[Updated] Due to a change in the placement of OHLC price data in Tiingo's feed, version 4.0 of the stand alone Excel spreadsheet should no longer be used! In beta version 5.0 the issue has been fixed by JH. Great job!
TrendXplorer
Exploring Smart Leverage: DAA on Steroids
 The constant leverage myth is busted: there is no
spoonnatural decay.  DAA’s fast protective momentum approach successfully detects lower volatility regimes with higher streak potential.
 Smart leverage through a clever separation of signals and trades can achieve considerable outperformance even on a risk adjusted basis.
Popular belief that constant leveraging results in decay over time is a myth. Michael Gayed and Charles Bilello busted the myth in their 2016 Dow Award winning paper “Leverage for the Long Run”. Their research shows that daily releveraging is not without risk. At times the act of releveraging can even be mathematically destructive. Yet the source of that risk does not come from some inherent form of natural decay. The authors single out high volatility and seesawing action as the (real) enemies of leverage, while low volatility and streaks in performance are its friends.
As stated in the paper daily releveraging combined with high volatility creates compounding issues, often referred to as the “constant leverage trap”. A systematic way of identifying lower volatility regimes with higher streak potential is key for achieving outperformance through smart leverage. Expanding on the authors application of moving averages for identifying those conditions, in the following article smart leverage is explored using the DAA framework with its fast 13612W protective momentum approach with a dedicated twoasset canary universe.
When the stock market is in an uptrend  positive 13612W momentum for all canary assets  favorable conditions for leveraged stock positions are assumed targeting positive streaks in performance. When the stock market is in a downtrend  negative 13612W momentum for one or more of the canary assets  a rise in volatility is expected and a (relatively) safe Treasury bond position is acquired to avoid the constant leverage trap for stocks.
On top of DAA’s dedicated 13612W protective momentum deployment for detecting favorable conditions for leverage, the smart leverage approach incorporates a clever separation of signals and trades. As proposed by Matthias Koch, a quant from Germany, nonleveraged asset universes are used for signaling momentum based position sizing while universes that hold a limited number of matching leveraged funds are used for actual trading.
Announcing Defensive Asset Allocation (DAA)
 Defensive Asset Allocation (DAA) builds on the framework designed for Vigilant Asset Allocation (VAA)
 For DAA the need for crash protection is quantified using a separate “canary” universe instead of the full investment universe as with VAA
 DAA leads to lower outofmarket allocations and hence improves the tracking error due to higher inthemarketrates
In our brand new SSRNpaper “Breadth Momentum and the Canary Universe: Defensive Asset Allocation (DAA)” we improve on our Vigilant Asset Allocation (VAA, see post) by the introduction of a separate “canary” universe for signaling the need for crash protection, using the concept of breadth momentum (see VAA). This protective universe functions as an early warning system similar to the canary in the coal mine back in the day. For DAA the amount of cash is governed by the number of canary assets with negative momentum. The risky part is still based on relative momentum, just like VAA. The resulting investment strategy is called Defensive Assets Allocation (DAA). The aim of DAA is to lower the average cash (or bond) fraction while keeping nearly the same degree of crash protection as with VAA.
Using a very simple model from 1925 to 1970 with only the S&P 500 total return index as investment asset, we arrive at a twoasset canary universe (VWO and BND) combined with a protective B2 breadth momentum setting, which defines DAA’s core elements.
The DAA concept turns out to be quite effective for nearly all four universes examined in our VAApaper from 1971 to 2018. The average cash fraction of DAA is often less than half that of VAA’s (below 30% instead of nearly 60%), while return and risk are similar and for recent years even better. Deploying a separate “canary” universe for signaling the need for crash protection also improves the tracking error with respect to the passive (buyandhold) benchmark due to higher inthemarketrates than with VAA. The separate “canary” universe also limits turnover. This makes DAA less sensitive for rising cash (or bond) yields, which is key in view of recent low rates.
To crystallize the DAA concept:
 When both canary assets VWO and BND register negative 13612W momentum, invest 100% in the single best bond of the cash universe;
 When only one of the canary assets VWO or BND registers negative momentum, allocate 50% in the top half of the best risky assets, while applying equal weights, and invest the remaining 50% in the best bond of the cash universe;
 When none of canary assets VWO and BND register negative momentum, indicating the risk of a crash is deemed low, invest 100% in the full top risky assets, again applying equal weights.
Presenting the Keller Ratio
 Many traditional return to risk measures are not apt for intuitive interpretation
 The Keller ratio is expressed as an adjusted return and therefore easy to interpret
 The Keller ratio allows for strategy selection optimally aligned with an investor’s risk appetite
In our VAApaper we introduced a new metric for assessing a portfolio’s equity line in terms of the reward to risk relationship: return adjusted for drawdown (RAD). We did choose RAD above the usual risk measures like the Sharpe and the MAR ratios (Sharpe: return divided by volatility, MAR: return divided by maximum drawdown), because most retail investors commonly identify true risk with maximum drawdown over volatility. Since RAD is an adjusted return, its interpretation is similar to any return (a simple percentage). For this reason we prefer RAD over MAR, which as such is just a numeric value with little context.
