Description
The document about Security Analysis and Portfolio Management
Security Analysis and Portfolio Management
PGP-2, Theme – 4, Batch 2008-2010
Testing Effectiveness of Technical Trading Rules in Indian Equity Markets
Abstract
This study examines the effectiveness of various technical trading rules in Indian equity markets for periods ranging from January 2004 to July 2009. The annualized returns from each trading rule are compared to a naive buy-and-hold strategy to determine profitability. Apart from RIL; all other Indexes (NIFTY, Auto Index, IT Index, Healthcare Index & Reality Index) and stocks (Ashok Leyland, SBI, Tata Steel & Tata Motors) under observation gave higher profits ( as high as 900% more than buy-hold strategy) . Disregarding statistical significance, the results reveal that 17 out of the 20 (85 per cent) trading rule variants tested on all data sets were profitable after accounting for transaction costs. The results are important because they provide investors with information about the Indian equity Index funds and stocks that can be used to determine optimal asset allocations and to further diversify portfolios. It simply shows how simply following trend can also help generate money.
Introduction Technical analysis is considered to be one of the earliest forms of investment analysis with
its origins dating back to the 1800s. It was among few of the first forms of investment analysis mainly because stock prices and volume levels have been publicly available prior to other types of financial information. Technical analysts search the past prices of a time series for recognizable patterns that have the ability to predict future price movements and earn abnormal returns. Various trading rules and indicators have been developed based on each identifiable pattern. The belief that historical data can be used to identify patterns that predict security movements violates the random walk hypothesis and the weak form of market efficiency. Technical trading rules are known to give abnormal returns. There have been a number of studies conducted on trading rules in the North American equity markets. The number of influential studies that support trading rules grew in the 1990s. Some of these studies include Jegadeesh and Titman (1993), Blume, Easley, and O’Hara (1994), Chan, Jagadeesh, and Lakonishok (1996), Lo and MacKinlay (1997), Grundy and Martin (1998), and Rouwenhorst (1998). We did an extension over the previous research work done and chose to go further
Effectiveness of Technical Trading Rules in Indian Market, Sep 2009, TAPMI Page 2
and test the effectiveness of these technical trading rules in Indian equities and indices. Technical trading strategies like MACD, RSI, Support – Resistance and Price trends are regularly seen by viewers/traders/investors in daily newspapers but very few implement on these strategies as their trading rules. This research acts as a mean to verify the profitability of such rules and also at the same time; the final outcome will boost the investor confidence be it any side of the coin.
Literature Review
Camillo Lento, Lakehead University, conducted a study in November, 2007 to examine effectiveness of three trading rules in eight Asian-Pacific equity markets for a period from January, 1987 to November, 2005.[1] Each of these trading rules had three variants, which could be followed as investment strategy. Returns were calculated by using each of these variants and compared with returns offered by buy-and-hold strategy for the same period. It was observed that technical trading rules helped achieve higher returns over buy-and-hold strategy in TSEC, Straits Times, Hang Seng, Jakarta, KOSPI and BSE equity markets whereas it wasn’t the same in case of Nikkei. Three rules tested were moving average cross-over (MAC-O) rules, filter rules and trading range break-out (TRBO) rules. In MAC-O rule, following short, long combinations were used to calculate the returns: (1, 50), (1, 200) and (5, 150). Filter rule was based on three parameters: one percent, two percent and five percent. And returns from TRBO rule were calculated by observing the maximum and minimum value of an index for last 50, 150 and 200 days. In general, the MAC-O trading rules performed the best of the three rules. It generated excess returns over buy-and-hold strategy for 22 of the 24 (91.7 per cent) variants tested, twelve of which were significant at five per cent. More importantly, the MAC-O (1, 50) trading rule outperformed other two MAC-O trading rules as it offered better return than buy-and-hold strategy did in all eight Asian-Pacific markets. The MAC-O (1, 50) earned excess returns in the range of 1.8 to 32.6 per cent per annum. The filter rules beat the buy-and-hold strategy as 17 of the 24 (70.8%) tests earned excess returns, however only 11 of the 24 filter rules were significant at five per cent level of
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significance. Nikkei was the only market, which offered better return to buy-and-hold strategy follower over any of the filter rules (one, two, or five per cent). The TRB-O rules also beat the market in 17 of the 24 (70.8%) variants tested, but only four of the TRB-O rules were significant at five percent. The TRB-O (50 days) performed excellently over two other TRB-O rules by beating the market in 7 of the 8 variants tested. In general, the results suggested that trading rules based on short-term momentum were better at generating statistically significant excess returns. In Another Study, titled “Tests of Technical Trading Strategies in the Emerging Equity Markets of Latin America and Asia” by Mitchell Ratner, Ph.D. Associate Professor of Finance Rider University, New Jersey and Ricardo Leal, Ph.D. Associate Professor of Finance COPPEAD/UFRJ, Rio de Janeiro, examines the effectiveness of technical trading strategies among 10 emerging equity markets of Latin America and Asia to earn profits in excess of buy hold strategy. Any form of past price patterns violates random walk hypothesis, the weak form of strong market hypothesis. In this study five variable length moving average (VMA) 1-50, 1-150, 5-150, 1-200, 2-200 have been analyzed over a period from 1982 to 1995, where the 1, 2, and 5 represent the number of days in the short moving average, and the 50, 150, and 200 represent the number of days in the long moving average. A buy signal is indicated when the short moving average exceeds the long moving average. As the daily inflation adjusted returns are used, no interest is earned when the money is not invested. The study found that VMA trading models do not possess the ability to outperform buy hold strategy in most of the emerging markets. Study found out three profitable strategies for Thailand, two for Philippines, one Brazil, Korea, Japan and Malaysia and none for Argentina, Chile, India and US.
