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Efficient market hypothesis research proposal

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This unparalleled rise of India and china to global economic superpowers has defied all expectation and shows no symptoms of waning. Throughout the cruel economic symptom in history couple of years both countries have registered growth rates that are close to the finest on the planet. But has this come at the cost of market efficiency? This report aims to evaluate empirical evidence and run other tests to uncover if both financial markets are weak form efficient.

Exams are conducted a duration of four years, since the global recession, to find out if market efficiency remains vulnerable to any means.

The empirical analysis is completed by performing various unit root tests along with the auto correlation tests. Event study is transported out across the two largest coal and oil companies in China and india, by searching on the market response to the announcement of dividends with the sample period. Market efficiency may also be affected by periodic anomalies. These anomalies vary from neighborhood holidays or any other factors. These studies aims to look at brought on by individuals holidays across the returns to investors.

Efficient markets may help attract domestic investments and significant foreign investments. The following decade is an important phase for countries since they vie for natural sources and investments. A dependable and mature market may well be a sign which from the neighbours will emerge as being a true global superpower inside the turn in the decade.

Introduction within the Research Problem

Using the Efficient Market Hypothesis suggested by Eugene Fama in 1970, a dependable information mill one where the worth of well worth the cost is reflected available on the market cost. Industry cost is unquestionably an aggregate of all of the historic and supply information available. These studies aims to comparatively test industry efficiency within the two emerging markets of China and india, with concentrate on selecting the final results around the globe Recession of 2008-2009 about this. The study may also explore the opportunity of extrapolating the findings as well as other emerging economies.

Efficient market hypothesis research proposal Test the result

A short study the origins and growth and development of the markets carrying out a colonial rule and economic reforms within the mid 1990, which introduced for the rapid development of the economies, will most likely be transported out. Both China and india are heavily influenced by their wealthy cultures and religion plays a vital role, it’s thus imperative that you test whether they have any effect on returns and efficiency within the markets. This can be created by performing holiday effect tests with the periods of Dusshera and Diwali in India and Chinese Year and National Day in China. Another critical approach to testing market efficiency is actually by performing event studies. Industry response to announcement of dividends by companies be studies to comment upon the informational efficiency and overall market efficiency.

Literature Review

Using the Efficient Market Hypothesis, which draws on the idea first forwarded by Samuelson (1965) then formally reviewed and presented by Fama (1970), no-you can consistently make gains more than the standard market returns. It further claims that anytime as time passes, the safety prices fully reflect everything supplied with no information or analysis should be expected outshine benchmark market returns. You will find three types of efficiency using the hypothesis:

Weak Form efficiency: Prices reflect all past freely available information and past market data. No type of technical analysis can lead to abnormal gains.

Semi-Strong Form efficiency: Prices reflect all past freely available information and past market data additionally to right away switch to reflect new information.

Strong Form efficiency: Prices reflect all past and new information additionally to mirror any hidden (insider) information.

In situation of semi-strong efficiency, in addition to technical analysis, fundamentals analysis can also be useless plus strong form even insider information doesn’t lead to outstanding gains. Random Walk Theory was the forerunner for that EMH. It absolutely was initially hypothesized by French math wizzard Louis Bachelier then produced by Cootner (1964). Based on it, although the financial markets are inefficient, you will never consistently beat the returns because of its natural unpredictability, similar to drunk scheming to make his way home.

An ‘efficient’ market is identified as a company and you will uncover large figures of rational, profit-maximizers positively competing, with every single attempting to predict future market values of human securities, where important current details are nearly freely open to all participants. In a efficient market, competition one of many intelligent participants creates a situation where, anytime as time passes, actual prices of human securities already reflect the final results of understanding based both from time to time which have already happened as well as on occasions which, presently, industry expects to occur later on. Essentially, in a efficient market anytime as time passes the particular cost from the safety is a superb estimate from the intrinsic value. (Fama, 1965)

“This helps to ensure that in a efficient market, equity research and valuation is really a pricey task that provided no benefits. The odds to discover an undervalued stock must be random (50/50). Also, a technique for at random diversifying across stocks or indexing for that market, transporting minimum information cost and minimal execution costs, may be superior on the vacation strategy, which has bigger information and execution costs. There’d be no useful by portfolio managers and investment strategists.In a efficient market, a technique for minimizing exchanging, i.e. developing a portfolio instead of exchanging unless of course obviously clearly cash was needed, may be a lot better than a method that needed frequent exchanging.” (Damodaran, A, 2010)

However, the paradox occurs when investors find no undervalued stock (i.e. inefficiencies), they’d stop trying to find their account, thus stop being “profit maximizing investors”, which makes all the markets inefficient. Therefore, it’s argues the efficient information mill a self-correcting mechanism where inefficiencies happened from time to time.

