Tactical Adaptive Income Strategy Whitepaper

Updated January 01, 2023

TAAS offers two Tactical Adaptive Strategies to investors:

  • Tactical Adaptive Global provides broad exposure to domestic and international equities, fixed income, real estate, commodities and precious metals.
  • Tactical Adaptive Income invests across a diversified basket of fixed income subclasses designed to provide a high level of income coupled with exceptionally low risk.

This paper describes the design and principles which underlie the Tactical Adaptive Income Strategy in considerable detail. It is written for subscribers and prospective subscribers who seek to more fully understand the strategy.

The Appeal Of Adaptive Income

Adaptive Income will appeal to three different types of investors:

  • Risk-averse investors seeking portfolio stability and a relatively high level of income from a strategy with a very low Ulcer Index and exceptionally low drawdowns.
  • Investors seeking an ultra-simple method to remain invested while earning high income.
  • Multi-strategy investors who prefer to withdraw distribution income for living expenses rather than “eat the seed corn”

Before moving on, let's set the stage with a brief explanation of Tactical Asset Allocation.

Tactical Asset Allocation

Tactical Asset Allocation (TAA) is among the best investment tools available for navigating Full Market Cycles. While TAA tends to lag in late bull markets, it offers opportunities for higher Compound Annual Growth Rates and lower Maximum Drawdowns across a full bull/bear market cycle. Among the greatest strengths of TAA is its mechanical, rules-based approach, which not only keeps the portfolio attuned with market conditions but reduces the anxiety of managing the portfolio.

Perhaps the single biggest distinction between Tactical Asset Allocation and Modern Portfolio Theory is that while Modern Portfolio Theory seeks to reduce risk by spreading it across several asset classes, Tactical Asset Allocation seeks to reduce risk by cutting it. - Earl Adamy

Please see What Is Tactical Asset Allocation? How Does It Improve Returns? for a more complete discussion of Tactical Asset Allocation.

Tactical Process For Adaptive Income

  • Calculate trend scores (TScores) for each fund in the basket. Adaptive Dynamic Momentum, the trend identification methodology used by our Tactical Model, provides the critical pathway to finding (and sticking with) winners while avoiding (and/or quickly discarding) losers. During the development process, the Tactical Model quickly identified the fact that a shortened primary trend length is most suitable for achieving the low risk objective of Adaptive Income. (For more detail, please see Adaptive Dynamic Momentum - How Does It Improve Trend Identification?)
  • Order and select the funds according to TScore. This is where we give priority to holding existing positions when conditions are favorable, and manage the holding and reentry requirements imposed by Open End Funds.
  • Allocate a portfolio percentage to each selected fund using Volatility Weighting

Building A Fixed Income Fund Basket

Fixed income is viewed by many equity-focused investors as the backwater of the markets although the bond and credit markets are in fact larger than the equity markets and very challenging.

The Fed’s aggressive management of monetary and interest rate policies have wrought critical long term changes in the credit markets. First we had historically easy fiscal and monetary policies which stoked the inflation genie. The Fed is now trying to stuff the stuff the genie back into the bottle with an extremely rapid series of rate increases. Adapting to more volatile yields means returns must be earned by exploiting more corners of the credit markets as well as shorter interest rate cycles.

I went through a process of testing nearly 40 different  fixed income funds before settling on a basket of six funds including corporate high yield, municipal high yield, corporate senior loans, government mortgage backed securities, corporate investment grade, and short term Treasuries. Five are Exchange Traded Funds and one is an Open End Fund.

Fund inception date and liquidity are among the important criteria used in fund selection. Ideally, each fund has a history which goes back into 2007 prior to the start of the Great Financial Crisis. This allows us to capture performance during a full market cycle. Even better is history back to 1999 allowing us to capture performance during two full market cycles.

Relatively few ETFs have history back to 2007 and even fewer back to 1999. Fortunately, in using indexed ETFs we are able to identify Open End Funds with same/similar indexing and/or characteristics, in many cases, predecessor funds from the same fund family.

Market Conditions Model

I tested the Market Conditions Model to constrain eligible fund selections during Hostile periods. Doing so provided a tiny increase in return at the expense of a doubling in monthly maximum drawdown. While the Market Conditions Model focuses on longer term cyclical trends, the Adaptive Income Strategy requires short term trend identification.

This is the only TAAStrategy which does not use the Market Conditions Model; however we include below performance statistics for all three market conditions to demonstrate the historical consistency of strategy performance.

