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Fundamental and Collar Weighting in the European Stock Market

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Fundamental and Collar Weighting in the European Stock Market FUNDAMENTAL AND COLLAR WEIGHTING IN THE EUROPEAN STOCK MARKET Niek M. Bijlefeld Thesis MScBA Risk and Portfolio Management Faculty of Economics and Business University of Groningen Student number: 1299867 Phone: 0031 (0)647138556 [email protected] Supervisor: A. Plantinga April 22, 2009 Abstract Fundamental weighting renders higher returns and lower risks than market capitalization weighting. It uses measures of company size that are indifferent of market capitalization. Using a combination of these two weighting methods provides mean-variance ratios that are higher than those of market capitalization weighted portfolios, but lower than those of fundamental weighted portfolios. None of the weighting methods provides a significant alpha. The return differences are significantly related to the value premium. JEL classification codes: G11, G14. 1 Introduction Sharpe (1964), Lintner (1964) and Black (1972) developed the Capital Asset Pricing Model (CAPM). According to CAPM a stock portfolio in which the invested amounts are allocated according to each stock’s market capitalization is mean-variance efficient. This form of efficiency means that the portfolio is optimal regarding its mean return and the variance of this return. Even though CAPM is based on some assumptions that do not hold in the real world, market capitalization is widely accepted as the basis for stock indexes. Worldwide many companies and individuals invest in market capitalization weighted (cap weighted) portfolios believing they will get the best possible combination of risk and return. But is this believe correct? Shiller (1981) argues that stock prices are subject to large fluctuations without apparent changes in the underlying company’s value. Siegel (2006) attributes these fluctuations to noise. Are cap weighted portfolios the best method for acquiring the optimal combination of return and variance of this return? Perhaps there are different methods for constructing portfolios that provide a better mean-variance combination. I construct European stock portfolios that select and weight stocks by measures other than market capitalization. In the last 10 years these fundamental weighted portfolios render higher returns and lower variances than the cap weighted reference portfolios. Also portfolios that use a combination of cap weighting and fundamental weighting are more mean-variance efficient than the cap weighted portfolios. The return premiums of these two methods have a significant relation with the value premium as presented by Fama and French (1992). In spite of these return and variance premiums I find no significant alpha for both the fundamental and collar weighted portfolios. 1 1.1 Noise Stock prices seem to be fluctuating around the fair value of the company they represent. According to Shiller (1981) these temporary deviations are too large to be attributed to new information. Summers (1986), Black (1986) and Fama and French (1988) suggest that noise causes stock prices to deviate from their fair values. I define noise as data that looks like information but actually is not. Noise traders trade on noise under the assumption that they are trading on information. This noise trading causes stock prices to reflect the information as well as the noise that traders trade on. Siegel (2006) refers to this market capitalization bias with the ‘noisy market hypothesis’, which is a variation on CAPM’s ‘efficient market hypothesis’. Following the noisy market hypothesis the market price of a stock at a certain moment in time is its fair value plus or minus the amount of noise at that specific moment. According to Siegel (2006) noise may last for days or for years and its unpredictability makes is hard to design a trading strategy that consistently produces superior returns. I suggest it is better to compose stock portfolios based on factors that are not influenced by this unpredictable and seemingly irrational noise. 1.2 Cap weighting vs. non-cap weighting Arnott et al. (2005) construct portfolios in the U.S. market based on measures of company size that are indifferent from market capitalization. Arnott et al. (2005) use revenue, equity book value, sales, dividends, cash flow, and employment as weight metrics and call their portfolios ‘fundamental indexes’. Over a time span of more than forty years these fundamental indexes outperform a cap weighted reference portfolio by an average of more than two percentage points per year. The volatility of the returns of the fundamental portfolio is approximately equal to those of the cap weighted reference portfolio. Also outside the U.S. fundamental weighting performs better than cap weighting. Morgan Stanley Capital International (MSCI) and Financial Times Stock Exchange (FTSE) Group examine 23 developed countries and find that from 1993 until 2007 fundamental weighting outperforms cap weighting in all these countries (Research Affiliates, 2007). 