Is Dhaka Stock Exchange Efficient? An Application of the Technical Trading Rule

Tashfeen Hussain

School of Business, North South University, Dhaka, Bangladesh Liton Chakraborty

School of Business, University of Liberal Arts Bangladesh, Dhaka, Bangladesh Mir Ahasan Kabir

Department of Economics, North South University, Dhaka, Bangladesh

Abstract This paper attempts to examine whether Dhaka Stock Exchange confirms to weak form of efficiency through the application of statistical tools and technical trading rule, namely the Moving Average Rule. The sample includes 5815 observations from Dhaka Stock Exchange General Indices (DSE-GEN) between 16th September 1986 and 30th June 2008. The results indicate that DSE does not follow the weak form of efficiency. Introduction It is an established fact that investors feel confident to trade in an efficient market. Market efficiency ensures that the stock price is fully reflected by all the available information in the market. Abnormal price changes cannot be predicted systematically in an efficient market. If an investor can gain abnormal/arbitrage profit by systematically predicting the abnormal price change, the market can be termed inefficient. The Efficient Market Hypothesis (EMH) has been accepted as one of the cornerstones of modern financial economics. The market is said to be efficient when new information is quickly incorporated into the price so that price becomes information. Under these conditions the current market price in any financial market could be the best-unbiased estimate of the value of the investment. Therefore, efficient market hypothesis is the idea that information is quickly and efficiently incorporated into asset prices at any point in time, so that old information cannot be used to anticipate future price movements. Consequently, three versions of EMH are being distinguished depending on the level of available information: (1) Weak-form of efficient market hypothesis, (2) Semi-strong form efficient market hypothesis and (3) Strong-form efficient market hypothesis. The Weak-form EMH assumes that current stock prices fully reflect all security market information, including the historical sequence of prices, rates of return, trading volume data and other market-generated information. This hypothesis implies that past rates of return and other historical market data should have no relationship with future rates of return (price and rates of return should be independent). The Semi-strong form EMH asserts that security prices adjust rapidly to the release of all public information; that is, current security prices fully reflect all public information. The Strong-form EMH contends that stock prices fully reflect all information from public and private sources. Therefore, this hypothesis contends that no group of investors should be able to consistently derive above-average risk-adjusted rates of return. The strong-form EMH encompasses both the weak-form and the semi-strong form EMH.

From the above discussion one can conclude that a market can attain the next level of efficiency only after attaining the earlier level. A very few number of studies concentrated on this issue in the case of Bangladesh. More over, none of them consider any technical trading rule. This study attempts to examine whether the Dhaka Stock Exchange confirms to the weak form of efficiency. Literature review Researchers show tiny interest on testing EMH on Bangladesh stock market. A small number of studies have been conducted on DSE for testing the EMH, up to now. There have been some research reports, Alam (1999); Hassan (2000); Mobarek (2002); Kader (2005); Islam (2005) and Rahman (2006), on testing the market efficiency of the DSE, results are conflicting. Applying a variance ratio test, Alam (1999) found that the monthly stock price index series of the DSE (during 1986 to 1995) follow a random walk. This implies the existence of weak-form efficiency. However, by applying runs and autocorrelation tests, Mobarek (2002) concluded that the daily price index series of the DSE (for the period of 1988 to 1997) did not follow a random walk. On the other hand, like Mobarek (2002); Hassan (2000); Kader (2005); Rahman (2006) conclude that the DSE is not efficient in weak form. So, it’s possible to earn abnormal profit following a specific trading rule on a regular basis that violates the hypothesis of random walk. Moreover Hassan (2000) also found a negative relation between risks and return in DSE, which is statistically significant also, though this result is not consistent with portfolio theory. Islam (2005) found evidence in favor of short-term predictability of share prices in the Dhaka Stock Exchange prior to the 1996 boom, but not during the post-breakdown period. Cooray (2005) found mixed result for Bangladesh, the classical unit root supports weak form efficiency but DF-GLS and ERS doesn’t. However, except Kader (2005) none of the researchers has conducted any test on any specific trading rule, which is very important to test the weak form of efficiency for a market. Using K-filter rule, Kader (2005) found that DSE is not efficient even at weak-form. Stock price behavior in developing economies like Lithuanian and Prague are studied by Milieska (2004) and Vošvrda (1998). Vošvrda (1998) concluded that Efficient Market Hypothesis did not apply to the Prague stock exchange for the period (1995-1997) using autocorrelation between subsequent returns. Milieska (2004) end result on Efficiency of Lithuanian Stock market is mixed. From the earlier empirical studies we can observe that Asian emerging equity markets diverge from random walk hypothesis, while the opposite is found for couple of Latin American countries. Özer (2001) analyzed the behavior of Istanbul Stock Exchange (ISE) that is a growing emerging market. The daily, weekly and monthly returns of four sectors stock price indexes between January 4, 1988 and June 1, 2001 are examined and conclude that weak efficiency condition in ISE is not satisfied. Robert L. Brown (1989) indicates that London Stock Exchange exhibited a degree of weak-form efficiency using data from 1821 to 1860 which is also comparable to that found in similar tests conducted using data from contemporary markets. Cuthbertson (1997) applied the Campbell-Shiller VAR methodology to an annual UK stock index to test market efficiency for the period 1918-1993 and clearly rejected the null that expected returns are constant or that they depend only on a safe rate of interest. Hall (2002) tested efficiency of Russian stock exchange (RSE) and demonstrated that RTE was initially inefficient and for last two and a half years it became efficient. The performance of ASPGEN index overall remains predictable. However, from both indexes there is evidence of a tendency towards being efficient.

