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Book Review: Adaptive Asset Allocation

February 29, 2016

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I recently read “Adaptive Asset Allocation” ( link to the book) by Butler, Philbrick and Gordillo of ReSolve Asset Management.  The book is the culmination of research  developed over the years by the ReSolve team towards the next generation approach of dynamic asset allocation. The core principles of this approach are the ability  to “go anywhere” and adapt to changes in the economic environment in the quest for greater risk-adjusted returns. (CSSA readers may recall a post we did a while back on adaptive asset allocation, if not it is worth a refresher along with one of the original whitepapers on AAA)    The book is extremely well-written, and the chapters are easy to read- developing the story persuasively from cover to cover.

This book is not a dense quantitative tome , but rather a summary of a coherent and rigorously developed investment philosophy that is carefully built around academic research and concepts. To that extent, Adaptive Asset Allocation is a true “tour de force” and a key contribution to the field of asset allocation theory.  Without this background, it is impossible to frame ideas properly within any trading system or tactical asset allocation model. It is far too easy to get confused over the wide range of possible approaches to portfolio management: should you use momentum? should you seek to minimize risk?  should you use long-term or short-term estimates? should you include or exclude certain asset classes? what time frame should you trade on? Ultimately the answers to these questions are driven by having a framework that neatly incorporates what input assumptions that you are confident in making versus those that you don’t know anything about. The book really helps to address these key issues in the development of trading models/systems.  Adaptive Asset Allocation also neatly ties in the natural link between an active asset allocation approach and financial planning. Much of this is both theoretical and also based on their experience working as financial advisors with wealthy clients.  The authors show that managing “volatility gremlins” with a portfolio management approach that is specifically designed to manage volatility itself is critical for investors in retirement.  Adaptive Asset Allocation is not just an investment philosophy or a quantitative approach, but rather the book proves that it is a coherent and comprehensive solution for wealth management.

Who should read the book?:  If you are a short-term trader that is looking for trading system ideas this probably isn’t for you. But if you are an investor, a portfolio manager, or a trader interested in longer frequency models, this is an essential book that will help to develop and crystallize your thinking towards asset allocation.


Volatility Futures and S&P500 Performance

February 23, 2016


We wrote a more quant-friendly  article on volatility futures and their impact on S&P500 performance recently on our blog for Blue Sky Asset Management. We will be dedicating a section of the blog specifically for more technical articles. This is the first of many that we have planned in the coming months so stay tuned for more  geeky quant goodness!

Investors Should Tread Lightly

September 4, 2015

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A tactical approach based on momentum would require that investors reduce their risk recently as volatility rose and trends deteriorated. Obviously no one knows what will happen going forward (ie is this a bull market or a correction) but from a purely qualitative perspective, the economic environment warrants more caution than usual. In general it is my belief that the global quantitative reflation is nearing the end of its course and overall efficacy. Like the last five episodes of “Breaking Bad”, investors can expect a wild ride with many different twists and turns.   Read our recent post here.

The Observation Model is the Best Defense

July 16, 2015










This post is a follow up to part one: Defense is the Best Offense

Sometimes the decisions we make in everyday life are good case studies for making effective investing decisions. My wife and I recently traveled to Santorini Island in Greece where we stayed in the village of Imerovigli. During our trip we planned to hike to Oia – which has the nicest views on the island. The hike would take roughly two hours through a couple small towns and some isolated mountainous terrain beside the Aegean Sea.  click here to read the full story

The Greek Domino Effect

July 3, 2015

Check out our latest post on Greece and how to trade it here.  We believe that a quantitative approach is the best way to trade macro events. Using discretionary calls becomes increasingly difficult as the complexity of the situation increases. A good post on the perils of discretionary macro investing by Barry Ritholz can be found here.

Think MPT Doesn’t Work? Clearing Up Some Misconceptions

June 25, 2015

A note to readers: I have posted some interesting material the last few weeks on Blue Sky Asset Management (BSAM) that may be of interest.  This includes our monthly commentaries on current market observations and also a  whitepaper series on Dynamic Asset Allocation that is definitely worth reading. I also recently posted a new blog article on the site as well: Defense is the Best Offense   Readers should keep  an eye out on the website as we plan to roll out some “geekier” research papers and interesting  current market analytics in the future.  

