This review will review the “Adaptive Asset Allocation: Dynamic Global Portfolios to Profit in Good Times – and Bad” book by the people at ReSolve Asset Management. Overall, this book is a definite must-read for those who have never been exposed to the ideas within it. However, when it comes to a solution that can be fully replicated, this book is lacking.

Okay, it’s been a while since I reviewed my last book, DIY Financial Advisor, from the awesome people at Alpha Architect. This book in my opinion, is set up in a similar sort of format.

This is the structure of the book, and my reviews along with it:

Part 1: Why in the heck you actually need to have a diversified portfolio, and why a diversified portfolio is a good thing. In a world in which there is so much emphasis put on single-security performance, this is certainly something that absolutely must be stated for those not familiar with portfolio theory. It highlights the example of two people–one from Abbott Labs, and one from Enron, who had so much of their savings concentrated in their company’s stock. Mr. Abbott got hit hard and changed his outlook on how to save for retirement, and Mr. Enron was never heard from again. Long story short: a diversified portfolio is good, and a properly diversified portfolio can offset one asset’s zigs with another asset’s zags. This is the key to establishing a stream of returns that will help meet financial goals. Basically, this is your common sense story (humans love being told stories) so as to motivate you to read the rest of the book. It does its job, though for someone like me, it’s more akin to a big “wait for it, wait for it…and there’s the reason why we should read on, as expected”.

Part 2: Something not often brought up in many corners of the quant world (because it’s real life boring stuff) is the importance not only of average returns, but *when* those returns are achieved. Namely, imagine your everyday saver. At the beginning of their careers, they’re taking home less salary and have less money in their retirement portfolio (or speculation portfolio, but the book uses retirement portfolio). As they get into middle age and closer to retirement, they have a lot more money in said retirement portfolio. Thus, strong returns are most vital when there is more cash available *to* the portfolio, and the difference between mediocre returns at the beginning and strong returns at the end of one’s working life as opposed to vice versa is *astronomical* and cannot be understated. Furthermore, once *in* retirement, strong returns in the early years matter far more than returns in the later years once money has been withdrawn out of the portfolio (though I’d hope that a portfolio’s returns can be so strong that one can simply “live off the interest”). Or, put more intuitively: when you have $10,000 in your portfolio, a 20% drawdown doesn’t exactly hurt because you can make more money and put more into your retirement account. But when you’re 62 and have $500,000 and suddenly lose 30% of everything, well, that’s massive. How much an investor wants to avoid such a scenario cannot be understated. Warren Buffett once said that if you can’t bear to lose 50% of everything, you shouldn’t be in stocks. I really like this part of the book because it shows just how dangerous the ideas of “a 50% drawdown is unavoidable” and other “stay invested for the long haul” refrains are. Essentially, this part of the book makes a resounding statement that any financial adviser keeping his or her clients invested in equities when they’re near retirement age is doing something not very advisable, to put it lightly. In my opinion, those who advise pension funds should especially keep this section of the book in mind, since for some people, the long-term may be coming to an end, and what matters is not only steady returns, but to make sure the strategy doesn’t fall off a cliff and destroy decades of hard-earned savings.

Part 3: This part is also one that is a very important read. First off, it lays out in clear terms that the long-term forward-looking valuations for equities are at rock bottom. That is, the expected forward 15-year returns are very low, using approximately 75 years of evidence. Currently, according to the book, equity valuations imply a *negative* 15-year forward return. However, one thing I *will* take issue with is that while forward-looking long-term returns for equities may be very low, if one believed this chart and only invested in the stock market when forecast 15-year returns were above the long term average, one would have missed out on both the 2003-2007 bull runs, *and* the one since 2009 that’s just about over. So, while the book makes a strong case for caution, readers should also take the chart with a grain of salt in my opinion. However, another aspect of portfolio construction that this book covers is how to construct a robust (assets for any economic environment) and coherent (asset classes balanced in number) universe for implementation with any asset allocation algorithm. I think this bears repeating: universe selection is an extremely important topic in the discussion of asset allocation, yet there is very little discussion about it. Most research/topics simply take some “conventional universe”, such as “all stocks on the NYSE”, or “all the stocks in the S&P 500”, or “the entire set of the 50-60 most liquid futures” without consideration for robustness and coherence. This book is the first source I’ve seen that actually puts this topic under a magnifying glass besides “finger in the air pick and choose”.

