The TAAStrategies, A Short History

(Note: this is an update to an earlier post which appeared in 2019. It is instructive in describing the approach and priorities which I bring to my strategy development process.)

A Short History of the TAAStrategies

Tactical Asset Allocation (TAA) has been a passion since I began studying TAA in 2011. Early in my research, I was content to study and experiment with strategies developed by others. However natural curiosity coupled with decades of software development experience led me to begin tinkering with spreadsheets. Tinkering was soon followed by the construction of ever more sophisticated TAA strategies.

I began using my own strategies for a small portion of my family portfolio in 2012 and gradually added to our investment as my confidence grew stronger based on observable results.

In early 2014, I completed my first large-scale Tactical Model which provided a platform for developing and testing increasingly sophisticated tactical strategies. A year later, I developed the first Market Conditions Model and began integrating it with the Tactical Model.

I began publishing Tactical Global Core and Satellite strategies in early 2016. Ultimately, the Core and Satellite strategies would be combined into a more sophisticated Adaptive Global strategy.

A third generation Tactical Model was completed in mid-2018. The centerpiece was Adaptive Dynamic Momentum, an innovative and powerful momentum strategy which required nearly 5 years to develop. Adaptive Dynamic Momentum uses sophisticated statistical techniques to dynamically discover and validate security trends for each fund in our baskets, each period. While requiring significant computing resources; the improvements in both higher return and reduced risk is significant. Adaptive Dynamic Momentum was incorporated into the flagship Adaptive Global Strategy.

Adaptive Dynamic Momentum, which is particularly adept at recognizing sustainable shifts in momentum, also provided the foundation for the development of Adaptive Income, our most conservative strategy, released during the summer of 2018.

Early-2019 saw the development of a second generation Adaptive Income strategy with an entirely new fund basket and major improvement in performance metrics. This was followed by the introduction of the Adaptive Innovation strategy which provides exposure to the leading edges of innovation without the high volatility.

The most recent incremental upgrades were released in mid-2019 and are described in considerable detail below together with the resulting outcomes. In each case, the focus has been on improving control over the upper bounds of portfolio volatility, improving the consistency of annual returns, and on reducing fund turnover.

I also demonstrate a trade-off between developing strategies for long term safety versus short term returns.

Tactical Model upgrades

Limited Portfolio Volatility Weighting

Summary: I developed an enhanced version of Volatility Weighting which allows us to specify a limit for the expected portfolio volatility. When expected portfolio volatility exceeds the cap, allocations are shifted from high volatility funds into lower volatility fixed income funds until the limit is met. This has the effect of lowering the volatility of monthly returns as well as reducing the depth and duration of drawdowns.

  • The Performance Summary included with each performance table includes two important measures of volatility. The first is the “Standard Deviation of Monthly Returns” which measures the degree to which monthly returns deviate from the average return. The lower the number, the more consistent are the monthly returns.
  • The second measure is the “Ulcer Index of Daily Drawdowns” which is a measure of the length and severity of downside (unfavorable) volatility.  “Ulcer Index measures the depth and duration of percentage drawdowns in price from earlier highs. The greater a drawdown in value, and the longer it takes to recover to earlier highs, the higher the UI. Technically, it is the square root of the mean of the squared percentage drawdowns in value. The squaring effect penalizes large drawdowns proportionately more than small drawdowns.”  I began including this some months ago at the suggestion of a subscriber and it has quickly become one of my goto measures of downside risk. As a point of reference an Ulcer Index of 5 and below is considered to be very low.
  • Fund volatility, as measured by annualized Standard Deviation (StDev), is not constant. For example, MDY, a mid-cap index fund used in Adaptive Global, has a full cycle average 20 day StDev of 21.8% which ranges from a weekly low of 4.9% to a high of 95.1%. The distribution of volatility is heavily lop-sided with 646 weeks (80%) below the average and 168 weeks (20%) above the average.
  • A low StDev means that most of the numbers are close to the average. As a general rule, volatility declines in strong trends and rises when trends weaken or change direction. Rising volatility is concomitant with rising risk. Two recent examples can be found on a weekly chart of MDY. An example of extreme low volatility in MDY would be the period from 08/25/17 through 01/26/18 where 20 day StDev dropped as low as 4.9%. An example of above average volatility in MDY would be the period from 04/26/19 through 11/01/19 where 20 day StDev rose to 23.4. (The high of 95.1% occurred during the Great Financial Crisis.) Clearly, we could pull the average volatility significantly downward if we could lop off some of the occurrences which are above the average 21.8%.
  • Two of our Strategies (Adaptive Global and Adaptive Innovation) employed Volatility Weighting which allocates portfolio weights inversely to volatility. Once the required number of funds are selected based on momentum, the funds with the lowest volatility get the largest allocations (within limits). This moderates the expected volatility of the strategy.
  • I developed the Limited Volatility Weighting algorithm to further moderate portfolio volatility while maintaining returns. First, the fund set is selected based on momentum. Next, the initial allocation is performed using Volatility Weighting and the resulting portfolio volatility is calculated. Finally, if the estimated portfolio volatility exceeds the target percentage specified for the Strategy; the algorithm begins shifting allocations away from the highest volatility funds into lower volatility fixed income funds until the target is met.
  • Limited Volatility Weighting is employed only when high levels of volatility in one or more selected funds causes expected portfolio volatility to exceed the cap. The effect is similar to that of putting a speed governor on a vehicle. The cap for each Strategy is commensurate with the Strategy objective and is selected based on the mid range of compromises between lowering volatility and lowering returns which are a function of both advances in price and reduction in the depth and duration of drawdowns.
  • Limited Volatility Weighting has been adopted for all of our Strategies because it lowers both the StDev of Monthly Returns and the Ulcer Index. This improves the consistency of returns at the expense of a slight reduction in returns.

