# Introduction

n Efficient Market Hypothesis (EMH) it is assumed that the current market price of a security reflects all the information of the respective security and investors by using the information content in the historical price cannot be able to predict current or future price to make abnormal return even if the market is efficient at its weak form (Fama 1970). So the tests of Weak Form Market Efficiency basically try to find out whether there is any relationship between the past prices and the current price, in other word whether the current price can be predicted by using the past prices of a security. By being one of the central areas of research interest during the last couple of decades, this area has showed its development in many phases.

The first formal test of market efficiency could be found when Kendall (1953) applied serial correlation coefficient test on weekly changes of nineteen indices of UK industrial stock prices and found near zero correlation coefficient, which supported the market to be complied with random walk model. After that so many studies have been conducted on some other developed markets. If we take a look at the literature up to the year 1970, most of the studies were found to be consistent with market efficiency (Kendall 1953;Fama 1965;Fama & Blume 1966;James 1968;Jensen & Benington 1970;etc.), though subsequently several studies came up with a totally opposite finding (Lo & MacKinlay 1988;Sweeny 1988;Brock et al. 1992;etc.). But to be more precise developed markets are found to be weak form efficient in so many studies (Kendall 1953;Fama 1965 Many studies have also been documented on emerging stock markets. But those are mainly based on statistical tests of independence like: serial correlation coefficient tests, runs tests, tests of normality, variance ratio test and stationarity test, etc. The evidence from emerging markets can be presented by dividing these markets into four areas. Firstly if we look at the Asian markets then we see Poshakwale (1996), Kumar & Dhankar (2011) and Gupta & Yang (2011) did their studies on Indian Stock Markets and concluded the market to be inefficient. In some other studies Moustafa (2004)  As the review from literature shows mixed result, the efficiency of these markets has remained always inconclusive. At the same time, chronological development of testing methods in this field like using time series regression models (Fama & French 1988;Poterba & Summers 1988), applying variance ratio test (Lo & Mackinlay 1988)  The next few chapters of this paper will include some reviews from the relevant literature and justification of this study, the research questions which it will address and the objectives of the research, data sources and detailed research methodology, findings of this research, and in the final section it will make concluding remark along with some recommendations.
and

# II.


# Literature Review

In order to test Weak Form Market Efficiency some of the researchers approached through statistical tests of independence like Serial Correlation Coefficient test (Kendall 1953;Fama 1965), runs test (Fama 1965) and some other approached through test of technical trading rules like filter rule (Alexander 1961 Practically capital market is not frictionless; hence the profitability of trading rules can be challenged with the existence of transaction costs. Fama & Blume (1966) confirmed that Alexander's results overstated the profit and x% filter rule would not outperform buy and hold policy considering the higher transaction costs. Van Horne & Parker (1967) also concluded the NYSE to be consistent with random walk hypothesis after examining it with moving average technical trading rules. Sweeny (1988) came up with a different conclusion from that of Fama & Blume (1966) on DJIA. He used some new filter rules and found the trading rules were more useful than Fama & Blume did. He added that floor traders can be able to obtain data at a lower transaction cost. James (1968) applied monthly moving averages trading rule on the listed common stocks of NYSE. His results were consistent with random walk hypothesis as he found buy and hold policy was better off in most of the cases. Jensen & Benington (1970) has conducted relative strength trading rule on twenty nine independent samples of two hundred securities of NYSE and confirmed that the market was efficient as the trading rule could not earn more than buy and hold policy after netting the transaction costs. Brock, Lakonishok & LeBaron (1992) used two technical trading rulesmoving average and trading range break on DJIA and found the evidence of profitability of trading strategies. The study of Brock et al. (1992) was replicated on UK market by Hudson, Dempsey & Keasey (1996) and they came up with the finding that technical trading rules cannot generate excess return if cost of transaction is considered. But they confirmed that technical trading rule may exhibit some predictability.