2 Fundamental weighting provides the capacity, liquidity, diversification and broad-market perception that Hsu and Campollo (2006) describe to be the most important benefits of traditional cap weighted indexes. In their research the fundamental weighted portfolios outperform cap weighted portfolios by 3.5 percent globally with a turnover that is only 4 percent higher. Estrada (2006) finds that dividend weighted fundamental weighting substantially outperforms cap weighting by 1.9 percent per year. This research represents over 93 percent of the world market capitalization. Treynor (2005) and Hsu (2006) show that cap weighted portfolios are less efficient than non-cap weighted portfolios when stock prices contain more noise. Arnott et al. (2008) consider high yield bonds and emerging market bonds to contain more noise and compare these with investment grade corporate indexes. They find a positive relationship between the outperformance of fundamental weighted bonds and the amount of noise in market prices from 1997 to 2007. According to Hsu (2006) cap weighting is sub-optimal in comparison with non-cap weighting since cap weighting assigns more weight to overvalued stocks than to undervalued stocks. Jun and Malkiel (2007) attribute the excellent performance of fundamental weighting to an increased exposure to stocks with low price-to-book value and small capitalization. Fama and French (1992) describe this advantage for non-cap weighting as the value premium and the higher expected stock return for smaller companies as the size premium. Stocks of companies with low book to market values and stocks of small companies are considered to be more risky. More risk implies a higher expected return. The noisy market hypothesis explains these size and value premiums. If the market price of a stock declines (increases) while its fair value remains unchanged it is likely that this stock will render above (below) normal returns in the future. Though non-cap weighting seems to outperform cap weighting Amenc, Goltz, and Le Sourd (2008) find that none of these non-cap weighted portfolios outperform equal weighted portfolios. However, rebalancing a large equal weighted portfolio implies high turnover costs. Also small listed companies do not have enough capacity to provide their stocks to investors if equal weighting would be used on a large scale. Within finance literature there is disagreement about the way fundamental weighting should be treated. Asness (2006), Perold (2007) and Blitz and Swinkels (2008) argue that fundamental indexation is merely a new name for value investing as presented by Fama and French (1992). Perold (2007) considers value investing to be an active stock selection strategy and he therefore states that 3 fundamental weighting should not be called an indexation method. Arnott (2006) defines an index as an objective, rules-based, transparent, replicable and low turnover way to compose a portfolio. I leave it up to the investment community to decide if and how fundamental weighting can be useful to them. Asness (2006) writes that if the fundamental weighting methodology will be used as an index by the general public the spreads between over- and undervalued stocks will narrow. If this will happen and the advantage of fundamental weighting is primarily caused by the value premium I agree that fundamental weighting will become less attractive. Even if fundamental weighting becomes as widely used as currently cap weighting, I don’t see serious drawbacks. As long as fundamental weighted portfolios select and weight stocks by their fundamental values fundamental weighting will suffer less from the day by day whims of investors than cap weighting. 1.3 Collar weighting According to Siegel (2006) noise is not directly observable. Cap weighting invests more money in overpriced and less in underpriced stocks. So, fundamental weighting has a small-cap market bias in comparison with cap weighting. Therefore Treynor (2005) argues that fundamental weighting still depends, inversely, on market values. Kaplan (2008) shows that fundamental values cannot be unbiased value estimators because their sources, risk and expected growth, are determinants of market values. While cap weighting contains noise, fundamental weighting ignores risk and expected growth. Combining cap weighting and fundamental weighting seems like a good idea, since both methods contain useful information for investors. Arya and Kaplan (2006) present the collar weighting approach which combines the advantages of cap weighting and fundamental weighting while minimizing their disadvantages. Collar weighting
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