Position Optimization for LTCG

  • Fund selection based purely on momentum selects the best performing funds … period. However, there are times when a fund we already own is performing strongly but does not quite make the cut. We end up selling the fund we own to replace it with the higher performing fund.
  • Position Optimization favors holding existing fund positions over trading into new positions when the existing fund meets high thresholds of momentum and confidence. The second generation algorithm incorporates improvements that can increase average holding periods.
  • It should be noted that while optimization succeeds in extending holding periods, those periods are often not long enough to qualify as Long Term.

Strategy Upgrades

Returns in the Tactical Adaptive Strategies lagged the market for much of 2018 and 2019. This was the result of severe chop in the equity market which failed to produce an enduring trend among the funds in our baskets. However, our drawdowns also lagged the market by half.

The upgrades to Adaptive Income and Adaptive Innovation would have improved upon returns in 2019 due to small changes in each basket which are detailed below for each strategy.

Adaptive Global already has a broad basket of 24 funds so there is no point in altering the basket. I did; however, take a look at what would have been required to improve Adaptive Global's return for 2019. Had we made these changes:

  • Remove the market condition constraints
  • Force the Adaptive Dynamic Momentum to use shorter momentum lengths
  • Use Equal Weighting instead of Volatility Weighting

We would have seen a 4.20% return for January through October instead of 0.36%. However, the cost of juicing the performance in 2019 would have been devastating across a full market cycle: CAGR drops from 15% to 11%, the Ulcer Index rises  from 3.7% to 8.1%, the StDev Of Monthly Returns rises from 9.5% to 12.5%, Max Report Drawdown rises from 8.7% to 20.6%, and the Up/Down efficiency drops from 229% to 156%. I don’t believe that is the type of long term performance and safety any of us are seeking. Even the best of investment strategies has times when it underperforms. Changing strategies to chase performance is seldom rewarding but at least we can play "what if?".

Adaptive Global

The primary benefit of minor changes to Adaptive Global is reduction in both Standard Deviation of Monthly Returns and the Ulcer Index:.

  • Fund basket and condition eligibility: no change was made to fund baskets or condition eligibility
  • Fund selection: no change to fund selection method (Adaptive Dynamic Momentum)
  • Weighting of selected funds: Volatility Weighting was replaced with Limited Portfolio Volatility Weighting which is employed to cap the expected total portfolio volatility when high volatility funds (especially equities and commodities) are used. This slightly reduces both risk and return.
  • Position Optimization: Upgraded which slightly improves holding periods and performance.
  • Results: CAGR decreased slightly,  Max Monthly Drawdown decreased, and the Up/Down Ratio decreased fractionally. The Ulcer Index declined and Standard Deviation of Monthly Returns declined reflecting improved consistency of returns.