So far most of the trading rules like moving averages used in academic research are basically trend indicators which practitioners do not use in predicting the stock price in isolation, as they think these would be too naive to capture the information content in the past prices. Loh (2007) applied a test of technical trading rule based on practitioner's approach on five developed Asian-Pacific stock markets: Australia (ASX), Hong Kong (HKSE), Japan (NIKKEI), South Korea (KOSPI) and Singapore (STI), for the time period 1990 to 1995. His test basically denoted as combined test of trend indicator (moving average) and confirming indicator (stochastic oscillator). He got two interesting results from his analysis-a combined strategy is more effective compared with a simple moving average technique, and weak form efficiency is not determined by technological progress but factors.

In fact there are a very few studies on test of technical trading rules in Dhaka Stock Exchange and was none before Kader & Rahman (2005) tested K% filter rule in Dhaka Stock Exchange and concluded it as weak form inefficient. After that Hussain, Chakraborty & Kabir (2008) have tested Moving Average 50, 100, and 200 rules over a big data set from 1986 to 2008 with 5815 observations. They found all the MA rules could outperform buy and hold strategy even considering 0.5% transaction cost for both buy and sell.

Most of the previous studies fail to address the practitioner's viewpoint. As the practitioners very often do not use the trend indicator solely rather they adopt some confirming indicator combined with the trend


# Global Journal of Management and Business Research

Volume XIV Issue VI Version I Year ( )
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indicator to create more accurate and sophisticated technical trading rules, the tests which have included only the simple trading rules may not be able to capture the complete information content in the past prices. Considering this fact Loh (2007) adopted moving averages combined with stochastic oscillator in his study and contributed some new findings. In addition to the methodology of Loh (2007) this study has included longer length moving averages to make better comparison among the trading rules. This is the first time applied test of its kind in this country market. It has compared the findings between the traditional approach (simple moving averages) and the practitioners' approach as well as showed the profitability of these rules over buy and hold strategy even after considering the transaction costs.


# III. Research Questions and Objectives

This study is going to address the following research questions: IV.
?

# Research Methodology and Data Sources

Fama (1991) suggested that the test of weak form market efficiency basically denotes to the test of return predictability, which means to find out whether the past return series can predict the future return series. Fama (1965a) described two approaches for test of return predictability for the researchers. The first approach is to use some statistical tools like Serial Correlation Coefficient test, Runs test, etc. to find out whether the past return series is random and statistically independent, so that the chartist or technical analyst cannot be able to predict the prices to earn more return than that of a buy and hold strategy. The second approach is to formulate some suitable technical trading rules and use these directly on the recent market prices to predict the market trend and to find out whether these trading rules are profitable or not. If the trading rules are profitable and can earn more return than that of a buy and hold strategy then it can be concluded that return is predictable and the market is weak form inefficient. Reilly & Brown (2004, p. 180) has named these two test approaches as firstly, Statistical Tests of Independence, and secondly, Tests of Trading Rules. In order to conduct the Test of Trading Rules, mainly the methodology of Loh (2007) has been followed here. Though Loh (2007)  This study is going to test the first three trading rules. A brief description of these rules and signaling process is provided here.


# Moving Average Trading Rule

Moving Averages are very famous but simple and easy to use trading rules. Moving Average Trading Rule basically works with buy and sell signals received from the movement of a short run moving average (SRMA) and a long run moving average (LRMA). The formula for calculating a moving average is given below:

Here, MA t is the moving average for the time period t, L denotes to the length of moving average. P is the stock or index price. The value of L in an SRMA t ranges from one to five days. On the other hand the length of moving average L in an LRMA t depends upon the investors' preferences as the investors may like to track run, intermediate or long run trends in the stock prices. Generally L is observed to be 200 days in a long run moving average. If the value of L in SRMA t is one then the moving average is known as Single Moving Average, because, then the price series directly can be After generating a buy (sell) signal the position will be continued before another sell (buy) signal is generated by the moving average process discussed above. Hence the holding period (days) in buy position will be (Db): SRMA t >LRMA t , and in the same way the holding period (days) in sell position will be (Ds): SRMA t <LRMA t .


