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Impact Of Efficient Market Hypothesis Finance Essay

Efficient Market Hypothesis is an important concept in portfolio investment and diversification. This concept is gaining significance because of integration of international markets helping in movement of investment across national boundaries. When the markets tend to be inefficient they provide arbitrage opportunities for the investors. This paper tries to test the weak form efficiency for the London Stock Exchange and New York Stock Exchange for the period April 1990 to March 2010. The evidence suggests that the series do not follow random walk thus rejecting the weak form efficiency hypothesis. This rejection shows the existence of arbitrage opportunity in the markets..

Financial market is a market providing both short term and long term access to finance. It helps in maintaining a balance between short term liquidity and long term profitability. Money market is the market for short-term debt securities providing for liquidity and easier access to credit. Capital market is market for long term securities in the form of debt and equity. A market is said to be efficient when current prices reflect all the available information about the scrip. An investor is willing to pay the maximum price for a share which is actually the basis of the concept of valuation. The maximum price is the present value of the future cash flows generated from the security. New information needs to be incorporated into the prices of the share. This situation generates an efficient market. Efficient market emerges when new information is quickly incorporated into the price so that price becomes information. In other words the current market price reflects all available information.

The term market efficiency in capital market theory is used to explain the degree to which stock prices reflect all available, relevant information. The concept of Efficiency Market Hypothesis is based on the arguments put forward by Samuelson (1965) that expected price of an asset to vary arbitrarily. Fama (1970) presented a formal review of presumption and proof for market efficiency and modified it further on the basis of research. Efficiency of equity markets is important for the investment policy of the investors. In an efficient market, prices of the assets will reflect market’s best estimate for the risk and expected return of the asset. Therefore, there will be no undervalued assets or overvalued assets in the market. Hence, concentrating the risk return tradeoff of an asset is the ideal investment strategy However if the markets are inefficient it is better for investors to research

The impact of efficient market hypothesis can be understood under two perspectives. They are the investor perspective and the financial manager perceptive. The random-walk evidence suggests that prices of securities are affected by intelligence and market gossip. Favourable news will have an eventful impact on the price. Financial manager’s perspective. Managers understand that investors will react to information. The company's share price will replicate the information (Annuar and Shamsher 1993). Emerging markets have received huge inflows of funds in the recent past and became viable alternative for investors looking for international diversification. With different financial market reforms beginning demand for investment funds have started growing significantly (Gupta and Basu 2007).

The next section of this paper provides a brief literature review of studies testing market efficiency in different financial markets and a brief background of the equity market of the choose country. Data and research methodology is given in Section 3. This section also explains the different hypothesis tested in the paper. Section 5 explains the results and analysis. This section also includes the trading patterns in the New York Stock Exchange and the London Stock Exchange. The last section summarizes the conclusions and their implications.

3. RESEARCH METHODOLOGY

This section explains the basis of research design and the tools used in the research mythology. The section also discusses the sample and the different tests used to analyse the efficient market hypothesis. The research methodology provides the logic for the different techniques used and the analysis made thereafter

3.1 RESEARCH DESIGN

The research design is a plan that helps in methodical management of data collection. Design and methodology order what you need to answer your research questions. Research designs can be broadly qualitative research design and quantitative research design. Qualitative research involves using techniques that try to gain an understanding of the continuation of characteristics and attributes. It then goes on to assess the span and the intensity of those attitudes. Qualitative research studies do not measure the emotion or opinion of the investigators but they may give an indication of the dominant feelings. Qualitative research also indicates the different biases involved in the research design. The biases can arise from both the investigator and the investigated. The different methods of qualitative research are in-depth interviews, focus groups, delphi technique, interviews etc.

Quantitative research is the methodical and systematic undertsanding of quantitative properties. This type of research also tries to explain the different relationships that exists between the various variables. The objective of quantitative research is to develop and use mathematical models, theories and/or hypotheses pertaining to natural phenomena. The process of measurement is critical to quantitative research as it provides the most vital connectivity between empirical observation and mathematical model created to explaint he realationship between the different variables used in the reesrach.