After Fama found that the American Dow jones johnson manley Manley Index was weak form efficient Solnik (1973) tested various European markets determined that they are efficient, however with a smaller degree compared to the DJI. However, an research into the handful of in the smaller sized sized sized European indices by Jennergren and Korsvold (1975) proven inconclusive results. Tests on some under-developed markets in 1985 proven that they are inefficient. Empirical studies on Asian stock markets offer been conducted round the massive. Research conducted by Yu and Mukherjee (1999) and Groenewold (2003) found that the SSE and SZSE were weak form inefficient. They found the B share index of both indices sweets a random walk. However, Seddighi and Nian (2004) found that the SSE was weak form efficient according to returns in the twelve several days period in 2000. Similarly, research inside the 90’s ensures that the Taiwanese exchange was indeed weak form efficient. Additionally, an extensive study by Ma and Barnes (2001) conducted round the SSE and SZSE couldn’t comprehensively accept or reject the null hypothesis.

There is a couple of studies across the Indian Market too. When using the BSE data for almost any period seven years from 1987 to 1994, Poshakwale (1996), figured the Indian market was inefficient. His findings were later using the studies of Pant and Bishnoi (2002), Pandey (2003) and Gupta and Basu (2007), using different tests over different time scales. However, earlier studies conducted in 1997 by Sharma Kennedy, Barua (1987) and Ramachandran (1985) found evidence the Indian Stock Markets were really weak form efficient.

Studies on Day’s Week effect are really less within the Indian and Chinese markets. Nath and Dalvi (2004) found that after the development of moving settlements within the NSE (an impact of Market Reforms), the returns on Mondays and Fridays was a higher deviation. This shows the existence of inefficiency within the maket. Similarly, Shiguang Ma (2004) also found the existence of inefficiency within the Shanghai and Shenzhen stock markets. Both studies used the Regression with bitweights and dummy variables to accomplish the tests.

The literature reviewed ensures that still cannot be completely pointed out if stock markets possess a random walk, particularly in emerging markets. There’s enough literature nonetheless the outcomes are really mixed. You have to conduct timely and accurate studies to uncover the efficiency of people markets due to the potential reward to investors. There’s always studies and debates regarding the applicability within the random walk model however, this really is becoming really important due to the recent global recession.


Despite being classed as emerging markets, the 2 largest stock markets of both countries would be the top 15 stock markets on the planet, according to their market capital. India’s Bombay Stock Market (BSE) and National Stock Market asia (NSE) will be in figures 8 and 9 correspondingly, obtaining a combined market capital of $ 3227 billion, while China’s Shanghai Stock Market (SSE) and Shenzhen Stock Market (SZSE) will be in figures 5 and 13, obtaining a complete market capital of $ 4048 billion. (IMF’s World Development Indicators, 2010).

Recently, regardless of the global turndown, China and india are really powerhouses of growth, with figures of 10% and 9% correspondingly. There’s even the finest amounts of domestic savings on the planet, which may be switched to investments in the existence of a dependable market. The 2 neighbours may also be a beautiful spot to choose Foreign Investments for this reason due to the aftereffect in the slowdown in developed markets. A company following random walk reaches equilibrium as equity is appropriately priced, while having less randomness skews the danger-return characteristics. Therefore effects the very best allocation of capital, which may be harmful to overall economic growth. Capital financial markets are imperative that you decision makers because the signals the investors send may affect the insurance coverage plan in the organization. Undervalued stock can trigger buybacks, which prevents allocation of capital to growth and investment activities.

India and China’s growth and histories will frequently have intertwined and faced lots of similar problems, but clarified with various responses. More often than not there’s been hostility backward and forward since a war in 1954 along with the geo-political situation in Southeast Asia. The 2 are really vying for dominance in Asia let’s focus on raw material to fuel their rapid growth. They are competing to attract investors and also to release their significant domestic savings. The outcome in the research may shed more light on who’s winning that competition as efficient markets gain investor confidence. These studies might also try and fulfill the growing demand that exists while using the present literature by updating it to incorporate the final results within the recession across the markets and getting less comparative quantitative research to the two markets.

Research Aims Objectives


The study aims to look for the efficiency of secondary markets in China and india.


See the origins and structure within the stock markets in countries.

Test weak form efficiency by Random Walk Behavior and performing ADF ensure Philips-Perron test.

To get a celebration study by searching within the aftereffect of dividend announcement by Reliance Industries in India and PetroChina in China.

