# Measuring Tactical Alpha Part II: Examples and Analysis

When we left off in Part 1, we promised to examine how select Global Tactical Asset Allocation products stack up against the Global Market Portfolio from the perspective of several performance measures – particularly Sharpe ratio, alpha and information ratio. Without further adieu:

Figure 1. Performance comparison of Global Tactical Asset Allocation products vs. ETF Proxy Global Market Portfolio, Jun 1, 2011 – Nov 28, 2014

Figure 2. Performance comparison of global risk parity products vs. ETF Proxy Global Market Portfolio, Jun 1, 2011 – Nov 28, 2014

Analysis: GestaltU, Data from Yahoo Finance and Bloomberg

A few notes about these tables. First, where stats are labeled (Incep), they are calculated from June 2011, or the product’s inception if it launched subsequent to that date, through the end of November 2014. Second, CAGR numbers are annualized, except where a fund has been operating for less than 1 year. All risk-adjusted performance numbers are annualized from daily data, regardless of the length of track record (daily ratios are multiplied by sqrt(252)). Betas, alphas and t-scores are all since inception, and all relative metrics (IR, alpha, beta, t-scores) are relative to the Global Market Portfolio and based on daily observations.

So what story do these tables tell? Well, first off the Global Market Portfolio hasn’t been a tough bogey to beat in terms of raw returns over the past three years or so, with less than 6% annualized returns. For comparison, the S&P (SPY ETF) has returned over 16% annualized over the period, and a US balanced fund (Vanguard US Balanced ETF) has gained 11% per year. Bear in mind US markets represent over 30% of the global index, so international diversification has been quite a performance drag.

I know many of you with US-centric portfolios are patting yourself on the back. Ain’t self attribution bias grand? Make no mistake, you are US-centric because of home market bias, not superior forecasting abilities, but I will be the first to admit that it’s better to be lucky than smart. I can state with some confidence that US-centric investors are unlikely to experience the same relative success over the next three years. If that’s the case, what are you going to do about it?

In terms of returns relative to the GMP, GTAA funds are a mixed bag. The fund with the highest returns appears to be SMIDX, the SMI Dynamic Allocation fund, but this is somewhat of a red herring because the fund has less than 1/2 the operating history of most other funds. On a risk adjusted basis, JP Morgan’s Efficiente (EFFE) mandate has delivered the highest risk adjusted performance, in terms of Sharpe, Sortino, and Omega over the entire observation period. More importantly, given its low beta and high alpha scores, EFFE has generated its returns with very little reliance on performance from the underlying indexes. This is a critical point, as funds with a high correlation to the GMP are vulnerable to a negative shift in performance when global markets turn at the end of this cycle.

Investor legend Rob Arnott’s GTAA behemoth, PAAIX, managed under the PIMCO banner, deserves an honourable mention. It also surpassed the GMP’s Sharpe ratio over the past few years, and delivered the second lowest alpha and beta of any fund, despite lower absolute returns.

We included the Good Harbor Tactical Core US fund in our analysis, despite the fact that it is US focused, because it highlights the risk of trying to market time strictly between the stocks and bonds of one market. This is the difference between market timing and GTAA: you make just one bet.We deal with this concept in more detail in our new paper (see below). In our testing, we’ve observed that market timing between stocks and bonds or stocks and cash is a much more difficult challenge than spreading bets across multiple asset classes, and Good Harbor’s unfortunate recent performance lends credence to our own findings.

Given higher average structural allocations to bonds in risk parity funds, products in this class have clearly benefitted from the global race to the bottom in long rates, as average Sharpe ratios are meaningfully higher than average GTAA Sharpe ratios. I strongly suspect this will reverse when the rate cycle finally turns (which admittedly could be quite a while). Setting aside QSPIX for a moment as a special case, note that Invesco’s Balanced Risk portfolio sports the highest Sharpe, Sortino, and Omego ratios over the past 3+ years, as well as the lowest beta and highest alpha. This is a large fund, with $10 billion in AUM according to Morningstar, yet it continues to deliver stellar returns year after year. Not for nothing, it has also generated the highest annualized returns over this recent period.

