Editorial Board

A Refereed Monthly International Journal of Management

Testing Weak- Form Efficiency of Indian Stock Market using High Frequency Data

Author

INTRODUCTION

Eugene Fama in 1965 has discussed three forms of financial market efficiency: the weak form, the semi-strong form and the strong form efficiency. The weak form of efficiency is defined by the situation when current asset prices reflect all the information enclosed in the past price movement. Hence, future price movement cannot be forecasted by examining the past price movement. This also implies that it is not possible to obtain excess returns by studying the assets’ prices history. (Ahmad, Ashraf, & Ahmed, 2006). If financial market does not follow weak form efficiency, it becomes predictable in nature. (Bessembinder and Chan, 1995).  This predicable characteristics of the financial market  provides the opportunity to investors or traders to earn supernormal profits. Various scholastic studies found asset market's to be least weak form efficient (Bessembinder and Chan, 1995; Coutts and Cheung, 2001). These studies bring out the success of technical trading strategies based on this inefficiency to produce abnormal profits to investors.

Previous studies related to weak form efficiency have mostly used the daily data. However, problem associated with the daily data is that it is an average of last 30 minutes of the trade, consequently, it is not suitable to bring out the dynamics of complete trading session.  But, advent of high speed electronic technology available these days have made the high quality intraday data accessible. This high quality high frequency data is expected to reveal limitations related to efficiency of markets, thereby providing a way of (legally) making an excess return from trading (Goodhart and Hara, 1997). Therefore, the present study tries to re-examine the weak form efficiency of Indian Stock Market using the 5- minute interval intraday return data.

REVIEW OF EXISTING LITERATURE

Majority of the previous studies provide an evidence for the financial markets to be weak form inefficient. However, some of the studies have found some markets to be weak form efficient which include Taiwan share market by Fawson et al. (1996); Hong Kong Stock market by Cheung & Andrew (2001); Hungary, Germany, Ireland, Portugal, Sweden and the United Kingdom markets by Worthington & Higgs (2003); Dhaka Stock Market by Rahman et al. (2004); Australia and Taiwan markets by Worthington & Higgs (2005); Insurance sector of Abu Dhabi Securities Market by Squalli (2006); Bahrain Stock Market by Asiri (2008). Table 1 depicts the existing literature that have examined weak form of efficiency in various financial markets.

The associated literature demonstrates various linear and non-linear dependencies in asset price behaviour. Nonlinear serial dependence has been recognized across various financial markets with different market structural systems (Al-Loughani & Chappell; 1997; Lim, 2009; Lim, Brooks, & Hinich; 2008). Various studies observed linear serial dependencies in financial data using simple serial correlation tests (Brown & Easton, 1989; Poshakwale, 1996; Laurence et al., 1997; Abrosimova et al., 2002; Hameed et al., 2006; Awad & Daraghma, 2009; Irfan et al., 2010; Gupta & Basu; 2011 etc.). Some studies used variance ratio test to investigate serial interdependence among various financial market asset's returns, return volatility, volume etc. (Urrutia, 1995; Cheung & Andrew, 2001; Buguk & Brorsen, 2003; Worthington & Higgs, 2003; Islam & Khaled, 2005; Worthington & Higgs, 2005; Squalli, 2006; Ntim, Opong, & Danbolt, 2007; Hamid et al., 2010). Various studies investigated weak form efficiency using GARCH models which include Milionis & Moschos (2000); Abrosimova et al. (2002); Ahmad et al. (2006); Hameed et al. (2006); Magnus (2008); Guidi et al. (2011); Alexeev & Tapon (2011); Mishra (2011) and Lean & Smyth (2015).