Data can be collected either on primary basis or on secondary basis. In primary data collection, data is collected by the investigator in the form of surveys, interviews and questionnaires. The key point here is that the data collected is unique to the investigator and the investigation until it is published. There are many methods of collecting primary data and the main methods include questionnaires, interviews, focus group interviews, observation, case-studies, diaries, critical incidents, portfolios etc.

All methods of data collection can supply quantitative data (numbers, statistics or financial) or qualitative data (usually words or text). Quantitative data may often be presented in tabular or graphical form. Secondary data is data that has already been collected by someone else for a different purpose to yours. Sources can be classified as paper-based sources like books, journals, periodicals, abstracts, indexes, directories, research reports, conference papers, market reports, annual reports, internal records of organisations, newspapers and magazines and electronic sources like CD-ROMs, on-line databases, Internet, videos and broadcasts.

3.2 SAMPLE AND DATA COLLECTION

The present research is based on the secondary data collection. The data used in this study is consisted of monthly FTSE 100 returns for the London Stock Exchange from 1st of April, 1990 to 31st of March, 2010 and monthly NASDAQ 100 returns for the National Association of Securities Dealers Automated Quotations Systems (NASDAQ), from 1st of April, 1990 to 31st of March, 2010. Mathematically, the natural logarithm of the relative price was computed for the daily returns to produce a time series of continuously compounded returns, such that Rt= Log (Pt/Pt-1)*100 where Pt and Pt-1 represent the stock index price at time t and t-1.

3.3 HYPOTHESIS

The different hypothesis tested in the paper is follows.

H0: β^= 0 (i.e. there is no significant influence of stock prices on stock returns)

Ha: β^≠0 (i.e. there is significant influence of stock prices on stock returns)

H0: The stock returns in LSE are random over the time period of the study.

H1: The stock returns in BSE are not random over the time period of the study.

H0: The stock returns in NASDAQ are random over the time period of the study.

H1: The stock returns in NASDAQ are not random over the time period of the study.

H0: No difference between the two samples

H1:Significant difference between the two samples

3.4 STATISTICAL TOOLS USED FOR ANALYSIS

The different statistical tools used for analysis have been discussed in this section.

DESCRIPTIVE ANALYSIS

Descriptive statistics are used to provide the basic features of the sample chosen. These provide summary information about the different variables. This tool is used to provide the quantitative descriptions in manageable forms. This provides the summary statistics of the returns of the two indices used in the research paper. This is shown in the following tables.

NASDAQ 100

Mean

631.3598

Standard Error

990.2668

Median

1492

Standard Deviation

15309.15

Sample Variance

2.34E+08

Kurtosis

8.11071

Skewness

-0.37658

Range

144565

Minimum

-70786

Maximum

73779

Sum

150895

Count

239

FTSE 100

Mean

1484.686

Standard Error

1270.896

Median

3120

Standard Deviation

19647.58

Sample Variance

3.86E+08

Kurtosis

1.480697

Skewness

-0.9002

Range

116170

Minimum

-73410

Maximum

42760

Sum

354840

Count

239

NASDAQ 100 AND FTSE 100 SUMMARY STATISTICS

The mean of Nasdaq 100 is 631.3598 whereas of FTSE100 is 1484.686. The standard deviation of Nasdaq 100 is 15309.15 and that of FTSE 100 is 19647.58. The kurtosis of Nasdaq 100 is 8.11071 while that of FTSE100 is 1.480697. The skewness of Nasdaq is -0.37658 while that of FTSE 100 is -0.9002. The maximum of Nasdaq is 79779 while the minimum is -70786. The maximum of FTSE 100 is 42760 while the minimum is -73410. The sum of all the observations of Nasdaq is 150895. The sum of all the observations under FTSE 100 is 354840. The total observations for both the samples are 239. The returns of both the indices are shown in the form of histograms over the years.

