To illustrate, in the graph below we show the aggregate allocation over the last 3 years, summarized by asset category. Note the increase in defensive allocation (ex. cash and bonds) during market weakness, the collapse in exposure to gold, and the pivot from US to international risk assets.

The Aggregate Allocation Report is not intended to be a trading signal – it’s meant to be a barometer, providing insight into what TAA as a trading style is saying about the market. But members often ask about just that; could the report be traded? The idea isn’t without merit; in a sense, it represents the ultimate diversified TAA portfolio.

In this analysis we explore that question. The short answer is, yes, the aggregate allocation has value as a trading signal if utilized correctly. It’s a blunt instrument though, and a diversified, handcrafted Model Portfolio, tailored to your specific goals, will likely outperform blindly trading the aggregate.

Test #1: Ignoring Trading Friction, Same Day Execution

We start with the simplest test.

We assume that every day, week, half-month or month-end, the investor matched today’s aggregate allocation (by individual ticker) in their own portfolio at today’s close.

This first test makes two generous assumptions: it ignores trading friction (transaction costs + slippage), and it assumes today’s aggregate allocation is available at the close. Think of these results as a best-case ceiling.

The result: every cadence from daily to monthly significantly outperformed the benchmark, especially in terms of risk-adjusted performance and managing losses. Slower cadences edged out faster cadences, but the difference was slight.

Test #2: With Trading Friction, Same Day Execution

Trading friction (transaction costs + slippage) is unavoidable. Even zero-commission ETFs carry slippage. We assume a reasonably conservative 0.1% per trade (0.2% round-trip). You may do better, especially trading large, liquid ETFs, but friction will never be zero.

In this test, we add trading friction to our test.

As expected, the drag from trading friction increases with trading frequency, nearing 2% p.a. when trading daily. That tracks with what we know: most TAA strategies are designed to trade monthly and trading them more frequently slightly hurts performance gross, but significantly hurts performance net (read more).

If an investor were to use the Aggregate Allocation Report as a trading signal, it would be most effective to do so with a monthly or semi-monthly cadence.

Alternatively, the investor could “tranche” the portfolio across the month so that only a portion of the portfolio trades each day, maintaining the monthly cadence for each slice.

Test #3: With Trading Friction, Next Day Execution

All the results shown so far are not technically possible on our platform. The Aggregate Allocation Report is generated nightly, and the tests above assumed that an investor had perfect foresight of that night’s results.

So, in the results below we’ve added a 1-day lag to execution. In other words, the investor reviewed the report last night but didn’t execute trades until today’s close.

We didn’t bother including an equity curve here, because the results are so similar. Across all cadences, there was little impact from delaying execution. This isn’t surprising. As we’ve shown previously, adding a 1-day lag to the execution of a diversified TAA portfolio has had little impact on long-term performance (read more).

Test #4: Individual Tickers, Categories and Risk On/Off

The Aggregate Allocation Report includes multiple levels of granularity:

• Individual asset tickers (which we’ve tested up to this point)
• Asset categories: e.g., “US Equities” includes everything from the S&P 500 to individual stock market sectors.
• Risk on/off: “Risk On” includes equities, real estate and high-yield bonds. “Risk Off” includes everything else.
• Roll up: This is not part of the Aggregate Allocation Report, but we’ve added it to our test to show a more practical approach to trading individual tickers. We assume that we “rolled up” allocations < 2% into more generic asset classes. See this analysis to learn more.

Below we show the results of trading each level of granularity. Each asset category and risk on/off is each represented by a single ticker (see the calculation notes at the end of this analysis for a list). Results include trading friction and a 1-day lag in execution.

Broadly speaking, less granular approaches have underperformed, but much of the difference came prior to 2010, and results have been fairly similar since.

The takeaway is that granularity matters, but only to a point. Investors could capture substantially all of the Aggregate Allocation Report’s performance using either asset categories or a simple 2% roll up.

Data dump:

This data can be sliced and diced many ways. Click for a CSV file showing all combinations of trading frequency, granularity, same/next-day execution, and trading friction.

