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More Markowitz Efficient Portfolios Featuring the USER Data and an Extension to Global Data and Investment Universes

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Abstract

In the previous chapter, we used the Barra Aegis system to create and measure portfolios using the USER model. The Barra Model is referred as a fundamental risk model because security fundamental data is used to create the risk, or style, indexes. In this chapter, we create portfolios using statistically-based risk models in the USA and global markets. In this chapter, we address several additional issues in portfolio construction and management with Guerard et al. (2012) USER data. First, we test the issue of whether Markowitz mean–variance, MV, portfolio construction model (1956, 1959, 1987), with a fixed upper bound on security weights, dominates the Markowitz enhanced index tracking, EIT, portfolio construction model (1987) in which security weights are an absolute deviation from the security weight in the index. We will refer to the absolute deviation from the benchmark weight-enhanced index portfolio construction weight as the equal active weighting, or EAW, portfolio construction model. Guerard, Krauklis, and Kumar (2012) reported that MV portfolios produced higher Information Ratios and Sharpe Ratios than EAW portfolios with weights less than EAW4. A newer approach to the systematic risk optimization technique is the Systematic Tracking Error optimization technique reported by Wormald and van der Merwe (2012). We will show the effectiveness of the Systematic Tracking Error approach using Global Expected Returns (GLER) data over the 2002–2011 period. Finally, we demonstrate using the Axioma system and its Alpha Alignment Factor (AAF) analysis reported in Saxena and Stubbs (2012) that the AAF is appropriate for USER and GLER Data and that the Axioma Statistical Risk Model dominates the Axioma Fundamental Model.

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Notes

  1. 1.

    In Chap. 6, we reported that asset selection was statistically significant in the Barra Aegis system. We report similar results with Sungard APT and Axioma. The author’s belief is that the three systems can be used to produce highly statistically significant asset selection and very good portfolio returns and great risk-return statistics. One needs to decide if one wants to set Lambda, as with Sungard APT, active risk, as with Axioma, and risk acceptance parameters, as with Barra. In the author’s view, APT, the system that the author has used since 1989 is outstanding and very adequate. Many (intelligent) people choose active risk (tracking error targets). As long as you are statistically significant in asset selection with the USER variable (or other proprietary forms) and are man-enough to implement the model to maximize the Sharpe Ratio and Geometric Mean (having a negative size exposure and positive momentum, growth, and value exposures), then the choice of APT and Axioma (and Barra) is analogous to the man who is asked if he prefers blondes, brunettes, or redheads; one prefers great minds, strong wills, good looks, and the hair color, preferably natural, is a lesser concern. Not all risk models and optimizers work, as we found out in the McKinley Capital Horse Race and research seminars of 2009 and 2011. Some systems are more expensive and their portfolios are dominated by APT, Axioma, and Barra on a risk-return analysis. We found a decidedly negative correlation between cost and performance.

  2. 2.

    Harry Markowitz often (always) reminds his audiences and readers that he discussed the possibility of looking at security returns relative to index returns in Chap. 4, footnote 1, page 100, of Portfolio Selection (1959).

  3. 3.

    The reader is referred to Chap. 2 of Guerard (2010) for a history of multi-index and multi-factor risk models.

  4. 4.

    Guerard (2012) decomposed the MQ variable into: (1) price momentum, (2) the consensus analysts’ forecasts efficiency variable, CIBF, which itself is composed of forecasted earnings yield, EP, revisions, EREV, and direction of revisions, EB, identified as breadth, Wheeler (1991), and (3) the stock standard deviation, identified in Malkiel (1963) as a variable with predictive power regarding the stock price-earnings multiple. Guerard (1997) and Guerard and Mark (2003) found that the consensus analysts’ forecast variable dominated analysts’ forecasted earnings yield, as measured by I/B/E/S 1-year-ahead forecasted earnings yield, FEP, revisions, and breadth. Guerard reported domestic (US) evidence that the predicted earnings yield is incorporated into the stock price through the earnings yield risk index. Moreover, CIBF dominates the historic low price-to-earnings effect, or high earnings-to-price, PE. We use CTEF, PRGR, and CIBF interchangeably in this monograph.

    It is extremely important for the author to state that this section draws heavily from the APT Analytics Guide, written originally by John Blin and Steve Bender. The author’s paper with Blin and Bender demonstrated how a statistically significant expected model can be used in the APT system to create portfolios that can produce excess returns (and statistically significant asset selection). The author is extremely grateful to APT and Sungard APT for its work with McKinley Capital Management.

  5. 5.

    There is a large literature on the application of optimization to portfolio construction, starting with Markowitz (1952, 1959) and reviewed in Fabozzi et al. (2002). A recent comprehensive overview can be found in the volume edited by Guerard (2010). An alternative approach might be pursued using ultrametrics and spanning trees rather than correlation shrinkage, see Onnela et al. (2003) for more on this approach.

  6. 6.

    The Bias statistic, shown is a statistical metric which is used to measure the accuracy of risk prediction; if the ex-ante risk prediction is unbiased, then the bias statistic should be close to 1.0 (see Saxena and Stubbs2010 for more details). Clearly, the bias statistics obtained without the aid of the AAF methodology are significantly above the 95% confidence interval thereby showing that the downward bias in the risk prediction of optimized portfolios is statistically significant. The AAF methodology recognizes the possibility of inadequate systematic risk estimation and guides the optimizer to avoid taking excessive unintended bets.

  7. 7.

    The author worked on the GLER analysis with Anureet Saxena. Any errors remaining in this section are the sole responsibility of the author.

  8. 8.

    It is interesting to note that initial Axioma analysis suggests that purchasing AWCG constituents produce similar Information Ratios and Sharpe Ratios to purchasing FactSet and Thomson Financial securities (with at least two analysts covering the stocks, a universe exceeding index constituents by a factor of 5–6 times). The similar Sharpe Ratios and IRs are very interesting given the very illiquid composition of many securities (trading volume of less than $15 MM USD, daily).

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    John B. Guerard Jr.

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Guerard, J.B. (2013). More Markowitz Efficient Portfolios Featuring the USER Data and an Extension to Global Data and Investment Universes. In: Introduction to Financial Forecasting in Investment Analysis. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5239-3_7

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