HISTOGRAM OF FTSE 100

HISTOGRAM OF NASDAQ

REGRESSION ANALYSIS

The influence of individual variables on the stock returns is analyzed through simple regression analysis. The analysis is done to find out whether there is a significant linear relationship between an independent variable xi and a dependent variable yi. The model’s unknown parameters are denoted by α (the intercept) and β (the slope). The assumed model for linear regression is given as;

yi = α + βxi + ei

This relationship is assumed to hold for all observations (i = 1, 2… n). Inclusion of random error ei is necessary because other unspecified variables may also have an effect on yi. It is assumed that ei is normally distributed random variable with a mean of 0 and standard deviation α. From the sample, the fitted model is estimated and is used to predict the expected value of yi for a given value of xi. The fitted linear model is given as;

yi^= α ^+β^xi

α ^ denotes the estimated intercept and β^ denotes the estimated slope. The difference between observed value yi and the fitted value yi^ is the residual and is denoted by ei.

ei = yi - yi^

ANALYZING INFLUENCE OF STOCK PRICES ON STOCK RETURNS OF NASDAQ 100

In the regression analysis stock return is the dependent variable and stock price is the independent variable. The null and alternate hypotheses are explained as follows.

H0: β^= 0 (i.e. there is no significant influence of stock prices on stock returns)

Ha: β^≠0 (i.e. there is significant influence of stock prices on stock returns)

The output is shown in the following table.

Regression Statistics

Multiple R

0.870872

R Square

0.005023

Adjusted R Square

0.000825

Standard Error

15302.84

Observations

239

ANOVA

 

Df

SS

MS

F

Significance F

Regression

1

2.8E+08

2.8E+08

1.196414

0.27515

Residual

237

5.55E+10

2.34E+08

Total

238

5.58E+10

 

 

 

 

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

-1201.44

1946.154

-0.61734

0.537602

-5035.41

2632.528

-5035.41

2632.528

X Variable 1

1.386628

1.267708

1.093807

1.093807

-1.11079

3.884045

-1.11079

3.884045

For this analysis a significance level of 0.05 is chosen. Using sample data, a linear regression is used to determine whether there is a significant influence of price on stock returns. The following equation shows the relationship between stock price and stock returns.

yi^= -1201.44+1.386628xi

The value of the slope β^ is -1.386628. The value of t-Stat is 1.093807 and the P-value is 1.093807. The value of R2 is 0.870872which explains 87% of the variation. Since the P-value is less than the significance value of 0.05, Ha is accepted and the H0 is rejected. This proves that there is significant influence of stock price on stock returns.

ANALYZING INFLUENCE OF STOCK PRICES ON STOCK RETURNS OF FTSE 100

In the regression analysis stock return is the dependent variable and stock price is the independent variable. The null and alternate hypotheses are explained as follows.

H0: β^= 0 (i.e. there is no significant influence of stock prices on stock returns)

Ha: β^≠0 (i.e. there is significant influence of stock prices on stock returns)

The output is shown in the following table.

SUMMARY OUTPUT

Regression Statistics

Multiple R

0.729558

R Square

0.000874

Adjusted R Square

-0.00334

Standard Error

19680.38

Observations

239

ANOVA

 

Df

SS

MS

F

Significance F

Regression

1

80270951

80270951

0.207248

0.649349

Residual

237

9.18E+10

3.87E+08

Total

238

9.19E+10

 

 

 

 

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

483.93

4507.784

-0.10735

0.914599

-9364.37

8396.515

-9364.37

8396.515

X Variable 1

0.433885

0.95308

0.455245

0.049349

-1.44371

2.311476

-1.44371

2.311476

For this analysis a significance level of 0.05 is chosen. Using sample data, a linear regression is used to determine whether there is a significant influence of price on stock returns. The following equation shows the relationship between stock price and stock returns.

yi^= -483.93+.0.433885xi

The value of the slope β^ is 0.433885. The value of t-Stat is 0.455245and the P-value is 0.049349. The value of R2 is 0.729558 which explains 72% of the variation. Since the P-value is less than the significance value of 0.05, Ha is accepted and the H0 is rejected. This proves that there is significant influence of stock price on stock returns.

