Research Topics: UK Stock Market FTSE 100 Index Papers

ECONOMETRIC ANALYSIS OF UK STOCK MARKET FTSE 100 INDEX

This section examined UK’s stock market using the FTSE 100 index. This issue has generated substantial debate in recent years and has significant implications for stock market efficiency. The study employs the regression-based tests of Fama and French (1988)[1] that focus on the autocorrelations of stock returns for increasing holding periods.   Previous studies have found predictability in return data, but most have relied on short-horizon data and found only a small degree of systematic change in prices (Lo and MacKinlay, 1988; French and Roll, 1986; O’Brien, 1987; Poterba and Summers, 1988; and Cochrane and Sbordone, 1988).[2] But stock prices may mean revert very slowly as Summers (1986)[3] argues; hence, predictability may be evident only at longer return horizons. The tests of Fama and French (1988)[4] explicitly capture Summers’ (1986)[5] insight. Their evidence, based on U.S. data for industry and decile price indexes, implies considerable predictability for the 1926 to 1985 period, especially for smaller firms. They find much less predictability, however, in the 1940 to 1985 subperiod. Because the present study covers the 1970 to 1989 period, the results are broadly consistent with those of Fama and French (1988).[6]   This section worked on three assumptions. Assumption 1 strengthens specifications by imposing a rate of convergence. This smoothness condition is typically imposed in spectral analysis (Lobato 1999).[7] This extension was recommended by a referee who suggested the case of a stationary long-memory series [X.sub.t] ([d.sub.X] [is less than] 1/2) observed with an added short-memory noise component for which [Beta] [is less than or equal to] 2[d.sub.X] [is less than] 1. This case has empirical relevance because it happens in some long-memory stochastic volatility models. In addition, in certain bivariate models of volume and volatility, the short-memory component could affect the long-memory components of both volume or volatility (leverage effect) implying that [Beta] [is less than] 1.   Assumption 2 establishes that the process is linear with finite fourth moment. The assumption of linear fourth-order stationary processes has also been employed in the parametric literature (Giraitis and Surgailis 1990; Hosoya 1997).[8] Notice that the restriction of constant conditional innovations variances could be relaxed by assuming boundedness of the eighth moment as shown by Robinson and Henry (1999).[9] Assumption 3 is a regularity condition similar to the ones imposed in the parametric case.   Assumption 3 [which is assumption A4' of Robinson (1995 cited in Robinson and Henry, 1999)[10] for the p = 1 case] imposes an upper bound in the rate of increase of m with n, necessary to control the bias from high frequencies. Notice that this upper bound is especially restrictive when [Beta] is small, suggesting that when the long-memory series is observed with an added noise or when the leverage effect is important, the chosen m should be smaller. It also imposes a mild lower rate for m when tapering is applied.   Fama and French (1988)[11] develop their test from a simple model of stock price behavior. The log of a stock’s price, p(t), is the sum of a random walk component, q(t), and a slowly decaying stationary component, z(t) = az(t-1) + e(t), where e(t) is white noise and a is close to, but less than, unity.   A test for the presence of stationary components in p(t) can be based on the estimated pattern of the |Beta~(T)s for increasingly large T. If a stationary component exists, one expects to see negative values for |Beta~(T) that become significantly different from zero at some horizon. The estimates of |Beta~(T) also provide insight into the economic (as opposed to just statistical) importance of the stationary component. From equation (2) and equation (3), the ratio of the variance of |z(t+T) – z(t)~ to the variance of r(t, t+T) equals 2|Beta~(T) when T is large. That is, the estimated |Beta~(T)s can be used to measure the percent of total return variance due to the stationary component, z(t).   The empirical analysis relied on the UK FTSE 100 index. Included stocks comprise a representative sample of large, medium, and small capitalization companies from each local market and are meant to replicate the industry composition of each market. The UK FTSE index attempt to include 60 percent of the total capitalization of each market. Stocks of nondomiciled companies and investment funds are excluded from the indexes, and companies with restricted float due to dominant shareholders or cross-ownership also are avoided. The MSCI indexes are calculated using Laspeyeres’ concept of a weighted arithmetic average together with the concept of chain linking. The weights equal the number of shares outstanding. The data are monthly and cover the period 1969:12 to 1989:12. All of the indexes share an identical base, December 1969 = 100.   Returns are computed as the log difference of a country’s price index for the appropriate return horizon. The returns are valued in local currencies and are annualized. Both nominal and real returns are studied. Consumer price data needed for the calculation of real returns are from the International Monetary Fund’s International Financial Statistics (1985=100).   |Beta~(T) is estimated for each of UK’s stock market using ordinary least squares for return horizons ranging from one month to four years.(3) For horizons greater than one month, returns are constructed using overlapping price data. Thus, the standard errors of the estimated |Beta~(T)s are adjusted for the sample autocorrelation of overlapping residuals using the method of Hansen and Hodrick (1980). When faced with a time series that shows irregular growth, such as Series #2 analyzed earlier, the best strategy may not be to try to directly predict the level of the series at each period (i.e., the quantity Y(t)). Instead, it may be better to try to predict the change that occurs from one period to the next (i.e., the quantity Y(t)-Y(t-1)). In other words, it may be helpful to look at the first difference of the series, to see if a predictable pattern can be discerned there. For practical purposes, it is just as good to predict the next change as to predict the next level of the series, since the predicted change can always be added to the current level to yield a predicted level. Here’s a plot of the first difference of the irregular growth series from UK’s FTSE 100 index analysis: In other words, I predict that this period’s value will equal last period’s value plus a constant representing the average change between periods. This is the so-called “random walk” model: it assumes that, from one period to the next, the original time series merely takes a random “step” away from its last recorded position. (Think of an inebriated person who steps randomly to the left or right at the same time as he steps forward: the path he traces will be a random walk.) Notice that (a) the one-step forecasts within the sample merely “shadow” the observed data, lagging exactly one period behind, and (b) the long-term forecasts outside the sample follow a horizontal straight line anchored on the last observed value. The error measures and residual randomness tests for this model are very much superior to those of the linear trend model, as will be seen below. However, the horizontal appearance of the long-term forecasts is somewhat unsatisfactory if we believe that the upward trend observed in the past is genuine. The result showed that most of the price indexes studied have exhibited behavior during the past two decades inconsistent with mean reversion. With regard to nominal returns, only eight of 18 indexes have predominantly negative |Beta~(T)s across return horizons. Recall that mean reversion in prices implies a negative |Beta~(T), as temporarily low returns. Fama and French (1988),[12] by contrast, find no significant stationary component for either the equal-weighted or value-weighted NYSE market portfolio from 1941 to 1985. (Their findings for market portfolios, as opposed to their results for industry and   A growing literature has revealed that stock prices are predictable to some extent. This paper examined whether UK’s stock price indexes mean revert using regression-based tests developed by Fama and French (1988).