There is a cool new website that tracks the performance of well-known tactical strategies. AllocateSmartly has collected an extensive list of strategies from well-known hedge fund managers like Ray Dalio along with several other portfolio managers and financial bloggers. The backtests for these strategies use a very detailed and comprehensive method that is both conservative and realistic. Where possible, the author uses tradeable assets rather than indices and factors in transaction costs along with careful treatment of dividends. The current allocations and performance are tracked in real-time which allows investors to be able to realistically trade these portfolios. Curiously the best performing model tracked on the website this year is the Minimum Correlation Algorithm from CSSA which says a lot about the importance of diversification in 2016 versus momentum and managing risk via trend-following/time-series momentum. In fact, if you dig deeper you will notice that most of the best performers have a structural or dynamic diversification element. The worst performers have been the most concentrated and oriented toward identifying the best performers. As the website correctly points out- the diversification oriented strategies tend to do well during normal market conditions but ultimately the dynamic and more tactical strategies outperform during bear markets. Over longer backtest periods, the more truly tactical performers had better long-term performance. Different market regimes will reward different approaches depending on how predictable and interrelated the markets happen to be that year. An umbrella is great for a rain storm but less than ideal for a sunny day. That is why it is important to understand the strategies you are following and why you are investing in them rather than blindly chase performance. While many quant developers and investors chase the best looking equity curves it is important to consider two primary factors: 1) the utility curve that works best for any one individual is a very personal choice (ie risk/reward and tracking error) 2) you need to choose a set of assumptions for capital markets either going forward or over the long-term: will returns, correlations or volatility be predictable and if so which will be the most predictable and why.

On a side note, I was informed that the very popular “A Simple Tactical Asset Allocation Strategy with Percentile Channels” by CSSA is also being added to the AllocateSmartly website very soon. This is a tactical and structural diversification hybrid that provides balanced factor risk with the ability to de-risk during market downturns. While it lacks the higher returns of more momentum-oriented or equity-centric strategies it provides a steady and low-risk profile across market conditions.

**Disclosure:** The author(s) principally responsible for the preparation of this material are expressing their own opinions and viewpoints, which are subject to change without notice and may differ from the view or opinions of others at BSAM or its affiliates. Any conclusions presented are speculative and are not intended to predict the future of any specific investment strategy. This material is based on publicly available data as of the publication date and largely dependent on third party research and information which we do not independently verify. We make no representation or warranty with respect to the accuracy or completeness of this material. One cannot use any graphs or charts, by themselves, to make an informed investment decision. Estimates of future performance are based on assumptions that may not be realized and actual events may differ from events assumed. BSAM is not acting as a fiduciary in presenting this material. Benchmark indices are presented or discussed for illustrative purposes only and do not account for deduction of fees and expenses incurred by investors.

The strategies discussed in this material may not be suitable for all investors. We urge you to talk with your investment adviser prior to making any investment decisions. Information taken from Minimum Correlation Algorithm strategy article is publicly available and used by a third party to generate the strategies and signals provided on AllocateSmartly.com. We have not reviewed and do not represent this information as accurately interpreted or utilized.

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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.

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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!

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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.

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*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**

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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……..**

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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:

For comparison, here is absolute momentum using SPY and SHY with the same 120-day parameter:

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:

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.

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The concept has always been appealing to me, and it makes sense to use this method to reduce the downside risk of holding a chosen asset class. In thinking about this concept, I could see why excess returns- or the return minus the risk free rate- was theoretically appealing since this is the basis of modern financial economic theory. But I also realized that investors do not earn nominal returns- they earn real returns net of inflation. The cost of living goes up, and so nominal returns must keep pace with inflation in order to provide an investor with a real return on their investments. It is rational for an investor to avoid assets with negative excess returns. If the excess return is negative net of inflation (or the real excess return is negative) then this should make an asset even less desirable for an investor.

The challenge is that inflation is somewhat elusive. Measures such as the CPI- Consumer Price Index- are released monthly with a lag, and are at best a vague measure of the change in the cost of goods for a typical consumer. Perhaps one of the best ways to get access to a real-time estimate of inflation is to look at yield curve of Treasury Inflation Protected Securities (TIPS) versus the comparable duration of a regular Treasury bond. The difference between these two represents expected inflation which is forward looking. Since there is often no matching bond duration for a TIP versus a traditional treasury, this real yield needs to be interpolated using a nonlinear estimation. A quick and convenient (albeit imperfect) way to capture this is to look at the difference in returns between the 7-10 year Treasury Bond (IEF) and the Treasury Inflation Protected Bond (TIP) which are both ETFs that trade daily. Both have an effective duration that is approximately 8 years, which makes them roughly equivalent. The daily difference in their total returns is essentially the change in expected inflation. Since this can be somewhat noisy, I chose to smooth this using a 10-day average. To proxy the risk-free rate, I use the short-term Treasury or (SHY). To calculate “Real Momentum”, I use an average of daily real excess returns. This is essentially the daily return of an asset minus the return of the risk-free rate (SHY) and the smoothed return of expected inflation (10-day sma of daily return difference between TIP and IEF).

