3. RESULTADOS DE LA AUDITORÍA
3.3 COMPONENTE CONTROL FINANCIERO
3.3.1 Estados Contables
3.3.1.14 Hallazgo administrativo por error al registrar la provisión del mes de
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n the indexing and ETF world, 2012 may be remem- bered as the year ETF closings reached sufficient num- bers to dominate industry gossip and news about ETFs. IndexUniverse’s ETF Watch lists about 100 ETF closures in 2012, almost twice the annual pace seen in 2008-2010 dur- ing the financial crisis and recession. There is little chance we will run out of ETFs or stop launching new ones any time soon: The total number of ETFs continues to expand, with nearly 180 added in 2012, and money continues to flow into ETFs, both new and old. By now, some 20 years after the launch of the SPDR S&P 500 ETF (NYSE Arca: SPY) kicked off the rise of the ETF industry, most new ETFs— and recently closed ones—are based on strategy indexes rather than broad-based market indexes. Strategy domi- nates the new issues because there are few, if any, markets left that aren’t already covered by ETFs. Further, strategy indexes—and hence the ETFs linked to those indexes— focus on various investor interests such as dividends or low volatility. Digging into the nature of strategy may explain the 2012 rise in ETF terminations.While we shouldn’t ignore some of the financial factors cited for ETF closures—rising operating expenses; increases in the minimum size needed to break even; or competition among ETF issuers—understanding the nature of strategies and the indexes that track them is important for understand- ing why some ETFs survive and others fade away.
Financial and economic research going back several decades focuses on why some stocks tend to outperform the market. The results of this research are the raw mate- rial of strategy indexes. Many investors are familiar with ideas that small-cap or value stocks tend to outperform large-cap or growth stocks. The more formal statement of these arguments is the three-factor model of Gene Fama and Ken French,1 who identified size measured by market
capitalization and a value bias measured by the ratio of book value to market value and then added market per- formance as the third factor driving stock performance. Later work by Mark Carhart2 introduced momentum
measured by the difference between short-term and intermediate-term performance as a fourth factor. Recent research has expanded some of these ideas with different measures of value or momentum and with new factors such as volatility or liquidity. The three- or four-factor models underlie the first generation of strategy indexes focusing on combinations of growth or value and large-, mid- or small-cap stocks and momentum.
These efforts were only the beginning of strategies. Other factors soon joined, including dividends, specific sectors or industries, mergers, acquisitions, spinoffs and other corporate actions or such company characteristics as family ownership or social policies. Strategy indexes are attempts to exploit times when the market deviates from the theory that all stocks offer the same returns after adjustment for risk and correlation. Some strategies—for example, buying stocks in only one sector—have limited lifetimes, since the market is constantly evolving. Other strategies seek longer lifetimes and more staying power; some claim to do well in various markets.
All strategies face three challenges that could limit their performance. An ETF based on a strategy that underperforms is living on borrowed time. Consider an ETF that holds stocks in only one sector: Markets shift over time, and what works one day may fail miserably the next day. Financial stocks were shunned in 2007-2009 but gained twice as much as the S&P 500 in 2012.
The second challenge is data mining: Given a big database, a fast computer and enough time, an analyst can “discover” some rule guaranteed to pick yesterday’s winning stocks.
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Despite high t-statistics, statistical and economic significance, and an R2 of 99.99 percent, the discovery most often doesn’t
work going forward. The cause is simple: Try enough models, equations and ideas, and a few are certain to look good. Even when data mining creates fool’s gold instead of the real thing, some of these do become filings for new ETFs.
The last challenge is “success.” Once word gets around that some new strategy works, everyone rushes in. Suppose you designed an index of technology stocks that pay dividends to combine the stability of dividend payers with growth and it beats the market quite handsomely. Other tech-dividend indexes appear, hedge funds buy up high-dividend tech stocks and CNBC runs a hot idea story, while IndexUniverse lists all the newly registered ETFs targeting the area. Prices of dividend-paying tech- nology stocks would be bid up, and performance going forward would collapse amid falling dividend yields for the group. There was no market rotation and the idea wasn’t data-mined, yet success breeds its own failure.