From table 1, it is clearly evident that most of the previous studies have used low frequency data (daily data) to test weak form efficient market hypothesis. With accessibility of high frequency data from few years have grabbed the attention of researchers to re-test the efficiency of financial markets. Few studies have tried to re-examine the weak form efficiency using high frequency data which includes Niarchos and Alexakis (2003); Strawinski and Slepaczuk (2008); Schulmeister (2009); Shmilovici et al. (2009); Wang and Yang (2010); Reboredo et al. (2012). Majority of these studies found financial markets to be weak form inefficient, except the study of Wang and Yang (2010) and Shmilovici et al. (2009). However, Wang and Yang (2010) have scrutinized the intraday efficiency of futures market using four major energy futures: crude oil, heating oil, gasoline, natural gas. Out of these four futures, crude oil and gasoline futures were found to weak form efficient. Commenting on weak form efficiency, Shmilovici et al. (2009) observed that intraday forex market tend to predictable above random. But, this predictability of the model is not enough to produce profitable trading strategy.

The present study endeavours to re-test the weak form efficiency using high frequency data in Indian Stock Market. High frequency literature is novel for Indian financial markets and have not yet been extensively researched. Therefore, this study contributes to the high frequency literature through re-testing basic weak form efficient market hypothesis in Indian financial markets.

Table 1: Empirical evidence for weak form of efficiency.

Source: Compiled from various research studies.

DATABASE AND METHODOLOGY

The sample used for the study is Nifty 50 and top 10 frequently traded stocks for the period 1st January 2009-31st March 2011 using 5-minute interval data for prices. During the period of our study, the stock market in India have seen two major significant structural changes in stock trading which are as follows:

1. On 1st January 2010: Advancement of trading hours (market opening changed from 9:55am to 9:00am).
2. On 18th October 2010: Pre-opening session (Pre-auction period) launched. This pre-open session lasts for 15 minutes from 9:00 AM to 9:15 AM.

The intention of these major structural changes in the stock market is to make market more liquid, less volatile and more efficient. Therefore, with the prevalence of these changes in stock market, re-testing the market efficiency theories has become the need of an hour. Consequently, complete data period was divided into 3 parts

Sub period 1: 1st January 2009-31st December 2009 (Before the change in trading time)

Sub period 2: 1st January 2010-17th October 2010 (After the change in trading time/ Before the launch of pre-opening session)

Sub period 3: 18th October 2010-31st March 2011(After the launch of pre-opening session)

Further, out of top 50 frequently traded stocks only top 10 frequently traded stocks are selected based on following filters:

1. Stocks whose prices are adjusted due to any corporate action such as issue of bonus shares, stock splits, merger or acquisition during the sample period are excluded.
2. Then top 10 stocks which have highest turnover (trading volume multiplied by share price) are included in the study.

Database for high frequency data for the Capital Market segment has been purchased from Dotex International Limited, a subsidiary of National Stock Exchange. This database is managed in two steps using software Visual Fox Pro:

1. Company Based Management: Data set includes complete transaction book for each trading date separately. In this step, the complete database is arranged company wise using Microsoft visual fox pro. Results output contains tick by tick information for each company separately.
2. Time Based Management: The second step of database management is time based management, in which proper interpolation rule is used to extract data at fixed intervals. Using nearest value and trading volume adjusted weighted average prices, desired companies or indices have been extracted at required fixed intervals (5-minute interval).

Statistical tests are applied in two steps, the first step includes preliminary analysis, which forms the basis of every time series statistical analysis. It includes the summary statistics using Mean, Standard Deviation, Skewness and Kurtosis. Unit Root test or Augmented Dickey Fuller (ADF) test has been used to check stationarity of the series. In a weak-form efficient market, there is no correlation between successive prices. Hence, the second step is applying the main time series statistics tools to check interdependence.

Various statistical analysis such as ARMA (Auto-regressive Moving Average) model and GARCH(1,1) is used to check presence of interdependence. An ARMA model is a special type of regression model in which the dependent variable has been stationarized and the independent variables are all lags of the dependent variable and/or lags of the errors.  The model consists of two parts, an autoregressive (AR) lags of the dependent variable part and a moving average (MA) part.