AUGMENTED DICKER FULLER TEST

Under the random walk hypothesis, a market is (weak form) efficient if most recent price has all available information and thus, the best forecaster of future price is the most recent price. In the most stringent version of the efficient market hypothesis, εt is random and stationary. This study has used returns and not prices for test of market efficiency as expected returns are more commonly used in asset pricing literature (Fama (1998). Augmented Dickey Fuller test (ADF test) is used to examine the aforesaid hypothesis. Three regression models (standard model, with drift and with drift and trend) are used in this study to test for unit root in the research, (Chan, Gupta and Pan 1997). The regression equations are given below.

St = αSt-1 + εt (1)

St = u* + α*St-1 + ε*t (2)

St = u** +β(t-T) + α**St-1 + ε**t (3)

Where: St = the stock price

u* and u** = the drift terms

T = total number of observations

εt, εt*, εt** = error terms

Where St is the logarithm of the price index seen at time t, u is an arbitrary drift parameter, α is the change in the index and εt is a random disturbance term. Equation (1) is for the standard model; (2) for the standard model with a drift and (3) for the standard model with drift and trend. (Brooks II)

PHILLIP PERRON TEST

The Phillips-Perron (PP) unit root tests differ from the ADF tests mainly in how they deal with serial correlation and heteroskedasticity in the errors. In particular, where the ADF tests use a parametric autoregression to approximate the ARMA structure of the errors in the test regression, the PP tests ignore any serial correlation in the test regression. The test regression for the PP tests is

Δyt = β0Dt + πyt−1 + ut

where ut is I(0) and may be heteroskedastic. Under the null hypothesis that π = 0, the PP Zt and Zπ statistics have the same asymptotic distributions as the ADF t-statistic and normalized bias statistics. One advantage of the PP tests over the ADF tests is that

the PP tests are robust to general forms of heteroskedasticity in the error

term ut. Another advantage is that the user does not have to specify a lag length for the test regression.

This study conducts a test of random walk for the National Association of Securities Dealers Automated Quotations Systems, for the US market and the London Stock Exchange for the UK market using NASDAQ 100 for the US market and FTSE100 for UK market. It employs unit root tests (augmented Dickey-Fuller (ADF)). ADF test results for the different forms conducted are shown in the following tables. The purpose of this study is to examine the efficiency of the NASDAQ and LSE at the weak-level. The null hypothesis states that the stock market will be efficient at the weak-form. The null and alternative hypothesis:

H0: The stock returns in LSE are random over the time period of the study.

H1: The stock returns in LSE are not random over the time period of the study.

H0: The stock returns in NASDAQ are random over the time period of the study.

H1: The stock returns in NASDAQ are not random over the time period of the study.

PARTICULARS

VALUES

PARTICULARS

VALUES

ADF Test statistic(14 lags with intercept and no trend)

-14.35573

ADF critical values (with intercept and no trend)

1%

5%

10%

-3.457745

-2.873492

-2.573215

ADF Test statistic(14 lags with intercept and trend)

-14.354559

ADF critical values (with intercept and no trend)

1%

5%

10%

-3.997083

-3.428819

-3.137851

PP unit root test (with intercept and no trend)

-14.42561

PP critical values (with intercept and no trend)

1%

5%

10%

-3.457757

-2.873492

-2.573215

NASDAQ 100 ADF AND PP TEST OUTPUT

PARTICULARS

VALUES

PARTICULARS

VALUES

ADF Test statistic(14 lags with intercept and no trend)

-14.90978

ADF critical values (with intercept and no trend)

1%

5%

10%

-3.457747

-2.873492

-2.573215

ADF Test statistic(14 lags with intercept and trend)

