comparison of MACD and RSI, Case Study on SET50 Index, Working Paper, Thammasat University. Puksamatanan, J, 2008, Comparing Lump-Sum Investment Versus Dollar-Cost Averaging, Constant Share Purchasing and Value Averaging Investment Strategies, Working Paper, Thammasat University. Ratner, M. and Ricardo, P.C. Leal. 1999, Tests of Technical Trading Strategies in the Emerging Market of Latin America and Asia, Journal of Banking and Finance, 23, 1887-1905. Thorley, S.R., 1994, The fallacy of Dollar Cost Averaging, Financial Practice and Education, Volume 4, Number 2 Fall/Winter, 138-143. Wilcoxon, F., 1945, Individual comparisons by ranking methods, Biometrics Bulletin, 1 (6), 80–83. 84