Part 4: and here’s where I level my main criticism at this book. For those that have read “Adaptive Asset Allocation: A Primer”, this section of the book is basically one giant copy and paste. It’s all one large buildup to “momentum rank + min-variance optimization”. All well and good, until there’s very little detail beyond the basics as to how the minimum variance portfolio was constructed. Namely, what exactly is the minimum variance algorithm in use? Is it one of the poor variants susceptible to numerical instability inherent in inverting sample covariance matrices? Or is it a heuristic like David Varadi’s minimum variance and minimum correlation algorithm? The one feeling I absolutely could not shake was that this book had a perfect opportunity to present a robust approach to minimum variance, and instead, it’s long on concept, short on details. While the theory of “maximize return for unit risk” is all well and good, the actual algorithm to implement that theory into practice is not trivial, with the solutions taught to undergrads and master’s students having some well-known weaknesses. On top of this, one thing that got hammered into my head in the past was that ranking *also* had a weakness at the inclusion/exclusion point. E.G. if, out of ten assets, the fifth asset had a momentum of say, 10.9%, and the sixth asset had a momentum of 10.8%, how are we so sure the fifth is so much better? And while I realize that this book was ultimately meant to be a primer, in my opinion, it would have been a no-objections five-star if there were an appendix that actually went into some detail on how to go from the simple concepts and included a small numerical example of some algorithms that may address the well-known weaknesses. This doesn’t mean Greek/mathematical jargon. Just an appendix that acknowledged that not every reader is someone only picking up his first or second book about systematic investing, and that some of us are familiar with the “whys” and are more interested in the “hows”. Furthermore, I’d really love to know where the authors of this book got their data to back-date some of these ETFs into the 90s.

Part 5: some more formal research on topics already covered in the rest of the book–namely a section about how many independent bets one can take as the number of assets grow, if I remember it correctly. Long story short? You *easily* get the most bang for your buck among disparate asset classes, such as treasuries of various duration, commodities, developed vs. emerging equities, and so on, as opposed to trying to pick among stocks in the same asset class (though there’s some potential for alpha there…just…a lot less than you imagine). So in case the idea of asset class selection, not stock selection wasn’t beaten into the reader’s head before this point, this part should do the trick. The other research paper is something I briefly skimmed over which went into more depth about volatility and retirement portfolios, though I felt that the book covered this topic earlier on to a sufficient degree by building up the intuition using very understandable scenarios.

So that’s the review of the book. Overall, it’s a very solid piece of writing, and as far as establishing the *why*, it does an absolutely superb job. For those that aren’t familiar with the concepts in this book, this is definitely a must-read, and ASAP.

However, for those familiar with most of the concepts and looking for a detailed “how” procedure, this book does not deliver as much as I would have liked. And I realize that while it’s a bad idea to publish secret sauce, I bought this book in the hope of being exposed to a new algorithm presented in the understandable and intuitive language that the rest of the book was written in, and was left wanting.

Still, that by no means diminishes the impact of the rest of the book. For those who are more likely to be its target audience, it’s a 5/5. For those that wanted some specifics, it still has its gem on universe construction.

Overall, I rate it a 4/5.

Thanks for reading.

This post will review the DIY Financial Advisor book, which I thought was a very solid read, and especially pertinent to those who are more beginners at investing (especially systematic investing). While it isn’t exactly perfect, it’s about as excellent a primer on investing as one will find out there that is accessible to the lay-person, in my opinion.

Okay, so, official announcement: I am starting a new section of posts called “Reviews”, which I received from being asked to review this book. Essentially, I believe that anyone that’s trying to create a good product that will help my readers deserves a spotlight, and I myself would like to know what cool and innovative financial services/products are coming about. For those who’d like exposure on this site, if you’re offering an affordable and innovative product or service that can be of use to an audience like mine, reach out to me.

Anyway, this past weekend, while relocating to Chicago, I had the pleasure of reading Alpha Architect’s (Gray, Vogel, Foulke) book “DIY financial advisor”, essentially making a case as to why a retail investor should be able to outperform the expert financial advisers that charge several percentage points a year to manage one’s wealth.
The book starts off by citing various famous studies showing how many subtle subconscious biases and fallacies human beings are susceptible to (there are plenty), such as falling for complexity, overconfidence, and so on—none of which emotionless computerized systems and models suffer from. Furthermore, it also goes on to provide several anecdotal examples of experts gone bust, such as Victor Niederhoffer, who blew up not once, but twice (and rumor has it he blew up a third time), and studies showing that systematic data analysis has shown to beat expert recommendations time and again—including when experts were armed with the outputs of the models themselves. Throw in some quotes from Jim Simons (CEO of the best hedge fund in the world, Renaissance Technologies), and the first part of the book can be summed up like this:

1) Your rank and file human beings are susceptible to many subconscious biases.

2) Don’t trust the recommendations of experts. Even simpler models have systematically outperformed said “experts”. Some experts have even blown up, multiple times even (E.G. Victor Niederhoffer).

3) Building an emotionless system will keep these human fallacies from wrecking your investment portfolio.

4) Sticking to a well thought-out system is a good idea, even when it’s uncomfortable—such as when a marine has to wear a Kevlar helmet, hold extra ammo, and extra water in a 126 degree Iraq desert (just ask Dr./Captain Gray!).

