May 16, 2017
Robert A. Gillam, CFA Chief Investment Officer McKinley Capital Management, LLC
Gregory S. Samorajski, CFA Director of Investments McKinley Capital Management, LLC
Momentum, as used in investment management, is the observed phenomenon that the prices of financial instruments appear to follow trends. Momentum is used to compute investment signals. Two practitioner-oriented articles have been recently distributed. Jim McKee (“McKee”), with the Callan Institute, published a short article explaining when and why momentum investing works and how to manage the risk of large losses1. Christopher Geczy and Mikhail Samonov wrote an article regarding the returns to momentum investing in the U.S. equity markets from 1801 to 20122. Among other findings, the article reports the empirical duration of positive momentum signals in the U.S. equity markets, over a period of approximately five months, and quantifies the risk of momentum investing around major market inflection points. McKinley Capital Management, LLC (“McKinley Capital” or “the firm”), is a prominent registered investment advisory firm that uses price momentum and earnings acceleration signals to manage global equity investments. The firm is one of the concept leaders in the application of momentum signals in global investing. In this paper, the firm summarizes recent articles, adds its own research insights, and analyzes the effectiveness and risks of momentum investing in the global developed equity markets for the past 20 years.
Momentum Investing: What is it, Why Might it Work, and What are the Risks?
Momentum investors seek to exploit the possibility that, for a period of time, security prices seem to follow trends. The idea is that investors buy or hold equities that have been the best performers during the prior six to twelve months, and short-sell or avoid equities that have underperformed in the same time period. In his article, McKee explains his opinion of the theory3. As a behavioral finance phenomenon, when new information arrives into the market, investors initially underreact. Thus, good (bad) news results in somewhat higher (lower) prices; which, never-the-less, remain below (above) those implied by the news. This effect creates the possibility for continued price change as the new information becomes more widely understood and incorporated. At some point, other behavioral shortcomings, such as confirmation bias, support the continuation of the trend until the market price represents over-reaction to the news. The final phase occurs when overreaction results in trend reversal. The Geczy and Samonov analysis indicates that in the U.S. equity markets, positive performance, based on momentum signals, has persisted for between five and eight months before systematically reversing4.
McKee explains that when the trend reverses, the correction can be sudden and large. This leads to the risk that momentum investing can be profitable in the long run, but is subject to occasional large losses, even when employing a long-short equity momentum strategy. These reversals often occur at major market inflection points5; the time period March to May 2009 comes to mind. McKee recommends diversification across markets, and the use of multiple factors and strategies to reduce risk6. Indeed, commodity trading advisors and other global macro strategists often diversify their trend-following positions across many markets – stock indexes, bonds, currencies, metals, agriculture, energy, etc. The hope is that not all markets inflect at the same time. Similarly, within the equity sector, institutional investors often employ managers when taken together incorporate diverse investment styles and factors.
Are there additional tactics available to managers who specialize in equity momentum investing, and to investors who seek the best managers in each style? Geczy and Samonov explain the challenge with market reversals7. Momentum calculations can be decomposed into stock specific and systematic market-based effects. Because of the market effect, it is not surprising to learn that following a prolonged up market, on average, the best raw momentum scores are calculated for high risk, high beta stocks. Similarly, low risk, low beta stocks, on average, are assigned the best scores following a prolonged down market. This “market-effect” is what makes inflection points treacherous. For example, the authors show that in the U.S., the beta of an unadjusted market neutral momentum portfolio has varied between .2 and .8 following an “up” market duration of between two to three years. Following similar duration “down” markets, unadjusted market neutral long-short portfolio betas have varied between -.4 and -1.08. When a down equity market reverses sharply, a low risk, negative beta portfolio is likely to underperform. The authors demonstrate that the time of highest risk for U.S. equity momentum investing is following a down market of two to two and one-half years9.