-14.92954

ADF critical values (with intercept and no trend)

1%

5%

10%

-3.997083

-3.428819

-3.137851

PP unit root test (with intercept and no trend)

-14.93213

PP critical values (with intercept and no trend)

1%

5%

10%

-3.457747

-2.873492

-2.573215

FTSE 100 ADF AND PP TEST OUTPUT

The null hypothesis that the time series is non-stationary is rejected when test statistic is more negative than the critical value at a given level of significance. The above tables clearly shows that in both the forms the ADF test statistic is more negative than the critical values which prove that unit root does not exist. So the study suggests that NYSE and LSE is not weak form efficient. We further test the series using the Phillips-Perron (PP) tests for a confirmatory data analysis. For both LSE and NYSE the results are statistically significant and the results of all the two tests are consistent suggesting these markets are not weak form efficient.

F TEST ANALYSIS

In order to prove that there is significant arbitrage opportunities that exist in both the markets a paired F-test is conducted which tests the impact of arbitrage opportunities on the returns of the indices. It tests whether the two samples are statistically same or different. The two hypothesis tested are as follows.

H0: No difference between the two samples

H1: Significant difference between the two samples

 

Variable 1

Variable 2

Mean

1484.686

631.3598

Variance

3.86E+08

2.34E+08

Observations

239

239

Df

238

238

F

1.647084

P(F<=f) one-tail

6.53E-05

F Critical one-tail

1.23821

 

The above output shows that the p value is 6.53E-05. This value is less than the significance level of 0.05. Thus the null hypothesis of no difference between the two samples is rejected and the alternate hypothesis is accepted. This shows that there is ample arbitrage opportunity in the two markets which reiterates the fact that the markets are inefficient.

4. ANALYSIS AND INTERPRETATION

This section explains the trend of the the two stock exchange looked into in this research paper.

4.1 LONDON STOCK EXCHANGE

The London Stock Exchange is the most important exchange in Europe and one of the largest in the world. It has over 3,000 companies listed with it and with 350 of the companies coming from 50 different countries. The London Stock Exchange is comprised of two different stock markets: the Main Market and the Alternative Investment Market (AIM) (Mitchie). The Main Market deals only with the well established organisations which are characteristic of sustainable performance and financial profitability. The listing requirements of the Main Market are very stringent and the listing fees are also high. Approximately 1,800 of the LSE's company listings trade on the Main Market, and the total market capitalization is over 3,500 billion. The Alternative Investment Market on the other hand trades with small-capitalizations, or new companies. These organizations show high potential to grow and sustain their profits. Over 1,060 companies list on this market, with a total capitalization of 37 billion. The LSE is completely electronic, but different shares are traded on different systems. Liquid shares are traded using the SETS automated system on an order driven basis. When the buy and sell price match the order is automatically executed. For securities that trade less regularly, the London Stock Exchange implements the SEAQ system, where market makers keep the shares liquid. These market makers are required to hold shares of a specific company and set the bid and ask prices, ensuring that there is always a market for the stock. The LSE also promotes the equity derivatives called EDX London, created in 2003. In 2004, EDX traded an average of 382,599 contracts per day.

4.2 FTSE 100 INDEX

The FTSE 100 index is an important index of the LSE. This is a composite index of 100 companies listed with the London Stock Exchange which shows the overall investment trend in the stock market. The returns of the the FTSE over the years is shown in the following table.

YEAR

PERCENT

1985

-10.5

1986

28.3

1987

0.1

1988

4.3

1989

25.8

1990

-12.5

1991

23.7

1992

8.7

1993

24.4

1994

-11.1

1995

21.5

1996

13.1

1997

25.4

1998

16.4

1999

6.2

2000

-6.5

2001

-14.7

2002

-23.4

2003

12.4

2004

8.7

2005

18.1

2006

9.7

2007

2.1

2008

-30.9

2009

22.1

During November of 2010, the FTSE 100 had a return of 

-2.59%. The top ranked index during November was the Nikkei 225 Index, with a 

return of 7.98%. The median return for all stock market indexes during November was 0.07%. The average return for the indexes during the month was 0.35%. During the last year the FTSE 100 had a return of 7%. 