The extent of excess returns in any market also depends on the persistence of autocorrelation in these markets. Harvey (1995) found that autocorrelation in emerging markets is much higher in emerging markets than in developed markets. Brock, Lakonishok, and Le Baron (1992) and Sweeney (1986) concluded that some of the trading rules have the ability to forecast price changes in the US equity and currency markets. Hudson (1996) found out that some of the trading rules have the ability to predict the returns. Returns are lost once trading cost is factored in. For period before 1995, Bessembinder and Chan (1995) found out that trading rules are profitable. Thus the topic of effectiveness of trading strategies is still open.
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Trading Rules
MACD MACD is an acronym for Moving Average Convergence Divergence. This tool is used to identify moving averages that are indicating a new trend, whether it's bullish or bearish. After all, our 1. priority in trading is being able to find a trend, because that is where the most money is made. Here we used MACO; i.e. cross- over of LMA and SMA and based on that we will generate calls to take positions. The calls are generated based on the concepts given below :-
RSI The Relative Strength Index (RSI) is a financial technical analysis momentum oscillator measuring the velocity and magnitude of directional price movement by comparing upward and downward close-to-close movements. Momentum measures the rate of the rise or fall in stock price. Is the momentum increasing in the "up" direction, or is the momentum increasing in the "down" direction. Here we use the 30 and 70 mark to generate calls.
Here we calculate the Relative Strength Index based on the formula RSI= 100 – (100/(1+RS)) RS = Average gain/ Average Loss Now; in the paragraph below we discuss the Methodology and analysis done.
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Methodology
Analysis of Moving Average Cross-Over (MAC-O) rule in Indian equity market
This study applies MAC-O technical trading rule on Indian equity market, sectoral indices and stocks. NSE was considered as equity market. Sector portfolio was consisted of Auto, IT, Realty and Healthcare sectors. Ashok Leyland, SBI, Tata Motors, RIL and Tata Steel were part of equity group. The period for this analysis was over five years from 1st January 2004 to 20th August, 2009. This period has witnessed both rising and falling markets. MAC-O 5-50 rule was applied for analysis purpose, which compares 5 days moving average of stock/index values to 50 days moving average. Reason behind taking this rule was better performance of short-term momentum as observed in previous study. Whenever short period moving average exceeds long period moving average, MAC-O method generates buy signal. Similarly, it generates sell signal when short period moving average drops below long period moving average. This analysis does not consider short position when market is expected to fall. It would rather invest this idle money, received after following sell signal, in saving account at 3.5% interest rate. Transaction charges are considered at adequate market rate. Investor in buy and hold strategy is assumed to invest in the beginning of period and to hold it till the end. Exhibit-1 shows the sample plots of 5 days and 50 days moving averages for IT index and Tata Motors. Many points can be observed where one curve crosses the other one generating buy-sell signals. Exhibit-2 shows the returns from both buy & hold and MAC-O strategies. Table-1 indicates the returns for entire cycle of more than 5 years considering boom and recession period together. Results indicate that MAC-O outperformed buy & hold strategy 8 out of 10 times (80%) significantly. Returns offered by both the strategies were quite close for the remaining two stocks. Table-2 indicates the returns from these strategies for two separate periods i.e. during upturn and downturn. Results from this part of study are quite surprising as MAC-O underperformed when market was rising and it outperformed buy & hold strategy during falling market phase. This is quite evident from consistency in result i.e. all six indices/scrips behaved in tandem.