The McKinley Capital Approach to Momentum Investing
McKinley Capital specializes in global growth equity investing utilizing its expertise and understanding of the benefits of price momentum and earnings acceleration. The firm has used momentum as a component of its long-only investment process since its founding in 1990. However, the firm’s specialized expertise in price momentum might be best expressed through momentum exposure implicit in its long/short alternative investment strategies. This exposure is derived from holding long positive momentum global equities and short selling low momentum equities. McKinley Capital does not currently manage a stand-alone, long/short global momentum strategy. Although, the firm believes it could create such a product. If unleveraged, McKinley Capital believes such a strategy could generate high single digit returns, below 8% volatility and low correlation with traditional asset classes10. In researching and managing its dynamic momentum component, McKinley Capital has had the opportunity to confirm and extend many of the U.S. results reported by Geczy and Samonov, into the global equity space.
Following are summary findings. Momentum as a risk factor has been rewarded in the global developed universe over the past 20 years. With regard to the momentum signal, there have been gains to diversification across multiple global developed markets. McKinley Capital concurs with McKee’s and Geczy and Samonov’s risk analysis. For the same reasons as in the U.S., global momentum investing is likely to be volatile at major market inflection points. As justified in the following chart, the firm seeks to limit any long-short risk mismatch of its portfolio regardless of the duration of the up or down market state. It does this by adjusting the long/short investment ratio as the unadjusted risk of each side changes. Finally, McKinley Capital incorporates a process to ensure that long and short exposure is reduced prior to the possibility of a major inflection point. Research results follow.
The firm is often asked why, in its early years as a U.S. based equity manager, it chose momentum as one of its primary investment factors. The answer is based on the observation that, long-term, momentum has been the factor most indicative of excess returns in the U.S. markets. Table 1 shows the Fama and French returns to momentum, value, and small size tilted portfolios in the U.S. from 1927 to 2017. Of the three main Fama and French factors, momentum has been the best on an absolute and risk adjusted basis. There is no comparable long-term non-U.S. data. The Fama and French database begins in 1990. However, the limited Fama and French data available seems roughly consistent.
Table 1: Fama and French U.S. Factor Returns
January 1927 – March 2017
(Winners Minus Losers)
|Annualized Standard Deviation
(Winners Minus Losers)
|Return / Risk|
Source: Kenneth R. French website, http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html. Data retreved May 2017. Factor winners minus losers (WML) returns are as follows: 1) Momentum – Top 30% ranked by momentum minus bottom 30% ranked by momentum segmented by market capitalization, 2) Value – Top 30% ranked by book to market minus bottom 30% ranked book to market, and 3) Size – Top 30% ranked by market capitalization minus bottom 30% ranked by market capitalization.
20 Years of Global Momentum Results
To test the effectiveness of momentum based global investing, the firm collected almost 20 years (12/31/97 – 11/30/2016) of monthly stock returns in USD across eight global developed regions – U.S., Canada, U.K, Euro Europe, Non-Euro Europe, Asia Ex-Japan (developed only), Japan, and Australia/New Zealand. Every month, the firm grouped each stock in each region into quintiles based on a simple momentum score, and measured the equally weighted average return difference between the top and bottom quintile stocks (W-L) for the subsequent month11. The results are portrayed in Figure 1. In seven out of eight regions, the W-L simulated portfolio (the “portfolio”) generated positive annualized returns. The returns ranged from a high of 14.60% in Non-Euro Europe to a low of -0.1% in Asia ex-Japan (developed only). These results provide evidence that momentum, as a compensated risk factor, is not limited to the U.S. In fact, with an annualized return of 4.20%, the U.S. ranked only 6th out of the 8 global regions12. These results are of the same order of magnitude as the long term U.S. results reported by Geczy and Samonov, and Fama and French. From 1801 to 2012, the Geczy and Samonov U.S. W-L U.S. momentum portfolios showed an annualized return of 3.8%. As indicated in Table 1, from January 1927 to March 2017, the Fama and French U.S. W-L U.S. momentum portfolios showed an annualized return of 6.5%.