From the earlier empirical test results on developing and developed countries stock markets, it can be said that emerging markets tend to be inefficient even at weak form whereas the developed markets tend to be weak form of efficient. One of the reasons behind this may be developed countries’ markets are established long time ahead and their economy is more stable than developing countries. Data and methodology The daily price indices are examined for the empirical analysis from DSE General Price Indices (DSE-GEN) for the period between 16th September 1986 and 30th June 2008. The whole sample includes 5815 daily observations that have been collected from the Research and Library Center of DSE. In order to avoid the possible bias originated from thin or infrequent trading, a longer time-period has been used, which has reduced the problem of non-trading bias. To compare the level of efficiency of DSE, before and after the crash of 1996, as well as to make the test result consistent and verified, two sub-samples have been used in this study. The first sub-sample (16th Sep 1986 to 31st July 1996) contains 2680 daily general index prices and the second sub-sample (3rd May 1997 to 30th June 2008) contains 2922 daily general index prices. Daily general index prices from 1st Aug 1996 to 2nd May 1997 are not included into any of the sub-sample to avoid biasness and this period has been considered as outlier, since this is the period during which the market crash occurred. There are two basic methodologies that can be used in measuring weak form of efficiency. These are regression analysis (parametric tests) and the runs test (non-parametric tests). If the two tests confirm the random walk theory the conclusion would be that the respective market is efficient in the weak form. A pure random walk with drift process can be expressed as follows:

Pt = Pt-1 + β + ξt or,

rt = ∆ Pt + β + ξt Where, Pt is the logarithm of the price index observed at time t, β is an arbitrary drift parameter, rt is the percentage change in the index and ξt is a random disturbance term satisfying E(ξt) = 0, σ2

ξ is constant, E(ξt , ξt-g) = σ2, where g = 0 and E(ξt , ξt-g) = 0, where g ≠ 0, for all t. Under the random walk hypothesis, a market is (weak-form) efficient if the most recent price contains all available information and therefore the best predictor of future prices is the most current price. In the strictest version of the efficient market hypothesis, is not only random and stationary, but exhibits no autocorrelation, since the disturbance term cannot possess any systematic forecast errors. On the other hand, for technical analysis, we employ moving average. Technical analysis is used to forecast the future direction of security prices through the study of past market data, primarily price. The general conclusion that emerges from the studies of Brock (1992) and Hudson (1996) is that technical trading rules have predictive ability if sufficiently long series of data are considered. Tabak (2004) in their study show the evidence of predictability using technical trading rules for emerging markets, even when analyzing short-term time series. The moving average is one of the most versatile and widely used of all technical indicators. A moving average is a distinct averaging method of a certain body of data. If a 30-day average of closing prices is desired, the prices for the last thirty days are added up and the sum is divided by thirty. Each new day is added and initial one is subtracted from the average.

Following Brock (1992), the moving average – oscillator is examined. The moving average – oscillator involves two moving averages (MAs) of the level of the index, Kst: a ‘short’ moving average of order n,

n – 1 st (n) = 1/n Σ xt-i

i = 0

and Klt: a ‘long’ moving average of order m (m > n) m – 1

lt (m) = 1/m Σ xt-i i = 0

In its simplest form, this rules generates a buy (sell) signal when st(n) rises above (falls below) lt(m) and when this happens, a ‘trend’ is said to be initiated. Hypothesis This study seeks evidence whether the Dhaka Stock Exchange follows weak form of efficiency or not. H0: Rate of return is not predictable \ efficient Ha: Rate of return is predictable \ inefficient Empirical tests and results The empirical results are presented in different subsections. Result of statistical tests is presented in first section and result of trading rules is presented in the second subsection for all sample period. Dynamic Regression Analysis It is obvious that results from simple statistical significance test are not sufficient to reach into a final decision about the existence of random walks or stock market efficiency. Therefore some dynamic regressions models are fitted, including all significantly important lags as independent variables. These regression models for different sample size are constructed as follows:

Rt = Rt-1 + Rt-2 + Rt-3 + Rt-4 + Rt-5 + Rt-6 + Rt-7 + Rt-8 + Rt-9 + et (Whole period) Rt = Rt-1 + Rt-2 + Rt-3 + Rt-4 + et (Excluding Outliers) Rt = Rt-1 + Rt-2 + Rt-3 + Rt-4 + Rt-5 + Rt-6 + Rt-19 + et (1st Sub Sample) Rt = Rt-1 + Rt-2 + Rt-3 + Rt-4 + et (2nd Sub Sample)

Where Rt represents the daily rate of returns at time, t, and i (= 1, 2, 3…) stands for lag period and et is the residual of the regression at time, t, having zero mean and constant variance.