Adaptive Income

The primary benefit of changes to Adaptive Income is a broadening of the fund basket and reduction in both Standard Deviation of Monthly Returns and the Ulcer Index:

  • Fund basket and condition eligibility: the number of fixed income ETFs was increased from 5 to 6 to broaden the basket to include an investment grade ETF.
  • Fund selection: no change to fund selection method (Adaptive Dynamic Momentum)
  • Weighting of selected funds: Adaptive Income used a single selection with a 100% weighting to the best performing fund. Limited Portfolio Volatility Weighting is now employed to cap the expected total portfolio volatility when higher volatility funds (especially high yield) are used. This forces the allocation across a second, lower volatility fund when the cap is exceeded.
  • Position Optimization: Upgraded which slightly improves holding periods and performance.
  • Results: CAGR increased slightly, Max Monthly Drawdown increased, by 1%, however Max Daily Drawdown remains unchanged, and the Up/Down Ratio rose sharply. The Ulcer Index declined slightly and Standard Deviation of Monthly Returns declined sharply.
  • Note: the increase in Max Monthly Drawdown is directly attributable to the addition of the investment grade ETF. Because the Max Monthly Drawdown remains exceptionally low, this appears to be a reasonable trade-off given the size of other improvements.

Performance Table For The Tactical Adaptive Income Strategy From 2000 To Now (Two Full Market Cycles)

Adaptive Innovation

This upgrade to Adaptive Innovation improves performance while significantly reducing volatility.

  • Fund basket and condition eligibility: The existing long duration Treasury ETF was added to the eligible funds for Balanced conditions. This allows strongly performing Treasuries to complete effectively with the Innovation equities for selection. A short duration Treasury ETF was added to provide a higher yielding alternative to cash.
  • Fund selection: no change to fund selection method (Adaptive Dynamic Momentum)
  • Weighting of selected funds: Volatility Weighting was replaced with Limited Portfolio Volatility Weighting which is employed to cap the expected total portfolio volatility when the higher volatility Innovation funds are used. While Adaptive Innovation typically employs 2 fund selections, this can force the allocation to a third fund when the volatility of the primary fund(s) exceeds the cap.
  • Position Optimization: Upgraded which made no change in holding periods and performance.
  • Results: CAGR increased slightly,  Max Monthly Drawdown and Maximum Daily Drawdown remained unchanged, and the Up/Down Ratio increased sharply. Both the Ulcer Index and Standard Deviation of Monthly Returns declined significantly.

Conclusion

The experience I acquired over many years of investing in a variety of managed portfolios and hedge funds taught me the importance of evaluating risk and reward over a full market cycle rather than chasing recent performance. This provides a more balanced perspective on risk and reward.

The TAAStrategies were developed using history for the full market cycle which began in 2007 with the shift from bull to bear market. I was then able to use out of sample history from the previous 2000-2007 full market cycle to validate the performance of the TAAStrategies. More recently, last year's Covid Crisis and bear market have provided real time affirmation of the careful design and development process which underpin the TAAStrategies.

Earl Adamy

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Compares performance of the Tactical Adaptive Strategies to the S&P 500 and Vanguard Balanced Index Fund

Supporting tables for Tactical Adaptive Global. S&P 500 (SPY) and Vanguard Balanced Index Fund (VBINX) can be found below

Our backtest results tables are constructed for two full market cycles beginning in January 2000.

The most recent market cycle covers October 2007 to date. The fund baskets for our tactical strategies are constructed from indexed Exchange Traded Funds (ETFs) with just two exceptions, an Open End Fund and a Closed End Fund, both with long history.

The earlier market cycle covers January 2000 through September 2007. A number of the ETFs we use were not created until later in the decade. For those cases, we infill using predecessor Open End Funds (OEFs) for which the indexing and/or subclass is substantially similar to the ETF. Aside from providing insight into possible strategy performance during a second, earlier, cycle, they also offer the advantage of completely out of sample data. The fact that the metrics of both cycles are very comparable appears to validate the process.

We have been asked if it is possible to extend backtests to earlier decades. While this appears to be a common practice with some services; it is not possible to produce credible results for many strategies due to the lack of funds with substantially similar indexing and/or subclass. Doing so would force me to stretch the term "substantial" far beyond my comfort level.

A Caveat

A 35+ year secular bull market in both equities and bonds began in 1982. The last cyclical bull market in equities (and to a lesser extent, bonds) began 10 years ago. Returns during these periods have been historically exceptional. Market returns for the next 10 years are highly unlikely to approach those of the past 10. In fact, there is at least some evidence that market returns have a high probability of being significantly lower and that bonds and equities (which have risen together) may actually begin working at cross purposes.

Investors should not use the statistics shown for our strategies to establish expectations of specific levels of returns or drawdowns. Investors should, however, appreciate that we believe the principles which underlie the Tactical Adaptive Global, Tactical Adaptive Income, and Tactical Adaptive Innovation Strategies are enduring enough to significantly outperform the market in the future, both in lowering risk and in improving returns.