Conclusion:

Yes, the Aggregate Allocation Report has value as a trading signal, assuming a slow enough cadence (monthly or semi-monthly, or tranching). Assets could be combined either by asset category or using a simple 2% asset rollup.

Understand however that this is a very blunt instrument. A diversified, handcrafted Model Portfolio, tailored to your specific goals, will likely outperform blindly trading the aggregate.

New here?

We invite you to become a member for about a $1 a day, or take our platform for a test drive with a free membership. Put the industry’s best tactical asset allocation strategies to the test, combine them into your own custom portfolio, and follow them in real-time. Learn more about what we do.

* * *

Calculation notes:

Semi-monthly execution: We assumed trades were executed on normalized trading days 10 and 21.

The post The Wisdom of 100 Strategies: Using Aggregate TAA Allocation as a Trading Signal appeared first on Allocate Smartly.

A unique feature of our platform is the ability to follow these monthly strategies on any day of the month. We’re not just executing the same signal on a different date – we’re recalculating the signal each day, while maintaining the integrity of the original strategy.

We have long championed “tranching”: splitting the execution of monthly strategies across multiple days of the month. But members often ask about a different approach – instead of splitting execution, why not go “all in” multiple times per month? In other words, what if we traded the entire portfolio weekly, or even daily?

In this analysis, we put that idea to the test. The verdict: it’s a bad idea. Monthly TAA strategies are tuned to that monthly cadence and trading them more frequently hurts performance.

Test Setup:

We strongly believe investors shouldn’t trade a single strategy in isolation, tactical or otherwise. Combining multiple strategies into what we call “Model Portfolios” diversifies away the risk of any one strategy underperforming – so that’s what we tested here.

We created 1,000 portfolios, each with 5 randomly selected monthly strategies (out of the 90 we track), equally-weighted at 20% each. We then backtested each of these 1,000 portfolios four ways, both with and without “trading friction” (transaction costs + slippage):

• Monthly, on the last trading day of the month (as designed)
• Semimonthly
• Weekly
• Daily

Geek note: See the “calculation note” at the end of this analysis for more details.

Test Results: With and Without Trading Friction:

In the tables below we show daily/weekly/semimonthly trading results relative to the baseline (the baseline is executing strategies as designed, trading monthly on the last trading day of the month). A negative number means that approach underperformed the baseline.

We start by ignoring trading friction (transaction costs + slippage). These first results should not be viewed as conclusive because they ignore a key component of return, but they do help to paint a picture of gross vs net results.

Even before accounting for friction, trading more often hurts performance. Max drawdown is the metric you’d most expect to see benefit from more frequent trading due to more quickly responding to changing markets, but even here, results were negative.

Next, we add trading friction (0.1% per trade, 0.2% round-trip):

These results paint a more pessimistic (but also realistic) picture. As expected, the drag from trading friction increases with trading frequency, exceeding 2% p.a. when trading daily. This additional unproductive trading friction is the nail in the coffin of trading monthly strategies more often.

Trading friction is unavoidable. Even zero-commission ETFs carry slippage. We assume a reasonably conservative 0.1% per trade (0.2% round-trip). You may be able to drive that lower, especially trading large, liquid ETFs, but it will never be zero.

Digging deeper (for we TAA geeks): EOM execution vs non-EOM execution

Savvy readers will recall we’ve shown that trading at month-end has historically outperformed trading on other days. See our previous discussions on this, along with the latest statistics.

Setting trading friction aside, much of the underperformance when trading daily, weekly, or semimonthly comes simply from exposure to those underperforming trading days. This holds true for tranching as well.

So our headline finding – “trading monthly strategies more frequently underperforms” – can be expressed with more nuance:

• Some of the underperformance when trading more frequently is simply the historical gap between EOM and non-EOM execution.
• Accepting this gap when “tranching” (i.e. when spreading execution across multiple days of the month) may be worth it, because:
  • We don’t know whether EOM’s historical edge persists going forward, and tranching diversifies away that risk, and –
  • It adds little in extra transaction costs (assuming no flat per-trade commissions on a small account).
• However, that same trade-off is not worth it when you trade the entire portfolio more often (i.e. the approach we’ve tested here) because:
  • Unlike tranching, it significantly increases trading friction, but –
  • Gross performance doesn’t improve to compensate.