Table 1: Coefficients of the Regressions for different sample Coefficients λ0 λ1 λ2 λ3 λ4 λ5 λ6 λ7 λ8 λ9 Whole sample 0.00 0.14* -

0.04* 0.03* 0.05* 0.03* -0.01 0.03* 0.01 0.01

Excluding outlier 0.00* 0.10* -

0.05* 0.05* 0.06*

Coefficients λ0 λ1 λ2 λ3 λ4 λ5 λ6 λ19 1st Sub Sample 0.00 0.05* 0.01 0.04* 0.05* 0.05* 0.02 -0.02

2nd Sub Sample 0.00 0.12* -

0.10* 0.05* 0.05*

*Denotes statistically significant coefficients at 5% level of significance, for a two-sided test

All statistically significant coefficients, in table 1, indicate that today’s market return is predictable from the previous day’s market return. Therefore, it is evident that the daily market return series do not follow random walk model which means DSE is inefficient at weak form. Technical Trading Rule This study employs a simple 50, 100 and 200 days moving averages (MA) for technical analysis, also calculate the profit for each time period using the buy and hold strategy. The results obtained while using the different moving averages are shown in the following table:

Table 2: Returns of the different Moving Average Strategies

MA 50 MA 100 MA 200 Buy and Hold Whole period 149.71% 140.19% 53.79% 51.08% Excluding outlier 51.46% 63.05% 18.68% 53.56% First Sub-Sample 38.06% 53.98% 22.87% 39.38% Second Sub-Sample 22.36% 21.46% 13.32% 19.55%

Source: Data from DSE library and the authors’ calculation Returns are calculated after adjusting 0.5 % transaction costs for both buy and sell. It can be concluded from the table that if an investor follows moving-average strategies (especially lower based, i.e, MA 50), (s)he can beat out the buy and hold benchmark, even after the price is adjusted for transaction costs. So, it is not incorrect to say that smart individuals can earn arbitrary profit on a regular basis from this market and this market is inefficient even at weak form. Another important signal from the result is that second sub-sample can generate less arbitrage profit compare to first sub-sample. So, it can be said that after crash period inefficiency is diminishing. Reasons behind inefficiency A scrutiny reveals that - most of the investors are not educated enough concerning the pattern of market; meaning investors lack the knowledge to verify quality shares based on fundamentals and consequently carrying the transaction or trading based on speculation or rumor only. The market practice suggests – flow of information is not asymmetric to all the investors at a time resulting into an advantage to a group of investors acquiring the information early ahead to make an abnormal profit. Another issue is the inequality between demand and supply of shares in the market. First, lack of quality shares instigates instability in the market. Second, religious sentiment forces some not to keep their money in the bank as they believe interest rate to be forbidden in Islam. And for this, they invest in stock market sometimes in poor quality shares mostly which again increases the demand against the supply of quality shares resulting into abnormal increase in price. Further the approval of 0% income tax on Capital Gain encourages investors to invest in the market which increases the demand where as the supply remains the same.

Improvement of inefficiency Study also indicates that the Level of Inefficiency of DSE is reducing right after the crash period. It implies that DSE is performing better than the early 90s. After the crash of 1996 the trading system has been improved a lot through computerization of transaction and introduction of new regulations like Circuit Breaking system, maturity of shares depending on the quality of shares, Credit Rating etc. In addition the market authority also introduced several awareness campaign to enlighten the investors which was also helpful for improving the market efficiency as the investors became capable of making sensible decision and started relying less on speculation & rumor and more on market information & financial analysis. Finally, a significant number of quality shares has been introduced in the market over last couple of years which diversified the investment portfolio As a result pressure on a specific group of quality shares decreased which helped in the process of equilibrate and stabilize the demand-supply condition resulting in to improved efficiency of the market. Recommendations Given the tests we have conducted, it is concluded that DSE is not efficient even at its weak form. Therefore, a need exists to take proper actions to improve the efficiency of the market. First, it is important to ensure asymmetric information among all of the investors. In addition to the existing awareness creation policy it is important to improve it on continuous basis to enlighten the investors about market structure, trading pattern, financial solvency and financial analysis of the enlisted companies. Proper implication of the rules of regulatory commission is also needed to be ensured so that there will not be any scope to violate the market structure to gain abnormal profit. It is also important so that the regulatory commission enforce policies to ensure improved quality of enlisted shares in addition to the initiative to encourage new companies to be enlisted in the stock market. Finally to make the market more efficient it is expected that the authority of the market would introduce sophisticated means of investment and tools in the near future. References Alam M. I., Hasan T. and Kadapakkam, P. (1999), "An Application of Variance Ratio Test to

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