We mentioned QSPIX is a special case, and it is. This fund, managed by AQR’s esteemed Andrea Frazzini and Ronen Israel, is based on a concept described in a 2012 paper by Antti Ilmamen, Ronen Israel, and Tobias Moskowitz, entitled “Investing with Style: The Case for Style Investing” (currently behind AQR paywall). Antti Ilmamen is one of the greatest investment thinkers alive today, and his books are required reading for every aspiring asset allocator. The authors present compelling evidence of the magnitude, persistence, and structurally low correlations, of the four primary sources of style premia: value, momentum, carry and ‘defensive’. Across all asset classes covered, the authors demonstrate that style premia correlations averaged -0.22, and ranged between -0.6 and +0.21 from 1990 – 2012. Long-term Sharpe ratios for style premia composites across all asset class buckets range from 0.9 for value to 1. 37 for carry over the same period. In simulation, when normalized to a 10% volatility, a combination style premia composites across all asset classes delivered a Sharpe of 2.52 before fees and expenses.

Of course, the authors are aware of the many frictions and pitfalls involved in implementing the strategy, so they included an analysis of the net historical performance after accounting for trading costs (Sharpe declines to 1.9); discounting for model overfitting (Sharpe declines to 0.98), and; risk-management and fees (Sharpe ratio declines to 0.85). This seems to be to be quite a conservative target (see Figure 5.)

Obviously, given the low expected average correlation with traditional 60/40 portfolios, and the high expected Sharpe ratio, QSPIX should substantially improve overall portfolio Sharpe, even with small allocations. For example, a 10% allocation to QSPIX carved out of a 60/40 portfolio might raise overall Sharpe from 0.3 to 0.44, according to the authors.

Overall, I’d say the short snapshot of performance we’ve seen over the past year since inception would not cause me to reject the possibility that QSPIX will deliver against expectations. However, the fund may be mildly vulnerable to liquidity shocks, as it has a gross leverage ratio of 8x (!!), so it should not play the role of a tail hedge in portfolios. In my opinion, the best structural tail hedge is a good CTA fund.

So what can we conclude from our analysis? This article wasn’t meant to recommend, or point fingers, at any particular strategy, but rather to highlight how we might think about the performance of global allocation funds, and what observed performance features might make them attractive. Above all, before committing any capital to these products, we would focus our scrutiny on the process underlying the strategy. What factors do the managers believe are driving returns? What evidence do they have that their methodology is effective? We would want to see much longer trading histories, analyze performance in multiple trading regimes, and understand how the strategy might interact with other holdings in portfolios. Where a long-term live history isn’t available (or even if one is available), we would be keen to see simulations of historical performance using the same process, and understand all the ‘moving parts’ that might affect the character of the strategy.

That said, if we only have live returns to go on, we would focus on performance relative to the only true passive global benchmark, the GMP, rather than making comparisons with specific regional indexes. Specifically, we would seek to harvest as much true alpha as possible relative to the GMP, as strategies with high alphas are less reliant on strong global market performance to deliver returns. After all, aren’t we after diversification? Next we would look at overall risk metrics, especially volatility, but with one eye on drawdowns and beta. Only then would we start to care about absolute returns and Sharpe ratios.

One other metric, Omega ratio, stands out as meaningful, since unlike all of the other performance metrics above, it makes no assumptions about the distribution of returns. The utility of Sharpe, Sortino, alpha, and beta all depend on the assumption of normally distributed returns, but Omega accounts for the fact that returns often stray far from normality, especially over shorter horizons. The formula for Omega ratio looks fancy, but it’s actually easy to calculate. First, since the Omega ratio reflects the relative probability of achieving returns above a minimum required return (MRR), we must first choose an MRR. We chose to use the risk free rate, which is currently zero, and which makes our calculations really easy. But here is the general formula in Excel-friendly language.

Omega={SUM(IF(returns>MRR,returns-MRR))/(SUM(IF(returns<MRR,MRR-returns)))}

Note that the returns variable refers to the vector of returns, so this is a matrix formula. In order for Excel to calculate it, you must hold down both the CTRL key and the ENTER key at the same time.

In any event, you will note that on this measure, and relative to a 0% risk free rate over the period studied, GTAA funds compare favourably relative to the GMP, almost across the board. This suggests that, after accounting for higher moments of the return distributions, an investor would have a higher probability of achieving positive returns using GTAA than the GMP. An interesting observation indeed.

Overall, there are a few worthy examples of successful GTAA mandates and several risk parity products worth considering for active global diversification. I should also mention that Meb Faber’s Cambria has recently launched a very interesting new GTAA ETF, GMOM, based on newer additions to Meb’s ubiquitous paper, “A Quantitative Approach to Tactical Asset Allocation“. Well worth a look.

Lastly, we are excited to get our own GTAA track record audited so that we can add our own numbers to this list as we launch our new firm, ReSolve Asset Management, in the new year.

© Dundee Goodman Private Wealth