Conclusion:

The takeaway is straightforward:

Most TAA strategies are built around a monthly cadence, and that design choice matters. Trading them more frequently doesn’t add precision – it adds churn and cost, without a commensurate increase in performance.

That doesn’t mean investors have no flexibility in how they execute monthly strategies. Tranching – spreading execution across multiple days – remains a reasonable way to smooth out single-day execution risk without abandoning the monthly cadence the strategy was designed around. But trading the entire portfolio more often is an entirely different thing, and the data makes clear: it raises costs without improving performance.

If a strategy was built to trade monthly it should be traded monthly, and investors who wish to trade more frequently, should use tranching.

Other Reading:

Other things we’ve written about deviating from a strategy’s original design:

• Delaying execution by a full day (i.e. signal today, execute tomorrow) has had little impact on long-term performance, and can make execution less stressful. Read more.
• “Rolling up” small positions into more generic assets has also had little impact on long-term performance, and makes position management easier. Read more.
• Read more about trading on days other than month-end, tranching and “timing luck.”

New here?

We invite you to become a member for about a $1 a day, or take our platform for a test drive with a free membership. Put the industry’s best tactical asset allocation strategies to the test, combine them into your own custom portfolio, and follow them in real-time. Learn more about what we do.

* * *

Calculation note: We took some shortcuts to make this analysis more manageable. None affect the basic conclusions of this analysis.

• Daily execution: Trades only occurred on days with an associated “normalized trading day” (which is the vast majority of days, but not all). Learn more.
• Weekly execution: Our platform is not oriented around calendar weeks, but we approximated weeks with a roughly 5 trading-day spacing. We assumed trades were executed on normalized trading days 5, 10, 15 and 21.
• Semimonthly execution: We assumed trades were executed on normalized trading days 10 and 21.

The post Does Trading TAA Strategies More Often Improve Performance? appeared first on Allocate Smartly.

Backtested results from 1990 follow. Results are net of transaction costs – see backtest assumptions. Learn about what we do and follow 100+ asset allocation strategies like this one in near real-time.

Logarithmically-scaled. Click for linearly-scaled results.

Note: Below we’ve shown the rolling 3-year max drawdown, rather than the spot drawdown we usually show (otherwise the chart becomes a pile of spaghetti).

A word of warning: this is an extremely volatile strategy, usually allocating the entire portfolio to a single stock market sector. Investors should think of it as a unique risk asset – not a total portfolio solution – and limit it to a reasonably sized allocation within a broader, diversified Model Portfolio.

How this strategy works:

We covered much of the underlying theory behind this strategy in our previous test of Growth and Inflation. Here’s a quick refresher:

Most investors are familiar with the classic “four seasons” framework, which categorizes the current market environment based on economic growth versus inflation. Variations of this concept underpin well-known strategies such as the Permanent Portfolio and All-Weather Portfolio.

Different sectors tend to perform best in each quadrant. The challenge, of course, is identifying the current market environment in real time.

Varadi uses the current trend of the S&P 500 to measure forward growth. The S&P 500 is inherently forward-looking and has been a good proxy for future US economic growth over the last 120 years.

His original Growth and Inflation strategy forecasted inflation using an indicator called “Sector-Implied Expected Inflation”. It measures the relative performance of sectors with positive beta to expected inflation (energy, financials, industrials and materials) versus sectors with negative beta (consumer staples, health care and utilities).

Varadi’s new strategy takes this idea a step further by also directly forecasting inflation via the 5-year Breakeven Inflation Rate (T5YIE), calculated as the spread between nominal Treasury yields and TIPS yields. This spread represents investors’ expectations for future inflation (plus some other things, more on this later).

Inflation is considered to be rising when:

• The breakeven inflation rate is above 2%, and –
• Either the breakeven inflation rate or sector-implied expected inflation is rising.

