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Blanchett continued from page 39 Figure 6 Monthly Returns Test: Morningstar Core Vs. Russell, July 1997- May 2010 Raw Return Difference Replicating Portfolio Alpha Difference Large Mid Small Large Mid Small Geomean -0.01% -0.07% 0.22% 0.02% -0.07% 0.27% Std Dev 1.67% 2.04% 2.18% 1.37% 1.43% 1.52% t-stat -0.07 -0.42 1.25 0.15 -0.57 2.21 Source: Morningstar Direct significance of the differences in the monthly returns for the Morningstar Core indexes versus the Russell indexes on both a raw return basis and on a market-factor-adjusted basis. While the Morningstar Large Core Index had a higher average monthly geometric return than the Russell 1000 (0.293 percent vs. 0.274 percent), it had a negative return when the differences were compared on a monthly basis. The results of the market- factor-adjusted test were generally the same as the raw return test, except the Morningstar Large Core Index had a higher return than the Russell 1000 when the returns were factor-adjusted. The outperformance of the Morningstar Small Core Index over the Russell 2000 becomes more pronounced versus the raw return at +0.27 percent per month (which works out to 3.27 percent per year). Over the entire test period, the cumulative Morningstar Small Core outperformed the Russell 2000 by over 50 percent. The outperformance is also statistically significant at the 5 percent level (i.e., has a t-statistic greater than 2 in absolute terms). Conclusion The results from this analysis suggest that Core appears to be a better “middle” investing strategy than Blend. The Morningstar Core indexes tended to have both higher relative returns and higher factor-adjusted returns than their respective Morningstar Blend and Russell capitalization peers, although most of the results were not statistically significant. Therefore, it may be worthwhile for investors seeking non- Value and non-Growth style exposures to consider investing in the true “middle,” which would be a Core approach, versus a Blend approach, which is currently the most popular. Works Cited Banz, Rolf. 1981. “The relationship between return and market value of common stocks.” Journal of Financial Economics, vol. 9: 3-18. Basu, S. 1977. “Investment Performance of Common Stocks in Relation to Their Price-Earnings Ratios: A Test of the Efficient Markets Hypothesis.” Journal of Finance, vol. 32, No. 3: 663-682. Blanchett, David. 2010. “Can Indexes Generate Alpha?” Journal of Indexes, vol. 13, No 2 (March/April): 32-37. Carhart, Mark M. 1997. “On Persistence in Mutual Fund Performance.” Journal of Finance, vol. 52, No 1: 57-82. Cremers, Martijn, Antti Petajisto, and Eric Zitzewitz. 2008. “Should Benchmark Indices Have Alpha? Revisiting Performance Evaluation.” Working paper version July 31, 2008: http:// www.mccombs.utexas.edu/Dept/Finance/fea/papers/f2-should-benchmark-indices.pdf. Fama, Eugene F., and Kenneth R. French. 1992. “The Cross-Section of Expected Stock Returns.” Journal of Finance, vol. 47, No 2: 427-466. Fama, Eugene F., and Kenneth R. French. 1993. “Common Risk Factors in the Returns on Bonds and Stocks.” Journal of Financial Economics, vol. 33: 3-53. Israelsen, Craig. 2007. “Variance Among Indexes.” Journal of Indexes, vol. 10, No 3 (May/June): 26-29. Appendix I Four Factor Results Morningstar Core Morningstar Blend Russell Large Mid Small Large Mid Small Large Mid Small Intercept 0.03% 0.03% 0.05% -0.02% 0.03% -0.07% 0.01% 0.09% -0.24% Mkt-RF 0.89 0.98 1.00 0.96 1.02 1.01 0.98 1.03 1.00 SMB -0.26 0.08 0.53 -0.23 0.18 0.61 -0.14 0.21 0.75 HML 0.12 0.48 0.48 -0.06 0.26 0.30 0.02 0.23 0.27 Mom. -0.02 -0.07 -0.08 -0.01 -0.04 -0.09 -0.02 0.01 0.01 R² 89.91% 87.79% 91.89% 98.66% 94.71% 96.38% 99.42% 95.62% 97.36% Sources: Morningstar Direct, Kenneth French www.journalofindexes.com January / February 2011 49
Talking Indexes Sorting And Digging Breaking down the broad market By David Blitzer Most of traditional securities analysis falls into one of two areas—sorting or digging. Sorting consists of assigning stocks to classifications, groups or categories; digging involves delving into the financial, legal or operational details one company at a time. Sorting begins when securities are divided into asset classes. Most investors believe that stocks are different from bonds, have different risks and returns, and usually behave differently. Any two stocks are likely to be more similar to one another than a stock is to a bond. This illustrates a key aspect of sorting: Two securities in the same category have less variation