This research indicates that stand-alone dollar neutral investing can be risky. The standard deviation of W-L returns varied from a low of 20.47% annualized in Australia/New Zealand to a high of 31.90% annualized in Asia Ex-Japan (developed only). In the center were Euro Europe at 23.80% annualized and the U.K. at 24.73% annualized. McKee’s recommendation to diversify helped manage the risk. The monthly standard deviation of a combined equal region weighted W-L portfolio was 19.02% annualized. The equal region weighted portfolio annualized return was 10.40%. The reason diversification across regions works is that momentum based returns have been lowly correlated across global developed countries. Table 2 reports data collected by McKinley Capital in an earlier study. The research suggests other ways in addition to diversification that may reduce risk. As discussed earlier, dollar neutral W-L momentum portfolios can have unequal long and short side risk – whether measured by total risk or by beta. This is due to the “market–effect.” Following periods of strong up (down) markets, the higher (lower) total risk, higher (lower) beta securities will be measured with the higher (lower) momentum scores, all other things being equal. It is not clear this market direction based momentum risk is compensated.
Table 2: Traditional Long/Short Momentum (Winners Minus Losers: WML) Monthly Return Correlation Matrix (Simulation),
Mar 1991 – Dec 2014
Source: FactSet, 12/23/16. Analysis by McKinley Capital Management, LLC, 12/28/16. Data is simulated not actual. Past performance is not indicative of future returns. Information is believed to be reliable but accuracy cannot be guaranteed.
To address this question, McKinley Capital measured the modeled13 total risk and beta for each long and short side portfolio for each region and the equal weighted combination14, and computed the ratio. For example, in the U.S., the ratio of long to short modeled total risk averaged .97, but varied from a high of 1.84 to a low of .52. The comparable ratio numbers for U.S. W-L modeled beta were .99 average, 2.44 high, and .49 low. Other regions showed similar dispersion. The firm formed equal risk portfolios by using a long side of 100%, but adjusting the percentage weight of the short portfolio by the inverse of the risk ratio. For example, if the risk ratio was .9 (shorts riskier than longs) the short side weight would be 90%. The results are shown in Table 3. For each region, the standard deviation was reduced using equal risk weighting within each region. At the aggregate level, the monthly standard deviation of the eight region W-L portfolio was reduced from 5.49% to 4.45% using total risk weighting, and to 4.52% using beta weighting15. The average monthly return results varied by region. At the aggregate level, the average monthly return for the eight region total risk weighted portfolio was .93%, and .94% for the beta weighted portfolio. Due to the miniscule reduction in return, the firm concludes that market direction based momentum risk has not been significantly compensated in the global developed equity markets. McKinley Capital offers equal risk weighted W-L portfolio construction for its dynamic momentum strategy.
Table 3: Simple Momentum Results – Equally Risk Weighted Return and Volatility (Simulation)
Jan 1997 – Nov 2016
|Monthly Return St.
St. Dev. (Beta Risk
|Monthly Return St.
Dev. (Total Return
|Euro – Europe||1.22||6.87||1.17||5.34||1.16||5.17|
|Equal Weighted Global||0.99||5.49||0.94||4.52||0.93||4.45|
Source: FactSet, 12/23/16. Analysis by McKinley Capital Management, LLC, 12/28/16. Data is simulated not actual. Past performance is not indicative of future returns. Information is believed to be reliable but accuracy cannot be guaranteed.
While risk adjusted long/short positioning is expected to mitigate risk, the firm notes that major market inflection points represent additional risk to momentum strategies. As suggested by Geczy and Samonov, the risk is highest when the market experiences a V-shaped recovery from a sustained bear market. Table 4 lists some of the worst months for momentum in the U.S., most having occurred following recoveries from sustained bear markets.
Table 4: Worst Momentum Months Driven by Outperformance of Prior Loser Stocks During Junk Rallies (Simulation)
Worst Months for Momentum, U.S. 1927-2014
|Worst 15 Avg||-36.3||7.6||43.9||15.9||-14.1||-37.2||33.8|
|All Mths Avg||1.2||1.5||0.3||0.9||5.7||24.9||16.2|
Source: Kenneth R. French Data Library, 2/26/15. Analysis by McKinley Capital Management, LLC, 2/26/15. Data is simulated not actual. Past performance is not indicative of future returns. Information is believed
to be reliable but accuracy cannot be guaranteed.