# Moving Average combined with Stochastic Oscillator

Most of the studies on Tests of Moving Averages as a trading rule have included only these single or double moving averages, but from the practitioner's view point depending only on the trend indicator might be misleading. Hence an inclusion of another confirming indicator named 'Stochastic Oscillator' along with the moving averages would be more meaningful. So this study is also going to test the combined signal of moving average and stochastic oscillator. The construction method of a stochastic oscillator is described below: DL t = P t -min (P t , P t-1 , ....., P t-m+1 ) HL t = max (P t , P t-1 , ....., P t-m+1 ) -min (P t , P t-1 , ....., P t-m+1 ) K t = (DL t / HL t ) × 100

Here, DL t denotes to the difference between the current closing price and the recent lowest price over a predefined period, m. Generally m is assumed as 14 by the practitioners, but this study has considered m equals to L in the LRMA t (Loh 2007). In the same way HL t is the difference between the recent highest closing price and lowest closing price. K t is the ratio of latest price range to recent price range. In order to generate a confirming signal K t is compared with another signal line named D t , which is calculated in the following way: So from the formula above it is quite clear that D t is an n-period moving average of K t . 'n' is commonly assumed as 3 by the practitioners. But in this study n equals to L in the SRMA t is assumed (Loh 2007). The main intuition behind this strategy is-if the line K t intersects D t from below then it is assumed by the traders that the market has moved from an oversold to an overbought position so a buy signal is generated and vice-versa. Finally a trading strategy based on moving average combined with stochastic oscillator will look like the same as below: Buy signal at time period t (BS t ): SRMA t >LRMA t and SRMA t-1 < LRMA t-1 and K t >D t Sell signal at time period t (SS t ): SRMA t <LRMA t and SRMA t-1 > LRMA t-1 and K t <D t A buy (sell) position will be continued until a sell (buy) signal is generated by both the indicators simultaneously.

Besides the profitability, this study will also compute the break even cost (BEC) of the trading strategies. The main idea behind this BEC is that, if the trading cost is below the BEC then the strategy will be profitable despite the existence of cost of trading. The formula for calculating BEC is given below:
BEC t = (D b × M b -D s × M s ) / (2 × (N b + N s ))
Here, D b and D m are the total number of buy and sell days and M b and M s are average daily return from buy days and sell days respectively. N b and N s denote to the number of buy and sell signal respectively. As each buy (sell) signal is associated with a sell (buy) signal, the total number of buy and sell signals are multiplied by 2.


# Data

This study has considered daily price data of two indices of Dhaka Stock Exchange (DGEN and DSE20) from the year 2002 to 2010. Here data after the year 2010 has been ignored intentionally, because, at the end of the year 2010 Bangladesh faced severe capital market turmoil which persisted for a long time and at the beginning of the year 2013 Dhaka Stock Exchanged replaced its previous two indices with new ones. In order to calculate the return series the following formula has been used (ignoring the dividends): Return on index at time period t = Ln (MI t ) -Ln (MI t-1 )

Here, Ln denotes to natural logarithm, MI t is the market index price at time period t and MI t-1 is the price of index at time period previous of t. The standard t-test (one tailed two sample unequal variances t-test) has been used to find out whether the mean return from trading strategy is significantly higher than the mean return from buy and hold strategy. If the profitability of these trading strategies is confirmed over that of the buy and hold strategy then the weak form efficiency of this market will be questionable.

V. combined with Stochastic Oscillator, MASO 5-50, MASO 5-200) will be applied on DGEN and DSE20 return series to find out whether these are profitable and can outperform the buy and hold strategy.  predicting movement of the stock price than that of the long length moving averages . For MA 1-50 trading rule the accuracy in predicting the immediate upward or downward movement of index price (DGEN) is 100%. But at the same time it should be noted that the number of transaction is also higher for the short length moving averages . So if the trading cost is very high in a market, the short length MA rules may not show that much profitability which is observed from the table 5.11. In this study, a confirming indicator which is known as stochastic oscillator is used with Moving Average to improve the predictability and profitability of the moving average trading rules. Here it is found that using stochastic oscillator with moving average improves only the short length moving average (compare MA 5-50 and MASO 5-50), but for the long length moving average (Compare MA 5-200 and MASO 5-200) the result becomes reversed. So the result does not fully support the findings of Loh (2007).