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Taking results from both the tables together, it can be comprehended that MAC-O technical trading rule outperformed buy & hold strategy during downturn as well as entire cycle of rise and fall of market. This study was further extended to compare the effectiveness of two MAC-O rules with each other. Returns were calculated for 50-200 rule for few scrips, which were far below than returns offered by 5-50 rule. This finding is consistent with the previous study’s finding of better performance of short-term momentum trading rule. There are other rules in moving average strategy such as 12-26, which goes one step forward in terms of complexity of calculations. It generates MACD line and trigger line, which are not just the averages of specific number of days’ index/scrip values. Analysis on the basis of this rule may further increase the accuracy of generating buy-sell signals.
Analysis of RSI in Indian equity Markets Once the values are obtained in such format then we plot the values in a graph sheet use 30/70 as our buy/sell call generator. As the value goes below 30 and again increases come back to 30; we generate a buy call and similarly when the values cross 70 mark and goes further and turns back to come to 70 we generate a sell call. Based on this simple logic the calls were generated and the positions were changed. The returns were calculated correspondingly. This analysis does not consider short position when market is expected to fall. It would rather invest this idle money, received after following sell signal, in saving account at 3.5% interest rate. Transaction charges are considered at adequate market rate. Investor in buy and hold strategy is assumed to invest in the beginning of period and to hold it till the end. Exhibit 3 shows the RSI values plotted for RIL in red and the blue line as Price. The calls were generated are the profitability based on same were calculated. The existence of this strategy alone is doubted in the industry and majorly used along with Trend line or MACD. In our analysis, we took assistance of Trend line at times to generate calls. The results were excellent for this indicator too. It gave 90% of the times the signals which over a period of time were much profitable compared to the buy-hold strategy. Also the average was return was 160% higher compare to buy and hold strategy.
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EMPERICAL RESULTS
Returns for period from Jan 2004 to Aug 2009 Index/Scrip Nifty Auto Index IT Index Reality Index Healthcare Index Ashok Leyland SBI RIL Tata Steel Tata Motors Buy & Hold 9.12% 20.27% 11.49% 33.24% 3.33% 4.00% 23.22% 24.68% 5.00% -1.34% MAC-O 15.31% 26.37% 20.71% 45.25% 9.84% 7.66% 23.05% 22.75% 16.99% 11.17% Relative performance of MAC-O 67.84% 30.08% 80.26% 36.13% 195.76% 91.38% -0.71% -7.81% 240.01% 935.34%
We can see that MACO has given better results compared to Buy-Hold strategy. Table denoting the performance of RSI compared to Buy-Hold Strategy
Buy & Hold RSI Relative 9.12% 15.21% 66.76% 20.27% 23.14% 14.17% 11.49% 22.50% 87.24% 33.24% 32.87% -1.10% 3.33% 4.00% 23.22% 24.68% 5.00% -1.34% 19.09% 376.86% 44.76% 92.79% 23.63% -4.23% 5.98% 547.13%
Index/Scrips Nifty Auto Index IT Index Reality Index Healthcare Index Ashok Leyland SBI RIL Tata Steel Tata Motors
Here again, we can see that results are much better than the plain vanilla- Buy & Hold strategy. The same can be seen in other tables of exhibit which are further divided into various periods of analysis. During the downturn and its recovery the market has clearly outperformed the buy and hold strategy.