When the Intraday returns variances are dependent of time, then above models were adjusted to take into account these Autoregressive Conditional Heteroscedasticity (ARCH) effects. A natural extension of an ARCH(q) model is a Generalized Autoregressive conditional heteroscedastic (GARCH) model, which is widely employed in practice. From GARCH model volatility clustering can be observed. High persistent volatility clustering represent the inefficiency of a stock returns. GARCH (1,1) is employed in present study, which is represented as follows:

PRELIMINARY ANALYSIS

This section deals with preliminary analysis through unit root test and descriptive statistics.

Unit Root Test

Present study deals with the time series data, it becomes the major concern if the time series data is non-stationarity. In the nonexistence of stationarity, outcome of time series statistical analysis will become spurious. In order to check the presence of unit root and determining the order of differencing required to bring stationarity, this study has used the Augmented Dickey-Fuller (ADF) test.

Table 2 reveals that all price series are non-stationary. The null hypothesis of a unit root for price series is not accepted at the 1%, 5% and 10% level of significance. However, null hypothesis of a unit root for price series at first difference is accepted at the 1% level of significance.

Transformed series of 5-minute interval return data is taken as a logarithmic transformation of the price series is taken for further analysis. The returns are calculated as the difference of the

Transformed series of 5-minute interval return data is taken as a logarithmic transformation of the price series is taken for further analysis. The returns are calculated as the difference of the

Rp = Ln (Pt - Pt-1)

Where Rp= Returns, Pt= Price at interval t and Pt-1 =Price at interval t-1

Descriptive Statistics

Descriptive statistics for entire sample are computed to study the distribution pattern. Descriptive statistics include analysis of mean, maximum values, minimum values, standard deviation, skewness and kurtosis. Further, normality has been checked by applying the Jarque bera test. Skewness and kurtosis helps to understand the characteristics of a distribution.

From table 3, 4 and 5, it is observed that mean returns are positive for complete sample in sub-period 1. In sub-period 2, mean returns are positive for Nifty, I C I C I Bank Ltd., State Bank Of India, Infosys Ltd., H D F C Bank Ltd., Axis Bank Ltd., D L F Ltd., Bharat Heavy Electricals Ltd., Hindalco Industries Ltd. However, NTPC Ltd. and Sesa Goa Ltd have negative returns for this period. However, in sub-period 3, negative returns are observed for complete sample. Standard deviation is a measure of the variability or dispersion of a statistical population. From descriptive statistics, it is clearly evident that all returns series have low standard deviation which depicts the fluctuation of a security around its mean or average return (the mean reverting behavior).

The coefficient of the Jarque-bera is significant at 1 percent for complete sample in three periods. It documents that the trading returns are asymmetric and do not have the normal distribution. Leptokurtic distribution (kurtosis>3) of all the trading returns for Nifty and all companies is evident.

Table 2: Unit Root test- Augmented Dickey-Fuller test statistic

Table 3: Descriptive statistics for period 1st January 2009-31st December 2009

Table 5: Descriptive statistics for period 18th October 2010-31st March 2011

Tests ForPresence of Autocorrelation

The Auto-correlation coefficient depicts the relationship between the values of a random variable at time t and its value in the preceding period. Kendall (1953), Fama (1965), Nordhaus (1987), Chen (1996), Mobarek & Keasey (2000) and many more researchers applied autocorrelation test in a variety of speculative markets over diverse periods.

Auto Regressive Moving Average (ARMA) Model.

Auto-correlations are confirmed using ARMA Model. The auto-correlation (ACF) and partial auto correlation (PACF) functions are used from correlogram to recognize appropriate ARMA model. An ARMA model is a special type of regression model in which the dependent variable has been stationarized and the independent variables are all lags of the dependent variable and lags of the errors. Table 6 reveals significant AR and MA terms in returns of sample understudy.