Test brought on by local holidays and/or festivals on returns.

Explore the opportunity of extrapolating the findings as well as other emerging markets.


Selection of relevant facts are vital for accurate findings. Choice of sample size and understanding source is discussed during this section combined with quantitative methods for use. Some time plan’s also set. Because this research depends a little more about quantitative data and figures, data collection would mainly involve secondary research. Because of the abundance of general research in random walk theory and market efficiency, the standard of information available is usually good. However, only recognized sources will know about ensure precision within the information.

Primary data will most likely be collected through market data aggregators like Datastream Worldwide and Digitallook. Future literature review will probably be performed through access various available databases and thru calls for the library.

(i) Data Collection and Sampling:

As pointed out above earlier, the stock markets selected would be the Bombay Stock Exchange’s BSE-100 along with the NSE’s CNX 100 for the analysis of India’s Markets and Shanghai Stock Exchange’s SSE Composite and Shenzhen Stock Exchange’s SZSE 100 Index will most likely be helpful for China. These studies uses the daily stock return data, computed inside the closing prices within the listings within the selected indices. This is achieved a duration of The month from the month of the month of january 2007 to December 2010. Today remains selected to fully see the pre and publish aftereffect in the worldwide recession of 2008-2009. The weekly returns depends across the closing value on Friday of every week and monthly returns across the closing cost from the ultimate morning within the month. The information will most likely be sourced from DataStream.

The daily return is calculated when using the formula –

(ii) Quantitative Analysis:

The random walk behavior might be tested on performing unit root tests. The tests that is frequently used are

Auto Correlation FunctionTest:

“The autocorrelation function (ACF) test is examined to understand the grade of autocorrelation presently series. Its measurements would be the correlation relating to the current and lagged observations of occasions volume of stock returns, which is identified as:

where k is the amount of lags, and Rt represents the specific rate of return calculated as:

Two critical factors for estimating of autocorrelation would be the standard error ensure the bradenton area Pierce Q (BPQ) test. The standard error test measures the autocorrelation coefficient for individual lags and identifies the important one, since the Box Pierce Q test, measures the important autocorrelation coefficients inside the group level.

The standard error k is identified as:

where N may be the final quantity of observations and k may be the autocorrelation at lag (k).

Box Pierce Q is called:”

(Islam and Watanapalachaikul, 2005)

Dickey – Bigger Test

“Dickey-Bigger statistic test for the unit root within the time series data Pt is regressed against Pt-1 to evaluate for the unit root presently series random walk that’s given as:

If is considerably much like 1, your stochastic variablr Pt is pointed out to get a unit root. A group with unit root is pointed out to obtain united nations-stationary and doesn’t continue with the random walk. You will find three Dickey-Bigger tests for testing unit root within the series.

The above mentioned pointed out stated equation is presented as

Where, = (p-1) and here if is equivalent to , Pt could be a random walk.

To look for the validity of market efficiency, random walk hypothesis might be tested through this test. Unit root test remains conducted across the index return time series function, by running the next regression equations

where, is constant term and may be the coefficient of trend term. The null hypothesis for every is

The null hypothesis that Pt could be a random walk may be rejected if calculated is a lot more when compared with tabulated . Calculation of resembles the estimation of -statistic however, this value is instead of tabulated statistic, whose critical values are really tabulated by Dickey Bigger based on Monte Carlo simulations. The null hypothesis that Pt could be a random walk may be rejected if calculated is a lot more when compared with tabulated .” Pant and Bishnoi (2003).

Philips – Perron Unit Root Test:

“Phillips-Perron (1988) developed numerous unit root tests which have popular within the analysis of monetary time series. The Phillips-Perron (PP) unit root tests vary from the ADF tests mainly when it comes to they cope with serial correlation and heteroskedasticity within the errors. Test regression for the PP tests is supplied by:

Where its I() and it is heteroskedastic. The PP tests correct for virtually any serial correlation and heteroskedasticity within the errors t by using OLS estimation and modifying test statistics t= and T. These modified statistics, denoted Zt and Z receive by:

Believed values of that is standard errors acquired from OLS is due to the ADF tests. The sample variance whatsoever squares residual u could be a consistent estimate of two, along with the Newey-West extended-run variance estimate of u using u could be a consistent estimate of w2.