**Real Momentum**= return of asset- risk free return- expected inflation

or the simple moving average of the:

Daily return of asset- Daily return of risk free proxy (SHY)- Daily return (smoothed) of expected inflation proxy (TIP-IEF smoothed)

For comparison with Absolute or conventional Time-Series Momentum it is important to use an average daily return proxy which is simply the average of the daily excess return of an asset minus the return of SHY. Here are the results comparing Real Momentum with Absolute Momentum from 2005 (June) to Present using the S&P500 (SPY). Note that there is limited data for TIP, so this was approximately the earliest start date that could accomodate the different lookbacks.

Over this 10-year period, it appears that Real Momentum is superior to Absolute Momentum which matches what we might expect theoretically. On average, the difference appears to be marginally significant on visual inspection. But I am not yet convinced with these preliminary tests that the difference is real (no pun intented). Trend-following strategies require a lot of data to have statistical significance because they don’t trade very frequently. A longer testing period would be preferable along with a test that incorporates the real yield instead of the TIP/IEF differential which is not a perfect basis for comparison (which is why smoothing is preferred to using the raw daily difference). Alternatively, one could use a proxy for TIP that goes back farther in time. Since this testing is in the preliminary stage, I would caution that it is difficult to draw any firm conclusions just yet. But the concept of a real absolute returns is appealing, it is just trickier to quantify in light of the fact that inflation itself can be calculated so many different ways. Feel free to share your ideas/comments and suggestions on this interesting topic.

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**Note:** *James Picerno of The Capital Spectator recently did an interesting piece evaluating the Self-Similarity Metric and provides some R code which is valuable for many of our readers. *

The principle of parsimony relates to being frugal with resources such as money or the use of computing time. It is closely tied to the principles of simplicity, elegance and efficiency. It also complements the philosophical theory of Occam’s Razor which states that the simplest explanation with the fewest assumptions is often closest to the truth. Whether doing statistical modelling or building trading systems, it would be wise to respect the power of this principle. **Parsimonious models or trading systems are often robust, while overly complex models with too many assumptions are not.** The difficulty is in telling the difference- which is not obvious even to a talented and experienced developer. The ability to distinguish between parsimony and excess complexity is virtually invisible to almost everyone else.

The backtest is the problem and great distractor in the quest for parsimony. It is like a picture of a beautiful woman that is scantily clothed beside a paragraph of important text– no one is interested in the fine print. A beautiful backtest is admittedly just as satisfying to look at (perhaps even more so for quants!) and can blind us from the details that got us to the end point. And while we all appreciate some good “chart porn”, there are some important questions to consider: What universe did we select and why? Why did we omit certain assets or choose certain parameters over others? Why did we choose one indicator or variable over another-and how do we know it is superior? Why do we trade at a certain rebalancing frequency versus another and is this relevant to the model? Most importantly is: **Can I create a trading system with similar results with far fewer assumptions and with less computational power? **That should be your goal- to achieve the maximum results with the least number of assumptions and resource usage.

For example, I am well aware than the Minimum Correlation Algorithm does not mathematically optimize the correlation matrix or find the most diversified portfolio. The Minimum Variance Algorithm does not minimize variance either relative to a true MVP solution. But they both use an intuitive and simple method that meets or often exceeds the results of the more complex solutions with less resources, and hence can be considered parsimonious.** **They are also less dependent on estimates for optimization inputs. Such systems are more likely to work in the uncertain and messy world that we actually live in. Cooking is a hobby of mine, and more recently I have strived to achieve the most with the least, and ensuring that all of my marginal choices of ingredients or differences in traditional technique are actually adding value. There is no point sounding fancy by adding exotic ingredients or using fancy techniques if they don’t change the taste for the better. These give the illusion of expertise to the unsophisticated, but to top chefs judging these dishes on FoodTV they only serve to highlight their deficiencies as cooks. **My advice is to work with things that you can understand or intuitively grasp and be very careful when trying newer and more complex methodologies**. Master what you can with the tools you have at your disposal instead of reaching for latest and greatest new toy. This may sound strange coming from a blog that was built around offering new ideas and concepts- but rest assured this is some of the best advice you will ever receive.

All of the questions I posed above relating to trading systems are quite material, and many cannot be answered quantitatively. Unfortunately for the quantitatively inclined, the principles of good logic often get lost while decoding proofs, cleaning data, or debugging computer code. Furthermore, the elegance of complex math is like comfort food for those that are highly intelligent and it is easy to forget that the assumptions of these models are a far cry from describing reality. Even for the more experienced developers that are aware of these problems, they may arrive at the wrong approach to system development. **The solution is not to avoid making ANY decisions or assumptions (although relying less on specific parameters or universes is desirable for example), but rather to make sensible choices with few assumptions. Another alternative is to build a methodology that directly makes choices quantitatively to create a parsimonious model. Both methods have their strengths and weaknesses. **

At the end of the day, there is no point making something more complicated than it needs to be unless the benefits are material. The same is true for the length of time/complexity of the run for the computer program that runs the trading. My brother is a professional hiker and has traversed extreme mountain terrain. Unlike most amateurs, he does not pack everything under the sun that might be useful for his trip. Instead he focuses only on the essentials and on minimizing weight. **More importantly, he focuses on planning for what can go wrong and makes his choice of gear and specific hiking route accordingly**. The black and white realities of survival bring these questions to the forefront. In contrast the more comfortable and forgiving world of offices and computers make trading system decisions seem almost like a video game. Rest assured, it is not…..

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