Question

Market efficiency is an important concept in explaining the functioning of financial markets. Empirical researchers have conducted tests on market efficiency using a variety of methods, including serial correlation and portfolio studies.

a) Fama and French (1992, 1993) observed that two classes of stocks tended to outperform the overall market. Identify the classes of stocks Fama and French tested and briefly explain how these factors may be used to explain portfolio returns.  

b) Outline how you would apply the portfolio study methodology used by Fama and French to test whether firms with faster sales growth are more likely to be over-valued.  

c) Tests of serial correlation are often used to examine whether the market is weak-form efficient. Discuss what type of serial correlation you would expect to find in very short-term price changes.

Answer 1

                                    Market efficiency refers to the degree to which market prices reflect all available, relevant information. If markets are efficient, then all information is already incorporated into prices, and so there is no way to "beat" the market because there are no undervalued or overvalued securities available.

Market efficiency have to be specific not only about the market that is being considered but also the investor group that is covered. It is extremely unlikely that all markets are efficient to all investors, but it is entirely possible that a particular market (for instance, the New York Stock Exchange) is efficient with respect to the average investor. It is also possible that some markets are efficient while others are not, and that a market is efficient with respect to some investors and not to others. This is a direct consequence of differential tax rates and transactions costs, which confer advantages on some investors relative to others.

1. Market efficiency refers to how well current prices reflect all available, relevant information about the actual value of the underlying assets.

2. A truly efficient market eliminates the possibility of beating the market, because any information available to any trader is already incorporated into the market price.

3. As the quality and amount of information increases, the market becomes more efficient reducing opportunities for arbitrage and above market returns.

Serial correlation and portfolio studies

1. Serial correlation

The serial correlation measures the correlation between price changes in consecutive time periods, whether hourly, daily or weekly, and is a measure of how much the price change in any period depends upon the price change over the previous time period. A serial correlation of zero would therefore imply that price changes in consecutive time periods are uncorrelated with each other, and can thus be viewed as a rejection of the hypothesis that investors can learn about future price changes from past ones. A serial correlation which is positive, and statistically significant, could be viewed as evidence of price momentum in markets, and would suggest that returns in a period are more likely to be positive (negative) if the prior period's returns were positive (negative). A serial correlation which is negative, and statistically significant, could be evidence of price reversals, and would be consistent with a market where positive returns are more likely to follow negative returns and vice versa.

2. Portfolio Study

In some investment strategies, firms with specific characteristics are viewed as more likely to be undervalued, and therefore have excess returns, than firms without these characteristics. In these cases, the strategies can be tested by creating portfolios of firms possessing these characteristics at the beginning of a time period, and examining returns over the time period.

a. Fama and French (1992, 1993) observed that two classes of stocks tended to outperform the overall market. Identify the classes of stocks Fama and French tested and briefly explain how these factors may be used to explain portfolio returns.

                                     The Fama and French Three-Factor Model (or the Fama French Model for short) is an asset pricing model developed in 1992 that expands on the capital asset pricing model (CAPM) by adding size risk and value risk factors to the market risk factor in CAPM. This model considers the fact that value and small-cap stocks outperform markets on a regular basis. By including these two additional factors, the model adjusts for this outperforming tendency, which is thought to make it a better tool for evaluating manager performance.

1. The Fama French 3-factor model is an asset pricing model that expands on the capital asset pricing model by adding size risk and value risk factors to the market risk factors.

2. The model was developed by Nobel laureates Eugene Fama and his colleague Kenneth French in the 1990s.

3. The model is essentially the result of an econometric regression of historical stock prices.

                         Fama and French highlighted that investors must be able to ride out the extra short-term volatility and periodic underperformance that could occur in a short time. Investors with a long-term time horizon of 15 years or more will be rewarded for losses suffered in the short term. Using thousands of random stock portfolios, Fama and French conducted studies to test their model and found that when size and value factors are combined with the beta factor, they could then explain as much as 95% of the return in a diversified stock portfolio.

                  Researchers have expanded the Three-Factor model in recent years to include other factors. These include "momentum," "quality," and "low volatility," among others. In 2014, Fama and French adapted their model to include five factors. Along with the original three factors, the new model adds the concept that companies reporting higher future earnings have higher returns in the stock market, a factor referred to as profitability. The fifth factor, referred to as investment, relates the concept of internal investment and returns, suggesting that companies directing profit towards major growth projects are likely to experience losses in the stock market.

                                              Fama and French point out that low price-book value ratios may operate as a measure of risk, since firms with prices well below book value are more likely to be in trouble and go out of business. Investors therefore have to evaluate for themselves whether the additional returns made by such firms justifies the additional risk taken on by investing in them

b. portfolio study methodology used by Fama and French to test whether firms with faster sales growth are more likely to be over-valued.