In 1st sub period, AR(1) is significant for Nifty, I C I C I Bank Ltd, H D F C Bank Ltd., Bharat Heavy Electricals Ltd returns. Whereas, MA (1) is significant for State Bank Of India, Infosys Ltd. Axis Bank Ltd. D L F Ltd. Bharat Heavy Electricals Ltd. Hindalco Industries Ltd. N T P C Ltd. Sesa Goa Ltd. returns. Additionally, MA(2) is also significant for D L F Ltd. under this study period.

In 2nd sub period, AR(1) is significant for Infosys Ltd. and H D F C Bank Ltd. returns, where as AR(2) and AR(3) is significant for Nifty returns. On the other hand MA(1) is significant for State Bank Of India, Infosys Ltd., H D F C Bank Ltd., Axis Bank Ltd., D L F Ltd., Bharat Heavy Electricals Ltd., Hindalco Industries Ltd., N T P C Ltd. and Sesa Goa Ltd.

In 3rd sub period, AR(1) is significant for Nifty, Infosys Ltd., H D F C Bank Ltd., Hindalco Industries Ltd. and Sesa Goa Ltd. AR(2) is significant for I C I C I Bank Ltd., H D F C Bank Ltd. and Axis Bank Ltd. AR(3) is significant for Axis Bank Ltd. and AR(17) is significant for D L F Ltd. On the other hand, MA(1) is significant for State Bank Of India, H D F C Bank Ltd., Bharat Heavy Electricals Ltd., N T P C Ltd. and Sesa Goa Ltd. MA(2) is significant for I C I C I Bank Ltd only. All AR and MA terms are significant at 1% or 5% significance level.

Various studies such as Mobarek & Keasey (2000) Abrosimova et al. (2002) Rahman & Hossain (2006) Mollah (2007) Asiri (2008) Irfan et al.(2010) applied ARMA model in a variety of financial markets over diverse periods. All of these studies observed various significant AR and MA terms and claimed various financial markets to be weak form inefficient. Corroborating the results of these earlier studies, findings of this study also found Indian markets to be weak form inefficient. In order to re-confirm and interpret dependency in return series of sample understudy, further investigation is required. This interdependence is further investigated by GARCH(1,1) model.

Table 6: Average (ARMA) terms in return series

TEST FOR VOLATILITY CLUSTERING USING GARCH (1,1)

Another important requirement of time series data is that error terms of this developed autoregressive moving average model for stock returns should exhibit constant variance. If error terms does not exhibit constant variance, they are said to be heteroscedastic. Table 7 depicts the test for Heteroskedasticity :ARCH effects.

Table 7: Heteroskedasticity Test: ARCH test

Figure 1: Plot of Returns residual from ARMA Model.

Table 8: Variance Equation of GARCH(1,1)

Table 7 represents the nature of residuals of ARMA model, for which null hypothesis is that residuals are homoscedastic. It is clearly evident from the table that residuals are heteroscedastic at 1% level of significance. Heteroscedastic nature of residual is again observed in plot of return residuals from ARMA model in figure 1. From this plot it is clearly evident that the period of high volatility is followed by the period of high volatility and the period of low volatility is followed by the period of low volatility, this suggests that the residuals are conditionally heteroscedastic, can be represented by ARCH and GARCH model.

Hence, GARCH (1,1) is employed to study the nature of the return residuals. From GARCH model volatility clustering can be observed. Variance equation depicts the nature of volatility or conditional variance of the return series. This variance equation of GARCH(1,1) have two terms: ARCH and GARCH. The sum of the coefficients (α1+ β) of these terms depicts high persistence in volatility clustering, if the value is very close to one. High persistent volatility clustering represent the inefficiency of a stock market (Hameed et al., 2006). Table 8 shows that sum of ARCH and GARCH coefficient are very close to 1 for complete sample under study. Thus, suggesting a high persistence of volatility clusters over the sample period in the market. Similar volatility clustering was observed in Indian stock market by Abrosimova et al. (2002) using the daily data.

