Financial Education

The value premium refers to the fact that stocks with priced at a low multiple of book value tend to perform better than would be explained by the Capital Asset Pricing Model. Proponents of the Efficient Market Hypothesis (EMH) argue that the value premium results from a risk factor not captured by Beta. Opponents argue that the value premium is evidence that the EMH is not valid.

Fama and French, among others, have developed models that attribute the value premium to risk factors. Typically these studies use company size as a proxy for liquidity risk. In the Review of Finance, Phalippou replicates these studies in their original form, and then using institutional ownership as an alternative liquidity proxy. In this second iteration, the value premium persists and cannot be explained by the risk factor models.

Phalippou concludes that if the value premium is attributable to risk factors, the risk factors are not the ones identified in previous research.

In a strong-form efficient market no group of investors should be able to generate excess risk-adjusted returns. Technical analysis, fundamental analysis, and even inside information will provide little value once the information is known.

Tests of the strong form efficient market hypothesis have generally examined the performance of four groups of investors.

1. Corporate insiders
2. Stock exchange specialists
3. Security analysts
4. Professional money managers

Studies of insider buying and selling have provided mixed support for the EMH. At one time, insiders and public investors following insider trades experienced excess risk adjusted returns. However, more recent studies have indicated that public traders can no longer profit after adjusting for transaction costs.

Stock exchange specialists have monopolistic access to certain market data such as unfilled limit orders. Data suggests that specialists are able to earn excess risk-adjusted returns due to their access to this data.

There is some evidence that certain analysts may possess superior information, and that following the recommendations of these analysts may permit excess returns. Often these anomalies appear to be incorporated, which would support the EMH. For example, the Value Line timeliness rating was considered enigmatic as it appeared to consistently predict returns. However, changes in rating are now incorporated in stock prices within a day or two, and transaction costs may limit any usefulness of the anomaly.

In general, tests of professional investors have supported the EMH. On average, such investors do not enjoy superior risk-adjusted returns.

Over time, small capitalization stocks have been shown to outperform large-capitalization stocks. However, timing changes in the relative performance between the two groups could lead to still-better performance. In the Fall 2007 Journal of Portfolio Management, L’Her, Mouakhar and Roberge test three nonparametric techniques derived from artificial intelligence and using 20 macroeconomic and financial variables as inputs.

The three approaches are recursive partitioning, a neural network and a genetic algorithm.

Each of the three techniques outperforms a naive small-minus-big strategy, but the best results are derived from taking the consensus of the three techniques.

Behavioral finance theory has pointed to research that shows short-term momentum and long-term reversals in pricing as signs that markets are not fully efficient. In the December 2007 Journal of Finance George and Hwang show that the trends may be at least partially attributable to the differential tax rates applied to short-term and long-term capital gains.

The authors first note an asymmetry between the reversals of stocks with capital gains and stocks with capital losses. In the latter case, there is no tax incentive for holding over longer periods. They then test the capital gains hypothesis by comparing U.S. results to Hong Kong, where there are no capital gains taxes. They find no evidence of reversals in Hong Kong, supporting the thesis that short term momentum may be a means of compensating holders for additional taxes, while long-term reversals result as the tax effects subside.

The study is reminiscent of Harti’s study showing underperformance around the anniversary dates of large price swings, which would also appear to be tax-driven.

In the November/December 2007 Review of the Federal Reserve Bank of St. Louis, Guidolin and Jeunesse examine the decline in the U.S. personal saving rate. Their purpose is to determine whether the decline results from methodological flaws in the measurement methods and whether it is a concern.

There are two primary measures of the U.S. personal savings rate: the NIPA measure used by the Bureau of Economic Analysis and the Flow of Funds measure used by the Federal Reserve. The NIPA measure is most commonly referenced, but is known to have several flaws, including:

• Capital gains are excluded from disposable income, understating the savings rate
• Pension benefits are excluded, but pension contributions are included
• Household interest payments are expressed in nominal rather than real terms
• Education outlays are treated as personal expenses rather than investme

Despite these issues, it becomes clear that the savings rate has declined sharply. The authors then consider three arguments that imply that the decline is not cause for concern:

1. Net wealth data rather than NIPA data should be used, capturing capital gains
2. The savings rate of the nonfinancial business sector should be taken into account
3. The Flow of Funds measure, though superior, also may understate savings rates by failing to account for productivity enhancements

Such theories and others do explain part of the decline, but none account fully for the drop. The authors conclude that neither methodological flaws nor economic theories account for the drop in savings, and that the decline remains cause for concern.