While it is impossible to forecast the imminent arrival of a major turning point, it is possible to observe conditions which set the stage for a possible major reversal. For example, only after the market has declined significantly is it possible for a strong recovery to occur. To address this possibility, we test a simple market state rule based on the research of Geczy and Samonov. When the underlying regional market has declined over the prior 24 months, we define the market state as negative, and would exit all long and short positions in that region16. The results are reported in Figure 2. For the eight region market state adjusted portfolio, the annualized return improves to 14.90% with a standard deviation of 15.20% annualized. The comparable numbers for the unadjusted portfolio were an annualized return of 10.40% and a standard deviation of 19.02% annualized. The total return risk weighted and market state adjusted portfolio had an annualized return of 13.30% and a standard deviation 13.40% annualized. This compares to an annualized return of 10.40% and a standard deviation of 15.30% annualized for the total return risk adjusted portfolio without the market state adjustment. Whichever version is selected, it appears that even a simple market state rule has the potential to increase return and reduce the risk of a global developed momentum strategy.
In this paper, the firm reviewed the research on the benefits and risks of momentum investing in the U.S. markets historically. The firm extended this research using 20 years of data covering all of the major global developed markets. McKinley Capital concluded that while there appears to be validity to the use of the momentum factor in global investing, there is also risk. In this paper, the firm reported the results of simple risk reduction techniques, and found efficacy to diversification across global regions, equal risk weighting and market state considerations. McKinley Capital would be pleased to provide you with the
information needed to understand the benefits and risks of the firm’s Dynamic Momentum approach.
1Jim McKee, “Momentum The Trend is Your Friend,” Callan Insitute Research, October, 2016.
2 Christopher C. Geczy and Mikhail Samonov, “Two Centuiries of Price-Return Momentum, Financial Analysts Journal, Vol 72, No. 5, September/October 2016. 32-56.
3 McKee, “Momentum,” 3.
4Geczy and Samonov, “Momentum,” 38-40.
5McKee, “Momentum,” 4.
6Ibid. 3, 5.
7Geczy and Samonov, “Momentum,” 43-50.
10Please contact your McKinley Capital account representative to receive detailed information and performance results for the firm’s alternative investment strategies.
11The momentum rank, PM71, is based on the rank of the unadjusted raw return of each security in a region over the past six months lagged by one month. For example, the rank on October 31st is based on the ranked raw returns of the region’s stocks from March 31st to September 30th. The one month lag accounts for the well-known short-term momentum reversal effect. See for example, Geczy and Samonov, “Momentum,” 35.
The offsetting effects of transactions costs and short stock rebates were ignored.
12Note that Geczy and Samonov’s grouping procedure was similar but not identical to the McKinley Capital method. Geczy and Samonov used a 12 lag 2 calculation. See, for example, Geczy and Samonov, “Momentum,” 35.
13Using the Axioma global fundamental model.
14For simplicity, the modeled beta and total risk of each portfolio was estimated as the average of modeled beta and total risk for each individual stock.
15McKinley Capital incorporates total risk weighting both within and across countries to manage risk in its Dynamic Momentum strategy.
16For other market state based strategies and detailed momentum investing references see “Global Momentum Engineering a Dynamic Exposure, McKinley Capital Management, LLC, January 2011.
Allen, Franklin, and Risto Karjalainen (1999). Using genetic algorithms to find technical trading rules. Journal of Financial Economics, 51(2), 245-271.
Amihud, Y., and Mendelson, H. (1986b). Liquidity and Stock Returns. Financial Analysts Journal, 42, 43-48.
Arnott R, Hsu J, Markowitz H (2015). Can noise create size and value effects? Management Science, 61(11):2569 –2568.
Asem, Ebenezer, and Gloria Tian (2010). Market Dynamics and Momentum Profits. Journal of Financial and Quantitative Analysis, 45(6), (December):1549-1562.
Asness, C. S., J. Liew, and Stevens (1997). Parallels Between the Cross-Sectional Predictability of Stock Returns and Country Returns. Journal of Portfolio Management, 23, 79-87.
Asness, C.S., T. J. Moskowitz, and L. H. Pedersen (2013). Value and Momentum Everywhere. Journal of Finance, 68(3), 929-985.