CONCLUSION

Financial market efficiency is an important issue for investors, researchers, analysts and regulators of emerging market like India. Evidence of weak-form inefficiency is an imperative signal of predictability, thus making traders to earn supernormal profits. Earlier studies investigating the weak form efficiency have used the daily data, however, re-testing the weak form of efficiency using high frequency data is required to capture the intraday predictability characteristics of the stock market. Additionally, problem associated with the daily data is that it is an average of last 30 minutes of the trade, consequently, it is not suitable to bring out the dynamics of complete trading session. Hence, this study tries to re-examine the weak form efficiency using high frequency data. Various statistical techniques are employed such as ARMA model and GARCH (1,1) for the return series. ARMA model confirms significant serial dependence in the 5-minute interval return series. GARCH (1,1) model symbolize high persistence in volatility clustering for the three sub-periods. The outcome of these statistical models present evidence for the nonexistence of the weak-form efficiency.

The results of this study do not hold up the validity of weak form efficiency for stock market returns for Nifty 50 and top 10 frequently traded stocks. Therefore, this gives an opportunity to traders to forecast future prices and earn abnormal profits. Hence, this study re-confirms the testable implications for traders and investors, so that they can exploit predictability of stock using the intra-daily data. On the other hand, this study also have an implications for the market regulators who have been introducing major structural changes in the stock market to improve liquidity, lessen volatility and improve efficiency. Results of the study shows that advancement of trading hours and introduction of pre-opening session have not improved weak form inefficiency. Hence, market regulators need to take some more stringent steps to improve the same.