[13] The results indicate that indexes in a majority of the countries are not stationary. For the indexes that do mean revert, stationary components account for an economically significant part of the total return variance. The mean reversion that does occur arises both from common and country-specific factors.   1 The text provides only an abbreviated description of the Fama/French model. Readers are directed to the original paper for a full explanation.   2 Because interest lies in examining the time-series properties of prices, the dividend yield is excluded from the return calculation.   3 Prior to estimation, Phillips-Perron unit root tests are performed on the log level and first difference of the log level of each index. The results show that log prices are integrated of order one, while first differences, which here constitute returns, are stationary.   Because stock-market trading volume is nonstationary, several detrending procedures for the volume data have been considered in the empirical finance literature. For instance, Gallant et al. (1992)[14] fitted a quadratic polynomial trend, Andersen (1996)[15] estimated nonparametrically the trend of the logarithm of the volume series using both equally and unequally weighted moving averages, and Bollerslev and Jubinski (1999)[16] fitted a linear trend. There is a lack of rigorous statistical theory, however, on the effects of detrending for the inference on the long-memory parameters of nonstationary long-memory processes. Hence, the determination of a detrending mechanism that would allow for inference on the long-memory parameter of stock-market volume is still an unsolved problem.   Moreover, because it has been repeatedly shown that a main feature of return volatility (for exchange-rate returns and for stock-market returns) is the presence of long memory (Ding, Granger, and Engle 1993; Bollerslev and Mikkelsen 1996; Baillie, Bollerslev, and Mikkelsen 1996; Lobato and Savin 1998),[17] it is of interest to test if stock-market volume exhibits long memory and, if this is the case, to test also if return volatility and volume share the same long-memory parameter d. These two implications were derived by Bollerslev and Jubinski (1999)[18]–see their equations (5) and (6)–but their testing procedure has two drawbacks. First, when applied to data linearly (or nonlinearly) detrended, the estimators of the long-memory parameters have unknown properties. Second, it is unknown a priori that the long-memory parameter of volume lies in the stationary region (d [is less than] .5), and in fact they reported some cases for which d [is greater than] .5. In this section, we use a tapering procedure to overcome both difficulties.   Modeling (conditional) variances has been one of the most important topics in the stochastic analysis of financial time series (Beran and Ocker, 2001).[19] These models are readily interpreted as autoregressive moving average (ARMA)- and autoregressive integrated moving average (ARIMA)-type models of the (conditional) variance (Bollerslev and Mikkelsen 1996).[20] That is, the dependence of the conditional variance on the past decays exponentially with increasing lag. Moreover, long-range dependence in the (conditional) variance of financial time series, in particular stock-market indexes, has recently attracted considerable attention in the literature (Ding, Granger, and Engle 1993; Crato and de Lima 1994; Bollerslev and Mikkelsen 1996; Ding and Granger 1996).[21]   In particular, Ding et al. (1993)[22] found substantially high correlation between absolute returns and power-transformed absolute returns of some stock-market indexes for long lags. Independently Baillie, Bollerslev, and Mikkelsen (1996)[23] arrived at similar results–namely, long memory in volatility series. Both studies appear to argue against short-range dependent specifications of the (conditional) variance based on squared return series. In practical applications, it is often very difficult to find the “right” model and, in particular, to decide whether a series is stationary or has a deterministic or stochastic trend or whether there may be long-range correlations. (In fact, often a combination of these may be present.)   Kevin Kelly, the author New Rules for the New Economy (Kelly, 1998, p.161)[24] predicted the following:   * Increasing returns. As the number of connections between people and things add up, the consequences of those connections multiply out even faster; so that the initial successes are not self-limiting, but self-feeding.   * Plentitude, [1] not scarcity. As manufacturing techniques perfect the art of making copies plentiful, value is created by abundance, rather than scarcity, inverting traditional business propositions.   * Follow the free. As resource scarcity gives way to abundance, generosity begets wealth. Following the free rehearses the inevitable fall of prices, and takes advantage of the only true scarcity: human attention.   * Opportunities before efficiencies. As fortunes are made by training machines to be ever more efficient, there is yet far greater wealth to be had by unleashing the inefficient discovery and creation of new opportunities.   One of the most remarkable features of the new economy has been the stock market capitalisations attached to recently established businesses (Kay, 2001).[25] In 2000 Gisco Systems, a leading producer of networking software and hardware, founded in 1986, acquired the highest market value of any company in the world, overtaking long-established businesses such as General Electric and Exxon Mobil.   In February 2000, Lastminute.com, a business established in April 1998, whose cumulative revenues were less than [pounds]1 million, was floated on the London Stock Exchange at a price which implied a market value of around [pounds]350 million. Fees to advisers and underwriters totalled [pounds]7.7 million and the company raised [pounds]70 million in cash. The purposes for which this money was to be used were largely unspecified and little of it appears to have been spent. Autonomy and Bookham Technologies – two companies which have never made a profit and whose 1999 revenues were [pounds]16.5 million and [pounds]3.5 million respectively – displaced the likes of Scottish and Newcastle Breweries ([pounds]3,300 million turnover, [pounds]400 million profit) and Thames Water ([pounds]1.4 million turnover, [pounds]380 million profit) from the FTSE index of the 100 leading UK companies.   Such valuations cannot be supported by conventional rules based on historic earnings, and this is an area in which the need for new approaches has been emphasized.   “In forecasting the performance of high-growth companies like Amazon, we must not be constrained by current performance. Instead of starting from the present – the usual practice of DCF valuations – we should start by thinking about what the industry and the company could look like when they evolve from today’s very high growth, unstable condition to a sustainable, moderate-growth state in the future; and then extrapolate back to current performance. The future growth state should be defined by metrics such as the ultimate penetration rate, average revenue per customer, and sustainable gross margins. Just as important as the characteristics of the industry and company in this future state is the point when it actually begins” (Desmet et al., 2000).   [1] Supra Fama and French (1988) [2] Supra Lo and MacKinlay; French, K.R., and Roll, R. (1986) Stock Return Variances: The Arrival of Information and the Reaction of Traders. Journal of Financial Economics, 17, pp. 5-26; O’Brien, J.M. (1987) Testing For Transitory Elements in Stock Prices. Mimeographed, Board of Governors of the Federal Reserve System; Supra Poterba and Summers; Cochrane, J.H. and Sbordone, A. (1988) Multivariate Estimates of the Permanent Components of GNP and Stock Prices. Journal of Economic Dynamics and Control, 12, pp. 255-296.   [3] Supra Summers [4] Supra Fama and French [5] Supra Summers [6] Supra Fama and French [7] Lobato, I. N. (1999) A Semiparametric Two Step Estimator in a Multivariate Long Memory Model. Journal of Econometrics, 90, 129-153.   [8] Giraitis, L., and Surgailis, D. (1990) A Central Limit Theorem for Quadratic Forms in Strongly Dependent Linear Variables and its Application to Asymptotic Normality of Whittle’s Estimate. Probability Theory and Related Fields, 86, 87-104; Hosoya, Y. (1997) A Limit Theory for Long-Range Dependence and Statistical Inference on Related Models. The Annals of Statistics, 25, 105-137.   [9] Robinson, P. M., and Henry, M. (1999) Long and Short Memory Conditional Heteroskedasticity in Estimating the Memory Parameter of Levels. Econometric Theory, 15, 299-336.   [10] Ibid [11] Supra Fama and French [12] Ibid [13] Ibid [14] Gallant, A. R., Rossi, P. E., and Tauchen, G. E. (1992), “Stock Prices and Volume,” Review of Financial Studies, 5, 199-242.   [15] Andersen, T. G. (1996), “Return Volatility and Trading Volume: An Information Flow Interpretation of Stochastic Volatility,” Journal of Finance 60, 169-204.   [16] Bollerslev, T., and Jubinski, D. (1999), “Equity Trading Volume a Volatility: Latent Information Arrivals and Common Long-run Dependencies,” Journal of Business & Economic Statistics, 17, 9-21. [17] Ding, Z., Granger, C. W. J., and Engle, R. F. (1993), “A Long Memory Property of Stock Market Returns and a New Model” Journal of Empirical; Bollerslev, T., and Mikkelsen, H. O. (1996), “Modeling and Pricing Long Memory in Stock Market Volatility,” Journal of Econometrics, 73, 151-184; Baillie, R. T., Bollerslev, T., and Mikkelsen, H. O. (1996), “Fractional Integrated Generalized Autoregressive Conditional Heteroskedasticit, Journal of Econometrics, 74, 3-30; Lobato, I. N., and Robinson, P. M. (1998), “A Nonparametric Test for I(0),” Review of Economic Studies, 65, 475-495.   [18] Supra Bollerslev and Jubinski [19] Beran, J. and Ocker, D. (2001) Volatility of Stock-Market Indexes–An Analysis Based on SEMIFAR Models. Journal of Business & Economic Statistics, Vol. 19.   [20] Bollerslev and Mikkelsen   [21] Supra Ding and Granger, 1993; Ding, Z., and Granger, C. W. J. (1996), “Modeling Volatility Persistence of Speculative Returns: A New Approach,” Journal of Econometrics, 73, 185-215; Supra Crato and de Lima; Supra Bollerslev and Mikkelsen.   [22] Ibid [23] Supra Baillie, Bollerslev, and Mikkelsen   [24] Kelly, K. (1998), New Rules for the New Economy: 10 Ways the Network Economy is Changing Everything, London, Fourth Estate.   [25] Kay, J. (2001). What became of the new economy? National Institute Economic Review.   Credit:ivythesis.typepad.com

ECONOMETRIC ANALYSIS OF UK STOCK MARKET FTSE 100 INDEX

This section examined UK’s stock market using the FTSE 100 index. This issue has generated substantial debate in recent years and ha...

INSIDER TRADING IN THE U.K. STOCK MARKET

Introduction Having one of the most advanced financial environments in the world, expectations to the UK stock market for efficient trading is huge.  There are several points covered in the research proposal that justifies confidence to the level of efficiency of the UK stock market which is best represented by the London Stock Exchange or LSE.  Thus, in studying the efficiency performance of LSE, we can identify potential threats and problems in the UK stock market in areas of capital allocation, corporate governance and financial stability.  This research has important contribution to the existing literature.  For example, the level of stock market efficiency in emerging economies is found to be similar to developed nations (Griffin, Kelly & Nardari, 2007).  If this is applicable to UK, then the differences in country characteristics such as rate of economic growth and political situation does not contribute in assuring the efficiency in UK stock market contrary to the findings of Cochran & Defina (1994).                 The key feature of this research is that it intends to prove the stock market efficiency of the LSE, UK’s best stock market representative, based on the most demanding criterion which is the efficient market hypothesis (EMH) under the strong-form.  The occurrence of strong-form EMH is cited as being an extreme case for any stock exchanges.   Under the strong-form EMH, abnormal returns that investors are pursuing will not happen even with technical and fundamental analysis of the situation.  More importantly, even with the guidance of an expert consultant, the stock exchange will still not award excess returns due to the fact that all pertinent information are already conceived by stock prices.  Therefore, without luck, investors cannot outperform the market return.  In this research, the role of corporate executives as insider traders, or simply insiders, will be studied.             There are doubts about the efficiency of UK stock market (Chen, Roll & Ross, 1986; Hon & Tonks, 2001; Gunsel & Cukur, 2007).  This research will verify these doubts whether have basis or otherwise.  The general rule is that pure luck is the main cause of the scenario wherein the market gained thirty percent for the year while an investor gained thirty five or forty percent for the same year.  The challenge to the strong-form EMH is that the rule’s applicability to insider trading.  Insiders have two distinct features that provide them with some abilities whose effects could achieve the outcome of luck in similar manner.  First, they have company information which is largely isolated to them which can be used as a guide in investing strategies.  Secondly, they have direct influence on the performance of the company which presents an accessible mechanism to direct corporate performance based on their investing strategies.              Insiders can be Board of Directors (BOD), corporate officers (e.g. CEO, COO and CIO), top-level managers and other executives.  Employees can acquire information and a certain degree of influence on corporate outcomes but is very limited compared to their relative intensity in the higher management.  Employees and their influence on stock price are exemplified in the studies of McConnell & Servaes (1990), Chang (1990) and Davidson & Worrell (1994).  In effect, insiders in this research are restricted to the preceding executive category.  Strong-form EMH is a very challenging characteristic that could be acquired by stock exchanges.  Insiders can use their abilities to outperform the market.  That is, they will buy the stock of their own company when they expect that its price will increase in the future at a point above the market.  Alternatively, they will sell their own stock when they expect that its price will decrease in the future at a point under the market performance.              There are other stances that insiders can outperform the market using what is called by Finnerty (1976) monopolistic information.  With immediate access to information, insiders have both better present (e.g. what the firm is doing) and future (e.g. what the firm is planning) information than outsiders.  With better information, there is a greater possibility of better decision-making and results.  For example, insiders will buy stocks if they are acquainted with the information that the firm’s future will have expansionary tendencies, improved efficiency and higher earnings.  Alternatively, they will sell stocks if they are acquainted with the information that the firm’s future will have slower growth, increased litigation and divestments.  In both scenarios, insiders have the firsthand information and speed in decision-making that makes them outperform the market.  They avoid loss immediately when they sell pending fall in price and exploit returns immediately when they buy pending rise in price.              Objectives of the Study The mission of the study is to prove that the UK stock market is efficient.  This mission will not be obtained with specific objectives in mind.  It will be observed that the following objectives will reflect how we will consider in construction the remaining parts of the research. 1.     To use the London Stock Exchange as the best representative to suggest efficiency conditions in the UK stock market, 2.     To focus the study to LSE-listed companies who comprise the major share of capitalization in the Exchange, 3.     To compare the return performance of insiders from these “elite” companies to the return performance of the market, 4.     To test whether the similarities or differences in returns and return behavior are significant, 5.     To discuss issues of corporate governance, stock market regulation and investor strategies based on the findings.                           Literature Review Tests for Market Efficiency There are at least two metrics to determine resemblance to an efficient market.  First, a test can be conducted though evaluation of the ability of the ongoing price to reflect market information.  