Now that we’ve determined both the growth and inflation regime, the strategy selects the best ETF for that environment:

XLE = energy, XLK = technology, XLU = utilities, XLP = cons. staples, IEF = US Treasuries

The strategy trades monthly. Positions are assumed to be executed at the close on the final trading day of each month and held for the following month.

Original vs Enhanced:

We’re tracking two versions of Inflation Compass.

“Original” is a replication of Varadi’s published strategy. We’ve extended his test with a decade-plus of additional data. Data prior to 2003 can be considered out-of-sample.

“Enhanced” incorporates several refinements proposed by the author. The most significant is parameter diversification (aka “ensembling”). Rather than relying on a single lookback period – for example, the 60-day change in the breakeven rate – the Enhanced version averages the signal across multiple parameter values. This diversification helps to smooth future returns by reducing the “specification risk” of any single parameter value underperforming.

We rarely state one strategy is better than another. We present the evidence and let members draw their own conclusions. That said, we prefer the Enhanced version. The Original version could certainly outperform going forward, but by reducing specification risk, the Enhanced version narrows the range of possible future outcomes.

Potential risks to future performance:

This strategy would have produced exceptional returns. When we cover high performing strategies like this, we want to be extra skeptical in our coverage. The strategy “sells itself”; it doesn’t need our help. Our focus should be on potential risks to future performance.

Below we discuss what we view as the two most significant:

Risk #1: Major asset classes are hard to predict; sectors are even harder

First, an obvious risk. It’s one thing to say that during a given economic condition a broad asset class like stocks or bonds will perform well. That’s difficult enough on its own. It’s even harder to identify the specific sector that will outperform.

The best example is the “reflation” asset, energy (XLE), which is also the strategy’s most frequently held asset. As global energy production and consumption changes in the coming decades, will XLE continue to be the most productive reflation asset? There’s a risk of that not being the case.

Perhaps a future version of the strategy could either (a) take a more adaptive approach to selecting sectors or (b) hold multiple diversifying sectors.

Risk #2: TIPS breakeven inflation forecasts vs alternative inflation forecasts

This second potential risk is more nuanced. As Varadi explains, TIPS are a quirky inflation predictor:

Let’s consider alternative measures of 5-year future inflation. Below is a comparison of the TIPS breakeven rate, inflation swaps (Bloomberg: USSWIT5) and the Cleveland Fed’s forecast (EXPINF5YR).

Each measure has its own strengths and weaknesses:

• Inflation swaps are market-based like the breakeven rate and avoid TIPS’ liquidity-related distortions, but they introduce their own unique quirkiness, such as counterparty risk.
• The Fed forecast is published monthly and is partly survey-driven. It tends to react more slowly than market-based measures and is more anchored to recent inflation levels.
• The Fed forecast will often match the TIPS breakeven rate more closely during calm markets, but inflation swaps match more closely during market stress.

If we re-backtest the strategy, replacing the TIPS breakeven rate with one of these other inflation forecasts, performance declines. Importantly however, it declines much less when using inflation swaps than when using the Fed forecast.

Below are the results for the Original strategy as designed, versus results based on the alternative inflation forecasts.

That suggests two explanations:

• The first is encouraging: TIPS contain unique, actionable information that isn’t reflected in the other forecasts, and the strategy is exploiting that.
• The second is less encouraging: the strategy is overfit to periods when TIPS diverged from other measures due to a lack of liquidity or other reasons not related to actually forecasting inflation, particularly in 2008/2009 and 2020/2021.

Our conclusion falls between the two.

There is clearly actionable information in the market-driven TIPS and inflation swap forecasts not accounted for in the Fed forecast, especially during periods of market stress when moving quickly matters.

But there’s probably not enough difference between the TIPS and inflation swap forecasts to say with 100% certainty one set of results is “more right” than the other in terms of what they say about the future. Investors should err on the side of caution and view the inflation swap results (which are still quite good) as a better reflection of strategy performance.

Having said all of that, there is a risk that the distortions introduced by both market-based inflation forecasts don’t play out in the future the way they have in the past and the strategy doesn’t perform as well during future periods of market stress. The fact that we have so little data to consider (by TAA standards), exacerbates that risk.

