EFFICIENT MARKET THEORY

EFFICIENT MARKET THEORY

The efficient market hypothesis is a central idea of a modern finance that has profound implications. An understanding of the efficient market hypothesis will help to ask the right questions and save from a lot of confusion that dominates popular thinking in finance. An efficient market is one in which the market price of a security is an unbiased estimate of its intrinsic value. Note that market efficiency does not imply that the market price equals intrinsic value at every point in time.

A corollary is that investors will also be less likely to discover great bargains and thereby earn extraordinary high rates of return. The requirements for a securities market to be efficient market are;

(1)  Prices must be efficient so that new inventions and better products will cause a firm s securities prices to rise and motivate investors to supply capital to the firm (i.e., buy its stock);

 (2) Information must be discussed freely and quickly across the nations so all investors can react to new information;

(3) Transactions costs such as sales commissions on securities are ignored;

(4) Taxes are assumed to have no noticeable effect on investment policy;

(5) Every investor is allowed to borrow or lend at the same rate; and, finally,

(6) Investors must be rational and able to recognize efficient assets and that they will want to invest money where it is needed most (i.e., in the assets with relatively high returns).

Forms of Efficient Market Hypothesis

Eugene Fama suggested that it is useful to distinguish three levels of market efficiency. They are

1) Weak-form efficiency - Prices reflect all information found in the record of past and volumes;

2)  Semi-strong form efficiency - Prices reflect not only all information found in the record of past prices and volumes but also all other publicly available information;

 3) Strongform efficiency - Prices reflect all available information, public as well as private.

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Weak form of EMH

The week form of market holds that present stock market prices reflect all known information with respect to past stock prices, trends, and volumes. This form of theory is just the opposite of the technical analysis because according to it, the sequence of prices occurring historically does not have any value for predicting the future stocks prices. The technical analysts rely completely on charts and past behavior of prices of stocks.

Three types of tests have been commonly employed to empirically verify the weak-form efficient market hypothesis: (a) serial correlation tests; (b) runs tests; and (c) filter rules tests.

Serial Correlation Test: Serial Correlation is said to measure the association of a series of numbers which are separated by some constant time period. One way to test for randomness in stock price changes is to look at their serial correlations. Is the price change in one period correlated with the price change in some other period? If such auto-correlations are negligible, the price changes are considered to be serially independent. Numerous serial correlation studies, employing different stocks, different time-lags, and different time-periods, have been conducted to detect serial correlations

Run Test: Ren Test was also made by Fama to find out it price changes were likely to be followed by further price changes of the same sign. Run Test ignored the absolute values of numbers in the series and took into the research only the positive and negative signs. Given a series of stock price changes, each price (+) id it represents an increase or a minus (-) if it represents a decrease. A run occurs when there is not difference between the sign of two changes. When the sign of change differs, the run ends and a new run begin. To test a series of price changes for independence, the number of runs in that series is compared to see whether it is statistically different from the number of runs in a purely random series of the same size. Many studies have been carried out, employing the runs test of independence. They did not detect any significant relationship between the returns of security in one period and the returns in prior periods and made a conclusion that the security prices followed a random walk.

Filter Rules Test: The use of charts is essentially a technique for filtering out the important information from the unimportant. Alexander and Fama and Blume took the idea that price and volume data are supposed to tell the entire story we need to know to identify the important action in stock prices. They applied filter rules to see how well price changes pick up both trends and reverses which chartists claim their charts do. If a stock moves up X per cent, buy it and hold it long; if it then reverses itself by the same percentage, sell it and take a short position in it.

Semi-Strong Form of EMH

The semi strong form of the efficient market hypothesis centers on how rapidly and efficiently market prices adjust to new publicly available information. In this state, the market reflects even those forms of information which may be concerning the announcement of a firm s most recent earnings forecast and adjustments which will have taken place in the prices of security. The investor in the semi-strong form of the market will find it impossible to earn a return on the portfolio which is based on the publicly available information in excess of the return which may be said to be commensurate with the portfolio risk. Many empirical studies have been made on the semi-strong form of the efficient market hypothesis to study the reaction of security prices to various types of information around the announcement time of the information. Two studies commonly employed to test semi-strong form efficient market are event study and portfolio study.

Event Study examines the market reactions to and the excess market returns around a specific information event like acquisition announcement or stock split. The key steps involved in an event study are as follows:

1. Identify the event to be studied and pinpoint the date on which the event was announced.

 2.  Collect returns data around the announcement date. In this context two issues have to be resolved: What should be the period for calculating returns weekly, daily, or some other interval? For how many periods should returns be calculated before and after the announcement date?

 3.  Calculate the excess returns, by period, around the announcement date for each firm in the sample. The excess return is calculated by making adjustment for market performance and risk.

 4. Compute the average and the standard error of excess returns across all firms

 5. Assess whether the excess returns around the announcement date are different from zero. To determine whether the excess returns around the announcement date are different from zero, estimate the T statistic for each day. The results of event studies are mixed. Most event studies support the semi-strong from efficient market hypothesis. Several event studies, however, have cast their shadow over the validity of the semi strong form efficient markets theory.

Portfolio study: In a portfolio study, a portfolio of stocks having the observable characteristic (low price earnings ratio or whatever) is created and tracked over time see whether it earns superior risk-adjusted returns. Steps involved in a portfolio study are as follows:

1. Define the variable (characteristic) on which firms will be classified. The proposed investment strategy spells out the relevant variable. The variable must be observable, but not necessarily numerical.

 2.  Classify firms into portfolios based upon the magnitude of the variable. Collect data on the variable for every firm in the defined universe at the beginning of the period and use that information for classifying firms into different portfolios.