Identifying The Trend

Five years of research and development went into Adaptive Dynamic Momentum (see Adaptive Dynamic Momentum: Improving Trend Identification), our ranking algorithm. While most tactical ranking is performed using fixed length periods; Adaptive Dynamic Momentum identifies the optimum ranking criteria for each fund, each week. It does this by identifying primary and secondary trends as well as applying additional filtering to minimize noise.

Adaptive Dynamic Momentum is used by our Tactical Model to provide the critical pathway to finding (and sticking with) winners while avoiding (and/or quickly discarding) losers.

Fund ranking is just the beginning of the process for selecting funds for the next rebalance. We are just as interested in how well a fund is likely to continue performing in the future as how well it has performed in the past. Adaptive Dynamic Momentum assigns a confidence level to each fund's trend ranking. We use the combined ranking and confidence level to make the final selections.

Ranking and Selection

Once the TScores have been calculated for each fund, the funds are filtered for special conditions.

One of the newest features in our Tactical Model reduces the number of trades. Before replacing an existing position with a higher trend scoring fund, the Model checks to see if it can hold the existing position without compromising expected performance. For the Adaptive Income Strategy, this slightly reduces trades while extending the average holding period for each fund by a little over 10%.

Open End Funds are also checked for restrictions on reinvestment.

Finally, the funds are ordered from best to worst and selected for inclusion in the rebalance.

Allocating Funds

Generally, when building tactical strategies, I prefer to work with a sizable basket of funds and spread the allocation across several of the best performing funds for diversification. Extensive testing with the fixed income basket revealed that the best results are obtained by selecting a single fund. In fact, including the “2nd best” fund in a combined allocation actually reduced returns while increasing drawdowns.

I have also come to prefer Volatility Weighting over the commonly used Equal Weighting which spreads the investment equally over each of the selected funds i.e. 4 funds at 25% to each fund.

Volatility tends to decline when a trend is strengthening and rise when a trend is correcting or changing direction. Rising volatility accompanied by an upward change in trend is “good volatility” while rising volatility accompanied by a downward change in trend is “bad volatility”.

In studying the characteristics of fund volatility, I learned that volatility (20 to 30 day) is skewed sharply to the low side. That is, for a given fund, there will be many more instances where volatility is below the average and relatively few instances where volatility is above average. 182 (66%) are equal to or below the average and only 91 are above. Of the 91 above 4.1% only 42 are more than 20% (5.8%) above the average and only 7 are more than twice the average. Not surprisingly, the highest volatility at 34% was in October of 2008.

Volatility Weighting spreads the allocation inversely to volatility with limits to prevent funds from taking an extreme allocation. The six funds in the Adaptive Income basket have average volatilities ranging from 1.3% to 4.5%. One would expect that the 1.3% fund would receive a large allocation and the 4.5% fund would receive very little. But higher volatility funds tend to deliver higher returns, have higher TScores, and are therefore selected more frequently. To take that one step further, funds with extremely high volatility are quite likely to have poor TScores due to poor momentum and sustainability.

Adaptive Income selects just one fund. So it is going to get 100% of the allocation regardless of volatility? Not quite.

I developed an algorithm I refer to as “Limited Portfolio Volatility” which permits the setting of a limit on expected portfolio volatility. The algorithm calculates expected portfolio volatility by multiplying and summing each fund allocation by its volatility. If the expected portfolio volatility exceeds the limit, it begins shifting allocations from high volatility funds to lower volatility funds with high TScores.

In the case of Adaptive Income, which has just one fund selection, we introduce a second, high TScore, lower volatility fixed income fund to absorb enough of the allocation to reduce expected portfolio volatility to the target. This has the effect of dampening strategy volatility.

Scoring The Results

The Tactical Model is run through two full market cycles to calculate Compound Annual Growth Rate (CAGR), Maximum Monthly and Daily Drawdown, and a number of other statistics including monthly and annual returns.

Performance table for TAAStrategies tactical adaptive income asset allocation

The benefit of looking at the full cycle is the perspective on bear market performance. This shows that the Adaptive Income Strategy performed comparatively very well during the 2000-2002, 2007-2009, 2020, and 2022 bear markets.

By way of benchmarking, the Vanguard Total Bond Index Fund, which invests in investment grade bonds, shows a CAGR of 3.8% and a MaxMD of 17.8% while the S&P 500 (SPY) shows a CAGR of 6.2% and a MaxMD of 50.8%.