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Conclusion and Discussion
An empirical study was conducted to determine if technical trading rules are profitable in the Indian equity markets. Profitability was defined as returns in excess of the buy-and-hold trading strategy after accounting for transaction costs and treasury deposit incomes in cse o idle money. Two technical trading rules were tested on nine Index and stocks of equity markets. The results demonstrate, on average, that profits (after estimated trading costs) can be earned by technical trading rules. The results also suggest that buy signals can provide relevant trading information This study differs from the current literature because it provides a more comprehensive test of technical trading rules on the Indian equity markets with more recent data and a different Methodology. This is also one of its kind research and among the first few research on Indian equity markets. As such, this study contributes to the overall understanding of the efficiency and price behaviour of the equity markets. The results of this study are consistent with the reasoning that some of the Asian-Pacific equity markets were informational inefficient, at least over the period analyzed as the trading rules were able to earn profits and generate relevant trading information. Further research should be conducted to explore the relationship between technical trading rules and market microstructure and order flows. Further study can focus on the combination of these rules and then generating calls using OR, AND options. The combination of technical indicators might give more accurate results and better performance. Future studies can also explore the investment behaviour (i.e. if Indian investors believe more in technical analysis than Europeans do) and the comparison across various markets.(to see Technical Analysis is more applicable to Indian markets or European/ American)
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APPENDIX
Exhibit-1 Moving Average Cross-Over (MAC-O) Plots IT INDEX
TATA MOTORS
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Exhibit-2 Comparison of returns from MAC-O with those of Buy & Hold strategy
Table-1 Returns for period from Jan 2004 to Aug 2009 Index/Scrip Nifty Auto Index IT Index Reality Index Healthcare Index Ashok Leyland SBI RIL Tata Steel Tata Motors Buy & Hold 9.12% 20.27% 11.49% 33.24% 3.33% 4.00% 23.22% 24.68% 5.00% -1.34% MAC-O 15.31% 26.37% 20.71% 45.25% 9.84% 7.66% 23.05% 22.75% 16.99% 11.17% Relative performance of MAC-O 67.84% 30.08% 80.26% 36.13% 195.76% 91.38% -0.71% -7.81% 240.01% 935.34%
Table-2 Returns for period from a) Jan 2004 to Dec 2007 and, b) Jan 2008 to Aug 2009 Jan 2004 - Dec 2007 Buy & Hold MAC-O 33.85% 29.53% 33.41% 18.51% 21.15% 15.97% 43.15% 34.42% 49.92% 34.50% 13.04% 6.60% Jan 2008 - Aug 2009 Buy & Hold MAC-O -51.84% -27.59% -3.26% 42.91% -8.28% 33.83% -15.93% -1.94% -21.17% -1.87% -31.26% 21.11%
Index/Scrip Nifty Auto Index IT Index SBI RIL Tata Motors
Relative -12.77% -44.60% -24.51% -20.23% -30.88% -49.39%
Relative 46.77% 1416.04% 508.81% 87.81% 91.17% 167.55%
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Exhibit-3 Relative Strength Index (RSI) Plots NIFTY
State Bank of India
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References
[1]
“Tests of technical trading rules in the Asian-Pacific equity markets: a bootstrap approach”, Academy of
Accounting and Financial Studies Journal, Nov 2007, Camillo Lento, Lakehead University ? ? ? ? ? ? ? ? ? ? ? ? Brock, W., J. Lakonishok, and B. LeBaron, “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns,” Journal of Finance. Harvey, C. “Predictable risk and returns in emerging markets,” Review of Financial Studies. Bessembinder, H. & K. Chan (1995). The profitability of technical trading rules in the Asian stock markets. Pacific-Basin Finance Journal, 3, 257-284. Bessembinder, H. & K. Chan, (1998). Market efficiency and the returns to technical analysis. Financial Management, 27 (2), 5-17. Brock, W., J. Lakonishok, & B. LeBaron (1992). Simple Technical Trading Rules and the Stochastic Properties of Stock Returns. Journal of Finance, 47, 1931-1764. Chan, L., N. Jegadeesh, & J. Lakonishok (1996). Momentum Strategies. Journal of Finance, 51, 1681-1713. Dooley, M. P. & J.R. Shaler (1983). Analysis of Short-Run Exchange Rate Behaviour: March 1973 to November 1981 in Exchange Rate and Trade Instability: Causes, Consequences, and Remedies. Cambridge, MA. Fama, E. & M. Blume (1966). Filter Tests and Stock Market Trading. Journal of Business, 39, 226-241. Bessembinder, H. and K. Chan, "The Profitability Of Technical Trading Rules In The Asian Stock Markets," Pacific-Basin Finance Journal 3 (2-3), July 1995 Sweeney, R. “Beating the Foreign Exchange Market,” Journal of Finance, 1986 Allen, F. & R. Karjalainen (1999). Using Genetic Algorithms to Find Technical Trading Rules. Journal of Financial Economics, 51, 245-271. ? Alexander, S. (1964). Price Movements in Speculative Markets: Trends or Random Walks, No. 2 in P. Cootnered (ed.), The Random Character of Stock Market Prices. Cambridge, MA: MIT Press.