REFERENCES

• Abrosimova, N., Dissanaike, G., & Linowski, D. (2002, February). Testing weak-form efficiency of the Russian Stock Market. Paper presented at European Finance Association Conference, Berlin. Available at: http://ssrn.com/abstract=302287 or http://dx.doi.org/10.2139/ssrn.302287
• Ahmad, K. M., Ashraf, S., & Ahmed, S. (2006). Testing weak form efficiency for Indian stock markets.Economic and Political Weekly, 41(1), 49-56.
• Ajao, M. G., & Osayuwu, R. (2012). Testing the weak form of efficient market hypothesis in Nigerian capital market.Accounting and Finance Research, 1(1), 169-179.
• Al-Ahmad, Z. (2012). Testing the Weak Form Efficiency of the Damascus Securities Exchange.International Research Journal of Finance and Economics, Issue 85, 154-165.
• Alexeev, V., & Tapon, F. (2011). Testing weak form efficiency on the Toronto Stock Exchange.Journal of Empirical Finance, 18(4), 661-691.
• Al-Loughani, N., & Chappell, D. (1997). On the validity of the weak-form efficient markets hypothesis applied to the London stock exchange.Applied Financial Economics, 7(2), 173-176.
• Al-Saleh, N., & Al-Ajmi, J. (2012). Weak-form efficiency in the Saudi stock market.International Research Journal of Finance and Economics, 27(3), 845-868.
• Asiri, B. (2008). Testing weak-form efficiency in the Bahrain stock market. International Journal of Emerging Markets,3(1), 38-53.
• Awad, I., & Daraghma, Z. (2009). Testing the weak-form efficiency of the Palestinian securities market.International Research Journal of Finance and Economics, Issue 32, 7-17.
• Bessembinder, H., & Chan, K. (1995). The profitability of technical trading rules in the Asian stock markets.Pacific-Basin Finance Journal, 3(2), 257-284.
• Brown, R. L., & Easton, S. A. (1989). Weak-form efficiency in the nineteenth century: A study of daily prices in the london market for 3 per cent consols, 1821-1860.Economica, 56(221), 61-70.
• Buguk, C., & Brorsen, B. W. (2003). Testing weak-form market efficiency: Evidence from the Istanbul Stock Exchange.International Review of Financial Analysis, 12(5), 579-590.
• Chen, Ping (1996),“A Random-Walk or Color-Chaos on the Stock Market?- Time-Frequency Analysis of S & P Indexes,” Studies in Nonlinear Dynamics & Econometrics, 1(2), 87-103.
• Cheung, K. C., & Andrew Coutts, J. (2001). A note on weak form market efficiency in security prices: Evidence from the Hong Kong stock exchange. Applied Economic Letters,8(6), 407-410.
• Dimson, E. and Mussavian, M. (1998). A Brief History of Market Efficiency. European Financial Management, 4(1), 1-14.
• Elango, D., & Hussein, M. I. (2008). An empirical analysis on the weak-form efficiency of the GCC markets applying selected statistical tests. International Review of Business Research Papers,4(1), 1-19.
• Fama, E. F. (1965). The Behavior of Stock-Market Prices.The Journal of Business, 38(1), 34-105.
• Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work.The Journal of Finance, 25(2), 383-417.
• Fama, E. F. (1991). Efficient capital markets: II.The Journal of Finance, 46(5), 1575-1617.
• Fawson, C., Glover, T. F., Fang, W., & Chang, T. (1996). The weak-form efficiency of the Taiwan share market.Applied Economics Letters, 3(10), 663-667.
• Gimba, V. K. (2012). Testing the Weak-form Efficiency Market Hypothesis: Evidence from Nigerian Stock Market.CBN Journal of Applied Statistics, 3(1), 117-136.
• Goodhart, C. A., & O'Hara, M. (1997). High frequency data in financial markets: Issues and applications.Journal of Empirical Finance, 4(2), 73-114.
• Guidi, F., Gupta, R., & Maheshwari, S. (2011). Weak-form market efficiency and calendar anomalies for Eastern Europe equity markets.Journal of Emerging Market Finance, 10(3), 337-389.
• Guney, Y., & Komba, G. V. (2016). Testing for the Weak-Form Market Efficiency of the Dar Es Salaam Stock Exchange. Handbook of Frontier Markets. 1st Edition. 1-30
• Gupta, R., & Basu, P. K. (2011). Weak form efficiency in Indian stock markets.International Business & Economics Research Journal,6(3), 57-64.
• Gupta, R., & Yang, J. (2011). Testing weak form efficiency in the Indian capital market.International Research Journal of Finance and Economics, Issue 75, 108-119. 