Another, market price can be tested whether it is an unbiased proxy of the investment cost.  In these methods, there are three levels of market efficiency hypothesis (EMH) which are tested; namely, weak form, semi-strong form and strong form (Cao 2003).  Without abnormal returns awaiting investors, the weak form has the assumption that using historical or past prices and the method of forecasting are useless investment strategies because the current price already absorbs/ reflects these tools.  Without steady surplus returns awaiting investors, the semi-strong form has the assumption the same as the weak form together with the current price absorption of fundamental analysis.  This means that public information is useless basis for investment decisions.  Lastly, without the opportunity for abnormal returns even through insider strategies, the extreme case strong form has the assumption of both weak and semi-strong form with the private information being useless.   With regards to test methods, the presence of weak form EMH is evaluated by means of random walk theory where past data of the prices are necessary to carry the test.  Another method, return predictability, uses two variables such as interest rates and dividend yields to predict future returns.  Further, the performance of trading strategies is studied in terms of their ability or inability to present abnormal returns.  This can be applied by using filters (e.g. purchase a stock if the price rises to a pre-determined level) framed in a fixed period gap and in comparison with buy-hold approach.  In addition, transaction costs of strategy implementation can also provide a more conservative view of this particular test.  Event studies serve as the primary testing tool for semi-strong form EMH.  Compared to highly-statistical approaches as means of testing weak form, event study is rooted for the purpose of studying capital markets.  Specifically, it measures how public information about current and future performance of a certain firm affects its price performance.  Event studies are applied to analyze the price-impacts of stock splits, celebrity-led advertisements, discriminatory litigations, change in manufacturing practices, takeover bids, announcement of profits, modification of corporate name, and promotion to higher quality standards (Subrani and Walden 2001).  In practice, event studies are represented by models such as ARit = rit – (αi+βirmt) where ARit, rit and rmt stands for abnormal return, actual return and market return respectively.  The accumulated AR (CAR) will be framed in time horizons and can be contained by simply summing up ARit.  Afterwards, test for significance of CAR will be executed. For testing the occurrence of strong form EMH, there are two methods.  The first method requires exploration study to indentify an investment entity that can manage the funds with superiority.  However, this practice is one of the factors that contributed to the presumption that the strong form has very low probability to occur.  It is hard to achieve.  The second method is relatively less complicated.  It necessitates evaluation of insider behaviors like the rising trend of share price in the post-report when insiders are in purchase position and a falling trend when they are in selling stance.  In addition, there are also past studies that prove the ability of insiders to derive abnormal returns in reference to CAPM.  In the contrast, the efforts of outsiders to the company to use insiders’ decision as a trail are commonly ended with profit frustrations especially when accounting for transaction costs.                        Prior Studies on Stock Market Efficiency Griffin, Kelly & Nardari (2007) applied standard tests to measure the strength of weak and semi-strong form market efficiency in 56 countries.  In terms of the former, it is found that stock prices of larger companies are relatively more efficient than small companies.  Further, although unexpected, emerging markets perform at par with mature/ developed markets when it comes to market efficiency.  The weak form variables that are used to come-up with these findings are response of stock returns to market factors, the size of portfolio and past price data of companies.  Time horizons are classified as days and weeks.  For evaluating semi-strong form EMH, before and after conditions of income publicities are used.  It is found that both emerging and developed markets experienced abnormal returns.  This research concludes that there is no support to the belief that superior legal systems, regulatory framework and governance structures is not a significant determinant for market efficiency.                            From voluminous studies of economists over time, proof for market inefficiencies are established and became capital market jargons (Pierce & Maberly 2002).  This includes January effect, Monday effect, holiday effect and turn-of-the-month effect.  Due to this evidence, it is not surprising to observe friction between the supporters of EMH and the contenders to offer anti-thesis.  In the US, another anti-thesis to the existence of EMH in the US stock markets is created.  This is referred as the Halloween effect.  Supporters argued that the months from May to October are usually where underperformance of stock returns happen while from November to April are the reverse (i.e. above average performance).  The anti-thesis suggests investors to place their bets on S&P 500 index because it is value-weighted index from November to April while shifting strategy by investing in risk-free assets such as US Treasury Bills from May to October.                              Mean reversion is a term widely used in stock markets and defined as the behavior of stock prices to result in temporary highs and lows while an average price will reign in the long-run.  This concept is studied in Cochran & Defina (1994).  Using the stock indices from 18 countries, it is found that most of their samples exhibited non-stationary (i.e. do not mean revert) while those that do attributed their pattern greatly to variations in total return.  Specifically, they concluded that mean reversion that emerged on few country samples aroused from country characteristics.  This study is a huge setback from previous studies that argued the predictability of stock returns such as the works of Poterba & Summers (1988).  This study also has extended samples of at least two-decade period that reduced anomalies that arise from short-period data samples that pervious researchers overlooked.                       Supplementary Studies: Variables for the Three Forms of EMH There are studies to support the cognition that increased managerial and directorial stock ownership can result to increased corporate performance at various indicators (Kim, Lea and Francis 1988).  However, there are challengers of these studies which proved otherwise such as the works of Kesner (1987).  In general, earlier studies are only able to use accounting as a metric of corporate results while others did not adjust returns on stock investments for risk (e.g. using government bond as a benchmark).  The preceding studies on both sides are conclusive of this fault.  In a more reliable and timely research, however, the findings of the former won as Oswald & Jahera (1991) supported positive relationship between insider ownership and corporate outcomes.  The same relationship is present for employee stock ownership (McConnell & Servaes 1990).                      Chang (1990) showed that the announcement pertaining to introduction of employee stock options resulted in positive response from the stock market except if it is rumored as a defense against takeover bids.  In a refined study, Davidson & Worrell (1994) found that the response of the stock market has high positive relationship with public disclosures of employee stock ownership plans (ESOP) in the short-run.  On the other hand, there is no long-run enhancement of operating performance in the post-ESOP announcement.  For some samples, their second year post-ESOP announcement results even accounted for underperformance.  This condition is explained by the fact that most ESOP initiatives are means to address takeover anxieties.  Speculative investment would accept them as is and concretize actions by exploiting the benefits that a target company would be able to share profits into the market.  