David Varadi is a pioneer in the online quant space

Our thanks to David Varadi for sharing this strategy and giving us the opportunity to put it to the independent test.

Readers familiar with David’s work know he has been producing thoughtful, unconventional ideas like Inflation Compass for a very long time. He’s one of the pioneers in the online quant space. We highly recommend following what David is doing at CSS Analytics.

New here?

We invite you to become a member for about a $1 a day, or take our platform for a test drive with a free membership. Put the industry’s best tactical asset allocation strategies to the test, combine them into your own custom portfolio, and follow them in real-time. Learn more about what we do.

The post Taming the Wildcard: David Varadi’s “Inflation Compass” appeared first on Allocate Smartly.

Thinking about this chart from John Hussman, showing margin debt relative to GDP spiking to all-time highs, with previous such instances seeming to foreshadow major market downturns:

It’s very easy to look at a chart like this with hindsight and identify the top of each spike. It’s much more difficult to do that in real-time. That’s doubly true because of the big shift in the data in the early 1990’s. Pretending like you would have seen the pre-1990 instances as spikes in real-time, or not seen the entire 1990’s as a spike, is a little silly.

Below we present the data a bit differently. Here we show the 12-month difference in Margin/GDP ^(*).

The three big spikes in 2000, 2007, 2021 and today now stand out clearly. All spikes prior pale in comparison.

How has the market performed following these spikes? Below we show S&P 500 returns (annualized) after the change in Margin/GDP spiked above 1% (first instance only, overlapping observations ignored).

Returns following the spikes in 2000 and 2007 were poor, but 2021 bucked the trend.

Perhaps a better criterion is a spike of more than 1% followed by a decline of any amount, to try to time the top. Those results look as follows:

These results are more uniformly negative, especially around the 18-month mark.

What does all this mean?

These results are bearish, but they don’t mean much in isolation. It’s dinner conversation (at a very boring dinner where we talk about market valuations).

At most, it’s another warning light indicating that the market is overvalued. Add it to the giant pile of other indicators warning that this market is too rich.

If that’s the case, what do we do about that now, today?

Nothing. The market can remain overvalued for years. We stay the course and take advantage of the current market trend until we see concrete signs of weakness.

Trying to precisely time market tops ahead of time is a fool’s errand.

New here?

We invite you to become a member for about a $1 a day, or take our platform for a test drive with a free limited membership. Put the industry’s best tactical asset allocation strategies to the test, combine them into your own custom portfolio, and follow them in near real-time. Not a DIY investor? There’s also a managed solution. Learn more about what we do.

(*) Calcuation notes:

(1) Calculated as margin debt today minus margin debt 12m prior, all divided by average monthly GDP over same period.

(2) As mentioned, there was a big shift in Margin Debt/GDP in the early 1990’s. In real-time, without the benefit of hindsight, we would have analyzed this data differently than we have here. A change of say +/- 0.25% may have appeared significant in the mid-1990’s, whereas now we’ve set our threshold at 4 times that. Our point is: take all of this with a grain of salt. Markets change and the nature of this metric may very well change over time as well.

The post Margin Debt Is at an All-Time High, What Does That Mean? appeared first on Allocate Smartly.

In our previous studies we selected strategies based on recent return. In this study, we select “distressed” strategies, or strategies nearing or exceeding their previous max drawdown.

We looked at this subject way back in 2020 and concluded that strategies tended to generate higher returns when distressed than on other days. That still holds true today, but there’s another piece to the puzzle. In short, it doesn’t make sense to select a strategy simply because it’s near a previous max drawdown (nor does it necessarily make sense to avoid it).

Note: We track 100+ strategies, making these findings broadly representative of TAA as a trading style.

Results from 1990 ^(*):

We advocate combining multiple strategies together into “Model Portfolios” to limit the risk of any single strategy underperforming. We’ve assumed the investor selected strategies for their portfolio at the end of each month by looking at the “distance to max drawdown” for all 100+ strategies.