Data Source - Prowess Database - www.nseindia.com Indicators - www.stockcharts.com - charting.bseindia.com - www.windsorbroker.com
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doc_647081137.docx
The document about Security Analysis and Portfolio Management
Security Analysis and Portfolio Management
PGP-2, Theme – 4, Batch 2008-2010
Testing Effectiveness of Technical Trading Rules in Indian Equity Markets
Abstract
This study examines the effectiveness of various technical trading rules in Indian equity markets for periods ranging from January 2004 to July 2009. The annualized returns from each trading rule are compared to a naive buy-and-hold strategy to determine profitability. Apart from RIL; all other Indexes (NIFTY, Auto Index, IT Index, Healthcare Index & Reality Index) and stocks (Ashok Leyland, SBI, Tata Steel & Tata Motors) under observation gave higher profits ( as high as 900% more than buy-hold strategy) . Disregarding statistical significance, the results reveal that 17 out of the 20 (85 per cent) trading rule variants tested on all data sets were profitable after accounting for transaction costs. The results are important because they provide investors with information about the Indian equity Index funds and stocks that can be used to determine optimal asset allocations and to further diversify portfolios. It simply shows how simply following trend can also help generate money.
Introduction Technical analysis is considered to be one of the earliest forms of investment analysis with
its origins dating back to the 1800s. It was among few of the first forms of investment analysis mainly because stock prices and volume levels have been publicly available prior to other types of financial information. Technical analysts search the past prices of a time series for recognizable patterns that have the ability to predict future price movements and earn abnormal returns. Various trading rules and indicators have been developed based on each identifiable pattern. The belief that historical data can be used to identify patterns that predict security movements violates the random walk hypothesis and the weak form of market efficiency. Technical trading rules are known to give abnormal returns. There have been a number of studies conducted on trading rules in the North American equity markets. The number of influential studies that support trading rules grew in the 1990s. Some of these studies include Jegadeesh and Titman (1993), Blume, Easley, and O’Hara (1994), Chan, Jagadeesh, and Lakonishok (1996), Lo and MacKinlay (1997), Grundy and Martin (1998), and Rouwenhorst (1998). We did an extension over the previous research work done and chose to go further
Effectiveness of Technical Trading Rules in Indian Market, Sep 2009, TAPMI Page 2
and test the effectiveness of these technical trading rules in Indian equities and indices. Technical trading strategies like MACD, RSI, Support – Resistance and Price trends are regularly seen by viewers/traders/investors in daily newspapers but very few implement on these strategies as their trading rules. This research acts as a mean to verify the profitability of such rules and also at the same time; the final outcome will boost the investor confidence be it any side of the coin.
Literature Review
Camillo Lento, Lakehead University, conducted a study in November, 2007 to examine effectiveness of three trading rules in eight Asian-Pacific equity markets for a period from January, 1987 to November, 2005.[1] Each of these trading rules had three variants, which could be followed as investment strategy. Returns were calculated by using each of these variants and compared with returns offered by buy-and-hold strategy for the same period. It was observed that technical trading rules helped achieve higher returns over buy-and-hold strategy in TSEC, Straits Times, Hang Seng, Jakarta, KOSPI and BSE equity markets whereas it wasn’t the same in case of Nikkei. Three rules tested were moving average cross-over (MAC-O) rules, filter rules and trading range break-out (TRBO) rules. In MAC-O rule, following short, long combinations were used to calculate the returns: (1, 50), (1, 200) and (5, 150). Filter rule was based on three parameters: one percent, two percent and five percent. And returns from TRBO rule were calculated by observing the maximum and minimum value of an index for last 50, 150 and 200 days. In general, the MAC-O trading rules performed the best of the three rules. It generated excess returns over buy-and-hold strategy for 22 of the 24 (91.7 per cent) variants tested, twelve of which were significant at five per cent. More importantly, the MAC-O (1, 50) trading rule outperformed other two MAC-O trading rules as it offered better return than buy-and-hold strategy did in all eight Asian-Pacific markets. The MAC-O (1, 50) earned excess returns in the range of 1.8 to 32.6 per cent per annum. The filter rules beat the buy-and-hold strategy as 17 of the 24 (70.8%) tests earned excess returns, however only 11 of the 24 filter rules were significant at five per cent level of
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significance. Nikkei was the only market, which offered better return to buy-and-hold strategy follower over any of the filter rules (one, two, or five per cent). The TRB-O rules also beat the market in 17 of the 24 (70.8%) variants tested, but only four of the TRB-O rules were significant at five percent. The TRB-O (50 days) performed excellently over two other TRB-O rules by beating the market in 7 of the 8 variants tested. In general, the results suggested that trading rules based on short-term momentum were better at generating statistically significant excess returns. In Another Study, titled “Tests of Technical Trading Strategies in the Emerging Equity Markets of Latin America and Asia” by Mitchell Ratner, Ph.D. Associate Professor of Finance Rider University, New Jersey and Ricardo Leal, Ph.D. Associate Professor of Finance COPPEAD/UFRJ, Rio de Janeiro, examines the effectiveness of technical trading strategies among 10 emerging equity markets of Latin America and Asia to earn profits in excess of buy hold strategy. Any form of past price patterns violates random walk hypothesis, the weak form of strong market hypothesis. In this study five variable length moving average (VMA) 1-50, 1-150, 5-150, 1-200, 2-200 have been analyzed over a period from 1982 to 1995, where the 1, 2, and 5 represent the number of days in the short moving average, and the 50, 150, and 200 represent the number of days in the long moving average. A buy signal is indicated when the short moving average exceeds the long moving average. As the daily inflation adjusted returns are used, no interest is earned when the money is not invested. The study found that VMA trading models do not possess the ability to outperform buy hold strategy in most of the emerging markets. Study found out three profitable strategies for Thailand, two for Philippines, one Brazil, Korea, Japan and Malaysia and none for Argentina, Chile, India and US.