In a different view, with lucrative retirement potential seen in ESOP, unproductive managers may retire early and this cognition can signal future positive performance from the market perspective.    Previous Stock Market Findings on UK Stock Market In the study of Gunsel & Cukur (2007) using data from LSE listed companies from 1980-1993, it is found that macroeconomic factors significantly affects stock returns.  These factors include interest rates, risk premium, money supply, unanticipated inflation and exchange rate.  Although indications of arbitrage pricing theory (APT) are tested by previous researchers such as Chen, Roll & Ross (1986), this recent finding extended the effect of macroeconomic variables to stock markets such that degree of impact can vary from one industry to the other (e.g. one is positive while the other is negative).   Further, industry return and unexpected inflation do not have significant relationship indicating the presence of efficiency semi-strong form.           UK stock market is observed with Halloween effect for samples as far as 1694.  This finding concretized that there is a historical evidence of UK stock market inefficiency.  Aggravatingly, the cause of the anomaly is not found.  It is not the risks, relation of markets, etc.   In their research, Hon & Tonks (2001) proved that momentum strategies can provide significant returns if these strategies are executed in the UK.  Except for long-run periods, momentum effect is present in the stock market as investor holds the rule “buy winning portfolios and sell losing ones”.  Specifically, a momentum strategy is present only on the other half of the period that the research has studied (i.e. 1977-1996).  The earlier half (e.g. 1955-1976) did not post an optimal environment for such strategy since profit levels at this juncture is relatively low and also the less volatile market prior to 1976.  In this stance, the UK stock market may create a blemished weak-form EMH that challenges the finding of Worthington & Higgs (2003).        Conceptual Framework The Assurance of EMH for a Fair Game In 1965 work of Fama, the first definition of efficient market hypothesis (EMH) is revealed wherein the perceived benefits of new information on implicit value of an asset are diluted due to competition (Malkiel 1999). The information, thus, will be reflected in current prices in abrupt manner.  Given that new information is the main source of price changes of assets, the assumption of EMH would regard the new information phenomena avoiding bias while price adjustment is conducted very quickly.  Therefore, the promises of investment banks and fund managers who sell their specialty on selecting overvalued and undervalued stocks for the pleasure of investors have no basis at all.  The eminent situation is that before investors could rip the promised profit the targeted stock have already adjusted on the new information.             If investors and their respective fund managers can indeed use strategies to limit the assurance of EMH, this can be seen as very profitable ability.  That is, they are buying shares less than their true value while selling other shares more than their really worth.  On the contrary, the success of these abilities is limited by the number of stock analysts in competition.  As the entities working to find mispriced shares increase, the incentive from gaining new information becomes slimmer.  This is a scenario wherein the value of new information diminishes as several analysts are able to foresee them and the higher likelihood that these finding will be leaked into the market.  As a result, EMH protects the market from exploitation of several investors of excessive profit.  If any consolation is maintained in favor of the investors and analysts, this is to reward only very few of them from their efforts.             The theory on capital market, thus, is a good framework where EMH is valid.  This theory states that the expected return on stock investments is determined by its risks (Fama 1991).  Specifically, stock prices mirror streams of cash flows including the volatility, liquidity and insolvency tendencies of the source of such flows.  With these arguments, there are two findings.  First, new information are very unpredictable in the sense that valuable information is tedious while access to them is commonly determined by luck.  Second, movements in stock prices follow a random walk wherein forecasts and historical data will not be able to measure.  Bearing this in mind, the three forms of EMH earlier presented are conceptually enforced in stock exchange transactions.  Therefore, even with insider greater access to information, excessive returns will happened if inside information is subsequently mediated with luck.                   The requirement of luck in every stock price investment is caused by the absence of penalty clause in capital market investments (Bernstein 1990).  Capital markets are able to incur inefficient investing strategies without substantial loss and even hand potential gains although rarely.  In contrast, product markets do not have the same operating environment.  If a fast-food company does not choose strategies efficiently and decided to locate its new stores without regard to location characteristics (e.g. population and household income), there is a corresponding punishment such as low market share and unprofitable operations.  Therefore, capital markets rely more on luck rather than governance structures and management strategies.  This concept is attributed to the fact that the absence of punishment makes random strategy selection an indifferent action to the point of view of stock market analysts and participants.             Aside from the structure of the market being supportive to EMH, behavior of individuals particularly investors and investment managers can also provide similar EMH support (Shefrin & Statman 1985).  First, the aversion of loss of some individuals is gained from distorted decisions.  For example, an individual bought the share of a company at .00 each.  In the event of falling price for five consecutive years, the investor will only sell his/ her stock investment once the price climbed to .00.  This strategy, however, is sub-optimal as the person did not consider the time value of money, compensation to risks and also other transaction costs from the initial purchase and later selling.  Second, people are often caught from the illusion of knowledge which a phenomena similar to belief on law of small numbers.  That is, with only little sample information, the person fells that his observation is reliable to make a decision.  As illustration, even if an investment analyst is able to outperform the market five consecutive times, it is not statistically possible that the sixth and later performance will be the same.             With EMH, the investing community affords two ways of outperforming the market; namely, pure luck or irrational decisions which eventually lead to pure luck.  Accepting EMH that requires subsequent disregard from the opportunities to profit from mispriced stock prices can be stimulated by reminding the concept of invisible hand.  In economics, invisible hand argues that individual benefits arising from an economic transaction will not be obvious and would only be classified as normal events because numerous individuals are doing the same thing.  Thus, self-interests caused each of them to take advantage of inefficiencies in the economy.  As increased competition persists, the tendency of initial participants is to exit the inefficient market because costs already outweigh the benefits.  For prospective participants, engaging in similar activity will be less attractive that contributes to decreasing benefits attached to the market.  For the remaining participants, they must confront competition and consequent costs with the belief that luck will struck them and that they have sufficient expertise to continue reliable investing strategies.    The Behavior, Abilities and the Environment of Insiders Agency problem generally occurs when managers use their position to derive personal benefits which in turn reduce and impede the wealth-creation objectives of shareholders (Bebchuk & Fried 2003).  There are several situations where the symptoms of agency problem is observed such as building an empire, inefficient investment programs and establishing platforms to prevent entrenchment of poorly performing managers.  