In some investment strategies, firms with specific characteristics are viewed as more likely to be undervalued, and therefore have excess returns, than firms without these characteristics. In these cases, the strategies can be tested by creating portfolios of firms possessing these characteristics at the beginning of a time period, and examining returns over the time period. To ensure that these results are not colored by the idiosyncrasies of one time period, this analysis is repeated for a number of periods.

The steps in doing a portfolio study are as follows –

1. The variable on which firms will be classified is defined, using the investment strategy as a guide. This variable has to be observable, though it does not have to be numerical. Examples would include market value of equity, bond ratings, stock price, price earnings ratios and price book value ratios.

2. The data on the variable is collected for every firm in the defined universe at the start of the testing period, and firms are classified into portfolios based upon the magnitude of the variable. Thus, if the price earnings ratio is the screening variable, firms are classified on the basis of PE ratios into portfolios from lowest PE to highest PE classes. The number of classes will depend upon the size of the universe, since there have to be sufficient firms in each portfolio to get some measure of diversification.

3. The returns are collected for each firm in each portfolio for the testing period, and the returns for each portfolio are computed, generally assuming that the stocks are equally weighted.

4. The beta (if using a single factor model) or betas (if using a multifactor model) of each portfolio are estimated, either by taking the average of the betas of the individual stocks in the portfolio or by regressing the portfolio's returns against market returns over a prior time period (for instance, the year before the testing period).

5. The excess returns earned by each portfolio are computed, in conjunction with the standard error of the excess returns.

6.

There are a number of statistical tests available to check whether the average excess returns are, in fact, different across the portfolios. Some of these tests are parametric8 (they make certain distributional assumptions about excess returns) and some are nonparametric

7. As a final test, the extreme portfolios can be matched against each other to see whether there are statistically significant differences across these portfolios.

c. Tests of serial correlation are often used to examine whether the market is weak-form efficient. Discuss what type of serial correlation you would expect to find in very short-term price changes.

                           Weak form efficiency, also known as the random walk theory, states that future securities' prices are random and not influenced by past events. Advocates of weak form efficiency believe all current information is reflected in stock prices and past information has no relationship with current market prices.

              The key principle of weak form efficiency is that the randomness of stock prices make it impossible to find price patterns and take advantage of price movements. Specifically, daily stock price fluctuations are entirely independent of each other; it assumes that price momentum does not exist. Additionally, past earnings growth does not predict current or future earnings growth.

Weak form efficiency doesn’t consider technical analysis to be accurate and asserts that even fundamental analysis, at times, can be flawed. It’s therefore extremely difficult, according to weak form efficiency, to outperform the market, especially in the short term.

Under weak form efficiency, the

Current price reflects the information contained in all past prices, suggesting that charts and technical analyses that use past prices alone would not be useful in finding undervalued stocks.

Under semi-strong form efficiency, the current price reflects the information contained not only in past prices but all public information (including financial statements and news reports) and no approach that was predicated on using and massaging this information would be useful in finding undervalued stocks.

Under strong form efficiency, the current price reflects all information, public as well as private, and no investors will be able to consistently find undervalued stocks.

                              The basic efficient market hypothesis posits that the market cannot be beaten because it incorporates all important determining information into current share prices. Therefore, stocks trade at the fairest value, meaning that they can't be purchased undervalued or sold overvalued.

1. The efficient market hypothesis posits that the market cannot be beaten because it incorporates all important information into current share prices, so stocks trade at the fairest value.

2. Though the efficient market hypothesis theorizes the market is generally efficient, the theory is offered in three different versions: weak, semi-strong, and strong.

3. The weak form suggests today’s stock prices reflect all the data of past prices and that no form of technical analysis can aid investors.

4. The semi-strong form submits that because public information is part of a stock's current price, investors cannot utilize either technical or fundamental analysis, though information not available to the public can help investors.

5. The strong form version states that all information, public and not public, is completely accounted for in current stock prices, and no type of information can give an investor an advantage on the market

Weak Form

The three versions of the efficient market hypothesis are varying degrees of the same basic theory. The weak form suggests that today’s stock prices reflect all the data of past prices and that no form of technical analysis can be effectively utilized to aid investors in making trading decisions. Advocates for the weak form efficiency theory believe that if the fundamental analysis is used, undervalued and overvalued stocks can be determined, and investors can research companies' financial statements to increase their chances of making higher-than-market-average profits.

Semi-Strong Form

The semi-strong form efficiency theory follows the belief that because all information that is public is used in the calculation of a stock's current price, investors cannot utilize either technical or fundamental analysis to gain higher returns in the market. Those who subscribe to this version of the theory believe that only information that is not readily available to the public can help investors boost their returns to a performance level above that of the general market.

Strong Form

The strong form version of the efficient market hypothesis states that all information—both the information available to the public and any information not publicly known—is completely accounted for in current stock prices, and there is no type of information that can give an investor an advantage on the market. Advocates for this degree of the theory suggest that investors cannot make returns on investments that exceed normal market returns, regardless of information retrieved or research conducted.