The S&P 600 Index was the index that provided maximum return 

of 26% to investors in the last 12 months. The average 

return for all stock market indexes over the year was 12%. Over the last five years, the FTSE provided a return of 0%. The Mexico IPC Index was the best performing index over the last 5 years. It provided a return of 121%. The worst performing index during that  period was the Nikkei 225 Index with a return of -30%.

Over the last 10 years, the FTSE 100 had a rank of 18 

with a return of -12%. The top ranked index during the period was the Mexico 

IPC Index, with a return of 520%. The returns of the FTSE 100 are shown in the following graph.

According to EIRIS Report named FTSE100 snapshot: Trends in ESG performance the following findings have been highlighted.

FTSE 100 companies are making good improvement on environmental, social and governance issues. However small companies are still in the nascent stage.

The greatest improvements have been in the environmental policy, human rights and supply chain management including logistics.

There has been less development in the areas of environmental disclosure.

A key driver for better corporate social responsibility has been responsible investment.

Other reasons for better performance include emphasis on regulation, more participation of different stakeholders, recognition of proactive management

There is scope for better implementation of these resources in the ambit of ESG policies.

4.3 NATIONAL ASSOCIATION OF SECURITIES DEALERS AUTOMATED QUOTATIONS SYSTEMS (NASDAQ)

The NASDAQ was established in 1971. This was founded by a group of dealers called the National Association of Securities Dealers (NASD). The ownership and control of NASDAQ is with the NASDAQ OMX Group. This groups manages and operates the NASDAQ stock exchange situated in the New York City. The company also manages stock exchanges in Europe. It enjoys stakes also in the Dubai Stock Exchange. The division between the NASDAQ National Market and the NASDAQ Small-Cap Market developed from 1982 to 1986. This helped the clear distinction between the large listed company and the small listed companies. The NASDAQ was seen as a competitor of the NYSE. In 1994 the NASDAQ outperformed the NYSE in terms of annual turnover. The NASDAQ-AMEX Market Group was formed in 1988 with the amalgamation of NASDAQ and the American Stock Exchange.

The NASDAQ is as an electronic exchange. It does not have any physical trading floor. All the trades are completed through the electronic media and telecommunication. The brokers buy and sell stocks through a market maker rather than from each other. The market maker helps in the proper maintenance of the stock market. He controls the fluctuation in the prices of shares by holding a number of scrips. The brokers directly buy and sell shares from the market maker. The stock exchange built the NASDAQ Market Site in New York's Times Square to create a physical presence. The listing requirements of the NASDAQ are very stringent and strict. However the NASDAQ also has a market for smaller companies unable to meet these and other requirements, called the NASDAQ Small Caps Market. NASDAQ will move companies from one market to the other as their eligibility changes.

4.4 NASDAQ 100

The NASDAQ-100 Index Is composed of the 100 of the largest domestic and international non-financial securities listed on The Nasdaq Stock Market. This is based on market capitalization. The Index is a reflection of organization across major industry groups including technology, telecommunications, retail, wholesale trade and biotechnology. It does not include investment and financial companies. The highest all time monthly closes in the NASDAQ 100 Stock Index was 4398 in 

March, of 2000. It was a decline of 2281 points below the NASDAQ 100 all time high.  The NASDAQ 100 Index is down 16% over the last 10 years. The November 2010 close was 7.12 points lower than the October, 2010 close of  2124, resulting in a 0.34% decline for November, 2010. The 5 year market low for the NASDAQ 100 Stock Index was 1117 in February of 2009.