The Board-of-Directors are traditionally believed to minimize the agency problems as they are working in favor of shareholders.  Specifically, they are the most active monitoring mechanism of shareholders against implications of agency problem.  However, BOD also has their personal interest as well particularly to have an extended directorship position.  There are various incentives of being so such as an attractive salary, prestige and important business connections.              The above situation increases the probability that collusion in implicit terms between CEOs and directors may accrue.  This could be observed in give-and-take relationship.  For example, the CEO has influence over nomination of directors while BOD has influence on designing CEO pay.  Returning the favor from one side to the other is very eminent.  Also, even if BOD and CEO are using an efficient and reliable bargaining platform, outsider criticisms would lead to undermining this feat.  This is where the limits of optimal contracting are emphasized and the managerial power perspective to executive pay introduced (Bebchuk & Fried 2003).  Due to several restrictions to executives to prevent a burgeoning agency problem, CEO and indeed directors and other line managers are using camouflage to overcome these restraints.                        The tendency of managerial camouflage is a component of managerial opportunism.  Managerial opportunism, like EMH, is a hypothesis that requires tests to ascertain validity.  It states that managers are rational and that their superior information compared to the public leads them to a number of opportunities (Chalmers, Dann & Hanford 2001).  Opportunism is taken as a negative condition because the primary role of managers as agents of shareholders is overtaken by managerial personal interests.  Shareholders have several means to minimize the risks of an agency problem such as increasing monitoring systems to reduce asymmetrical information held by managers.  Most of the time, however, costs of increased monitoring systems outweigh benefits.  With this practice, it is apparent that managers have indeed superior information in both ends; namely, superior information against the public and superior information against the shareholders.             Individuals defined under EMH are bounded and biased rationality and that they participating in an intense capital market competition.  On the contrary, insiders are competing based on almost complete information about their company stock and that competition with regards to their corporate stock are controllable by them.  In effect, insiders view the risks of their own corporate stocks as totality where avoidance of risk is not only accessible but also open to maneuvering (Smith, 2000).  In the hedge fund sector, investment managers are referred as absolute return managers which are characterized by several attributes.  They undercut the importance of long-term returns in favor of short-term ones, they construct market trends rather than following them and they trade without reference to benchmarks.  In similar fashion, insiders can also have their own niche where their superior qualities can be highlighted.              In some instances, insiders can reduce the restrictions of excessive returns imposed by EMH in an open market.  In the case of Enron scandal, financial statements are poorly disclosed and largely lacking in major components (Thomas 2002).  Before the scandal erupted, off-balance sheet (OBS) entities are tapped in order for the firm to have higher rating from rating agencies.  Although OBS is currently regulated, the past management of Enron had used OBS to increase some important performance indicator.  In effect, insiders are able to make the stock price of Enron excessively high.  Eventually, the market unveiled the impacts of price escalation without basis of performance.  That is, major leaders of Enron colluded and freeze corporate control in order to satisfy their personal interests.  In this case, it is not hard to assume that the reason why managers acted illicitly is that they have substantial stock holdings that waited for a maximum price and right away rip-off profits if it emerges.                The ability of insiders to trade their own stocks legally provides them with the incentive to earn while the disincentive to cost of investors who do not have the same level of information.  Increased oversight such as the recently enforced Sarbanes Oxley Act is able not only to reduce the excessive returns from information asymmetry but more importantly avoid corporate scandals.  However, several studies about persistent outperformance of insiders against the market returns continuous to flourish.  When managers or directors become significant shareholders, the motivation to use such position to obtain personal interests rises.  Conflict of interest would persist as the former agents (i.e. CEO and managers) and monitoring platform (i.e. directors) are accomplishing their interests rather the interests of their principals.  In general, the agency rule holds that shareholders are naturally risk-takers while the managers act as agents to help the former achieve their goals (Jensen & Meckling 1976).              There are two main factors how insiders can outperform the market thus rejecting the presence of EMH in strong-form.  First is the information factor and the other is the influence factor.  Accepting that capital market theory holds, insiders can have excessive returns simply because they have inside knowledge of how risky is the investment in their own corporate securities.  Also, accepting that insiders are gamblers wherein their initial expected return is negative, they can influence the outcome through corporate control which makes them part of the casino administrators.  In analogy, insiders are able to take part in the role of market trend initiators which is able to upgrade their initial position with regards to expected return as positive.  Simply put, they are not just merely gamblers who are inclined to luck to be successful in outperforming the market.  Indeed, their information and influence have functional merits in insider trading.                          Hypothesis From the conceptual framework above, there are three primary hypotheses that could be tested.  The first two hypotheses are derived from the functional merits of insider information and corporate influence/ control while the third hypothesis wants to test whether the returns are persistent.  1.     When insider buys the stocks of its own company, it will be able to outperform future stock price performance. 2.     When insider sells the stocks of its own company, it will be able to underperform the future stock price performance. 3.     The above hypotheses will persist in both short-run and long-run.    Methodology The sample is selected listed companies whose shares are traded in the London Stock Exchange (LSE).  There is an important requirement before an LSE firm will be qualified for random sampling.  It must belong to the FTSE 100 which is the list of top-100 firms in terms of market capitalization.  This is a crucial criterion in sample selection since it serves to simplify the study by taking firms who have substantial interest in the stock exchange.  FTSE 100 comprises at least 80% of the capitalization of LSE which is to some extent is a practical yet major entities that take part the most number of LSE operational transactions.  As the volume and the value of FTSE 100 stocks are relatively larger than the entire market, there is a larger opportunity and motivation for insider trading.  Also, as the component firms under FTSE 100 are well-known and established global companies, information and influence gaps between insiders and common investors are substantial success factors.             Random sampling will be applied to FTSE 100 with the primary purpose of selecting thirty (30) samples randomly.  This number is a statistical minimum of 30%.  The reason of this selection is that there are a number of FTSE 100 listed firms that do not have insider trading activities.  Reducing the samples to 30 firms is a key to avoid for being bias on how to select those firms that have data in insider trading.  A scientific calculator with random selection capability will be used to select 30 samples from FTSE 100.  