This is all well and good—essentially making a very strong case for why you should build a system, and let the system do the investment allocation heavy lifting for you.
Next, the book goes into the FACTS acronym of different manager selection—fees, access, complexity, taxes, and search. Fees: how much does it cost to have someone manage your investments? Pretty self-explanatory here. Access: how often can you pull your capital (EG a hedge fund that locks you up for a year especially when it loses money should be run from, and fast). Complexity: do you understand how the investments are managed? Taxes: long-term capital gains, or shorter-term? Generally, very few decent systems will be holding for a year or more, so in my opinion, expect to pay short-term taxes. Search: that is, how hard is it to find a good candidate? Given the sea of hedge funds (especially those with short-term track records, or only track records managing tiny amounts of money), how hard is it to find a manager who’ll beat the benchmark after fees? Answer: very difficult. In short, all the glitzy sophisticated managers you hear about? Far from a terrific deal, assuming you can even find one.

Continuing, the book goes into two separate anomalies that should form the foundation for any equity investment strategy – value, and momentum. The value system essentially goes long the top decile of the EBIT/TEV metric for the top 60% of market-cap companies traded on the NYSE every year. In my opinion, this is a system that is difficult to implement for the average investor in terms of managing the data process for this system, along with having the proper capital to allocate to all the various companies. That is, if you have a small amount of capital to invest, you might not be able to get that equal weight allocation across a hundred separate companies. However, I believe that with the QVAL and IVAL etfs (from Alpha Architect, and full disclosure, I have some of my IRA invested there), I think that the systematic value component can be readily accessed through these two funds.

The momentum strategy, however, is much simpler. There’s a momentum component, and a moving average component. There’s some math that shows that these two signals are related (a momentum signal is actually proportional to a difference of a moving average and its last value), and the ROBUST system that this book proposes is a combination of a momentum signal and an SMA signal. This is how it works. Assume you have $100,000 and 5 assets to invest in, for the sake of math. Divide the portfolio into a $50,000 momentum component and a $50,000 moving average component. Every month, allocate $10,000 to each of the five assets with a positive 12-month momentum, or stay in cash for that asset. Next, allocate another $10,000 to each of the five assets with a price above a 12-month simple moving average. It’s that simple, and given the recommended ETFs (commodities, bonds, foreign stocks, domestic stocks, real estate), it’s a system that most investors can rather easily implement, especially if they’ve been following my blog.

For those interested in more market anomalies (especially value anomalies), there’s a chapter which contains a large selection of academic papers that go back and forth on the efficacies of various anomalies and how well they can predict returns. So, for those interested, it’s there.

The book concludes with some potential pitfalls which a DIY investor should be aware of when running his or her own investments, which is essentially another psychology chapter.
Overall, in my opinion, this book is fairly solid in terms of reasons why a retail investor should take the plunge and manage his or her own investments in a systematic fashion. Namely, that flesh and blood advisers are prone to human error, and on top of that, usually charge an unjustifiably high fee, and then deliver lackluster performance. The book recommends a couple of simple systems, one of which I think anyone who can copy and paste some rudimentary R can follow (the ROBUST momentum system), and another which I think most stay-at-home investors should shy away from (the value system, simply because of the difficulty of dealing with all that data), and defer to either or both of Alpha Architect’s 2 ETFs.
In terms of momentum, there are the ALFA, GMOM, and MTUM tickers (do your homework, I’m long ALFA) for various differing exposures to this anomaly, for those that don’t want to pay the constant transaction costs/incur short-term taxes of running their own momentum strategy.

In terms of where this book comes up short, here are my two cents:
Tested over nearly a century, the risk-reward tradeoffs of these systems can still be frightening at times. That is, for a system that delivers a CAGR of around 15%, are you still willing to risk a 50% drawdown? Losing half of everything on the cusp of retirement sounds very scary, no matter the long-term upside.

Furthermore, this book keeps things simple, with an intended audience of mom and pop investors (who have historically underperformed the S&P 500!). While I think it accomplishes this, I think there could have been value added, even for such individuals, by outlining some ETFs or simple ETF/ETN trading systems that can diversify a portfolio. For instance, while volatility trading sounds very scary, in the context of providing diversification, it may be worth looking into. For instance, 2008 was a banner year for most volatility trading strategies that managed to go long and stay long volatility through the crisis. I myself still have very little knowledge of all of the various exotic ETFs that are popping up left and right, and I would look very favorably on a reputable source that can provide a tour of some that can provide respectable return diversification to a basic equities/fixed-income/real asset ETF-based portfolio, as outlined in one of the chapters in this book, and other books, such as Meb Faber’s Global Asset Allocation (a very cheap ebook).

One last thing that I’d like to touch on—this book is written in a very accessible style, and even the math (yes, math!) is understandable for someone that’s passed basic algebra. It’s something that can be read in one or two sittings, and I’d recommend it to anyone that’s a beginner in investing or systematic investing.

Overall, I give this book a solid 4/5 stars. It’s simple, easily understood, and brings systematic investing to the masses in a way that many people can replicate at home. However, I would have liked to see some beyond-the-basics content as well given the plethora of different ETFs.

Thanks for reading.