Compares performance of TAAStrategies tactical asset allocation strategies to the S&P 500 and Vanguard Balanced Index Fund

Performance Across Market Conditions

As mentioned above, we do not use the Market Conditions Model because the Adaptive Income Strategy relies upon very short term trend identification. Never-the-less, we are able to break out strategy performance by market condition for the two full market cycles from January 2000 through July 2022:

  • Favorable condition: CAGR @ 9.9%, MaxRD (3.9%), Up/Down Ratio 530%
  • Balanced condition: CAGR @ 7.5%, MaxRD (3.6%), Up/Down Ratio 585%
  • Hostile condition: CAGR @ 10.2%, MaxRD (2.8%), Up/Down Ratio 341%

Withdrawals

The table’s statistical summary suggests a Sustainable Withdrawal Rate of 5.9%. How is this calculated?

  1. Assume that the Maximum Monthly Drawdown (4.0%) occurs on the day that a $100,000 investment in the strategy takes place. That leaves $96,000 to invest.
  2. Multiply $96,000 by the CAGR of 9.2% leaving or $8,832
  3. Withdraw 2/3 of the $8,832 which equals $5,888 (internal rounding accounts for the 5.9%). Leave the remaining 1/3 invested to cover inflation and a safety factor

The calculated Sustainable Withdrawal Rate is well above the yields on all of our funds. How is this possible? It is due to the large capital gains in the high yield funds during the early stages of each bull market. While capital gains are great, we want to identify a rate which is closer to the fund distributions.

I use distribution adjusted closing prices so the discrete distributions are unavailable to the Tactical Model but we can use the Tactical Model to provide more information by testing different withdrawal rates. We also use the past 5 years which avoids including the high returns of the early bull market.

The withdrawal rate which stabilizes the remaining balance is 4.9% during the past 5 years. That said, one should not lose sight of the fact that returns during the next 10 years are unlikely to match those of the past 10.

When it comes to withdrawals, I strongly prefer a fixed monthly amount which levels the portfolio volatility risk. Those with RMDs can always adjust or supplement the final withdrawal to meet requirements.

Conclusion

The Adaptive Income Strategy smoothly and effectively transitions between risk on and risk off while delivering exceptional returns.

It combines the use of low-cost, passive index funds and one actively managed fund, with an active management strategy to reduce losses and improve returns. There is a large body of academic research which is both substantive and compelling in making the case for the use of Tactical Asset Allocation to manage all or part of an investment portfolio. With historically extreme equity valuations and increased volatility in credit markets, investors should consider using Adaptive Income for all or part of their portfolios to improve returns and reduce risks.

The Adaptive Income strategy presented here can be used as a standalone income strategy or it can be paired with our two other tactical strategies for portfolio and strategy diversification.

Strategy Notes

Inclusion of a municipal bond fund, which pays dividends exempt from Federal tax, has no bearing on taxable versus non-taxable accounts. It is the yield and performance of the municipal bond fund which determines selection and not after-tax characteristics.

There are three issues attendant with mixing Open End and Exchange Traded Funds in the same tactical strategy. I have included an Open End fund in this strategy only because it significantly improves performance.

  • Early Redemption Fees: ETFs trade with no minimum holding period. Some brokers impose an Early Redemption Fee (typically $50) on purchases held for less than 90 to 180 days, particularly if purchased through a No Transaction Fee program. Solution: the Tactical Model optimizes fund allocations to extend holding periods wherever practical.; however it will exit if risk conditions warrant. An Early Redemption Fee should have minimal impact on the strategy’s total return.
  • Repurchase Limitation: Some funds and/or brokers impose a 60 day limitation on repurchase following a sale. Solution: the Tactical Model will not repurchase an OEF position for a minimum of 60 days after exiting.
  • Settlement of T+1 versus T+2: ETFs, like all stocks, settle in two business days (T+2) while some OEFs settle in one business day (T+1). An issue arises when selling an ETF, which settles in T+2, to buy an OEF which settles in T+1.  Solution: enter the order to purchase the OEF Market On Close effective for the day following the rebalance, or margin may be used to cover the 1 day difference in timing.

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Read How Our Strategies Reduce Risk And Improve Returns

Tactical Adaptive Global

Tactical Adaptive Income

Read About The Principles Which Underlie The TAAStrategies

#1 What is Tactical Asset Allocation? How does it improve returns?

#2 Adaptive Dynamic Momentum: A Major Advance In Tactical Asset Allocation

#3 What is a Full Market Cycle and Why Should I Care?

#4 What is a Market Conditions Model? How does it lower risk?

Tactical Asset Allocation Strategies, LLC is the developer of the TAAStrategies Market Conditions Model, the TAAStrategies Tactical Model, the TAAStrategies Tactical Adaptive Global Strategy and the TAAStrategies Tactical Adaptive Income Strategy 

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