Market Efficiency and Data

Oil Nationalisation and Managerial Disclosure: The Case of Anglo-Iranian Oil Company, 1933-1951

Chapter 5: The AIOC’s Stock Market reaction to nationalisation: Event Analysis and empirical results


As previously mentioned, the event should be unanticipated and the magnitude of abnormal performance is consistent with market efficiency since it measures the impact of the event on the wealth of the firm‟s shareholders[673]. Toms[674] argued that testing for market efficiency is an approach that allows the investigator to look behind technical conditions for the reasons why accounting disclosures might or might not have information content. The major role of the capital market is allocation of ownership of the economy‟s capital stock. The ideal is a market in which firms can make production-investment decisions, and investors can choose among the securities that represent ownership of firms‟ activities under the assumption that security prices at any time fully reflect all available information[675]. If information fails to be quickly and fully reflected in the stock market prices then the stock market is said to be inefficient because those who had privately gained access to such information can benefit by anticipating the course of such prices. Hence, the lack of efficiency in stock markets does not allow price mechanisms to work correctly. Fama[676] determined the conditions at which the capital market is efficient. First of all, there should be no transaction costs in trading securities. Second, all available information should be available, without cost, to all market participants. Finally, all agree on the implications of current information for the current price and distributions of future prices of each security. Hence, in such a market, the stock prices fully reflect available information.

The Efficient Market Hypothesis (EMH) assumes that the stock prices adjust
rapidly to the arrival of new information, and consequently, current prices fully reflect all available information and should follow a random walk process.[677]This means that stock returns are independently and identically distributed (IID), and therefore future price changes cannot be forecasted from historical price changes. Fama[678] formalized the theoretical and empirical evidence on efficient market hypothesis and divided it into three levels. First, the weak-form EMH, states that current stock prices fully reflect all historical market information such as prices, trading volumes, and any market-oriented information. Second, the semi-strong form EMH asserts that prices fully reflect not only the historical information but also all public information including non-market information, such as earning and dividend announcements, economic and political news. Finally, the strong-form EMH contends that stock prices reflect all information from historical, public, and private sources, so that no one investor can realize abnormal rates of return. To sum up, the categorization of the tests into weak, semi-strong, and strong form will help in testing the null hypothesis and determining the level of information at which the hypothesis breaks down. Stock market efficiency is an essential component of the performance of capital markets and their contribution to the development of a country‟s economy. The EMH has significant implications for both investors and authorities. For instance, if the stock market is efficient, the prices will represent the correct values of the stocks and in turn this will serve in a way that benefits both the individual investors and the country‟s economy as well. The Random Walk Model (RWM) is one of the mathematical models that assume that consecutive price changes are independent of identically distributed random variables so that future price changes cannot be predicted from historical price changes. A number of statistical tests have been used in the literature to examine the validity of weak-form EMH and the RWM. Autocorrelation tests are the most popular ones, so this study employs serial correlation to test the statistical independence between rates of return. Serial correlation is a parametric test assuming normality of the stock price time series and hence measures the association between two elements of returns time series, separated by a fixed number of time periods. Fama[679] explained that tests enrich our knowledge of the behaviour of returns across securities and through time and that stock index returns may show positive autocorrelation if some of the securities in the index trade infrequently.[680]
Statistically, the absences of statistical significance in autocorrelations tests indicate that the market is efficient at weak-level which implies that the market prices follow a random walk. Thus, the RWM has some testable implications for the weak-form EMH. To test for weak form efficiency, the study employs the random walk model and serial correlation (or autocorrelation) tests to measure the correlation coefficient between a series of returns and lagged returns in the same series. An autocorrelation is the slope in a regression of the current return on a past return. A significant positive serial correlation implies that a trend exists in the series, whereas a negative serial correlation indicates the existence of a reversal in price movements. A return series that is random will have a zero serial correlation coefficient. The beta coefficient from the following regression equation measures the serial correlation of stock i with a lag of K periods:

represents random error, and k represents different time lags. The serial correlation tests assume normal distribution for the stock price changes (or returns). The independence of increments implies not only that increments are uncorrelated, but that any nonlinear functions of the increments are uncorrelated. Changes in stock price are used as the dependent variable in linear regression while one lag of change in stock price is the independent variables. Semi-strong form tests of efficient market models are concerned with whether
current prices “fully reflect” all publicly available information. The test is concerned with the adjustment of security prices to one kind of information generating event (e.g. publication of AIOC annual reports on 16th November 1951 and announcement of nationalisation on 30th April 1951). Hence, the test brings supporting evidence for the impact of the release of information on the current stock prices.

