Global Tactical Asset Allocation Is More Than Just Market Timing
Just the Facts, Part 2

At our research blog, we recently posted an article discussing how many noteworthy investment commentators either misunderstand or misconstrue the salient qualities of Tactical Asset Allocation strategies. In particular, we hope to clarify that:

  • TAA does not require discretionary market calls;
  • TAA should not be about market timing; and,
  • It’s neither appropriate nor helpful to benchmark Global TAA (GTAA) funds against narrow domestic benchmarks

In Part 1 of this series we explored why quality TAA strategies do not rely on ‘expert’ market calls, noting that the terms ‘discretionary’ and ‘tactical’ ought not to be viewed as two sides of the same coin. Rather, the best tactical managers increasingly use systematic approaches to harvest persistent risk premia from factors such as ‘value’ and ‘momentum.’ This article will show why investors should not equate TAA with market timing.

First, let me be clear: tactical asset allocation is an active strategy. It involves regular shifts in portfolio composition – asset allocation – in response to changes in expected asset class returns, risks, and correlations, usually on an intermediate horizon. Some strategies are ‘binary’ in nature: a strategy will shift from 100% invested in a risky asset, such as a stock market ETF, to 100% invested in a ‘cash-like’ asset, like t-bills or Treasuries. These strategies are clearly ‘market timing’, as the goal is to be fully invested when market expectations are positive, and on the sidelines when expectations are negative.

However, market-timing strategies represent a very small sub-category within the broader TAA space. Most TAA strategies (such as Global Tactical Asset Allocation or GTAA) employ their methodology by choosing the best mix from a much broader and more diverse set of asset classes, with few ‘on-or-off’ type decisions. And there is a very powerful reason for this: market timing between two or three assets is much harder than choosing the best mix of a larger group of assets – in fact, there is reason to believe it’s more than 3x as hard!

A few years ago Que Nguyen (now Director of Portfolio Strategy and Analytics at Willett Advisors, NY Mayor Michael Bloomberg’s personal investment firm) studied the expected performance from market timing between stocks and bonds (or cash) versus selecting a mix from 28 global asset classes (18 stock markets and 10 bond markets). In essence, she simulated the performance of 75 different managers, each of whom selected assets once a month at random from his eligible universe over the period 1985 – 2003, with 55% accuracy. Chart 2 from her findings, copied below, describes the results of her experiment.

Note that when the performance of market timing managers and GTAA managers was standardized for active risk, the GTAA managers delivered 4x the alpha and 4x the Information Ratio (it’s easiest to think of the Information Ratio like you would a Sharpe ratio, but where the returns are adjusted for active risk rather than standard deviation). In addition, the likelihood of achieving positive alpha was 90% for the GTAA strategies, vs. just 63% for the market timers.

The point is, a small number of TAA strategies are truly market timers who are trying to call stock market tops and bottoms, but these types of strategies are rare. Furthermore, given how hard it is to succeed as a market timer, we would expect the proportion of market timing strategies within the TAA category to decline substantially over time.

However, the vast majority of TAA strategies do not fall into the market timing camp. Rather, the best TAA strategies harness value and/or momentum effects across a much more diverse global asset universe. And since the probability of achieving strong performance is a function of the breadth and diversity of one’s investment universe, we expect these more diverse Global TAA strategies to produce the best results going forward.


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Forward-Looking Information. The contents hereof may contain “forward-looking information” within the meaning of the Securities Act (Ontario) and equivalent legislation in other provinces and territories. Because such forward-looking information involves risks and uncertainties, actual performance results may differ materially from any expectations, projections or predictions made or implicated in such forward-looking information. Prospective investors are therefore cautioned not to place undue reliance on such forward-looking statements. In addition, in considering any prior performance information contained herein, prospective investors should bear in mind that past results are not necessarily indicative of future results, and there can be no assurance that results comparable to those discussed herein will be achieved. The contents hereof speaks as of the date hereof and neither ReSolve nor any affiliate or representative thereof assumes any obligation to provide subsequent revisions or updates to any historical or forward-looking information contained herein to reflect the occurrence of events and/or changes in circumstances after the date hereof.

General information regarding returns. Performance data prior to August, 2015 reflects the performance of accounts managed by Dundee Securities Ltd., which used the same investment decision makers, processes, objectives and strategies as ReSolve has used since it became registered and commenced operations in August, 2015. Records that document and support this past performance are available upon request. Performance is expressed in CAD, net of applicable management fees. Indicated returns of one year or more are annualized. Past performance is not indicative of future performance.

General information regarding the use of benchmarks. The indices listed have been selected for purposes of comparing performance with widely-known, broad-based benchmarks. Performance may or may not correlate to any of these indices and should not be considered as a proxy for any of these indices. The S&P/TSX Composite Index (Net TR) (“S&P TSX TR”) is the headline index and the principal broad market measure for the Canadian equity markets. The Standard & Poor’s 500 Composite Stock Price Index (“S&P 500”) is a capitalization-weighted index of 500 stocks intended to be a representative sample of leading companies in leading industries within the U.S. economy.

General information regarding hypothetical performance and simulated results. These results are based on simulated or hypothetical performance results that have certain inherent limitations. Unlike the results in an actual performance record, these results do not represent actual trading. Also, because these trades have not actually been executed, these results may have under- or over-compensated for the impact, if any, of certain market factors, such as lack of liquidity. Simulated or hypothetical trading programs in general are also subject to the fact that they are designed with the benefit of hindsight. No representation is being made that any account or fund managed by ReSolve will or is likely to achieve profits or losses similar to those being shown. The results do not include other costs of managing a portfolio (such as custodial fees, legal, auditing, administrative or other professional fees). The contents hereof has not been reviewed or audited by an independent accountant or other independent testing firm. More detailed information regarding the manner in which the charts were calculated is available on request. Any actual fund or account that ReSolve manages will invest in different economic conditions, during periods with different volatility and in different securities than those incorporated in the hypothetical performance charts shown. There is no representation that any fund or account will perform as the hypothetical or other performance charts indicate.

General information regarding the simulation process. The systematic model used historical price data from Exchange Traded Funds (“ETFs”) representing the underlying asset classes in which it trades. Where ETF data was not available in earlier years, direct market data was used to create the trading signals. The hypothetical results shown are based on extensive models and calculations that are available for any potential investor to review before making a decision to invest.