Badrinath, S.G. Wahal, S. (2002). Momentum trading by institutions. Journal of Finance, 57(6), 2449–2478.
Barroso, Pedro, and Pedro Santa-Clara (2014). Momentum has its moments. Journal of Financial Economics, 116(1), (April):111-120.
Blitz, David, Joop Huji, and Martin Martens (2011). Residual Momentum. Journal of Empirical Finance, 18(3), (June):506-521.
Blume, Lawrence, David Easley, and Maureen O’Hara (1994). Market statistics and technical analysis: The role of volume. Journal of Finance, 49, 153-181.
Brown, David, and Robert Jennings (1989). On technical analysis. Review of Financial Studies, 2, 527-551.
Brush J (2001). Price momentum: A twenty year research effort. Accessed March 3, 2017, https://sumgrowth.com/downloads/Columbine_Price_Momentum.pdf.
Brush, J. S. and K.E. Boles. (1983). The Predictive Power in Relative Strength and CAPM. Journal of Portfolio Management, 20-23.
Brush, J. (2007). A Flexible Model of Price Momentum. Journal of Investing 16.
Carhart, M. (1997). On Persistence in Mutual Fund Performance. Journal of Finance, 52(1), (March):57-82.
Chan, Louis K.C., Narasimhan Jegadeesh, and Josef Lakonishok (1996). Momentum Strategies. Journal of Finance, 51(5), (December):1681-1713.
Chordia, T., Roll R., and Subrahmanyam, A. (2000). Commonality in Liquidity. Journal of Finance, 50, 1147-74.
Cole, Arthur H., and Edwin Frickey (1928). The Course of Stock Prices, 1825-66. Review of Economics and Statistics, 10(3), (August):117-139.
Conrad J, Gultekin M, Kaul G (1997). Profitability of short-term contrarian portfolio strategies: implications for market efficiency. Journal of Business and Economic Statistics, 15(3):379–386.
Conrad J, Kaul G (1989). Mean reversion in short-horizon expected returns. Review of Financial Studies, 2(2):225–240.
Conrad J, Kaul G (1998). An anatomy of trading strategies. Review of Financial Studies, 11(3):489–519.
Daniel, Kent, David Hirshleifer and Avanidhar Subrahmanyam (1998). Investor Psychology and Security Market Under- and Overreactions. Journal of Finance, 53(6), 1839-1886.
Daniel, Kent D., and Tobias J. Moskowitz (2014). Momentum Crashes. Columbia Business School Research Paper 11-03.
DeBondt, Werner F.M., and Richard Thaler (1985). Does the Stock Market Overreact? Journal of Finance, 40(3), (July):793-805.
Demir, I., Muthuswamy, J. and Walter, T. (2004). Momentum returns in Australian equities: the influences of size, risk, liquidity and return computation. Pacific-Basin Finance Journal, 12, 143-158.
Edwards, Robert, and John Magee (1966). Technical Analysis of Stock Trends, 5th ed. (John Magee, Boston, Mass.)
Fama EF, French KR (1995). Size and Book-to-Market Factors in Earnings and Returns. Journal of Finance, 50(1), (March):131–155.
Fama EF, French KR (2008). Dissecting Anomalies. Journal of Finance, 63(4), (August):1653–1678.
Grinblatt, M., Titman, S. and Wermers, R. (1995). Momentum investment strategies, portfolio performance and herding: A study of mutual fund behavior. American Economic Review, 85, 1088–1105.
Grinblatt, Mark, and Tobias J Moskowitz (2004). Predicting Stock Price Movements from Past Returns: The Role of Consistency and Tax-Loss Selling. Journal of Financial Economics, 71(3), 541-579.
Grundy, B.D., and J.S. Martin (2001). Understanding the Nature of the Risks and the Source of the Rewards to Momentum Investing. The Review of Financial Studies, 14(1), 29-78.
Guerard JB Jr (2016). Investing in global markets: Big data and applications of robust regression. Accessed May 9, 2017, http://journal.frontiersin.org/article/10.3389/fams.2015.00014/full.
Guerard JB Jr, Markowitz HM, Xu G (2014). The role of effective corporate decisions in the creation of efficient portfolios. IBM Journal of Research and Development, 58(4):6:1–6:11.