# Empirical Results


# In

Table 5.12 shows all most in all the cases (except for two cases) the holding days are found correct for more than 50% of days. Differences are significantly different from '0' at 5% and 1% level of significance. BUY must be positive and SELL must be negative in order the trading rules to be held successful in predicting the market movements. Besides, using stochastic oscillator as a confirming indicator improves the predicting capacity of MA 5-50 rule in both buy and sell cases, which also complies with the findings of Loh (2007). But the same is not true for MA 5-200 rule. Rather the predictive ability has been decreased when we used stochastic oscillator. So it can be inferred that using stochastic oscillator as a confirming indicator may not applicable for improving the performance of longer length moving averages as Loh himself used MA 5-20 and MA 5-60 in his study. Up to this it has not been revealed whether a complete strategy can outperform the buy and hold strategy and Table 5.14 will be helpful in this regard. We assume here a complete strategy like holding the index in buy position in buy signal days and holding it in short position in sell signal days rather comparing these as a separate strategy. Average daily return of the whole holding period is calculated. This is the most interesting part of this study as now it becomes quite apparent that only MA 1-50 rule can outperform the buy and hold strategy. Because, though all other trading rules showed greater return than buy and hold strategy (except for only a few exceptions) in Table 5.11, only MA 1-50 (for both DGEN & DSE20) shows statistically significant higher return than that of buy and hold strategy in Table 5.14. This result also complies with the findings of some earlier studies where short length moving averages performed better than the long length moving averages in the markets bearing short trend in price series (Isakov & Hollistein 1999;Lento 2007).


# VI.


# Conclusion

Tests of trading rules conducted in this study reveal the profitability of trading rules set on Dhaka Stock Exchange even after considering transaction costs. It implies that investors of Bangladesh can apply different technical trading strategies to predict the market movement and earn return without bearing appropriate risk. Moreover, by combining the confirming indicator along with the trend indicator the return predictability of short length moving averages can be increased significantly. As the trading rules are found to


# Global Journal of Management and Business Research

Volume XIV Issue VI Version I Year ( ) C significantly outperform the buy and hold strategy, the market is not following weak form of efficiency. Findings from the previous chapter and also from the previous studies confirm that Dhaka Stock Exchange is suffering from weak form market inefficiency and the information content in the past prices does not reflect in the current market price of securities, which is alarming for the general investors of this market. To address the issues related to weak form market inefficiency, policies should be taken to remove information asymmetry, i.e. improving the disclosure policies, creating awareness among the investors, etc. In order to uphold the general investors' interest the regulatory authority can control the opportunities of making excess return by violating the market structures. Educational and training initiatives should be taken and increased to disseminate basic investment knowledge to the investors so that they can make informed decision. Besides growing number of actively traded securities and quality IPOs can also help to enhance the securities trade flow, which may further help in raising informational efficiency in this market.


# Global Journal of Management and Business Research

Volume XIV Issue VI Version I Year ( ) C
![; Fama & Blume 1966; James 1968; Jensen & Benington 1970; Hudson, Dempsey & Keasey 1996; etc.).](image-2.png "")
![Hassan, Abdullah & Shah (2007) have concluded the stock markets of Pakistan and United Aram Emirates respectively as inefficient. Evidence from several literatures also suggests about the weak form inefficiency of the capital markets of Bangladesh (Mobarek & Keasey 2000; Mobarek, Mollah & Bhuyan 2008; Hussain, Chakraborty & Kabir 2008; Khan & Huq 2012, 2013). In Chion & Veliz C. (2008) and Metghalchi, Garza-Gomez, Glasure & Chang (2008) studies the emerging markets of South America (Peru, Argentina, Brazil, Chile, Columbia, Mexico, Venezuela) are found to be weak form inefficient. From the studies of Gilmore & McManush (2001) on European emerging markets (Czech Republic, Hungary and Poland) and Aly, Mehdian & Perry (2004), Ntim, Opong & Danbolt (2007) on African emerging markets (Egypt and Ghana) the evidence of weak form inefficiency has revealed. But basically for methodological and data differences some findings contrary to previous evidence have also been documented in several studies, where some emerging markets are found to be weak form efficient (Vaidyanathan & Gali 1994; Gilmore & McManush 2001; Akhter & Misir 2005; Cooray & Wickremasinghe 2007; Rehman, Masood, Arshed & Shah 2012).](image-3.png "")
![Are technical trading rules profitable in Dhaka Stock Exchange (DSE)? ? Do the confirming indicators improve the return predictability of the trading strategy when they are combined with trend indicators? ? Can the technical trading rules outperform the buy and hold strategy even after considering the transaction costs? From the research questions the following objectives of this research can be formulated: ? To test the trading strategies like single moving averages, double moving averages and moving averages with stochastic oscillator to conclude about the profitability of these rules in this market. ? To conclude about the profitability of the strategy even after considering the transaction costs. ? To find out whether the technical trading rules can outperform the simple buy and hold strategy and whether combining the confirming indicator along with the trend indicator can improve the return predictability of the trading rules.](image-4.png "")