With the growth in the hedge fund industry has come a decline in the value added by hedge fund managers. Given the high fees typically charged by hedge funds, some have questioned whether passive approaches can be constructed that would provide returns similar to those of hedge funds while offering greater transparency and liquidity.

In the Winter 2007 Journal of Wealth Management Harry Kat discusses three general approaches to hedge fund replication:

• Factor Models
• Mechanical Trading Rules
• The author’s FundCreator product

In a factor model, linear regressions determine the market exposures experienced by a hedge fund or hedge fund index. Factors may include stock, bond, commodity and currency returns, or changes in credit spreads and market volatility. These exposures can then be taken via index products or derivative instruments.

In the case of funds that add value by timing short-term changes in market exposure, the investor’s trading behavior can be compared to mechanical trading rules.

The FundCreator product is a risk management tool that allows the investor to target the risk and correlation properties desired in order to maximize diversification potential.

The momentum anomaly refers to the fact that a strategy of buying past winners and selling past losers produces returns that are not explained by the Capital Asset Pricing Model framework. Proponents of the efficient market hypothesis (EMH) attribute the momentum anomaly to risk factors that are not captured in Beta, while opponents point to the anomaly as evidence against the EMH.

In the October 2007 Journal of Finance, Avromov et. al. find that the influence of momentum is limited to a small sample (4% of market capitalization) of companies with high credit risk. This study offers support to the efficient market hypothesis and the argument that the excess returns are attributable to a risk factor (in this case, credit quality.)

If the semistrong form of the efficient market hypothesis holds, investors should not be able to earn excess risk-adjusted returns if their decisions are based on information that has already been made public. Neither technical analysis nor fundamental analysis would provide a predictable edge.

There are two types of studies frequently used to test the semistrong form of the EMH:

1. Return prediction studies attempt to predict the future rates of returns for the market or individual stocks using public information such as valuation, dividend yield, or risk premium. Another type of return prediction study is event studies, which examine abnormal returns immediately following a major announcement to determine whether returns predictably persist or reverse.
2. Cross-sectional return prediction studies test whether variables such as valuation to predict the relative returns of all stocks in a sample.

Return prediction studies have generally shown little success in predicting short-term returns. However, they have successfully been used to predict long-term returns. For example, high dividend yields, high default spreads and high term structure spreads all tend to predict higher long-term returns for stocks.

Studies have also demonstrated that markets do not rapidly process the information related to earnings surprise, or unanticipated changes in earnings. If markets are semistrong efficient such information should be reflected on the day of the earnings announcement. However, as much as half of the total change in stock price can occur in the 90 days following the day of the announcement.

Cross-sectional studies have demonstrated several anomalies that appear to contradict the efficient market hypothesis. On a risk adjusted basis: low P/E stocks tend to outperform high P/E stocks; small stocks tend to outperform large stocks; stocks with low price/book ratios outperform stocks with high price/book ratios.

Event studies of stock splits, IPOs (after issuance), accounting changes and corporate finance events generally support the efficient market hypothesis in that the news of such items is quickly and fully incorporated into the market price.

Deep out-of-money options tend to trade with higher implied volatility than near-money options, a phenomenon known as the volatility skew. In the October 2007 Journal of Futures Markets, Doran, Peterson and Tarrant extend this observation to ask whether the implied volatility skew becomes more positive immediately prior to a market spike or more negative immediately prior to a market crash.

They find that at the short end of the term structure, the skew does give information regarding an impending crash. There is less information conveyed from positive skew. Further along the term structure, information content from volatility skew is weak.

If the weak form of the efficient market hypothesis holds, security market information should have no relationship with future returns. Technical analysis and trading rules should not allow investors to earn excess returns.

Researchers testing weak form market efficiency generally use one of two groups of tests when studying weak-form market efficiency.

1. Statistical tests of independence measure either the significance of positive or negative correlation over time (autocorrelation) or by comparing the number of runs (consecutive moves in the same direction) with that expected in a normal sample. In general, statistical tests of independence have shown no relationship between current and future price movements.
2. Tests of trading rules seek to mechanically simulate various trading strategies. For example, testing whether a strategy of buying when the stock price closes above the 50 day moving average and selling when the price closes below the moving average. In general, these tests have supported the weak-form efficient market hypothesis by showing no excess returns (after trading costs, compared to a buy-and-hold strategy) from following such rules. However, the results are not unanimous - some rules have been shown to offer superior returns.

Technical analysts criticize the existing tests as being too naive or simplistic to capture the