• Hamid, K., Suleman, M. T., Shah, S. Z. A., & Akash, R. S. I. (2010). Testing the weak form of efficient market hypothesis: Empirical evidence from Asia-Pacific markets.International Research Journal of Finance and Economics, Issue 58, 121-133.
• Irfan, M., Irfan, M., & Awais, M. (2010). Investigating the weak form efficiency of an emerging market by using parametric tests: evidence from Karachi stock market of Pakistan.Electronic Journal of Applied Statistical Analysis, 3(1), 52-64.
• Islam, A., & Khaled, M. (2005). Tests of weak‐form efficiency of the Dhaka stock exchange.Journal of Business Finance & Accounting, 32(7‐8), 1613-1624.
• Jamaani, F., & Roca, E. (2015). Are the regional Gulf stock markets weak-form efficient as single stock markets and as a regional stock market?.Research in International Business and Finance,33(1), 221-246.
• Jiang, Z. Q., Xie, W. J., & Zhou, W. X. (2014). Testing the weak-form efficiency of the WTI crude oil futures market.Physica A: Statistical Mechanics and its Applications, 405(July), 235-244.
• Kendall, M. G., & Hill, A. B. (1953). The analysis of economic time-series-part i: Prices.Journal of the Royal Statistical Society. Series A (General), 116(1), 11-34.
• Khan, A. Q., Ikram, S., & Mehtab, M. (2011). Testing weak form market efficiency of Indian capital market: A case of National Stock Exchange and Bombay Stock Exchange. African Journal of Marketing Management,3(6), 115-127.
• Korkmaz, M., & Akman, G. (2010). Testing the weak form market efficiency on Istanbul stock exchange.Trakia Journal of Sciences, 8(3), 39-49.
• Laurence, M. M., Cai, F., & Qian, S. (1997). Weak-form efficiency and causality tests in Chinese stock markets.Multinational Finance Journal,1(4), 291-307.
• Lean, H. H., and Smyth, R. (2015). Testing for weak-form efficiency of crude palm oil spot and future markets: new evidence from a GARCH unit root test with multiple structural breaks.Applied Economics, 47(16), 1710-1721.
• Lim, K. P. (2009). Weak-form market efficiency and nonlinearity: evidence from Middle East and African stock indices.Applied Economics Letters,16(5), 519-522.
• Lim, K. P., Brooks, R. D., & Hinich, M. J. (2008). Nonlinear serial dependence and the weak-form efficiency of Asian emerging stock markets. Journal of International Financial Markets, Institutions and Money,18(5), 527-544.
• Lim, K. P., Habibullah, M. S., & Hinich, M. J. (2009). The Weak-form Efficiency of Chinese Stock Markets Thin Trading, Nonlinearity and Episodic Serial Dependencies.Journal of Emerging Market Finance, 8(2), 133-163.
• Loh, E. Y. (2007). An alternative test for weak form efficiency based on technical analysis.Applied Financial Economics, 17(12), 1003-1012.
• Magnus, F. J. (2008). Capital market efficiency: An analysis of weak-form efficiency on the Ghana Stock Exchange.Journal of Money, Investment and Banking, Issue 5, 5-12.
• Mazviona, B. W., & Nyangara, D. (2013). A test of the weak form efficiency of the Zimbabwe Stock Exchange after currency reform. International Journal of Business, Economics and Law, 2(2), 43-48.
• Milionis, A. E., & Moschos, D. (2000). On the validity of the weak–form efficient markets hypothesis applied to the London stock exchange: comment.Applied Economics Letters, 7(7), 419-421.
• Mishra, P. K. (2011). Weak form market efficiency: evidence from emerging and developed world.The Journal of Commerce, 3(2), 26-33.
• Mobarek, A. and Keasey, K. (2000),“Weak-Form Market Efficiency of an Emerging Market: Evidence from Dhaka Stock Market of Bangladesh,” Paper Presented at the ENBS Conference held at Oslo, May 2000, 1-30.
• Mobarek, A., & Fiorante, A. (2014). The prospects of BRIC countries: Testing weak-form market efficiency.Research in International Business and Finance, 30(1), 217-232.
• Mollah, A. S. (2007). Testing weak-form market efficiency in emerging market: evidence from Botswana Stock Exchange.International Journal of Theoretical and Applied Finance, 10(6), 1077-1094.
• Moustafa, M. A. (2004). Testing the weak-form efficiency of the United Arab Emirates stock market.International Journal of Business, 9(3), 309-325
• Niarchos, N. A., & Alexakis, C. A. (2003). Intraday stock price patterns in the Greek stock exchange.Applied Financial Economics, 13(1), 13-22.