Now the process of generating buy and sellsignals from a moving average will be discussed. A buy(sell) signal is generated when SRMA t intersects LRMA tfrom below (above):Buy signal at time period t (BS t ): SRMA t >LRMA t andSRMA t-1 < LRMA t-1Sell signal at time period t (SS t ): SRMA t <LRMA t and SRMA t-1 > LRMA t-1YearVolume XIV Issue VI Version I( )Global Journal of Management and Business ResearchCused as SRMA t
5Trading RulesN bN sF b %F s %AbsoluteAbsolute Return from BuyReturn * %and Hold Strategy %DGENMA 1-504848100100506236MA 1-2001515100100319224MA 5-50313070.9776.67295236MA 5-20010108060243224MASO 5-50282578.5784309236MASO 5-2008787.5085.71110224DSE 20MA 1-5050509496437236MA 1-200191994.7468.42284224MA 5-50302963.3368.97254236MA 5-200887575232224MASO 5-50262773.0870.37271236MASO 5-2006883.3375196224Note:
5YearTrading Rules DGEN MA 1-50 MA 1-200 MA 5-50D b 1427 1298 1462D s 777 698 774B c % 62.16 59.24 58.28S c % 58.82 51.86 52.45BUY= M b -M h 0.001546** (3.8413) 0.001045* (2.4282) 0.000763SELL= M s -M h -0.002789** (-5.6396) -0.001728** (-3.2456) -0.00143**B -S =M b -M s 0.004335** (8.1858) 0.002773** (4.8518) 0.002194**Volume XIV Issue VI Version I ( ) C(1.9097) 0.000761 (1.7651) 0.000786* (1.9756) 0.000603 (1.2943) 0.00166** (3.9482) 0.001049* (2.3864) 0.0009295* (2.2078) 0.000827 (1.8842) 0.001024* (2.4232) 0.000882 (1.9407) Note: D b (D s ) means holding period in buy (sell) days; B c (S Global Journal of Management and Business Research (-2.8900) (4.1558) MA 5-200 1292 702 58.13 49.57 -0.001162* (-2.1886) 0.001923** (3.3689) MASO 5-50 1482 754 58.23 52.65 -0.001535** (-3.0691) 0.002321** (4.3681) MASO 5-200 1017 977 57.32 46.57 -0.000457 (-0.9675) 0.001059 (1.9421) DSE 20 MA 1-50 1240 997 62.74 55.47 -0.002058** (-4.5515) 0.003718** (7.3601) MA 1-200 1212 786 59.74 51.40 -0.001435* (-2.8175) 0.002485** (4.4562) MA 5-50 1235 1002 59.43 51.30 -0.001139* (-2.5237) 0.0020689** (4.0970) MA 5-200 1218 778 58.95 50.13 -0.001102* (-2.1540) 0.0019287** (3.4517) MASO 5-50 1221 1016 60.11 51.97 -0.001224** (-2.7250) 0.002248** (4.4579) MASO 5-200 1092 904 59.24 49.11 -0.000899 (-1.8556) 0.001782** (3.2548)c
5Trading RulesBreak Even Cost (BEC) %DGENMA 1-502.63MA 1-2002.24MA 5-502.42MA 5-2002.43MASO 5-502.91MASO 5-2003.67DSE 20MA 1-502.18MA 1-2003.73MA 5-502.15MA 5-2007.26MASO 5-502.56MASO 5-2007.02
5TradingMean ReturnMeant-Rules(TradingReturnstatisticRules)(Buy andHoldstrategy)DGENMA 1-50.00226.001053.425 *MA 1-200.00154.001071.230MA 5-50.00131.001050.745MA 5-200.00117.001070.243MASO 5-50.00138.001050.922MASO 5-200.00053.00107-1.448DSE 20MA 1-50.00195.001052.540 *MA 1-200.00137.001070.784MA 5-50.00113.001050.227MA 5-200.00112.001070.121MASO 5-50.00121.001050.444MASO 5-200.00095.00107-0.335Note:
			© 2014 Global Journals Inc. (US) 1
			© 2014 Global Journals Inc. (US)
		