The extent of excess returns in any market also depends on the persistence of autocorrelation in these markets. Harvey (1995) found that autocorrelation in emerging markets is much higher in emerging markets than in developed markets. Brock, Lakonishok, and Le Baron (1992) and Sweeney (1986) concluded that some of the trading rules have the ability to forecast price changes in the US equity and currency markets. Hudson (1996) found out that some of the trading rules have the ability to predict the returns. Returns are lost once trading cost is factored in. For period before 1995, Bessembinder and Chan (1995) found out that trading rules are profitable. Thus the topic of effectiveness of trading strategies is still open.
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Trading Rules
MACD MACD is an acronym for Moving Average Convergence Divergence. This tool is used to identify moving averages that are indicating a new trend, whether it's bullish or bearish. After all, our 1. priority in trading is being able to find a trend, because that is where the most money is made. Here we used MACO; i.e. cross- over of LMA and SMA and based on that we will generate calls to take positions. The calls are generated based on the concepts given below :-
RSI The Relative Strength Index (RSI) is a financial technical analysis momentum oscillator measuring the velocity and magnitude of directional price movement by comparing upward and downward close-to-close movements. Momentum measures the rate of the rise or fall in stock price. Is the momentum increasing in the "up" direction, or is the momentum increasing in the "down" direction. Here we use the 30 and 70 mark to generate calls.
Here we calculate the Relative Strength Index based on the formula RSI= 100 – (100/(1+RS)) RS = Average gain/ Average Loss Now; in the paragraph below we discuss the Methodology and analysis done.
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Methodology
Analysis of Moving Average Cross-Over (MAC-O) rule in Indian equity market
This study applies MAC-O technical trading rule on Indian equity market, sectoral indices and stocks. NSE was considered as equity market. Sector portfolio was consisted of Auto, IT, Realty and Healthcare sectors. Ashok Leyland, SBI, Tata Motors, RIL and Tata Steel were part of equity group. The period for this analysis was over five years from 1st January 2004 to 20th August, 2009. This period has witnessed both rising and falling markets. MAC-O 5-50 rule was applied for analysis purpose, which compares 5 days moving average of stock/index values to 50 days moving average. Reason behind taking this rule was better performance of short-term momentum as observed in previous study. Whenever short period moving average exceeds long period moving average, MAC-O method generates buy signal. Similarly, it generates sell signal when short period moving average drops below long period moving average. This analysis does not consider short position when market is expected to fall. It would rather invest this idle money, received after following sell signal, in saving account at 3.5% interest rate. Transaction charges are considered at adequate market rate. Investor in buy and hold strategy is assumed to invest in the beginning of period and to hold it till the end. Exhibit-1 shows the sample plots of 5 days and 50 days moving averages for IT index and Tata Motors. Many points can be observed where one curve crosses the other one generating buy-sell signals. Exhibit-2 shows the returns from both buy & hold and MAC-O strategies. Table-1 indicates the returns for entire cycle of more than 5 years considering boom and recession period together. Results indicate that MAC-O outperformed buy & hold strategy 8 out of 10 times (80%) significantly. Returns offered by both the strategies were quite close for the remaining two stocks. Table-2 indicates the returns from these strategies for two separate periods i.e. during upturn and downturn. Results from this part of study are quite surprising as MAC-O underperformed when market was rising and it outperformed buy & hold strategy during falling market phase. This is quite evident from consistency in result i.e. all six indices/scrips behaved in tandem.