Guerard JB Jr, Markowitz HM, Xu G (2015). Earnings forecasting in a global stock selection model and efficient portfolio construction and management. International Journal of Forecasting, 31(2):550–560.
Guerard JB Jr, Rachev RT, Shao B (2013). Efficient global portfolios: Big data and investment universes. IBM Journal of Research and Development, 57(5):11:1–11:11.
Guerard JB Jr, Xu G, Gultekin MN (2012). Investing with momentum: The past, present, and future. Journal of Investing, 21(1):68–80.
Hameed, A. and Kusnadi, Y. (2002). Momentum strategies: evidence from Pacific Basin stock markets. Journal of Financial Research, 25(3), 383-397.
Harvey CR, Liu Y (2014). Evaluating trading strategies. Accessed March 6, 2017, https://faculty.fuqua.duke. edu/~charvey/Research/Published_Papers/P116_Evaluating_ trading_strategies.pdf.
Harvey CR, Liu Y (2015). Lucky factors. Accessed March 6, 2017, http://jacobslevycenter.wharton.upenn.edu/wp-content/uploads/2015/05/Lucky-Factors.pdf.
Hasbrouck, J., and Schwartz, R. (1988). Liquidity and Execution Costs in Equity Markets. Journal of Portfolio Management, 14, 10-16.
Haugen RA, Baker N (1996). Commonality in the determinants of expected results. Journal of Financial Economics, 41(3):401–440.
Haugen RA, Baker N (2010). Case closed. Guerard JB, ed. The Handbook of Portfolio Construction: Contemporary Applications of Markowitz Techniques (Springer, New York).
Hong, Harrison, Terence Lim, Jeremy C. Stein (1999). Bad News Travels Slowly: Size, Analyst Coverage, and the Profitability of Momentum Strategies. Journal of Finance, 55(1), 265-296.
Jegadeesh, N. (1990). Evidence of Predictable Behavior of Security Returns. Journal of Finance, 45(3), (July):881-898.
Jegadeesh, N. and Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1), (March):65-91.
Jacobs B, Levy K (1988). Disentangling equity return regularities: New insights and investment opportunities. Financial Analysts Journal, 44(3):18–43.
Jacobs B, Levy K (2017). Equity Management: The Art and Science of Modern Quantitative Investing (McGraw-Hill, New York).
Jorion, P. (1986). Bayes-Stein Estimation for Portfolio Analysis. Journal of Financial and Quantitative Analysis, 21(3), (September):279-292.
Kang, J., Liu, M. and Ni, S.X. (2002). Contrarian and momentum strategies in the China stock market: 1993-2000. Pacific-Basin Finance Journal, 10, 243-265.
Lee, Charles M.C., Bhaskaran Swaminathan (2000). Price Momentum and Trading Volume. Journal of Finance, 55(5), 2017-2070.
Lo, Andrew W., and A. Craig MacKinlay (1988). Stock market prices do not follow random walks: Evidence from a simple specification test. Review of Financial Studies, 1, 41-66.
Lo, Andrew W., and A. Craig MacKinlay (1997). Maximizing predictability in the stock and bond markets. Macroeconomic Dynamics, 1, 102-134.
Lo, Andrew W., and A. Craig MacKinlay (1999). A Non-Random Walk down Wall Street (Princeton University Press, Princeton N.J.).
Lo, Andrew W., Mamaysky, and Wang, J. (2000). Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation. Journal of Finance, 55(4), 1705-1765.
Lo, Andrew W., and Wang J. (2000). Trading Volume: Definitions, Data Analysis, and Implications of Portfolio Theory. Review of Financial Studies, 13, 257-300.
Menchero J, Morozov A, Shepard P (2010). Global equity modeling. Guerard JB, ed. The Handbook of Portfolio Construction: Contemporary Applications of Markowitz Techniques (Springer, New York), 439-481.
Moskowitz, T.J. and M. Grinblatt (1998). Do industries explain momentum, Journal of Finance, 54(4), 1249-1290.