than two securities in different categories. While asset classes may be obvious, things become more interesting and useful when we divide stocks into style (growth and value); size (large, mid and small); or sectors. Most broad-based indexes sort stocks by style, size and sector. Further divisions are often included, such as more narrowly defined industries, style versus pure style or financial measures such as dividend yield. Since the ultimate objective is to understand why some stocks perform better than others, a successful categorization should tell us something about past returns and risks at different periods of time. Unfortunately, a categorization of stocks—like all other securities analysis—cannot provide truly reliable predictions of future returns. Of course, that doesn’t stop analysts from trying. The three categorizations mentioned—style, size and sector—are the most oft-used sorts. These weren’t just the creation of index providers searching for more ways to slice and dice their indexes; rather, they are the result of an established and still-growing body of literature on comovement; that is, how some groups of stock tend to comove or move together. The basic concept behind comovement is that fundamental factors such as earnings, capital structure or a company’s business model determine a stock’s underlying value. If one brings together a group of stocks with similar fundamentals, the stocks should have similar risks and returns. Dividing stocks into growth and value, usually done with various financial measures such as price-to-book value, dividend yield or others, is one example. Some fundamental factors such as earnings per share or dividends are easy to measure, others such as industry supply conditions or shifts in demand for a group of products are harder to identify and quantify. One way to deal with hard-to-measure factors is to find an easy-to-measure proxy such as what industry a company is in. Grouping stocks by sector or industry works in the sense that stocks within a sector are more alike than stocks in different sectors. Another example of a proxy measure is size. Smallcap stocks often behave differently than large-cap stocks; one group may rise when the other falls. While there are lots of ideas about why small-caps don’t mimic large-caps, the key idea is that size can be a useful way to classify stocks. All these sorts relate, more or less, to some fundamental factor affecting a stock’s returns. However, comovement can be a useful tool to examine situations where other, nonfinancial or noneconomic, factors are involved. One that should be familiar is home-country bias—despite globalization, many investors prefer stocks in their home market and therefore grouping stocks by nationality is common. Another might be whether the stock is listed on the NYSE or Nasdaq, even though the listing requirements can be quite similar. A more reasonable, and more studied, factor in stock performance is the extent of analyst coverage and the speed with which news about a company spreads through the markets. Widely followed stocks are heavily analyzed and discussed, while stocks with little analytical coverage are sometimes seen as 50 January / February 2011
Page 1 and 2: Improved Beta? Noël Amenc, Felix G
Page 3 and 4: Contributors Noël Amenc Noël Amen
Page 5 and 6: FREE SUBSCRIPTION OFFER! The Journa
Page 7 and 8: David Smith Publisher dsmith@fa-mag
Page 9 and 10: Editor’s Note Considering Alterna
Page 11 and 12: Improved Beta? A comparison of inde
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Page 15 and 16: Figure 2 Risk/Reward Difference Com
Page 17 and 18: show marginal statistical significa
Page 19 and 20: Figure 5 Appendix: Risk/Reward Diff
Page 21 and 22: The Evolution Of Equal Weighting Ad
Page 23 and 24: Figure 3 1 0.8 0.6 0.4 0.2 0 Figure
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Page 27 and 28: structures in the U.S. large- and s
Page 29 and 30: negative alpha is observed for both
Page 31 and 32: The Weight Debate A roundtable 30 J
Page 33 and 34: for perceived growth is constantly
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Page 39 and 40: that given size range. In contrast,
Page 41 and 42: Benchmarking Microfinance Equity In
Page 43 and 44: Figure 1 Illustration: Determining
Page 45 and 46: Figure 3 Establishing MFI SVIX Annu
Page 47 and 48: Figure 5 WSAS MFI SVIX Series - Bas
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Page 53 and 54: News Indexes Regain Edge Over Activ
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