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Taking results from both the tables together, it can be comprehended that MAC-O technical trading rule outperformed buy & hold strategy during downturn as well as entire cycle of rise and fall of market. This study was further extended to compare the effectiveness of two MAC-O rules with each other. Returns were calculated for 50-200 rule for few scrips, which were far below than returns offered by 5-50 rule. This finding is consistent with the previous study’s finding of better performance of short-term momentum trading rule. There are other rules in moving average strategy such as 12-26, which goes one step forward in terms of complexity of calculations. It generates MACD line and trigger line, which are not just the averages of specific number of days’ index/scrip values. Analysis on the basis of this rule may further increase the accuracy of generating buy-sell signals.
Analysis of RSI in Indian equity Markets Once the values are obtained in such format then we plot the values in a graph sheet use 30/70 as our buy/sell call generator. As the value goes below 30 and again increases come back to 30; we generate a buy call and similarly when the values cross 70 mark and goes further and turns back to come to 70 we generate a sell call. Based on this simple logic the calls were generated and the positions were changed. The returns were calculated correspondingly. This analysis does not consider short position when market is expected to fall. It would rather invest this idle money, received after following sell signal, in saving account at 3.5% interest rate. Transaction charges are considered at adequate market rate. Investor in buy and hold strategy is assumed to invest in the beginning of period and to hold it till the end. Exhibit 3 shows the RSI values plotted for RIL in red and the blue line as Price. The calls were generated are the profitability based on same were calculated. The existence of this strategy alone is doubted in the industry and majorly used along with Trend line or MACD. In our analysis, we took assistance of Trend line at times to generate calls. The results were excellent for this indicator too. It gave 90% of the times the signals which over a period of time were much profitable compared to the buy-hold strategy. Also the average was return was 160% higher compare to buy and hold strategy.
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EMPERICAL RESULTS
Returns for period from Jan 2004 to Aug 2009 Index/Scrip Nifty Auto Index IT Index Reality Index Healthcare Index Ashok Leyland SBI RIL Tata Steel Tata Motors Buy & Hold 9.12% 20.27% 11.49% 33.24% 3.33% 4.00% 23.22% 24.68% 5.00% -1.34% MAC-O 15.31% 26.37% 20.71% 45.25% 9.84% 7.66% 23.05% 22.75% 16.99% 11.17% Relative performance of MAC-O 67.84% 30.08% 80.26% 36.13% 195.76% 91.38% -0.71% -7.81% 240.01% 935.34%
We can see that MACO has given better results compared to Buy-Hold strategy. Table denoting the performance of RSI compared to Buy-Hold Strategy
Buy & Hold RSI Relative 9.12% 15.21% 66.76% 20.27% 23.14% 14.17% 11.49% 22.50% 87.24% 33.24% 32.87% -1.10% 3.33% 4.00% 23.22% 24.68% 5.00% -1.34% 19.09% 376.86% 44.76% 92.79% 23.63% -4.23% 5.98% 547.13%
Index/Scrips Nifty Auto Index IT Index Reality Index Healthcare Index Ashok Leyland SBI RIL Tata Steel Tata Motors
Here again, we can see that results are much better than the plain vanilla- Buy & Hold strategy. The same can be seen in other tables of exhibit which are further divided into various periods of analysis. During the downturn and its recovery the market has clearly outperformed the buy and hold strategy.