Frankly, albeit return adjusted for drawdown states exactly what RAD is all about, it is quite a mouthful. Therefore, and not only because RAD is his brainchild, but also to commemorate Wouter Keller’s contributions to the TAA literature (FAA, MAA, CAA, EAA, PAA, and VAA; see SSRN) it only seems fitting to accredit the return adjusted for drawdown indicator with his name. So henceforward RAD is to be named the “Keller ratio”.
Celebrating Wouter Keller's 70th birth year 
Every investor with skin in the game acknowledges a large portfolio drawdown as the ultimate investing risk. Large drawdowns are devastating to long term returns. For example, during the 2008 subprime crisis the S&P 500 Total Return index crashed over 50% in approximately 1.5 years from its late 2007 peak, needing 3 years for recovery to breakeven. This left Buy & Hold investors without any positive returns for over nearly five years, not to speak of the excruciating anxiety along the way.
The following table illustrates how severe drawdowns wreak havoc to portfolio performance. Total loss of principal is the biggest risk of all.
Matrix Iterations for Adaptive Asset Allocation
 Adaptive Asset Allocation (AAA) is based on the Nobel Prize winning portfolio theory of Markowitz (1952)
 AAA combines asset’s momentum, volatilities, and crosscorrelations for building diversified investment portfolios
 In a tactical application AAA exploits momentum for crash detection and results in consistent returns at mitigated risk levels
Actually, their encounter was coincidental. The fortuitous conversation between a stockbroker and a young mathematician in the early 1950’s proved to be seminal. After the stockbroker learned about the mathematician’s expertise, linear programming and utility maximization, and its reallife applications, he suggested to apply the math to financial portfolios. Fastforwarding four decades, in 1990 Harry Markowitz shared the Nobel Prize in Economics for his pioneering work on Modern Portfolio Theory (MPT).
Matrix rain animation courtesy TheCodePlayer.
AniGif created with Gif Brewery.

The mathematical framework of MPT combines asset’s expected returns, volatilities, and crosscorrelations for assembling wellbalanced and diversified portfolios while maximizing the expected return for a given level of risk. Its key proposition: for a multi asset portfolio returns can be maximized for a given level of risk. Likewise, risk can be minimized for a desired level of return. With the efficient frontier as its famous graphical depiction (see graph below), Markowitz’ MPT is also known as “meanvariance analysis” since the “mean” or expected return is maximized given a certain level of risk, defined as the portfolio variance (which is volatility squared).
Efficient Frontier
MPT proposes a mathematical framework how investors can reduce overall risk while maximizing return by holding a diversified portfolio of noncorrelated asset classes. Instead of looking at the riskreturn characteristics of each single asset class, MPT assesses risk and return as cumulative factors for the portfolio as a whole. The Markowitz Efficient Frontier is the graphical depiction of the collection of portfolios that offer the lowest risk for a given level of return. In an excellent video Arif Irfanullah explains in merely 3 minutes how the efficient frontier represents the set of portfolios that will give the highest return at each level of risk or the lowest risk for each level of return (highly recommended).
To illustrate key elements of MPT, let’s bring to bear the top selection from a diversified investment universe SPY, EWJ, VGK, EEM, and DBC (both the full universe population as well as the selection methodology are explained in the next section).
The portfolio concept under consideration for this contribution is the long only minimum variance portfolio without leverage, located at the magenta dot on the outer left side of the purple portfolio cloud (see statistics in bold font in the table below the following graph). For this special case portfolio risk is minimized for all feasible long only combinations. To localize this particular portfolio an Adaptive Asset Allocation (AAA) approach is applied. Please note the purple long only portfolio cloud is only a subset of the full unconstrained long/short portfolio space demarcated by the blue portfolio envelop hyperbola.
Speed readers may jump to the next section, others please bear with me while painting the full picture.
Update For Global Equities Momentum Excel VBA
By popular demand a new beta update is available for the Global Equities Momentum Excel VBA spreadsheet. The new edition sources data from Tiingo's. Following the same work flow as before, the spreadsheet allows to backtest Gary Antonacci's popular GEM strategy (see post).
GEM with mutual funds for longer historical backtest 
NB! Backtested results do not reflect actual trading. Furthermore, trading costs, slippage, and taxes are disregarded. Results are therefore purely hypothetical. Terms and conditions apply.Next to some bug fixes a new pie chart and an annual returns table have been added to the spreadsheet. The pie chart shows the average allocations over the test periode. And the annual returns table specifies GEM's annual returns along with those of the underlying components and the classical 60/40 benchmark.
Strategy Signals Powered By Tiingo's
As the new kid on the block, Tiingo is shaking up the data community. Tiingo offers Freemium access to high quality data for an extensive collection covering the full historical record. Starting today historical dividend adjusted data for the Strategy Signals page is sourced from Tiingo's, with delayed current day's NYSE data grabbed "real time" from Google Finance.
For improved performance a newly designed dedicated backend caches updates from Tiingo's data servers, and operates as ondemand feed for the various Strategy Signal tables.
Tiingo's Freemium service consists of two tailored plans: a free basic service and a power service for $10/m. For details see Tiingo's Pricing page.
For improved performance a newly designed dedicated backend caches updates from Tiingo's data servers, and operates as ondemand feed for the various Strategy Signal tables.
Subscribe to:
Posts (Atom)