Mauritius is Efficient or Inefficient Finance Essay

This chapter provides a review of existing literature which is relevant to the study of testing whether the Stock Exchange of Mauritius is efficient or inefficient. Section 2.2 examines the Efficient Market Hypothesis (EMH) that underpins this study. Section 2.3 continues to look at previous research on EMH and section 2.4 provides an overview of factors leading to improved market efficiency.

2.2 Theoretical Evidence

The issue of stock market efficiency is one of the most longstanding and contentious issues in the financial economics literature. In fact, there is much emphasis on this issue because of the importance of market efficiency and the difficulties in measuring it. It has been originated by a French mathematician Louis Bachelier who published his PhD thesis “The Theory of Speculation” in 1900. However, Bachelier’s work was not taken into consideration since it was way ahead of his time but it was rediscovered by Savage in 1955. His observations led to further development of the Random Walk Theory. According to Fama (1970), an efficient market is one where prices fully reflect available information. He further stated that this efficiency could be measured by how much the market price differs from its intrinsic value, that is, the value justified by the facts. Therefore, EMH implies that prices reflect all available information and can adjust rapidly to new information 

[1] . As a consequence, prices are always at levels consistent with ‘fundamentals’. It is impossible for investors to purchase undervalued stocks or sell stocks for inflated prices. Consequently, it is not worthwhile to have recourse to expert stock selection or market timing since it is not possible to outperform the market. Dimson and Mussavain (1998) identified an efficient market as a market where all relevant information is reflected in the price of a security or stock.

Hamid et.al (2010) highlighted that stock prices in an efficient market follow the “Random Walk Hypothesis” whereby prices cannot be predicted because when new information is released, the degree to which prices will change in response to this cannot be anticipated. In a perfectly efficient market it is impossible to beat the market and earn an abnormal profit, that is, investors are constantly paying a fair price (intrinsic value). Efficiency is important because of the crucial role that stock markets play in the pricing and allocation of capital and the pricing of risk. Since the 1960s, there have been numerous studies questioning the degree of market efficiency and the static assumptions behind for example EMT & CAPM 

[2] . In fact, Market efficiency involves three related concepts which are allocative efficiency, optional efficiency and informational efficiency. Allocation efficiency – Does capital flow to the projects with the highest risk-adjusted returns? Operational efficiency – Are transactions completed on a timely basis, accurately and at low cost? Informational efficiency – Does the observed market price of a security reflect all information relevant to pricing the security? However, financial economics tends to focus on informational efficiency when discussing market efficiency. Generally, the efficient market hypothesis states that markets are efficient if the prices of securities fully reflect all available information. That is, the prices of securities observed at any point in time are based on a correct evaluation of all information available in that given time period. Blake (2000) highlighted the three forms of EMH and separates them according to their information sets: Weak form efficiency states that prices will immediately and fully reflect all past information, therefore investors cannot use information on past prices to predict future prices to earn excess returns. That is, one cannot beat the market by using historical information on prices and volumes. 2) Semi-strong efficiency is when prices instantaneously and completely reflect all publically available information.

Under this tenet any relevant news will be quickly incorporated into market prices and will cause the prices to rise or fall accordingly. 3) Strong form efficiency states that market prices immediately and completely reflect all known information that is both public and private (insider) information into asset prices. With strong form efficiency, prices change so quickly in response to new information that even those with privileged information cannot profit from trading with that information. Based on these definitions, Malkiel (1999) described the weak-form efficiency as a situation where the stock price changes were independent, the semi-strong form efficiency as a market where prices quickly reflected new value changing information and the strong form efficiency as a market where professional managers were unable to accurately forecast future prices of individual stocks. For the purposes of this paper, we will focus only on the weak-form efficiency and semi-strong form efficiency in the Mauritian Stock Market as for the case of strong form efficiency; it is too extreme and complex.

Under the weak form efficiency, the expected excess return conditional on past historical data should be zero. This implies that one should not be able to predict future excess returns on the basis of historical excess returns. The weakly efficient market is basically a refutation of technical analysis. The technical analyst asserts that market prices are not a random process, that is, expected price changes are not independent of past price changes nor are distributions of rates of return independent from past distributions. Market prices exhibit identifiable patterns that are bound to be repeated.