The list of FTSE 100 will be encoded to excel spreadsheet alphabetically and will incur the row number of the spreadsheet.  Once the scientific calculator is manipulated to select random numbers from 1-100, the firm that corresponds to scientific calculator computation will comprise the 30 samples.             Once the 30 samples are determined, Yahoo! Finance Website will be used to gather the needed data.  There are two main data that must be extracted in the webpage.  First is the incidence of insider trading which are organized in chronological order starting from the latest incident extending as far as incidences recorded in the year 2000.  The second data is the stock price which corresponds to the date when insider transaction (e.g. buy and sell own corporate shares) takes place.  In this respect, the one-month, 6-month and 12-month stock price performance is also collected.  The preceding requirement will address stock price performance and its evaluation of how insider trading gains/ loss after those periods.  Webpage data will be gathered manually using the Internet and the excel spreadsheet.             In analyzing the data, the functions used in excel spreadsheet will be used.  The means and standard deviation of stock prices will be computed.  The percentage change in the stock price will be determined using the formula: (Period Price – Insider Trading Price)/ Insider Trading Price and the value is multiplied with 100.  As this will result to a single-average number, comparison to performance of a market index would be probable.  FTSE 100 and FTSE All Share indices will be used to compare if insider trading in FTSE 100 results to underperformance or outperformance against the market.  The presentation involves the use of percentages, bar charts and line graphs to reflect how the returns of insider trading in FTSE 100 perform against the market index.                             Results and Findings There are a total of thirty (30) firm samples derived from the FTSE 100.  There is empty firm observation totaling five (5) randomly selected samples since they do not have records of insider trading data from the source.  Firm sample observation with regards to insider trading is not similar as there are samples that exhibited high insider trading while there are also samples that have few insider trading.  The requirement in order for the insider trading to be included in the data sample is that the trading must happened within March 5, 2006 and March 5, 2007.  This enabled to have a reference point in post-12 month’s stock price performance up to March 5, 2008.  There are a total of one hundred sixty one (161) firm observations which are classified as a buying and selling transaction having one hundred thirty one (131) stances and thirty (30) stances respectively.  After the transaction date, the 1-month, 6-month and 12-month performance of the stocks are recorded.  This aided in the computation of the percentage change of the succeeding stock prices to the initial purchase/ trading of stock by insiders.             Table 1 Table 1 shows the mean of price performance of stocks that are bought and sold by insiders compared to the performance of FTSE 100 Index.  There are three dots in the line representing the 1-month, 6-month and 12-month percentage change of stock price after its acquisition or vending by the insider.  The FTSE index represents the performance of the FTSE 100 stock prices in aggregate.  As observed, insiders who bought the stocks underperform the market (i.e. FTSE 100) after the first month but outperformed the market in the sixth and twelfth month.  On the other hand, those who sold their stocks outperformed the market in the first and twelfth month but underperformed it in the sixth month.  It should be noted that outperforming (underperforming) the market in the buying scenario requires the purchased stock to have a higher (lower) percentage increase change in each period.  The reverse is true for outperforming (underperforming) the market in the selling scenario wherein the sold stock must have a lower (higher) percentage change in each period.     Table 2 Tables 2 shows the standard deviation of buy and sell transactions.  It is an important figure as it shows that the volatility of stock price changes for the samples are proportionally similar in both buying and selling stances.  In effect, this could mean that the gains and losses of insiders have a definable boundary which translates to the possibility that they have a safety level when they are investing in their own company stock.  With minimal volatility, say 10% to 50%, insiders have the advantage in assessing their risks compared to other investors that do not have the same safety level.  Within the safety level, insiders could use their capabilities and financial depth to maneuver their investments into gains and avoid excessive risks. Table 3 Table 3 shows a more reliable presentation than Table 1 because Table 3 is computed based on the median.  This indicates that the effect of extreme values are controlled which preserves the representativeness of the computed data.  In the 1-month period, buying insiders underperform the market but beat the market in both 6-month and 12-month period.  As observed, however, Table 1 presents insiders as having a continuously increasing gains but Table 2 undermines this finding.  For the selling insiders, they only outperformed the market after the first month and eventually unperformed it.  Again, compared to Table 1, Table re-evaluated the accuracy of the computed data.  It is in conclusion, therefore, that the 12-month period caused many insiders to loose money.  This event is explicit in the 12-month median price change of selling insiders and implicit in the 12-month median price change of the buying insiders (e.g. due to sudden decrease in price).        Table 4 The number of outperformers and underperformers for buying insiders are presented in Table 4.  After the first month, there are forty three (43) outperformers and eighty eight (88) underperformers.  In the sixth and twelfth months, there are eighty (80) and seventy-nine (79) outperformers and fifty one (51) and fifty two (52) underperformers respectively.  Generally, the number of short-term winners is smaller compared to the number of long-term winners.  This shows that buying insiders who have a long-term investment view may found success in purchasing their own stocks.  On the contrary, it should be noted that the short-term investors can also learn from them.  Table 5 The number of outperformers and underperformers for selling insiders are presented in Table 5.  Relative to Table 4, Table 5 seems a reverse scenario wherein there are a greater number of insiders who ripped money from a short-run perspective.  After the first month, there are twenty (20) outperformers and ten (10) underperformers.  In the sixth and twelfth months, there are thirteen (13) and twelve (12) outperformers and seventeen (17) and eighteen (18) underperformers respectively.  Generally, the number of long-term winners is smaller compared to the number of short-term winners.  This shows that selling insiders who have a short-term investment view may found success in selling their own stocks.    Conclusion Insider trading has been receiving continuous criticism in the investment world simply because insiders are generally agents of their own companies.  As agents, it is the responsibility and benefit of an insider to influence the strategic direction of the firm, its operating decisions and to be in proximity with information whether past, present or future.  There are studies that supports efficient market hypothesis in several stock exchanges but most of them are finding the weak and semi-strong form EMH.  This tendency is apparent because the strong-form EMH includes historical data, public and private information as indifferent variables to obtain excess returns.  The third variable which is private information seemingly is the force that is difficult to deal with.  The UK Stock market has one of the largest and most modern financial environments in the world.  In effect, this study is conducted to measure how this market could be influenced by insiders using London Stock Market and FTSE 100 as sample.             Using systematic methods, it is found that the when an insider bought a stock there is a tendency that he/ she can outperform the market in the long-term.  