• Nisar, S., & Hanif, M. (2012). Testing Weak Form of Efficient Market Hypothesis: Empirical Evidence from South-Asia.World Applied Sciences Journal, 17(4), 414-427.
• Nordhaus, D. W. (1987). Forecasting Efficiency: concepts and Applications. The Review of Economics and Statistics, 69(4), 667-674.
• Ntim, C. G., Opong, K. K., & Danbolt, J. (2007). An empirical re-examination of the weak form efficient markets hypothesis of the Ghana stock market using variance-ratios tests.African Finance Journal, 9(2), 1-25.
• Ntim, C. G., Opong, K. K., Danbolt, J., & Senyo Dewotor, F. (2011). Testing the weak-form efficiency in African stock markets.Managerial Finance, 37(3), 195-218.
• Onour, I. A. A. (2004). Testing weak-form efficiency of Saudi Stock Exchange Market. Available at SSRN: http://ssrn.com/abstract=611209
• Patel, N. R., Radadia, N., & Dhawan, J. (2012). An empirical study on weak-form of market efficiency of selected Asian stock markets.Journal of Applied Finance and Banking, 2(2), 99-148.
• Poshakwale, S. (1996). Evidence on weak form efficiency and day of the week effect in the Indian stock market.Finance India, 10(3), 605-616.
• Rabbani, S., Kamal, N., & Salim, M. (2013). Testing the weak-form efficiency of the stock market: Pakistan as an emerging economy.Journal of Basic & Applied Sciences, 3(4), 136-142.
• Rahman, M. Z., Salat, A., & Bhuiyan, M. M. H. (2004). Testing Weak-form Efficiency of the Dhaka Stock Exchange.Available at SSRN: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=997748.
• Rahman, S., & Hossain, M. F. (2006). Weak-form efficiency: Testimony of Dhaka stock exchange.Journal of Business Research, 8(1), 1-12.
• Reboredo, J. C., Matías, J. M., & Garcia-Rubio, R. (2012). Nonlinearity in forecasting of high-frequency stock returns.Computational Economics, 40(3), 245-264.
• Robinson, J. (2005). Stock price behaviour in emerging markets: Tests for weak form market efficiency on the Jamaica Stock Exchange.Social and Economic Studies, 54(2), 51-69.
• Schulmeister, S. (2009). Profitability of technical stock trading: Has it moved from daily to intraday data?.Review of Financial Economics, 18(4), 190-201.
• Shaker, A. T. M. (2013). Testing the weak-form efficiency of The Finnish and Swedish stock markets.European Journal of Business and Social Sciences, 2(9), 176-185.
• Shmilovici, A., Kahiri, Y., Ben-Gal, I., & Hauser, S. (2009). Measuring The Efficiency of The Intraday Forex Market With a Universal Data Compression Algorithm.Computational Economics, 33(2), 131-154.
• Squalli, J. (2006). A non-parametric assessment of weak-form efficiency in the UAE financial markets.Applied Financial Economics, 16(18), 1365-1373.
• Srinivasan, P. (2010). Testing weak-form efficiency of Indian Stock Markets. Asia Pacific Journal of Research in Business Management,1(2), 134-140.
• Stănculescu, A., & Mitrică, E. (2012). Testing weak form informational efficiency on the Romanian capital market.Theoretical and Applied Economics, 9(9), 29-36.
• Strawiński, P., & Ślepaczuk, R. (2008). Analysis of high frequency data on the Warsaw Stock Exchange in the context of efficient market hypothesis. Journal of Applied Economic Sciences,3(3), 306-319.
• Tsay, R. S. (1986). Nonlinearity tests for time series.Biometrika, 73(2), 461-466.
• Urrutia, J. L. (1995). Tests of random walk and market efficiency for Latin American emerging equity markets.Journal of Financial Research, 18(3), 299-309.
• Wang, T., & Yang, J. (2010). Nonlinearity and intraday efficiency tests on energy futures markets.Energy Economics, 32(2), 496-503.
• Working, H. (1960). Note on the correlation of first differences of averages in a random chain.Econometrica: Journal of the Econometric Society, 28(4), 916-918.
• Worthington, A.C. and Higgs, H. (2005). Weak-Form Market Efficiency in Asian Emerging and Developed Equity Markets: Comparative Tests of Random Walk Behaviour. University of Wollongong, Working Paper No. 3, 1-12.
• Worthington, A. C. and Higgs, H. (2003). Weak-form Market Efficiency in European Emerging and Developed Markets, Discussion paper no. 159, School of Economics and Finance, Queensland University of Technology, Brisbane, Australia.