Growth Investing — The McKinley Capital Style

May 12, 2017

Robert A. Gillam, CFA Chief Investment Officer McKinley Capital Management, LLC
John B. Guerard Jr., Director of Quantitative Research McKinley Capital Management, LLC

Gregory S. Samorajski, CFA Director of Investments McKinley Capital Management, LLC

Executive Summary

McKinley Capital Management, LLC (“McKinley Capital” or the “firm”) has developed a proprietary definition of growth, which it refers to as “earnings acceleration” or E’ (see below for selected academic references). E’ is incorporated as one component of the firm’s standard alpha model (“MQ”). In this paper, we detail two global portfolio construction techniques which use the E’ factor as the stand alone alpha model. The simulated returns of these global portfolios were calculated and compared to appropriate benchmarks and to portfolios constructed using traditional growth variables. In both cases, the E’ portfolios generated superior simulated returns. While single factor investing is a specialized application, the firm believes that its E’ model can be used effectively for those institutions who seek growth exposure with the added possibility of superior excess returns. An E’ based portfolio might be especially useful for an institution seeking to diversify a large portfolio with existing exposure to other factors.

What We Do

McKinley Capital, a global growth equity specialist, constructs long-only and long-short portfolios that are systematically exposed to the factors of momentum and growth. Instead of using standard definitions of these factors, McKinley Capital uses proprietary, and what it believes to be superior formulations. The firm believes that its definitions, and other portfolio construction and stock selection techniques can lead to long-term outperformance, not only against standard benchmarks, but also against generic “smart-beta” portfolios. Further, McKinley Capital has the ability to construct long-only and long-short portfolios using proprietary definitions and proprietary factor rotation models.1

Recently, clients and prospects have asked the firm to design single-factor, long-only, and long-short portfolios. In response, the firm has launched products that contain a long-short dynamic momentum component. Dynamic momentum is a strategy where the firm buys long global developed securities exposed to momentum (as defined by the firm) and shorts securities negatively exposed to momentum (as defined by the firm). There is also a factor timing model which allows the firm to reduce long and short exposure when it forecasts that momentum, as a factor, is likely to experience increased return risk.

McKinley Capital has added and tested a protocol for long-only and long-short portfolios with primary exposure to its proprietary growth factor,  E’. The firm simulated the performance of these portfolios, using several construction methodologies, and compared performance to the market benchmark, portfolios constructed with primary exposure to Axioma Growth, and portfolios constructed with primary exposure to McKinley Capital’s full factor set MQ.

Simulated Performance of E’ in Optimized Long-Only Portfolios

Sophisticated market participants often view growth exposure as a risk rather than a return factor. There is occasionally a belief that historic and forecasted future earnings growth is fully discounted by the market. If that were true, traditional growth exposure would be unlikely to add value. McKinley Capital shares this view, which is why it created its own definition of growth. The E’ model ranking is calculated using earnings acceleration measures such as estimate revision, breadth, and return on invested capital, and ignores historic and forecasted growth rate. Typical McKinley Capital long-only portfolios are concentrated, containing approximately 75 stocks, with exposure to E’ and momentum. Portfolios are constructed using mathematical optimization and traditional techniques, with a qualitative check on the quantitatively constructed portfolios. To test the ability of the E’ model to add value, the firm constructed three sets of optimized global portfolios of long stocks, rebalanced monthly, from December 31, 2003 to March 31, 2017. The first set used E’ scores as the alpha model, the second set used Axioma Growth scores as the alpha model2, and, for comparison, the third set used the full McKinley Capital quantitative scores, MQ , as the alpha model. The simulated return results are reported in Table 1. In Figure 1, the simulated growth of a dollar invested in the E’ portfolio is compared to the growth of a dollar invested in the MSCI ACWI Growth index, and in the simulated Axioma Growth portfolio. (Please review the important notes on page four for more information regarding the simulations.)

Table 1 – Simulated Portfolio Risk and Return

January 2004 – March 2017 Annualized Return Standard Deviation Sharpe Ratio Tracking Error Information Ratio
Simulated E’ Portfolio
10.26% 15.75% 0.57 6.04% 0.49
Simulated Axioma Growth Portfolio (Gross) 4.83% 17.59% 0.20 6.12% -0.41
Simulated MQ Portfolio (Gross) 12.42% 13.56% 0.83 5.47% 0.93
MSCI ACWI Growth 7.31% 15.37% 0.40  –

Simulated returns calculated by McKinley Capital Management, LLC. Portfolio risk and return statistics calculated using Zephyr StyleADVISOR. 05/09/2017.

Figure 1: Simulated Growth of $1

Figure One: Simulated Growth of One Dollar
Simulated Returns calculated by McKinley Capital Management, LLC. Growth of $1 chart created using Zephyr StyleADVISOR. 05/09/2017.

During the test period, the E’ portfolio achieved simulated annualized returns of 10.26% with a 0.57 Sharpe ratio, and a 0.49 information ratio. This compares to the index annualized returns of 7.31% with a Sharpe ratio of 0.40. The Axioma Growth portfolio achieved simulated annualized returns of 4.83% with a Sharpe ratio of 0.20. During the test period, the traditional growth modeled portfolio demonstrated negative simulated excess returns. The E’ formed portfolio not only outperformed the index, but did even better on a simulated basis when compared to the traditional growth portfolio. The correlation of simulated excess returns between the E’ and Axioma Growth portfolios was 0.51. This correlation is consistent with the firm’s understanding that the E’ model operates in the growth universe, but remains distinct from traditional models. These distinctions led to superior simulated returns in the test period. As expected, the full MQ model performed most effectively. However, investors looking for primary portfolio exposure to the growth factor, constructed in a way that might generate positive excess returns, could  consider a McKinley Capital E’ long-only mandate.