		
			

				


* 
	
		Capital Markets Efficiency: Evidence from the Emerging Capital Market with Particular Reference to Dhaka Stock Exchange
		
			Shelina&Akhter
		
		
			MMisir
		
		
			Abu
		
	
	
		South Asian Journal of Management
		
			12
			3
			
			2005
		
	




* 
	
		Price Movements in Speculative Markets-Trends or Random Walks
		
			SidneySAlexander
		
	
	
		Industrial Management Review
		
			2
			2
			
			1961
		
	




* 
	
		Price Movements in Speculative Markets-Trends or Random Walks
		
			SidneySAlexander
		
	
	
		Industrial Management Review
		
			5
			2
			
			1964
		
	




* 
	
		Using genetic algorithms to find technical trading rules
		
			FAllen
		
		
			RKarjalainen
		
	
	
		Journal of Financial Economics
		
			51
			
			1999
		
	




* 
	
		An Analysis of Day-of-the-Weak Effects in the Egyptian Stock Market
		
			HassanAly
		
		
			Mehdian
		
		
			&Seyed
		
		
			MarkJPerry
		
	
	
		International Journal of Business
		
			9
			3
			
			2004
		
	




* 
	
		Simple Technical Trading Rules and the Stochastic Properties of Stock Returns
		
			WilliamBrock
		
		
			Josef&Lakonishok
		
		
			BlakeLebaron
		
	
	
		Journal of Finance
		
			47
			5
			
			1992
		
	




* 
	
		On the Normality of Stock Return Distributions: Latin American Markets
		
			SergioJChion
		
		
			CVeliz
		
		
			NCarlos
		
	
	
		Journal of CENTUM Cathedra
		
			1
			2
			
			2008. 2000-2007
		
	




* 
	
		The Efficiency of Emerging Stock Markets: Empirical Evidence from the South Asian Region
		
			Arusha&Cooray
		
		
			GuneratneWickremasinge
		
	
	
		The Journal of Developing Areas
		
			41
			1
			
			2007
		
	




* 
	
		Filter Rules and Stock Market Trading
		
			EFFama
		
		
			MarshallEBlume
		
	
	
		The Journal of Business
		
			39
			1
			
			1966
		
	




* 
	
		Permanent and Temporary Components of Stock Prices
		
			EFFama
		
		
			KennethRFrench
		
	
	
		The Journal of Political Economy
		
			96
			2
			
			1988
		
	




* 
	
		The Behavior of Stock Market Prices
		
			EFFama
		
	
	
		The Journal of Business
		
			38
			1
			
			1965
		
	




* 
	
		Random Walks in Stock Market Prices
		
			EFFama
		
	
	
		Financial Analyst Journal
		
			21
			5
			
			1965a
		
	




* 
	
		Efficient Capital Markets: A Review of Theory and Empirical Work
		
			EugeneFFama
		
	
	
		The Journal of Finance
		
			25
			2
			
			1970
		
	




* 
	
		Efficient Capital Markets: II
		
			EugeneFFama
		
	
	