YEAR

RETURNS

1991

3479.66667

1992

-4520.25

1993

681.25

1994

2872.41667

1995

44.5

1996

478.25

1997

1730.08333

1998

-282.83333

1999

-5350.5

2000

-12783.917

2001

9923.66667

2002

7474.5

2003

1942.66667

2004

2558.54545

2005

1160.58333

2006

1481.08333

2007

219.583333

2008

1081.58333

2009

783.916667

All calculations are based on the monthly market close in the 

NASDAQ 100 Stock Index, excluding dividends.

This page provides a five year chart and a forecast for the NASDAQ 100.

Just one glance at our long term charts can provide 

tremendous insight into the historical trends of the financial markets. The table 

above presents historical data on the NASDAQ 100 Stock Index categorized by 

the months of the calendar. The trend of the NASDAQ 100 is shown in the following figure.

4.5 ARBITRAGE OPPORTUNITY IN THE DIFFERENT MARKETS

With the global financial services scenario having changed in the recent past, there is a growing drift of mergers and acquisitions which has taken place recently in numerous financial institutions. This has been particularly observed in the U.S.A and U.K where takeover activity has been most observed in the 1990s. In Risk Arbitrage, large excess returns have been documented in literature i.e. in previous studies. Two reasons can be attributed to this-

a) Transaction Costs and other practical limitations prevent investors from realizing these extraordinary returns.

b) Risk arbitrageurs receive a risk premium to compensate for the risk of deal failure.

The risk arbitrage premium is defined as the spread between the current market price and the price to be paid for the shares in the deal at the time of the tender offer announcement. (Johan and Markus (2003)) Another possible explanation for the extraordinary returns to risk as documented in previous studies is that they simply reflect the compensation for bearing extraordinary risk. The recent market corrections have led to increased competition and a vital reduction in the bid-ask spread. Because Nasdaq listed stocks trade in multiple trading venues, markets in these stocks are frequently locked or crossed during the trading day. A locked market occurs when the best bid price, across all markets, equals the best offer price in one or more of the markets. A crossed market occurs when the national best bid price is greater than the best offer price in one or more of the markets. While the recently adopted regulations discourages market participants from locking and crossing the overall market, locks and crosses are difficult to prevent in the current market environment (Roger 2002).

Crossed markets are particularly interesting to study because they appear to represent arbitrage opportunities. Our study examines whether or not this is the case. In order to do so, we first consulted with a National Securities Dealer that runs an arbitrage operation in order to profit from crossing prices. We were provided with a list of the firm’s ten most actively traded stocks for the year, as well as what it cost the firm to operate their proprietary trading desk. We then augmented these stocks with a sample of ten large market capitalization stocks.

It was found actively traded stocks are crossed approximately 0.5% of the trading day

in October 2003. The top 10 stocks traded by our firm were crossed less than the other 10 large cap Nasdaq stocks. However, crosses in the firm stocks were longer duration and had greater size available to trade. Crossed markets are often initiated by Nasdaq, they are more frequent when market share is less concentrated, and they are fleeting in nature. Crossing prices last for 18 three seconds on average, although median duration is only one second. The average crossed amount is usually one cent. However, we find adequate size (an average of about 2,000 shares on the offer side) was available for some traders to take advantage of market crosses (Garvey and Murphy 2005). Based on our simulated trading analysis, we conclude that institutional traders, who act fast and pay little in trading costs, should be able to profitably exploit the arbitrage opportunities presented by market crosses. Retail traders, who try to exploit crossed markets, are unlikely to cover their trading costs. Identifying arbitrage opportunities in other markets and examining market crosses in international cross-listed stocks are interesting topics for future research.

In the London Stock Exchange the cost of arbitrage could be controlled by controlling the transactions between jobbers and the non-members. It was seen that there was no difference between the members and the non-members in the transactions with the jobbers. The presence of arbitrage was benefiting the jobber as the risk involved was much less. But as the arbitrage went on the transaction length of buy/sell reduced drastically.