The art lies in devising the proper technique to identify trends, interpret them, and interpret any deviation from them (Khoury, 1983: 68). The Random Walk Hypothesis 

[3] of stock market prices is concerned with the question of whether one can predict future prices from past prices. In its simple form, it states that price changes cannot be predicted from earlier changes in any meaningful manner. Successive price changes in individual securities are independent over time and price changes occur without any significant trends or patterns. Thus, past prices contain no useful information as to their future price behaviour. The more efficient a market is, the more random will be the sequence of price changes. However, it should be noted that the EMH and the random walks are not synonymous, that is, do not amount to the same thing. A random walk of stock prices does not mean that the stock market is efficient with rational investors. Shleifer (2000) identified three main arguments for EMH: 1. Investors are rational and hence value securities rationally. 2. Some investors are irrational but their trades are random and cancel each other out. 3. Some investors are irrational but rational arbitrageurs eliminate their influence on prices. If all these exist, then both efficient markets and stock prices would be very unpredictable and thus would follow a random walk. Samuelson (1965) mentioned that asset prices in an efficient market should fluctuate randomly through time in response to the unanticipated component of news.

Prices may exhibit trends over time, in order that the total return on a financial asset exceeds the return on a risk-free asset by an amount commensurate with the level of risk undertaken in holding it. However, even in this case, fluctuations in the asset price away from trend should be unpredictable. Active fund managers such as fundamental analysts clearly believe that the semi-strong form EMH is not true, otherwise there would be no reason for their existence. On the other hand, many academics 

[4] do believe that the semi-strong form EMH is true. For small investors who believe in the semi-strong form EMH, the only course of action is to achieve a diversified portfolio by investing in passively managed funds (for example, tracker funds). In order to test for semi-strong efficiency, we need to employ a test which is named event study or event analysis. These tests often study an event window around news announcements regarding certain stocks. If news announcements convey new information to the market or if they remove uncertainty regarding rumours in circulation prior to the announcement, shares of the company which are affected by the news will obtain abnormal returns. It was found that event studies provide the strongest possible evidence in favour of the EMH. Countless studies have shown that the stock market reacts rather quickly to new information, whether we measure returns on a monthly, weekly or daily basis. Some would argue that the evidence suggests that information is incorporated in prices within a few minutes. 

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2.3 Empirical Evidences

Researchers have examined comprehensively the extent to which markets are efficient. There are various studies on the Efficient Market Hypothesis. However, some researchers focused on the assumptions of EMH while others identified a series of anomalies in the market. In support for the EMH, Mauboussin (2005) highlighted that rationality and the utility theory leads to efficient markets as investors always want to profit and will follow those who do in modern markets.

Also, Malkiel (2005) critically analysed the EMH and looked at whether it was the reason why managers of investment funds could not beat the market in terms of predicting prices to earn excess returns. Malkiel’s (2005) study supports the notion that market prices will reflect all available information, and therefore there is no significant gain to be had from holding a managed fund versus an indexed fund. Moreover, Russel (2003) assumed that very few active managers make money on stock exchange. On the other hand, Wilks (2003) added that active managers only beat the market because they take excessive risks. Fama (1998) refused to abandon the EMH theory by arguing that anomalies found in the market are just an illusion and are economically or statistically insignificant. He qualified the market imperfections found as the result of the changes made in the method of estimating abnormal returns. Grossman and Stiglitz (1980) found that if information was costly, there must have been a financial incentive to obtain it. But if the information was already ‘fully reflected’ in asset prices, then there would not be any financial incentive. This is a strong version of the hypothesis that could be literally true if all available information was costless to obtain. Alternatively, Jensen (1978) added a weaker but economically a more realistic version of the hypothesis. It is when prices reflect information up to the point where marginal benefits of acting on the information do not exceed the marginal costs of collecting it. Campbell, Lo and MacKinlay (1997) analysed the US sharemarket and it showed that above-average stock returns over a daily, weekly or monthly interval increase the likelihood of further above-average returns in the subsequent period.

However, for instance, only about 12 per cent of the variance in the daily stock price index can be predicted using the previous day’s return. Portfolios of small stocks display a greater degree of predictability than portfolios of large stocks.

There is also some weak evidence that the degree of predictability has diminished over time. Much of the EMH literature before LeRoy (1973) and Lucas (1978) revolved around the random walk hypothesis and the martingale model, two statistical descriptions of unforecastable price changes that were initially taken to be implications of the EMH. One of the first tests of the RWH was developed by Cowles and Jones (1937), who compared the frequency of sequences and reversals in historical stock returns, where the former are pairs of consecutive returns with the same sign, and the latter are pairs of consecutive returns with opposite signs. Cootner (1962; 1964), Fama (1963; 1965a), Fama and Blume (1966), and Osborne (1959) perform related tests of the RWH and, with the exception of Cowles and Jones (who subsequently acknowledged an error in their analysis – Cowles, 1960). All of these articles indicate support for the RWH using historical stock price data. Furthermore, Lo and MacKinlay (1988) examined the weekly US stock returns indexes from 1962 to 1985 by constructing a variance ratio test. They exploited the fact that return variances scale linearly under the RWH and if the random walk hypothesis holds, the variance of a two-week return is twice the variance of a one week. Particularly, they found that variances grow faster than linearly as the holding period increase which implies positive serial correlation in weekly returns.