Simulated Performance of E’ in Tranched Long-Only Portfolios

For its Dynamic Momentum strategy, McKinley Capital uses a different portfolio construction methodology. Instead of constructing concentrated portfolios using mathematical and traditional portfolio construction systems, the firm buys all of the stocks that achieve a certain rank (and pass qualitative and other filters). These stocks are held for a specific holding period (for example, six months). Each month the procedure is repeated, creating, in this example, six overlapping tranches each containing a large number of stocks. This methodology may be appropriate for a client looking for a portfolio of many stocks with exposure to a factor or factors.

Using a similar methodology, the firm simulated the returns of a long-only, “tranched,” E’ portfolio, and a long-only, “tranched,” Axioma Growth portfolio. Each monthly tranche consisted of the top decile of stocks — equally weighted — in the MSCI ACWI ranked either by E’ or by the Axioma Growth factor. The overall portfolio consisted of six overlapping portfolios, each containing several hundred stocks, and each held for six months. The time period was December 31, 2003 through March 31, 2017. Transaction costs were ignored for tranched tests. The simulated return results, and growth of a dollar charts are contained in Table 2 and Figure 2. (Please review the important notes on page four for more information regarding the simulations.)

Figure 2: Simulated Growth of $1

Figure Two: Growth of One Dollar

Simulated returns calculated by McKinley Capital Management, LLC. Growth of $1 chart created using Zephyr StyleADVISOR. 05/09/2017.

The simulated E’ tranched portfolios again outperformed the simulated Axioma Growth tranched portfolios (11.62% annualized versus 6.40% annualized). The E’ simulated information ratio was 0.67, compared to -0.11 for the simulated Axioma Growth portfolio. These results provide further confirmation that traditional growth portfolios are not likely to generate excess returns, but that McKinley Capital’s E’ portfolios might be expected to do so. Due to different transaction cost assumptions, the simulated return of the E’ tranched portfolio is not comparable to the return of the E’ optimized portfolio. However, the standard deviation of the tranched simulated returns was higher than the standard deviation of the optimized E’ portfolio simulated returns (19.23% versus 15.75%). This result is to be expected since part of the purpose of optimization is maximizing the return/risk tradeoff. The choice of version can depend on whether the client manages risk in-house, or whether risk management is considered the responsibility of the manager. Besides these approaches, McKinley Capital has the ability to create optimized, tranched hybrids, as well as long-short versions of any variety. The firm would be pleased to discuss all options with any prospective client.

Table 2 – Simulation Risk and Return

January 2004 – March 2017 Annualized Return Standard Deviation Sharpe Ratio Tracking Error Information Ratio
Simulated E’ Tranched Portfolio
(Gross of all costs)
11.62% 19.23% 0.54 6.45% 0.67
Simulated Axioma Growth Tranched Portfolio
(Gross of all costs)
6.40% 21.38% 0.24 8.42% -0.11
MSCI ACWI Growth 7.31% 15.37% 0.40  –

Simulated returns calculated by McKinley Capital Management, LLC. Portfolio risk and return statistics calculated using Zephyr StyleADVISOR. 05/09/2017.


McKinley Capital, a global growth equity specialist, has an extensive history of successfully building portfolios with specific factor exposure that have outperformed the relevant market benchmark. We believe this paper details a process that extends this skill set into a much broader application, and provides actionable information to investors. As always, your McKinley Capital account representative is available to discuss our research and investment process.

Important Notes:

The Simulated E’, Axioma Growth, and MQ Portfolios were constructed using the MSCI ACW Index as the initial investment universe. The simulation period was January 2004 through March 2017. McKinley Capital used the Axioma World-Wide Fundamental Equity Risk Model when preparing the simulation with the objective of maximizing return and minimizing total risk as estimated by variance. The portfolios were constrained to the benchmark, MSCI ACWI Growth, weight plus or minus two percent, a cash weight not above two percent, monthly turnover not to exceed sixteen percent, and tracking error to six percent. The number of securities held at any one time was limited to between 75 to 95 securities. Additionally, the ITG estimated cost curves with a default value of 1.5% were used in the mathematical optimization process.

The Simulated E’ and Axioma Growth Tranched Portfolios were constructed using the MSCI ACW Index as the initial investment universe. The simulation period was January 2004 through March 2017. McKinley Capital limited purchases to those securities with a long score of 90 or higher to construct each of the six equally weighted tranches which were rebalanced on a rolling six month schedule.

¹Robert A. Gillam, and Gregory S. Samorajski, Smart Beta Factor Allocation Portfolios, a McKinley Capital Management, LLC White Paper, June 8, 2016.

2Axioma Growth weightings, converted to ranks, are used to represent traditional returns to growth. While the exact formulas are proprietary, the firm believes that Axioma Growth scores are based on both historic and future forecasts of earnings growth. Please refer to the disclosure provided at the end of the document.


Elton, E.J., M.J. Gruber, and M.N. Gültekin. 1981. “Expectations and Share Prices.” Management Science 27, 975-987.

Hawkins, E.H., S.C. Chamberlain, and W.E. Daniel. 1984 “Earnings Expectations and Security Prices.” Financial Analysts Journal 24-39.

Guerard, J.B., Jr., M. Gültekin, and B.K. Stone. 1997. “The Role of Fundamental Data and Analysts’ Earnings Breadth, Forecasts, and Revisions in the Creation of Efficient Portfolios.” Research in Finance 15, Edited by A. Chan.

Ramnath, S., S. Rock, and P. Shane. 2008. “The Financial Analyst Forecasting  Literature: A Taxonomy with Suggestions for Further Research.” International Journal of Forecasting 31, 550-560.


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