Asset Allocation Strategies

March 16, 2015

McKinley Capital Management, LLC (“McKinley Capital”) has observed a growing interest in funds that allocate on a dynamic basis between major asset classes including equities, fixed income, real assets, commodities, and cash. The firm believes the combined use of momentum and volatility models is the best method to implement asset allocation decisions.

McKinley Capital is a pioneer in the application of innovative and sophisticated momentum strategies. The firm’s approach to momentum investing differs from generic industry models in its use of risk adjustment techniques, penalty functions, and dynamic momentum term-structure rotation. Generic momentum models, while historically effective long-term, have been plagued with occasional large losses (referred to as negative skew). These losses have generally occurred at major market inflection points. As an example, the early 2009 time period was difficult for equity strategies due to the v-shaped transition from a substantial down market to a rapid but non-earnings related rally. Proprietary risk adjustment and other techniques can be used to mitigate this risk without sacrificing the upside.

Can McKinley Capital use its proprietary process to successfully manage a broad-based asset allocation fund? To answer this question, the firm started with a benchmark: a composite of the MSCI ACW Index and the Barclay’s 7 to 10 Year Treasury Index in a 60% to 40% ratio. Quarterly returns were computed from January 2002 to June 2014. As reported in Table 1 below, the annualized benchmark return was 7.88%, with a standard deviation of 9.89%, a Sharpe ratio of 0.646 and, by definition, a zero information ratio. For comparison, the annualized S&P 500 return over the same time period was 6.49%, with a standard deviation of 14.94% and a Sharpe ratio of 0.334.


1504 Asset -01 Tab1

McKinley Capital constructed and tested three alternative active strategies. The first strategy, labeled “Alpha 12”, assigned asset class policy weights of 30% to U.S. Equity, 30% to International Equity, 35% to U.S. Fixed Income and 5% to Real Assets. Twenty-two ETFs or their corresponding indexes were considered and divided into the four classes1. There was no allocation to cash. An individual ETF policy weight was computed by dividing the corresponding class policy weight by the number of ETFs in the class. Each of the 22 ETFs was ranked from 1 to 22 using McKinley Capital’s proprietary momentum indicator. Active weight (difference from policy weight) was assigned to each ETF based on its momentum rank. Bands were established for class weights. In testing this strategy, the firm assumed a conservative 50 basis point transaction cost per side. The portfolio was rebalanced quarterly and averaged 13% quarterly turnover. The results are reported in Table 1 above. The annualized strategy return was 9.49%, with a standard deviation of 12.18%, a Sharpe ratio of 0.657 and an information ratio of 0.43. Chart 1 below shows the class weights over time.

1504 Asset -02 Chart2

The second strategy, labeled “Modified Alpha 12”, was constructed in the exact same way with the exception of the ranking model. Instead of ranking only by momentum, the ETF ranking algorithm included a short-term momentum reversal component and a volatility component. The annualized strategy return was 8.77%, with a standard deviation of 10.37%, a Sharpe ratio of 0.702 and an information ratio of 0.37.

In the third strategy, labeled “Optimized Alpha 12”, ETFs were ranked only by momentum. Instead of algorithmic weighting, weights were determined using a proprietary mathematical optimization system. Each of the 22 ETF weights was constrained to the range of 1% to 12% and bands were used for each class. The annualized strategy return was 9.51%, with a standard deviation of 8.53%, a Sharpe ratio of 0.941 and an information ratio of 0.59. Based on these results, McKinley Capital is now managing a portfolio utilizing the Optimized Alpha 12 strategy.

The firm conducted a second study using an expanded list of 87 asset classes where futures are used to gain exposure. Using a similar composite benchmark for comparison, the simulation revealed improved performance from the broader list (Table 2). Annualized gross return over the trailing 12 year period was 12.97% compared to the 7.75% return of the benchmark. Volatility was reduced with a demonstrated standard deviation of returns of 7.31% vs. 9.20% for the composite benchmark.

1504 Asset -03 Tab2

The dynamic nature of the asset allocation decisions can be seen in Chart 2 where the use of cash was particularly apparent in the downturn of 2008 and early 2009

1504 Asset -04 Chart2Chart 3 indicates the average exposure over the entire time period and Table 3 indicates the intended exposures as of December 31, 2014.

1504 Asset -05 Chart3In conclusion, what are some of the practical differences between a portfolio of ETF holdings and a portfolio of future contracts?

1. Futures involve the use of leverage, which some investors choose to avoid. 2. The ETFs used in this construction are liquid and deep. 3. The futures portfolio has many more asset classes to choose from, with the corresponding potential benefit of enhanced return and reduced volatility.

It appears that investment models employed by McKinley Capital have the potential to perform in the context of a broad-based, asset allocation fund. As always, your McKinley Capital account representative is available to discuss the firm’s research and investment process and answer your questions.

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