		The Journal of Finance
		
			46
			5
			
			1991
		
	




* 
	
		Random-Walk and Efficiency Test of Central European Equity Markets
		
			ClareGGilmore
		
		
			GinetteMMcmanus
		
	
	
		European Financial Management Association Conference at
		
			2001. June
		
	




* 
	
		Testing Weak form Efficiency in the Indian Capital Market
		
			Rakesh &Gupta
		
		
			JunhaoYang
		
	
	
		International Research Journal of Finance and Economics
		
			75
			
			2011
		
	




* 
	
		Testing of Random Walks and Market Efficiency in an Emerging Market: An empirical Analysis of Karachi Stock Exchange
		
			ArshadHassan
		
		
			Abdullah
		
		
			MuhammadShoib
		
		
			&Shah
		
		
			ZulfiqarAli
		
	
	
		The Business Review
		
			9
			1
			
			2007
		
	




* 
	
		Inference on via generalized spectrum and nonlinear time series models
		
			YHong
		
		
			T.-HLee
		
	
	
		The Review of Economics and Statistics
		
			85
			
			2003
		
	




* 
	
		A note on the weak form efficiency of capital market: The application of simple technical trading rules to UK stock prices-1935-1994
		
			RobertHudson
		
		
			Dempsey
		
		
			KevinKeasey
		
	
	
		Journal of Banking & Finance
		
			20
			
			1996
		
	




* 
	
		Is Dhaka Stock Exchange Efficient? An Application of Technical Trading Rule
		
			TashfeenHussain
		
		
			Chakraborty
		
		
			&Liton
		
		
			MirKabir
		
		
			Ahasan
		
	
	
		Paper presented at International Conference on Recent Development in Statistical Sciences (ICSS)
				Dhaka, Bangladesh
		
			2008. December
		
	




* 
	
		Application of Simple Technical Trading Rules to Swiss Stock Prices: Is it Profitable? Research Paper N2
		
			Isakov
		
		
			MarcHollistein
		
	
	
		International Center for Financial Asset Management and Engineering
		
			1999
		
	




* 
	
		Monthly Moving Average-An Effective Investment Tool?
		
			FEJames
		
		
			Jr
		
	
	
		Journal of Financial and Quantitative Analysis
		
			3
			3
			
			1968
		
	




* 
	
		Random Walks and Technical Theories: Some Additional Evidence
		
			MichaelCJensen
		
		
			GeorgeABenington
		
	
	
		Journal of Finance
		
			25
			2
			
			1970
		
	




* 
	
		Testing the Weak-Form Efficiency of an Emerging Market: Evidence from the Dhaka Stock Exchange References Références Referencias of Bangladesh
		
			MirKader
		
		
			AfmAbdul & Rahman
		
		
			Ataur
		
	
	
		AIUB Journal of Business and Economics
		
			4
			2
			
			2005
		
	




* 
	
		The Analysis of Economic Time-Series Part I: Prices
		
			MGKendall
		
	
	
		Journal of the Royal Statistical Society
		
			116
			1
			
			1953
		
	




* 
	
		Distribution of Risk and Return: A Statistical Test of Normality on Dhaka Stock Exchange
		
			MdKhan
		
		
			&Haque
		
		
			UmmaHuq
		
		
			Rumana
		
	
	
		Research Journal of Finance and Accounting
		
			3
			3
			
			2012
		
	




* 
	
		An Empirical Test of Weak Form Market Efficiency on an Emerging Market: Evidence from Dhaka Stock Exchange
		
			MdKhan
		
		
			&Haque
		
		
			UmmaHuq
		
		
			Rumana
		
	
	
		Dhaka University Journal of Business Studies
		
			34
			2
			2013
		
	




* 
	
		Distribution of Risk and Return: A Test of Normality in Indian Stock Market
		
			Rakesh &Kumar
		
		
			RajSDhankar
		
	
	
		South Asian Journal of Management
		
			18
			1
			
			2011
		
	




* 
	
		Tests of Technical Trading Rules in the Asian-Pacific Equity Markets: A Bootstrap Approach
		