There are several factors that disable the chance of buying insiders to earn substantially in the short-run which is largely affected by their “hiding-positions” to prevent speculation that they manipulating or exploiting their role in the company.  In effect, to avoid criticisms, they will use their information as well as influence capabilities to reach their vested interests beyond the scope of public’s disapproval.  This means that the public would allow insiders to rip money as long as the process is forgettable and not instant.  If this is the case, buying insiders must send the message to the public that they are doing their job and will only rip-off benefits when the period of their effort elapse and positive outcomes emerge.            For the selling insider, there is minimal chance of long-run incentive because stock prices tended to rise in the long-run making the selling stance sub-optimal.  However, the selling insider can benefit from a short-term investment.  There are several factors affecting this tendency but one of the most obvious is that insiders that tend to sell their stocks strategically into the future can be dubbed as “not-working”.  This means that the insider who is part of the management and directorship team is liable to shareholders and the public of not doing their best to support their company.  Having a long-term view and underlying speculation from the selling insider that the company would be weaker in the future is bad for the business both in capital and product markets.  This is the reason that many have lost when they are selling for the long-term.  These reasons also support the fact that there are relatively small number of insiders who sold their stock compared to those who bought them.   Recommendation For the stock price, an increase in expected dollar earnings of a firm with unchanged risk will increase its value while an increase in expected dollar earnings with lower risks will significantly increase its value (Bolten 2000 p. 4-10).  Due to this scenario, higher-ranked common stocks are more preferred than lower-ranked stocks and the latter tends to be sold in favor of the former.  The former is defined as those stocks with highest expected rate of return for the risk or the lowest risk for an expected rate of return.  As earnings per share (EPS) indicates the dollar return for every stock an investor purchased, it is one of the most items in the firm’s financial information that receives highest interests.  For its part, the firm should solidify its claim of having high-ranked stocks to be able to compete for the limited available capital in the economy.  As debt financing is more risky than equity financing (since the firm is obligated to pay debts even minimal or no profits is obtained), this task of the firm should be convincing.                       Although corporate earnings have positive relationship with stock price (Bolten 2000 p. 4-10) (that is, the greater earnings are, the higher stock price will be), external forces both political and economic in nature would barred the firm from optimal realization of such relationship.  In effect, being inclined or parallel with economic indicators is as crucial as internally generated earnings.  One indication of this is that expected earnings should exceed interest rate of government bond to be able to be competitive.  Another is that it should flow consistently with changes in interest rate, inflation and economic activity.  This will make the firm’s investors to avoid unguided speculation when making investment decisions.  It is a well-known dogma that no participant in a stock exchange can control the entire trend of stocks and so the consensus counts.  And where will a rational firm/ investor/ government base their decisions?  Of course, on what is really happening to the economy with small number of them speculating for short-term gains, that is, a mere buy and sell strategy.        Too much inflation (hyperinflation) has negative effect on stock price which can either be stimulated by pull and/or push consumer/ producer effects.  To dispute this, common stocks are viewed as inflation hedgers (Bolten 2000 p. 56) as they can easily maneuver the cost of firm’s goods to follow inflation in the cost of production.  Generally, the adverse inflationary effects on stock price is rather of short-term basis while the long-run inflationary functions of common stocks can be derived by an investor who is willing to place his investment longer in the hands of the firm.      There are at least three types of traders/ investors in the stock market (Reuters 1964).  This will serve as a guide for every trading/ investment strategy.  For portfolio managers, one profitable trading strategy especially for portfolio managers is top-down stock strategy wherein the economic and market environments are analyzed before making a decision.  Bottom-up approach emphasizes financial analysis of a firm’s data including comparison of stock performance against its competitors.  For technical traders, there is also a technique to forecasts stock price movements by using previous charts to identify trends and there signals of actually happening including their impact.  Hedging and arbitraging strategies are also used to aid in analysis.  For sales traders, another is the strategy that aims to determine the best price by having knowledge of trading partners and relative prices to identify who is liquid in the market and exploit such identification.   Bibliography Bolten, S 2000, Stock Market Cycles: A Practical Explanation, Quorum Books, Westport, CT.   Cao, Y. (2003). Testing China Stock Market Efficiency and the Evidence of “Alpha Plus” Strategy. Cass Business School, Dissertation.    Greis, J. & Bacon, F. (2007). Stock Split Announcements: A Test of Market Efficiency, Proceedings of ASBBS [Internet]. Cited on 26 December 2007. Available at <http://www.asbbs.org/proceedings/PDF/G/Greis.pdf>   London Stock Exchange (LSE) Website (2007). Cited March 6, 2008 in <http://www.londonstockexchange.com>   McKinley, K. (1999), “Stock Market Efficiency and Insider Trading”. Issues in Political Economy, vol. 8 [Internet]. Cited on 26 December 2007. Available in <http://org.elon.edu/ipe/mckinley.pdf>   PcGive Vendor (2007), Cited March 7, 2008 in < http://www.oxmetrics.net/>   Raines, J. & Leathers, C. (2000). Economists and the Stock Market: Speculative Theories of Stock Market Fluctuations, Edward Elgar, Cheltenham, UK.   Reuters; 1964; viewed March 13, 2008; www.reuters.com   SPSS Vendor (2007). Cited March 6, 2008 in <www.spss.com>   Subramani, M. & Walden, E. (2001). “The Impact of E-Commerce Announcements on the Market Value of Firms,” Information System Research vol. 12, no. 2, pp. 135-154.   Yahoo! Finance Website (2007). Cited March 10, 2008 in <http://finance.yahoo.com/>   Malkiel, B., , A Random Walk Down Wall Street, 7th ed., W.W. Norton & Co., New York, N.Y, 1999.   Fama, E.F., “Efficient Capital Markets: II,” Journal of Finance (December 1991).   Shefrin, H. and M. Statman, 1985, The disposition to sell winners too early and ride losers too long: theory and evidence, The Journal of Finance, 40, July, 777–790.   Bernstein, P., 1990, Of crap, black jack, and theories of finance, The Journal of Portfolio Management, 17, Fall, 1.   Executive Compensation as an Agency Problem Lucian Arye Bebchuk and Jesse M. Fried Journal of Economic Perspectives—Volume 17, Number 3—Summer 2003—Pages 71–92   Chalmers, J., Dann, L. & Hanford, J. 2001, Most of the time, however, costs of monitoring systems outweigh benefits. Most of the time, however, costs of monitoring systems outweigh benefits.    Smith, D 2000, The Measurement Nightmare: How the Theory of Constraints Can Resolve Conflicting Strategies, Polices, and Measures, Boca Raton, FL: St. Lucie Press.   Thomas, C 2002, “The Rise and Fall of Enron; When a Company Looks Too Good to Be True, It Usually Is”, Journal of Accountancy, vol. 193, no. 4, pp. 41+.   Jensen, M & Meckling, W 1976, “Theory of the Firm: Managerial Behavior, Agency Costs, and Ownership Structure”, Journal of Financial Economics, vol. 3, pp. 305-360.                                            Credit:ivythesis.typepad.com

INSIDER TRADING IN THE U.K. STOCK MARKET

Introduction Having one of the most advanced financial environments in the world, expectations to the UK stock market for efficient tra...