A recent research paper by McLean and Pontiff3 examines
the loss in stock predictability and strategy return caused by research publication. Their study explores 82 investment ideas going back over 20 years, testing losses that might be blamed on data mining as well as crowds. The impact of data mining and statistical analysis is mixed and not statistically significant. The average effect of popularity through publi- cation reduces the expected returns after publication by 35 percent of the returns before publication.
Investment strategies, like many other investment ideas, are often ephemeral. Moreover, the attractiveness of strategy ETFs differs from the attractions of investing in an ETF that tracks a broad-based market index like the S&P 500 or a total market index. An investor owning a strategy ETF hopes it is a good idea that will last long enough; the investors who choose an ETF tracking the S&P 500 or a total market index believe in low costs and participating in the stock market.
Endnotes
1 Fama, Eugene F. and French, Kenneth R. (1993). “Common Risk Factors in the Returns on Stocks and Bonds,” Journal of Financial Economics 33 (1): 3–56 2 Carhart, Mark M. (1997). “On Persistence in Mutual Fund Performance,” Journal of Finance 52 (1): 57–82
3 McLean, R. David and Pontiff, Jeffrey E. “Does Academic Research Destroy Stock Return Predictability?” (Oct. 3, 2012). AFFI/EUROFIDAI, Paris, December 2012 Finance
Meetings Paper. Available at SSRN: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2156623
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post for both opportunities arising from negative geopolitical events as well as a sanity check against bubbling stock markets. Comparing global equity markets on a relative basis allows the portfolio manager to create portfolios of cheap stocks markets, while avoiding or even shorting expensive markets.
Appendix: Other Valuation Models
Samuel Lee has a great article titled “The Hedgehog’s Error”9 on Morningstar’s website that sorts glob-
al countries based on value (price/book) using the French/Fama database. Not surprisingly, he finds that sorting on value works well.
We utilize the database to sort the countries (12 in 1975 and rising to 20 by 1991) based on various measure of value. In Figure 12, we demonstrate the results of sorting the countries on a yearly basis and choosing the cheapest x percent of the universe (from 10 to 33 per- cent). Results are U.S. dollar based, nominal.
Endnotes
1
Shiller maintains a website with an Excel download that includes historical data with formulas illustrating how to construct his 10-year CAPE: http://www.econ.yale. edu/~shiller/data.htm. For a step-by-step guide, Wes Gray at Turnkey Analyst has a good post that walks through the steps necessary to construct the metric: http://turn- keyanalyst.com/2011/10/the-shiller-pe-ratio/
2 “Estimating Future Stock Market Returns” by Adam Butler and Mike Philbrick tackles the issue of different measurement periods from one to 30 years (as well as other
valuation models).
3 John Hussman has a few good articles on this topic: “Estimating the Long-Term Returns on Stocks” and “The Likely Range of Market Returns in the Coming Decade”;
Joachim Klement also recently published the paper “Does the Shiller-PE Work in Emerging Markets?” that performs a similar analysis.
4 Rob Arnott of Research Affiliates touches on this important topic in his white paper “King of the Mountain” (http://www.researchaffiliates.com/Our%20Ideas/Insights/
Fundamentals/Pages/F_2011_Sept_King_of_the_Mountain.aspx). Two other books speak of CAPEs and inflation/deflation levels. The first is “Unexpected Returns: Understanding Secular Stock Market Cycles” by Ed Easterling, and John Mauldin’s “Bull’s Eye Investing: Targeting Real Returns in a Smoke and Mirrors Market.”
5
One such resource is Russell Napier, who authored Anatomy of the Bear: Lessons From Wall Street’s Four Great Bottoms, and who discusses global CAPEs in a video here: http://video.ft.com/v/946244201001/Long-View-Historian-sees-S-P-fall-to-400 . We also found two great recently published papers: “Does the Shiller-PE Work in Emerging Markets?” by Joachim Klement (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2088140), and “Value Matters: Predictability of Stock Index Returns” by Angelini, Bormetti, Marmi and Nardini (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2031406).
6 http://www.tweedy.com/research/papers_speeches.php 7 http://www.iijournals.com/doi/abs/10.3905/jpm.1991.409327 8 http://www.mebanefaber.com/2011/11/17/sorting-countries-by-dividend-yield-2/ 9 http://etf.morningstar.com/BlogArticle.aspx?postid=3281399
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