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Conclusion and Discussion
An empirical study was conducted to determine if technical trading rules are profitable in the Indian equity markets. Profitability was defined as returns in excess of the buy-and-hold trading strategy after accounting for transaction costs and treasury deposit incomes in cse o idle money. Two technical trading rules were tested on nine Index and stocks of equity markets. The results demonstrate, on average, that profits (after estimated trading costs) can be earned by technical trading rules. The results also suggest that buy signals can provide relevant trading information This study differs from the current literature because it provides a more comprehensive test of technical trading rules on the Indian equity markets with more recent data and a different Methodology. This is also one of its kind research and among the first few research on Indian equity markets. As such, this study contributes to the overall understanding of the efficiency and price behaviour of the equity markets. The results of this study are consistent with the reasoning that some of the Asian-Pacific equity markets were informational inefficient, at least over the period analyzed as the trading rules were able to earn profits and generate relevant trading information. Further research should be conducted to explore the relationship between technical trading rules and market microstructure and order flows. Further study can focus on the combination of these rules and then generating calls using OR, AND options. The combination of technical indicators might give more accurate results and better performance. Future studies can also explore the investment behaviour (i.e. if Indian investors believe more in technical analysis than Europeans do) and the comparison across various markets.(to see Technical Analysis is more applicable to Indian markets or European/ American)
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APPENDIX
Exhibit-1 Moving Average Cross-Over (MAC-O) Plots IT INDEX
TATA MOTORS
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Exhibit-2 Comparison of returns from MAC-O with those of Buy & Hold strategy
Table-1 Returns for period from Jan 2004 to Aug 2009 Index/Scrip Nifty Auto Index IT Index Reality Index Healthcare Index Ashok Leyland SBI RIL Tata Steel Tata Motors Buy & Hold 9.12% 20.27% 11.49% 33.24% 3.33% 4.00% 23.22% 24.68% 5.00% -1.34% MAC-O 15.31% 26.37% 20.71% 45.25% 9.84% 7.66% 23.05% 22.75% 16.99% 11.17% Relative performance of MAC-O 67.84% 30.08% 80.26% 36.13% 195.76% 91.38% -0.71% -7.81% 240.01% 935.34%
Table-2 Returns for period from a) Jan 2004 to Dec 2007 and, b) Jan 2008 to Aug 2009 Jan 2004 - Dec 2007 Buy & Hold MAC-O 33.85% 29.53% 33.41% 18.51% 21.15% 15.97% 43.15% 34.42% 49.92% 34.50% 13.04% 6.60% Jan 2008 - Aug 2009 Buy & Hold MAC-O -51.84% -27.59% -3.26% 42.91% -8.28% 33.83% -15.93% -1.94% -21.17% -1.87% -31.26% 21.11%
Index/Scrip Nifty Auto Index IT Index SBI RIL Tata Motors
Relative -12.77% -44.60% -24.51% -20.23% -30.88% -49.39%
Relative 46.77% 1416.04% 508.81% 87.81% 91.17% 167.55%
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Exhibit-3 Relative Strength Index (RSI) Plots NIFTY
State Bank of India
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References
[1]
“Tests of technical trading rules in the Asian-Pacific equity markets: a bootstrap approach”, Academy of
Accounting and Financial Studies Journal, Nov 2007, Camillo Lento, Lakehead University ? ? ? ? ? ? ? ? ? ? ? ? Brock, W., J. Lakonishok, and B. LeBaron, “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns,” Journal of Finance. Harvey, C. “Predictable risk and returns in emerging markets,” Review of Financial Studies. Bessembinder, H. & K. Chan (1995). The profitability of technical trading rules in the Asian stock markets. Pacific-Basin Finance Journal, 3, 257-284. Bessembinder, H. & K. Chan, (1998). Market efficiency and the returns to technical analysis. Financial Management, 27 (2), 5-17. Brock, W., J. Lakonishok, & B. LeBaron (1992). Simple Technical Trading Rules and the Stochastic Properties of Stock Returns. Journal of Finance, 47, 1931-1764. Chan, L., N. Jegadeesh, & J. Lakonishok (1996). Momentum Strategies. Journal of Finance, 51, 1681-1713. Dooley, M. P. & J.R. Shaler (1983). Analysis of Short-Run Exchange Rate Behaviour: March 1973 to November 1981 in Exchange Rate and Trade Instability: Causes, Consequences, and Remedies. Cambridge, MA. Fama, E. & M. Blume (1966). Filter Tests and Stock Market Trading. Journal of Business, 39, 226-241. Bessembinder, H. and K. Chan, "The Profitability Of Technical Trading Rules In The Asian Stock Markets," Pacific-Basin Finance Journal 3 (2-3), July 1995 Sweeney, R. “Beating the Foreign Exchange Market,” Journal of Finance, 1986 Allen, F. & R. Karjalainen (1999). Using Genetic Algorithms to Find Technical Trading Rules. Journal of Financial Economics, 51, 245-271. ? Alexander, S. (1964). Price Movements in Speculative Markets: Trends or Random Walks, No. 2 in P. Cootnered (ed.), The Random Character of Stock Market Prices. Cambridge, MA: MIT Press.
Data Source - Prowess Database - www.nseindia.com Indicators - www.stockcharts.com - charting.bseindia.com - www.windsorbroker.com
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