Mulvey, J.M., and Kim, W.C. (2008). Active equity managers in the U.S.: Do the best follow momentum strategy? Journal of Portfolio Management, 34(2), (Winter):126-134.
Mulvey,J.M., Simsek, K., and Zhang, Z. (2006). Improving investment performance for pension plans. Journal of Asset Management, 7(2), 99-108.
Nofsinger, J. and Sias, R.W. (1999). Herding and feedback trading by institutional and individual investors. Journal of Finance, 54, 2263–2295.
Pastor, L., and Stambaugh, R. (2002). Liquidity Risk and Expected Stock Returns. Journal of Political Economy, 111(3), (2003).
Perold A. (1988). The Implementation Shortfall: Paper Versus Reality. Journal of Portfolio Management, 14, 4-9.
Rouwenhorst, K.G. (1998). International momentum strategies. Journal of Finance, 53(1), 267-284.
Rosenberg, B. Extra-Market Components of Covariance in Security Returns. Journal of Financial and Quantitative Analysis, 9, 263-274.
Rosenberg B, Marathe V (1979). Tests of capital asset pricing hypotheses. Levy H, ed. Research in Finance 1, 115–224., Greenwich, CT: JAI Press.
Rudd, A. and H. K. Clasing (1982). Modern Portfolio Theory: The Principals of Investment Management. Homewood, IL: Dow-Jones-Irwin.
Sapp, T., and Tiwari, A. (2004). Does stock return momentum explain the “smart money” effect? Journal of Finance, 59, 2605-2622.
Tinic, S., (1972). The Economics of Liquidity Services. Quarterly Journal of Economics, 86, 79-93.
Treynor, Jack, and Robert Ferguson (1985). In defense of technical analysis. Journal of Finance, 40, 757-773.
Wagner, W., and Banks, M. (1992). Increasing Portfolio Effectiveness Via Transaction Cost Management. Journal of Portfolio Management, 19, 6-11.
G. Xu and Guerard, JB (2017). Data Mining and Stock Selection.
McKinley Capital Management, LLC (“McKinley Capital”) is a registered investment adviser under the U.S. Investment Advisers Act of 1940. McKinley Capital is not registered with, approved by, regulated by, or associated with the Financial Conduct Authority (“FCA”), the Prudential Regulation Authority (“PRA”), the Securities & Futures Commission of Hong Kong or the China Securities Regulatory Commission. Additionally, none of the authorities or commissions listed in the previous sentence has commented on the firm, the content of any marketing material or any individual suitability assessments.
This report contains back tested and/or model information; any performance is hypothetical and may not be relied upon for investment purposes. Back tested performance was derived from the retroactive application of a model with the benefit of hindsight and does not represent an actual account. Models may not relate or only partially relate to services currently offered by McKinley Capital and model results may materially differ from the investment results of McKinley Capital’s clients. Returns are absolute, were generated using McKinley Capital’s proprietary growth investment methodology as described in McKinley Capital’s Form ADV Part 2A, are unaudited, and do not replicate actual returns for any client. McKinley Capital’s investment methodology has not materially changed since its inception but it has undergone various enhancements.
No securities mentioned herein may be considered as an offer to purchase or sell a firm product or security. Any comment regarding an individual security is presented at the client’s request, may only be used for client reference, and is not reflective of composite or individual portfolio ownership. McKinley Capital may or may not have held or currently hold a specific security. In addition, any positive comments regarding specific securities may no longer be applicable and should not be relied upon for investment purposes. No security is profitable all of the time and there is always the possibility of selling it at a loss. With any investment, there is the potential for loss. Investments are subject to immediate change without notice. Comments and general market related perspectives are for informational purposes only; were based on data available at the time of writing; are subject to change without notice; and may not be relied upon for individual investing purposes.
Global market investing, including developed, emerging and frontier markets, also carries additional risks and/or costs including but not limited to: political, economic, financial market, currency exchange, liquidity, accounting, and trading capability risks. Derivatives trading and short selling may materially increase investment risk and potential returns. These risks may include, but are not limited to, margin/mark-to-market cash calls, currency exchange, liquidity, unlimited asset exposure, and counter-party risk. Future investments may be made under different economic conditions, in different securities and using different investment strategies. McKinley Capital’s proprietary investment process considers factors such as additional guidelines, restrictions, weightings, allocations, market conditions and other investment characteristics. Thus returns may at times materially differ from the stated benchmark and/or other disciplines and funds provided for comparison.