 3.  Compute the returns for each portfolio on the returns for each firm in each portfolio for the testing period and calculate the return for each portfolio, assuming that the stocks included in the portfolio are equally weighted.

 4. Calculate the excess returns for each portfolio. The calculation of excess returns earned by a portfolio calls for estimating the portfolio beta and determining the excess returns

Assess whether the average excess returns are different across the portfolios. Several statistical tests are available to test whether the average excess returns differ across these portfolios. Some of these tests are parametric and some nonparametric. Many portfolio studies suggest that it is not possible to earn superior riskadjusted returns by trading on some observable characteristics. However, several portfolio studies have documented inefficiencies and anomalies.

Strong-Form of EMH

The strong-form efficient market hypothesis holds that all available information, public or private, is reflected in the stock prices. The strong form is concerned with whether or not certain individuals or groups of individuals possess inside information which can be used to make above average profits. If the strong form of the efficient capital market hypothesis holds, then and day is as good as any other day to buy any stock. This the most extreme form of the efficient market hypothesis. Most of the research work has indicated that the efficient market hypothesis in the strongest form does not hold good.

Market Efficiency and Anomalies

Anomalies are situations that appear to violate the traditional view of market efficiency, suggesting that it may be possible for careful investors to earn abnormal returns. Some stock market anomalies are Low Price-Earnings Ratio: Stock that are selling at price earnings ratios that are low relative to the market Low Price-Sales Ratio: Stocks that have price-to-sales ratios that are lower competed with other stocks in the same industry or with the overall market. Low Price-to Book value Ratio: Stocks whose stock prices are less that their respective book values High Divident Yield: Stocks that pay high dividends relative to their respective share prices Small companies: Stock of companies whose market capitalization is less than 100 million Neglected Stocks: Stocks followed by only a few analysts and/or stocks with low percentages of institutional ownership Stocks with High Relative Strength: Stocks whose prices have risen faster relative to the overall market January Effect: Stock do better during January than during any other month of the year Day of the Week:

Stock of poorer during Monday than during other days of the week Most of these anomalies appear to revolve around four themes:

1. Markets tend to overreact to news, both good and bad.

2. Value investing is contrarians in nature and is beneficial because markets overreact.

3. The market consistently ignores certain stocks, especially small stocks.

Let  us examine what anomalies mean for investors and the concept of market efficiency.

Financial Market Overreaction: One of the most intriguing issues to emerge in the past few years is the notion of market overreaction to new information (both positive and negative). Many practitioners have insisted for years that markets to overreact. Recent statistical evidence for both the market as a whole and individual security has shown errors in security prices that are systematic and therefore predictable. Overreactions are sometimes called reversals. Stocks that perform poorly in period suddenly reverse direction and start performing well in a subsequent period, and vice versa. Several studies have found that stock returns over longer time horizons (in excess of one year) display significant negative serial correlation.

Profiting from Reversals: Market overreactions or reversals suggest several possible investment strategies to produce abnormal profits. Some possibilities include buying last year s worst performing stocks, avoiding stocks with high P/E rations, or buying on bad news. At the risk of oversimplifying, any investment strategy based on market overreaction represents a contrarian approach to invest, buying what appears to be out of favour with most investors.

Calendar-Based Anomalies: Are there better times to own stocks than others? Should you avoid stocks on certain days? The evidence seems to suggest that several calendar-based anomalies exist. The two best known, and widely documented, are the weekend effect and the January effect.

Weekend Effect: Studies of daily returns began with the goal of testing whether the markets operate on calendar time or trading time. In other words, are returns for Mondays (i.e., returns over Friday-to-Monday periods) different from the other day of the week returns? The answer to the question turned out to be yes, the trend was called the weekend effect. Monday returns were substantially lower than other daily returns. One study found that Mondays produced a mean return of almost-35 percent. By contrast, the mean annualized returns on Wednesdays was more than +25 per cent.

The January Effect: Stock returns appear to exhibit seasonal return patterns as well. In other words, returns are systematically higher in some months than in others. Initial studies found that returns were higher in January for all stocks (thus this anomaly was dubbed the January effect) whereas later studies found the January effect was more pronounced for small stocks than for large ones. One widely accepted explanation for the January effect is tax-loss selling by the investors at the end of December. Because this selling pressure depresses prices at the end of the year, it would be reasonable to expect a bounce-back in prices during January. Small stocks, the argument goes, are more susceptible to the January effect because their prices are more volatile, and institutional investors (many of whom are tax-exempt) are less likely to invest in shares of small companies.

Calendar-Based Trading Strategies: Both seasonal and day-of-the-week affects are inconsistent with market efficiency because both suggest that historical information can generate abnormal profits. As will all anomalies, however, a more important issue is whether seasonal and/ or day-of-the-week effects can create profit opportunities for investors.

Small-Firm Effect: Generally the stocks of small companies substantially outperform stocks of large companies. Of course, history has also shown that small stocks have exhibited more year-to-year variation than large stocks. However, even after correcting for differences in risk, some studies suggest that investors can earn abnormal profits by investing in shares of small companies, exploiting the small-firm effect. Two explanations for the small-firm effect seem plausible to us. The first is that analysts have applied the wrong risk measures to evaluate returns from small stocks. Small stocks may well be riskier than these traditional risk measures indicate.

Performance of Investment Professionals: Investment professionals such as mutual fund managers seem to have a difficult time beating the overall market. In a particular year, some professionals will beat the market, whereas others will not. The key question is whether some professionals can consistently outperform the market. Some evidence suggests that the answer to this question may be yes.