Benchmark S&P 500 (SPY)

Benchmark Vanguard Balanced Index Fund (VBINX)

Compares performance of the Tactical Adaptive Strategies to the S&P 500 and Vanguard Balanced Index Fund

Supporting tables for Tactical Adaptive Income, S&P 500 (SPY) and Vanguard Balanced Index Fund (VBINX) can be found below

Our backtest results tables are constructed for two full market cycles beginning in January 2000.

The most recent market cycle covers October 2007 to date. The fund baskets for our tactical strategies are constructed from indexed Exchange Traded Funds (ETFs) with just two exceptions, an Open End Fund and a Closed End Fund, both with long history.

The earlier market cycle covers January 2000 through September 2007. A number of the ETFs we use were not created until later in the decade. For those cases, we infill using predecessor Open End Funds (OEFs) for which the indexing and/or subclass is substantially similar to the ETF. Aside from providing insight into possible strategy performance during a second, earlier, cycle, they also offer the advantage of completely out of sample data. The fact that the metrics of both cycles are very comparable appears to validate the process.

We have been asked if it is possible to extend backtests to earlier decades. While this appears to be a common practice with some services; it is not possible to produce credible results for many strategies due to the lack of funds with substantially similar indexing and/or subclass. Doing so would force me to stretch the term "substantial" far beyond my comfort level.

A Caveat

A 35+ year secular bull market in both equities and bonds began in 1982. The last cyclical bull market in equities (and to a lesser extent, bonds) began 10 years ago. Returns during these periods have been historically exceptional. Market returns for the next 10 years are highly unlikely to approach those of the past 10. In fact, there is at least some evidence that market returns have a high probability of being significantly lower and that bonds and equities (which have risen together) may actually begin working at cross purposes.

Investors should not use the statistics shown for our strategies to establish expectations of specific levels of returns or drawdowns. Investors should, however, appreciate that we believe the principles which underlie the Tactical Adaptive Global, Tactical Adaptive Income, and Tactical Adaptive Innovation Strategies are enduring enough to significantly outperform the market in the future, both in lowering risk and in improving returns.

Benchmark S&P 500 (SPY)

Benchmark Vanguard Balanced Index Fund (VBINX)

Compares performance of the Tactical Adaptive Strategies to the S&P 500 and Vanguard Balanced Index Fund

Supporting tables for Tactical Adaptive Innovation, S&P 500 (SPY) and Vanguard Balanced Index Fund (VBINX) can be found below

The Innovation ETFs used in the Innovation Strategy were not established until 2014-2015 so our history is limited. There are no predecessor funds which are similar enough to use for infill.

A Caveat

A 35+ year secular bull market in both equities and bonds began in 1982. The last cyclical bull market in equities (and to a lesser extent, bonds) began 10 years ago. Returns during these periods have been historically exceptional. Market returns for the next 10 years are highly unlikely to approach those of the past 10. In fact, there is at least some evidence that market returns have a high probability of being significantly lower and that bonds and equities (which have risen together) may actually begin working at cross purposes.

Investors should not use the statistics shown for our strategies to establish expectations of specific levels of returns or drawdowns. Investors should, however, appreciate that we believe the principles which underlie the Tactical Adaptive Global, Tactical Adaptive Income, and Tactical Adaptive Innovation Strategies are enduring enough to significantly outperform the market in the future, both in lowering risk and in improving returns.

Benchmark S&P 500 (SPY)

Benchmark Vanguard Balanced Index Fund (VBINX)

This strategy is intended to capitalize on trending moves in both precious metals and bitcoin if and when they occur while managing volatility to reduce risk. The strategy, which uses a basket of precious metals, bitcoin, and Treasury funds, selects the single best fund each month although it provides blended allocations when necessary to manage volatility.

Bitcoin is a relative newcomer to investable assets and we take no position as to whether bitcoin is or is not a sustainable asset class. The Grayscale Bitcoin Trust used in this strategy was not established until 2015 so our history is limited. There are no predecessor funds which are similar enough to use for infill.

The CAGR in 2017 is extraordinary; however the bitcoin trust rose 1893% from $1.17 to $22.15 during this period. While the strategy remained invested in bitcoin for 10 of the 12 months, volatility weighting significantly reduced the weighting to bitcoin from a low of 14.6% to a high of 68.0%. The use of Treasuries to manage bitcoin volatility accounts for the high percentage invested in fixed income assets.

Volatility weighting has little effect on precious metals which typically receive a 100% weight when selected.