No fees or expenses of any kind have been deducted. Trading activity, asset allocation, and portfolio decisions are based on the management style that McKinley Capital may have followed had it been actively managing a discretionary account for that period. Returns are calculated using the internal rate of return; do not adjust for external cash flows; do not include brokerage commissions; ignore cash interest during adverse states and when deleveraged, are based on fully discretionary accounts; reflect the reinvestment of dividends and interest; are gross of all investment management and all other costs, expenses and commissions associated with client account trading and custodial services fees; and do not take individual investor tax categories into consideration. Returns do include the reinvestment of hypothetical gains, dividends and other income. The currency used to calculate hypothetical performance is the USD, and no specific benchmark is used unless otherwise noted in the presentation. Individual and actual returns may vary and additional fees or charges will negatively impact an investor’s absolute returns. Clients should realize that net returns would be lower and must be considered when determining absolute returns. Clients should contact the McKinley Capital institutional marketing manager for additional details on such returns.
Decisions and information provided were based on available research at the time and data contains hypothetical results. Back tested and model results do not represent actual trading and may not reflect the impact material economic and market factors might have had on McKinley Capital’s decision-making if it were managing an actual account. Material economic and market factors may have changed and certain investment restrictions may have affected performance. Back tested and model results are not GIPS compliant. Trading strategies that have been retroactively applied may not have been available during the periods presented.
Charts, graphs and other visual presentations and text information were derived from internal, proprietary, and/or service vendor technology sources and/or may have been extracted from other firm data bases. As a result, the tabulation of certain reports may not precisely match other published data. Data may have originated from various sources including but not limited to Bloomberg, FactSet, MSCI, Axioma, Russell Indices, FTSE and/or other systems and programs. With regards to any material, if any, accredited to FTSE International Limited (“FTSE”) ©FTSE : FTSE™ is a trade mark of London Stock Exchange Plc and The Financial Times Limited and is used by FTSE International Limited under license. All rights in the FTSE Indices vest in FTSE and/or its licensors. Neither FTSE nor its licensors accept any liability for any errors or omissions in the FTSE Indices or underlying data. Please refer to the specific service provider’s web site for complete details on all indices. McKinley Capital makes no representation or endorsement concerning the accuracy or propriety of information received from any other third party. With regards to any materials, if any, accredited to MSCI: Neither MSCI nor any other party involved in or related to compiling, computing or creating the MSCI data makes any express or implied warranties or representations with respect to such data (or the results to be obtained by the use thereof), and all such parties hereby expressly disclaim all warranties of originality, accuracy, completeness, merchantability or fitness for a particular purpose with respect to any of such data. Without limiting any of the foregoing, in no event shall MSCI, any of its affiliates or any third party involved in or related to compiling, computing or creating the data have any liability for any direct, indirect, special, punitive, consequential or any other damages (including lost profits) even if notified of the possibility of such damages. No further distribution or dissemination of the MSCI data is permitted without MSCI’s express written consent.
Fees are billed monthly or quarterly, which produces a compounding effect on the total rate of return net of management fees. As an example, the quarterly effect of investment management fees on the total value of a client’s portfolio assuming (a) $1,000,000 investment, (b) portfolio return of 5% a year, and (c) 1.00% annual investment advisory fee would be $10,038 in the first year, and cumulative effects of $51,210 over five years and $110,503 over ten years. Actual client fees vary. A fee schedule, available upon request, is described in the firm’s Form ADV part 2A. To receive a copy of the firm’s ADV or a description of all McKinley Capital’s composites, please contact McKinley Capital at 3301 C Street, Suite 500, Anchorage AK 99503 or 1.907.563.4488. All information is believed to be correct but accuracy cannot be guaranteed. Clients should rely on their custodial statements for the official investment activity records. Clients should contact their custodian with any questions regarding monthly/quarterly receipt of those statements.