I have spent  many years toiling with creating different asset allocation methodologies including the application of traditional and non-traditional portfolio optimization. Given the recent flare of articles on this topic in the blogosphere, I felt it was worthwhile to share my two cents. Applying optimization to a tactical approach is a topic that readers may already be familiar with, I recently posted an article on the subject on my LinkedIn :  Think MPT Doesn’t Work? You Are Probably Using it the Wrong Way .  Wouter Keller of Flex Capital, Adam Butler of BPG,  and Ilya Kipnis of Quantstratrader wrote a great paper that was referenced in the post that readers are encouraged to take a look at; Momentum and Markowitz; A Golden Combination. They show that using the MPT algorithm in a dynamic context with shorter-term data helps to capture the momentum effect as well as producing diversified portfolios with good risk-adjusted returns. This paper is in many ways a very important contribution to a stream of research and practitioner debate that is at times imbalanced and one-sided— and without good logical reasons. MPT happens to be widely and roundly criticized in the industry for perceived algorithm-specific flaws and research that shows poor out-of-sample performance.  Of course, this is primarily because it is used the wrong way–at intermediate or longer time horizons that are ill-suited to the approach. It is also important to keep in mind that industry heavyweights such as AQR and Goldman Sachs have used variants of a dynamic MPT approach to build sophisticated portfolios that have performed very well for decades.

Some other related articles on the same topic that are quite interesting include The Universal Investment Strategy by Frank Grossman of Logical Invest, and Momentum and Diversification by Andrew Gogerty of Newfound Research – 3rd Place winner of the prestigious NAAIM Wagner Award. The methodology in these two articles for optimization is nearly identical. They both find maximum Sharpe  portfolios by using brute force to combine equity curves with a constrained set of choices into a portfolio instead of using MPT. It is important to understand that both MPT and these approaches are essentially interchangeable for the most part (MPT finds the brute force optimal solution mathematically). Grossman uses a variant on the objective function with a risk-aversion parameter. Newfound introduces the twist of allowing for different rebalancing windows in the lookback window which is more similar to a dynamic programming approach. In both cases, I wanted to clarify to readers that finding the sharpe ratio by combining equity curves (assuming daily rebalance) is identical to using the calculated correlation/volatility and return to compute sharpe optimal portfolios- so there is no escaping “estimation error” it is just implicit as opposed to explicit.

Wes Gray of Alpha Architect is always a good source of research and demonstrates the more traditional use of MPT (not the tactical) in asset allocation in his post; Beware of Geeks Bearing Formulas. Unfortunately, this post is not comparing apples to apples since the MPT lookback parameters are longer-term than the simple tactical benchmarks being compared. As a consequence this post happens to be biased against the use of MPT in a dynamic format which is common within the industry and in my opinion a bit unfair since there is more good to work with than bad.  It just happens to be the case that using MPT in a tactical format comes with a set of unique complexities that do not plague simpler methods- these include higher turnover, concentrated portfolios and greater sensitivity to estimation error. The higher level of estimation error occurs for several reasons. One is greater dimensionality since there are many more inputs to estimate. The other is that in MPT the magnitude of returns dictate weights as well as the ranks of returns—in contrast a basic momentum approach only pays attention to rank. This puts greater pressure on return estimation in MPT versus  a simple momentum approach. Another issue is the integration of noisy/random correlations which interfere with errors in return estimates. Adding correlations is important for stressing diversification but only to the extent that they are not highly error-prone.  Using MPT for allocating across investment strategies rather than asset allocation is even more challenging since strategies have far more complex inputs to estimate, and some inputs cannot be estimated quantitatively.  On the positive side,  using MPT in a tactical approach carries much less room for data mining bias than building a simple tactical system using rules. This is especially true if the system builder is free to vary multiple parameters and may also choose their investment universe through repeated testing. Using one algorithm that is mathematically compact like MPT with one lookback parameter is far less subject to these insidious data mining problems.

I think the most important takeaway from the debate in the industry is that many algorithms, trading methods, or indicators are often unfairly discarded through improper or unsuitable analysis (or use) rather than for true deficiencies. The skilled cook can take a few mediocre or exotic ingredients and create a masterpiece while less knowledgeable cooks can find the same box of ingredients to be wholly deficient for creating a suitable meal. There are plenty of examples of people that have been successful even with the ultimate black-box machine-learning approach–it is a hazardous path much like climbing Mount Everest but apparently there are some good climbers out there (see Renaissance Technologies). Of course in good quantitative system design as in cooking, using great simple ingredients makes it easy to create a great meal without a lot of manipulation or effort. Pushing the edges by exploring the more exotic applications creates greater risk of  failure but also greater opportunity- and that is a risk worth taking in highly competitive markets. You just need to have a good understanding of where to draw the line. To that extent, I  guess the decision to incorporate MPT within tactical asset allocation is ironically a matter concerning utility curves……..