Underneath the null hypothesis that =, the Zt and Z statistics within the PP test have similar asymptotic distribution as ADF t-statistic and normalized bias statistics. One benefit in the PP tests within the ADF tests may be the PP exams are robust to general types of heteroskedasticity within the error terms ut.” (Onour, 2009)

(iii) Event Analysis:

Event analysis is the one other important test for efficiency. Industry model enables you to search for abnormal returns. Under normal conditions, an ordinary is anticipated to provide coming back because the market portfolio. Only within the light of unpredicted news, a stock’s return will fall outdoors industry return This is often known as arrogance interval. Thus an abnormal return AR is identified as the after-event return that falls outdoors the arrogance interval.

The CI acquainted with define record power an AR is:

CI = r^ i,t+e ± tcv * se(r^ i,t+e)

Where r^ i,t+e may be the after event return

, tcv may be the critical value

(iv) Time Period:

Following may be the monthly distribution of energy, getting an each week breakdown:

Begin Secondary Research.

Review texts along with other thesis

Refine Objectives and Thesis Methodology.

Discuss new objectives and methodology with supervisor.

Implement Feedback from Supervisor.

Prepare rough draft of literature review and methodology.

Complete essential data collection.

Present findings for review by Supervisor.

Complete literature review and methodology.

Run empirical analysis across the data collected.

Present findings to Supervisor.

Make correction and re-run the tests.

Start rough draft of ultimate report.

Finish rough draft of ultimate report.

Present the draft to supervisor for review making necessary changes.

Finish final report and send for binding.

Presentation of ultimate Dissertation.

Resource Needs:

Because the thesis mainly involves secondary research, convenience library catalogue along with other databases is vital. Usage of Datastream Worldwide is needed, that’s provided by the library. Likewise use computer system labs is needed to function the quantitative tests.


The goal of the study should be to comparatively test industry efficiency of two world’s fastest growing economies. This can be created if you take empirical analysis additionally to performing a meeting study. The empirical analysis is completed by performing various unit root tests along with the auto correlation tests. Event study is transported out across the two largest coal and oil companies in China and india, by searching on the market response to the announcement of dividends with the sample period. Market efficiency may also be affected by periodic anomalies. These anomalies vary from neighborhood holidays or any other factors. These studies aims to look at brought on by individuals holidays across the returns to investors.


Samuelson, P. (1965). Proof that Correctly Anticipated Prices Fluctuate At random. Industrial Management Review. Spring 6, 41-49.

Fama, E. (1970). Efficient Capital Markets: Review of Theory and Empirical Work, Journal of Finance, 25, 283-306.

Cootner, Paul H. (1964). The Random Character of Stock Exchange Prices. Durch Press.

Solnik, H. B. 1973, “Note across the Validity within the Random Walk for European Share Values,”Journal of Finance, 28(5), 1151-1159.

Groenewold, N. Tang, S. HKay. and Y. Wu (2003), “The Efficiency within the Chinese Stock Exchange along with the Role within the Banks”, Journal of Asian Economies, Vol.14, pp. 593-609.

Mookerjee, R. and Yu, Q. (1999), “An Empirical Research to the Equity Markets in China”, Review of Financial Immediate and ongoing expenses, Vol.8, pp. 41-60.

Seddighi, H. R. and Nian W. (2004), “The Chinese Stock Market Market: Operations and Efficiency”, Applied Financial Immediate and ongoing expenses, Vol.14, pp. 785-797.

Ma, S and Barnes, M (2001), “Are China’s Stock Markets Really Weak form Efficient?”, Adelaide College.

Barua, SKay. (1980), “Valuation of Securities and Inflence useful on Financial Decision in the Firm”, Doctorate Dissertation, Indian Institute of Management, Ahmedabad.

Gupta, R. and Basu, PKay. (2007), “Weak Form Efficiency in Indian Stock Markets”, IBER Journal. Vol.6, No.3, pp. 57-64.

Pandey, A. (2003), “Efficiency of Indian Stock Market”, Part of Project Report, Indira Gandhi Institute of Development and Research (IGIDR), Mumbai, India.

Pant, B. and Bishnoi, T. R. (2002), “Testing Random Walk Hypothesis for Indian Stock Exchange Indices”, Working Paper series, IIM, Ahmadabad.

Poshakwale, S. (1996), “Evidence on weak Form Efficiency and Day effect within the Indian Stock Market”, Finance India, Vol.10, No.3, pp. 605-616.

Ma, S. (2004), “The Efficiency of China’s Stock Market”, Ashgate Publishing.

Onour,I. (2009), “Testing Efficiency Performance of Saudi Arabia Market” JKAU: Econ. Adm. Vol. 23 No. 2, pp: 15-27

Islam, S and Watanapalachaikul, S (2005), “Are Emerging Markets Efficient? Some Evidence in the sorts of the Thai Stock Market” Victoria College, Financial Modelling Program.

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