The study will focus on the AIOC return index and the daily security return index
for 30 firms in the FT30 Industrial Index over the period from May 1950 to May 1951. This period was chosen for two reasons. Firstly, May 1950 was the date of the nationalisation of the AIOC so this period covers the influential events leading up to nationalisation and ends with the event itself on 1st May 1951. Secondly, this period is essential because it assists in defining the control period which is needed for undertaking the event study methodology. We need to bear in mind that the market price during the control period was before any nationalisation would have taken place. The process of data collection involving the AIOC index and FT30 index will be explained thoroughly in this section. The daily prices of the AIOC employed in this event study are generally “closing” prices which represent the prices at which the last transaction occurred during the trading day. The company‟s stock price quoted on the stock exchange is assumed to present the “fair” value of the stock and when the stock exchange values all the stocks fairly then it is considered as an “efficient market”. The dividends paid are assumed to convey important information to the market concerning the management‟s policy and dividend-paying potential. In view of this expectation, the AIOC return index is adjusted with the dividends paid to the shareholders during the period because it might be expected to have stock market information content. It must be noted that the AIOC left its dividend unchanged for a period of five years from 1947 to 1951 where the annual net payment to the shareholders was 16 pence per share in these years[681]. Thus, the stock price daily returns for AIOC are calculated as follows,

The Stock Exchange has been progressive in disclosing information from the companies whose shares are quoted and traded[682]. The Stock Exchange publishes a daily “Official List” that prints for all shares the different prices at which bargains had been struck during the previous business day[683]. The Financial Times Industrial Ordinary Shares Index (FT30) was the first major UK share index on the London Stock Exchange and its computation began on the 1st July 1935 [684].
The index consists of 30 heavily traded securities chosen to provide almost 30% of the market value of the securities quoted on the London Stock Exchange and to this extent they reflect movements of the whole market quite effectively. The principal purpose of the index was to measure market movements over the short term and not to provide any estimates of market return or to act as a benchmark portfolio. Nonetheless, the FT30 index has the advantage that it is the only one which is readily available, it has a small base and thus this may potentially lead to some inaccuracy. However, AIOC tends to be one company out of 30 companies from the list and for any price increase the difference computed will be relatively very small[685]. The FT30 index was initially adopted from Loughborough University[686]
and for the purpose of this
research it was modified by defining the corresponding dates for the Index values and also by excluding weekends and public holidays from the index for the period under study[687]. Using daily data takes into account the market‟s daily reaction to the signal during
the event month. Daily returns for FT30 index are calculated as follows,

Comparing the AIOC‟s Return Index (RI) with the FT 30 will provide a clear
picture about the performance of AIOC in relation to the market, which is very useful for assessment of the company[688]. Therefore, the FT30 index is ideal for investigating the performance of AIOC during its nationalisation.

673. Brown and Warner, Measuring security price performance, 205.

674. Toms, Information content of earnings in an unregulated market: The cooperative cotton mills of
Lancashire 1880-1900, 189.

675. Fama, Efficient capital markets: A review of theory and empirical work, 383.

676. Ibid, 387.

677. Samuelson, Proof that properly anticipated prices fluctuate randomly.

678. Fama, Efficient capital markets: A review of theory and empirical work.

679. Fama, Efficient capital markets: II, 1577.

680. Fama, The behaviour of stock market prices.

681. Bamberg, British Petroleum and Global Oil 1950-1975, 40.

682. Littlewood, The Stock Market: 50 years of capitalism at work, 13.

683. Ibid.

684. Arsad and Coutts, Security price anomalies in the London International Stock Exchange: a 60 year
perspective, 456.

685. FT30 includes 29 companies in addition to AIOC. Thus, when prices increase by 10% this means that 0.1/30= 0.0003 will correspond to AIOC‟s proportion. Obviously, the computed value is very small and will have a minor impact and will not lead to biasness and inaccuracy.

686. For review of FT30 index, see Terence C. Mills and Raphael N. Markellos, The Econometric
Modelling of Financial Time series, Data Appendix.

687. For review, see Appendix.

688. FT30 did not contain information about dividend payments due to the unavailability of the data in
the London Stock Exchange but any dividend bias which occurs from not employing dividend adjusted returns will relatively be small and will not have an impact on the statistical significance of
any results. However, sensitivity tests were conducted for AIOC return including and excluding dividend payments and there was relatively a very small difference in the results.


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