Oddly enough, Lo and MacKinlay also show that individual stocks in general do satisfy the RWH. Liu and He (1991) tested five weekly exchange rates and they rejected the random walk hypothesis. On the other hand, Ayadi and Pyun (1994) applied the same test to stocks in the Korean Stock Exchange and showed that after adjusting for serial correlation and heteroscedasticity, the random walk hypothesis cannot be rejected. Fama (1965) found evidence that there was no long-term profitability to be found in technical trading strategies. Malkiel (2003) also supported this view and provided us with evidence that more often than not traders find it difficult to perform better than the benchmark indices and when they do, their success is often not reported in the long run. In a study, Scholes (1972) observed how prices reacted to non information by seeing how share prices reacted to large share sales by large investors. This study was important as it directly dealt with the issue of the availability of close substitutes for individual securities 

[6] . Scholes found that they led to small price changes and that this could be due to negative news regarding the share sale. Thus, the results support the random walk theory. In contrast, various studies have criticised the assumptions of EMH, that is, there are several arguments that reject the EMH. For instance, Bogle (2003) argued that the EMH does not account for transaction costs and as such, market efficiency did not matter since investors, as a group, would fall short of the market return by the amount of costs they incurred. Hence, there is no doubt that transactions costs play an important role in investment strategies. Furthermore, Ball (1994) postulated that cost for information is not zero but positive, opposing the assumption of EMH. Besides, Shleifer and Summers (1990) questioned the assumption that investors are rational where there are noise traders that act on imperfect information causing the prices to deviate from their intrinsic values. In additional, Russel and Torbey (2002) argued that individuals are often prone to make mistakes and tend to rely on the opinion of others. In fact, human beings do not process information with machine-like speed, efficiency or rationality where as EMH assumes that information is processed correctly and immediately. While conducting a critical review of the literature on EMH, Akintoye (2008) highlighted the support for weak and semi strong efficiency but also highlighted the lack of evidence for strong form efficiency.

Akintoye explored market anomalies which caused the market to depart from efficiency and attributing those departures to a notion of behavioural finance finding that where emotions and cognitive biases were present and these factors can cause anomalies in market price movements. Brealey et al (2008) defined behavioural finance to be the use of human psychological evidence to interpret investor reactions. Behavioural finance assumes that investors are adverse to small losses especially when stock performance has been poor recently. Investors can also be slow to react to new information and over confident with their stock price predictions. The argument for behavioural finance in rejection of the EMH is also highlighted by Daniel et al (1998), where under or over reaction to market reaction to news is present due to psychological biases such as investor over confidence.

This study concluded that price movements in the market are distorted as investor biases are reflected in price as opposed to reflecting only the information available. Kulkarni (1978) investigated the weekly RBI stock price indices for Bombay, Calcutta, Delhi, Madras and Ahmedabad stock exchanges and monthly indices of six different industries by using spectral method. He concluded that there is a repeated cycle of four weeks for weekly prices and seasonality in monthly prices. This study has thus rejected the hypothesis that stock price changes were random. In addition, some studies have produced evidence against the random walk hypothesis, showing that stock returns do contain predictable elements. Much of this work has centered on the world’s largest stock markets, including the United States, developed economies in Europe, and Japan and were studied by Poterba & Summers (1988) and Lo and MacKinlay (1988). More recently, mixed evidence on the random-walk hypothesis has been found for emerging markets in Latin America (Urrutia, 1995); (Grieb & Reyes, 1999) and in Asia (Ayadi & Pyun, 1994); (Huang, 1995); (Chang & Ting, 2000). Apart from these arguments, some studies are based on anomalies present in the stock market. For instance, Rozeff and Kinney (1976) suggested that the month of January experiences higher returns than other months on the New York Stock Exchange. This stock market anomaly was dubbed henceforth as the “January Effects” 

[7] . Another anomaly related to stock returns on a given day of the week is known as the day of the week effect.

French (1980) claimed that there was a tendency for returns to be negative on Mondays whereas they are positive on the other days of the week similar to the findings of Tandon (1994). There also exists a size effect on the stock markets. For instance, Banz (1981) stipulated that holding stocks of low capitalization firms yielded excess returns, though it is argued that these excess returns may be only a compensation for exposure to the risks associated with small firms.

Similarly, some authors argued for the presence of the price earnings ratio effect on some stock markets. For example, in contradiction of the EMH theory, Basu (1977) has demonstrated that investors holding low price earnings ratio portfolio earned higher returns than an investor holding an entire sample of stocks.

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