The fact that the deviations from true value are random implies, in a rough sense, that there is an equal chance that stocks are under or over valued at any point in time, and that these deviations are uncorrelated with any observable variable. For instance, in an efficient market, stocks with lower PE ratios should be no more or less likely to under value than stocks with high PE ratios. If the deviations of market price from true value are random, it follows that no group of investors or companies should be able to consistently find under or over valued stocks using any investment strategy.

There is an internal contradiction in claiming that there is no possibility of beating the market in an efficient market and then requiring profit-maximizing investors to constantly seek out ways of beating the market and thus making it efficient. If markets were, in fact, efficient, investors would stop looking for inefficiencies, which would lead to markets becoming inefficient again. It makes sense to think about an efficient market as a self-correcting mechanism, where inefficiencies appear at regular intervals. (Damodaran)

Modern financial markets quickly react so that prices reflect all available information. Any new information can often cause prices to rise or fall on the world's major stock exchanges to adjust appropriately within only minutes. Prices of individual securities, market sectors and style segments, and entire stock and bond markets are therefore always "correct" in the sense that they always reflect the collective beliefs of all investors taken together as a whole about their future prospects. One major consequence of the EMH is that unless an investor is just plain lucky, it is impossible to exploit the market to make an abnormal profit by using any information that the market already knows. The total market is always perfectly diversified. Other portfolios that deviate from the total-market portfolio are never "more diversified" than the total-market portfolio.

5. CONCLUSIONS

This paper examines the weak form efficiency in the London Stock Exchanges and NASDAQ which represent the majority of the equity market in U.S.A and U.K. The weak form of efficiency has been tested in both the markets using FTSE 100 index and NASDAQ 100 index. Different tests like ADF, PP etc have been used to find similar results. The results of these tests find that both the indices are not weak form efficient. These results support the common notion that the equity markets in most economies are not efficient and to some degree can also explain the less optimal allocation of portfolios into these markets. For future research, using a computationally more efficient model like generalized autoregressive conditional heteroskesdasticity (GARCH) could help to clear this.

Markets will discount information about the stocks as they perceive the news to be. Since there is no one way to standardise this methods markets continue to be inefficient. Most individuals that buy and sell securities do so under the assumption that the securities they are buying are worth more than the price that they are paying, while securities that they are selling are worth less than the selling price. But if markets fully reflect all information, then buying and selling securities in an attempt to outperform the market will effectively be a game of chance rather than skill.

The paradox is that if every investor believed a market was efficient, then the market would not be efficient because no one would analyze securities. In effect, efficient markets depend on market participants who believe the market is inefficient and trade securities in an attempt to outperform the market. Those that accept the EMH generally reason that the primary role of a portfolio manager consists of analyzing and investing appropriately based on an investor's tax considerations and risk profile. The role of the portfolio manager in an efficient market is to tailor a portfolio to those needs, rather than to beat the market.

Investors should follow a passive investment strategy, which makes no attempt to beat the market. This does not mean that there is no role for portfolio management. Returns can be optimized through diversification and asset allocation, and by minimization of investment costs and taxes. In addition, the portfolio manager must choose a portfolio that is geared toward the time horizon and risk profile of the investor. The appropriate mixture of securities may vary according to the age, goals, tax bracket, employment, and risk aversion of the investor.

The efficient market debate plays an important role in the decision between active and passive investing. Active managers argue that less efficient markets provide the opportunity for out performance by skilful managers. However, it’s important to realize that a majority of active managers in a given market will under perform the appropriate benchmark in the long run whether markets are or are not efficient. This is because active management is a zero-sum game in which the only way a participant can profit is for another less fortunate active participant to lose. However, when costs are added, even marginally successful active managers may under perform.

One of the most enduring anomalies documented in the finance literature is the empirical observation that stock prices appear to respond to earnings for about a year after they are announced. Prices of companies experiencing positive earnings surprises tend to drift upward, while prices of stocks experiencing negative earnings surprises tend to drift downward. Companies need to understand that while investing in efficient markets it is better to invest replicating indexed funds as they are highly insulated from the market shocks and volatility.

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