Real Momentum: A Longer-Term Backtest

May 9, 2015

In the last post I introduced the concept of “real momentum” which is a trend following signal based on real returns. In the post I used both expected inflation and risk-free returns to net out from the S&P500 to create a real excess return. This was done to make the hurdle for buy positions higher than the standard method. Several comments from readers indicated that this is”double-counting” and obviously from an economic standpoint this is true:  real returns should only subtract out the return of inflation (or expected inflation).  Theory would dictate taking this approach versus a real excess return. Since this is a simplification, that is desirable since it better avoids claims of “data-snooping.” Furthermore, since this was a preliminary study,  in the previous post I did a quick test using only 10 years of data with the ETFs available. Clearly this is not ideal for assessing whether the concept has merit or is robust. To obtain more data, I used mutual fund proxies for TIP and IEF I was able to extend results back to 1995 ( for TIP I used Loomis Sayles Inflation Protected Secutities Mutual Fund (LSGSX) and for IEF I used T Rowe Price US Treasury Intermediate Fund). Following the advice of readers I subtracted out the expected inflation rate only- which is the differential return between TIP and IEF (smoothed using an optional lookback- anywhere between 3-10 days yields similar results, I chose 5 for these tests)- from the daily returns of the S&P500 (SPY) and then take the average of those returns. If the return is positive then go long, if negative then go to cash. Without assuming a return on cash here are the results compared to a traditional absolute/time-series momentum strategy that uses a risk-free rate or proxy such as short-term treasurys (SHY). Note that rebalancing was done on a monthly basis.

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The results seem to clearly favor Real Momentum- which is impressive considering we simplified the calculation and also extended the lookback for 10-years that are “out of sample.”  On average, the Real Momentum signal produces nearly a 1% advantage in CAGR annualized and a near 15% improvement in the sharpe ratio. It seems on the surface that real equity risk premiums may be more important to large investors that can move markets. But as my colleague Corey Rittenhouse points out, if you aren’t going to invest in something that has a negative real rate of return then you need to have an alternative.  I agree with this point, and one logical option is to hold TIP- or Inflation-Protected Treasurys when Real Momentum is negative.  Using a 120-day  Real Momentum with the strategy parameters above, a baseline strategy goes long SPY/S&P500 when Real Momentum is >0 and holds TIP when momentum is <0. Here is what this looks like:
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For comparison, here is absolute momentum using SPY and SHY with the same 120-day parameter:
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As you can see the Real Momentum strategy outperforms the Absolute Momentum strategy, with higher accuracy on winning trades and higher gains per trade along with higher return and a higher sharpe ratio with a similar maximum drawdown. Some readers may point out that this comparison may not be fair because TIP returns more than SHY as the cash asset. As the first table shows, the timing signal itself is superior so that is unlikely to be the driving factor. But just to prove that, here is the Absolute Momentum strategy using SHY as the asset to trigger the signal but holding TIP as the cash asset:
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This is substantially worse than the  Real Momentum strategy and worse than the Absolute Momentum strategy using SHY as the cash asset. While not shown, using TIP as the signal asset and the cash asset does the worst of all. So apparently there is something there with respect to looking at Real Momentum- or effectively the expected real return to the broad equity market/S&P500. This is not the final word on the strategy, and it would be helpful to run an even longer-term test (one can never have too much data as they say….). But after looking at the performance on other risk assets using this signal, I can’t reject the hypothesis that there isn’t something there at first pass.  It is something that makes sense, and seems to be supported by data even after simplification and an out-of-sample test. It would be interesting to run a deeper analysis to see what is going on and whether this is merely a spurious result that is driven by some other factor.  A basic Real Momentum strategy that holds the S&P500 when expected real returns are positive and holds Treasury Inflation Protected Securities when they are negative earns very good returns and risk-adjusted returns and beats buy and hold over a 20-year period by nearly 5% annualized. The strategy also happens to be relatively tax-efficient compared to more complex strategies which is a bonus.