			CamilloLento
		
	
	
		Academy of Financial and Accounting Studies Journal
		
			11
			2
			2007
		
	




* 
	
		Stock Market Prices Do not Follow Random Walks: Evidence from a Simple Specification Test
		
			AndrewWLo
		
		
			AMackinlay
		
		
			Craig
		
	
	
		The Review of Financial Studies
		
			1
			1
			
			1988
		
	




* 
	
		An alternative test for weak form efficiency based on technical analysis
		
			ElaineY LLoh
		
	
	
		Applied Financial Economics
		
			17
			
			2007
		
	




* 
	
		Are Moving Average Trading Rules Profitable? Evidence From The Mexican Stock Market
		
			MassoudMetghalchi
		
		
			Garza-Gomez
		
		
			Xavier
		
		
			Yong&Glasure
		
		
			Yung-HoChang
		
	
	
		The Journal of Applied Business Research
		
			24
			1
			
			2008
		
	




* 
	
		Technical Analysis and the London Stock Exchange: Testing Trading Rules Using the FT30
		
			TerenceCMills
		
	
	
		International Journal of Finance & Economics
		
			2
			4
			
			1997
		
	




* 
	
		
		
			Asma&Mobarek
		
		
			KeavinKeasey
		
		
			2000. May
		
	




* 
	
		Weak-form market efficiency of an emerging Market: Evidence from Dhaka Stock Market of Bangladesh. Paper presented at The ENBS Conference
		
			Oslo, Norway
		
	




* 
	
		Market Efficiency in Emerging Stock Market: Evidence from Bangladesh
		
			AsmaMobarek
		
		
			AMollah
		
		
			RafiqulSabur & Bhuyan
		
	
	
		Journal of Emerging Market Finance
		
			7
			1
			
			2008
		
	




* 
	
		Testing the Weak-Form Efficiency of the United Arab Emirates Stock Market
		
			MAMoustafa
		
	
	
		International Journal of Business
		
			9
			3
			
			2004
		
	




* 
	
		An Empirical Re-Examination of the Weak Form Efficient Markets Hypothesis of the Ghana Stock Market Using Variance-Ratios Tests
		
			CollinsNtim
		
		
			Gyakari
		
		
			Opong
		
		
			KKwaku
		
		
			JoDanbolt
		
	
	
		African Finance Journal
		
			9
			2
			2007
		
	




* 
	
		Evidence on Weak Form Efficiency and Day of the Weak Effect in the Indian Stock Market
		
			SunilPoshakwale
		
	
	
		Finance India
		
			10
			3
			
			1996
		
	




* 
	
		Mean Reversion in Stock Prices
		
			JamesMPoterba
		
		
			Summers
		
		
			HLawrence
		
	
	
		Journal of Financial Economics
		
			22
			
			1988
		
	




* 
	
		Evaluation of Weak Form of Efficiency: An Empirical Study of Emerging South Asian Stock Markets
		
			AwaisRehman
		
		
			Ur
		
		
			Masood
		
		
			Mubashar
		
		
			Arshed
		
		
			&Saleem
		
		
			SyedShah
		
		
			Zulfiqar Ali
		
	
	
		International Research Journal of Finance and Economics
		
			88
			
			2012
		
	




* 
	
		Investment Analysis and Portfolio Management
		
			FrankKReilly
		
		
			Brown
		
		
			CKeith
		
		
			2004
			Thomson
			USA
		
	




* 
	
		Some New Filter Rule Tests: Methods and Results
		
			RichardJSweeny
		
	
	
		The Journal of Financial and Quantitative Analysis
		
			23
			3
			
			1988
		
	




* 
	
		Efficiency of the Indian Capital Market
		
			RVaidyanathan
		
		
			KantiGali
		
		
			Kumar
		
	
	
		Indian Journal of Finance and Research
		
			2
			
			1994
		
	




* 
	
		The Random-Walk Theory: An Empirical Test
		
			VanHorne
		
		
			JamesCParker George
		
		
			GC
		
	
	
		Financial Analyst Journal
		
			23
			
			1967