For example, if a strategy’s previous max drawdown was -10% and current drawdown is -5%, the distance to max drawdown = 50%. We assume the investor only knew about drawdowns up to that moment in time (no lookahead bias).

The investor divided the portfolio into Y equally-weighted slots (from 1 to 5) and selected the Y strategies closest to their previous drawdown if the distance was >= X% (50-100%).

Important: Any slots not filled by a distressed strategy earned the average strategy’s return the following month. In most months, no strategies meet our criteria. We want to evaluate whether distressed strategies outperform other competing strategies when they do appear.

We’ll show results for our hypothetical portfolios across four metrics, starting with annualized return. For comparison, we also include the average strategy we track, as well as the 60/40 benchmark ^(*).

There has been a small benefit to this approach when the number of slots in our Model Portfolio is large enough to still provide some diversification (3+), and the minimum distance to max drawdown is low enough (i.e. the bottom left of the table).

If we set our minimum distance to max drawdown too high, there simply aren’t enough strategies that meet our criteria to move the needle.

Next, we look at two measures of loss: Max Drawdown and the Ulcer Performance Index (UPI). Max Drawdown is of limited value; it’s capturing a single moment in time. UPI is our preferred measure because it considers return relative to both the depth and length of all drawdowns.

These results are basically in line with annual return and the Sharpe Ratio. There has been some benefit to this approach near the bottom left of the table, but that benefit has been small.

A mea culpa, sort of…

Again, we looked at this subject back in 2020 and concluded that strategies tended to generate a higher return when distressed than on other days. We’ve more than doubled the number of strategies in our test since then, but that basic observation still holds. However, we missed a piece of the puzzle in that previous analysis.

We were looking at the question at a strategy level; i.e. how has this strategy performed when distressed versus this strategy on all other months? In our analysis today we’re looking at the question at a portfolio level, and at a portfolio level there are two additional considerations.

The first is more obvious: distressed strategies tend to “cluster”. On most days there are few or no distressed strategies, and then suddenly market volatility spikes and many strategies simultaneously meet the criteria (think 2022, 2007-08, etc.) At a strategy level that doesn’t matter – every instance is treated equally – but at a portfolio level it does – individual strategy performance is diluted.

The second consideration is really the important one though, and it came as a big surprise to us (although in hindsight it makes sense).

When at least one strategy is distressed, all strategies tend to perform better the following month. For example, when at least one strategy was within 70% of its previous max drawdown, the average return of all strategies the following month was about 50% higher than other months.

The more distressed that single strategy is, the better all strategies perform. Likewise, the more strategies that are distressed, the better all strategies perform.

What voodoo is this?

We tend to see one or more strategies approach their previous max drawdown during spikes in market volatility. Concurrently, all strategies – including non-distressed ones – tend to generate higher returns during those same spikes in volatility.

Put another way, most of the higher return observed when strategies are distressed is not specific to the distressed strategy, it’s across all strategies.

Where does that leave us?

Should investors invest in distressed strategies? There is limited evidence for that. It’s not the worst approach assuming the portfolio is reasonably diversified, but it’s not a great approach either. Our advice for selecting strategies remains the same:

Select a broad range of diverse, robust, high-quality strategies based on long-term performance. Consider avoiding strategies that have consistently failed to live up to expectations until performance is better understood (refer to the Underperformer Watchlist), but beyond those extreme cases, do not consider recent performance in strategy selection.

As mentioned, this is the fourth article in this series. Be on the lookout for additional tests in search of an effective approach for selecting strategies based on recent performance.

New here?

We invite you to become a member for about a $1 a day, or take our platform for a test drive with a free limited membership. Put the industry’s best tactical asset allocation strategies to the test, combine them into your own custom portfolio, and follow them in near real-time. Not a DIY investor? There’s also a managed solution. Learn more about what we do.

(*) Calculation notes:

(1) This analysis is shorter than in the three previous installments in this series. That’s because we use the first 20 years of backtested data for each strategy to find the max drawdown up to that point in time. The earliest any of the strategies on our platform begins is 1970, hence this analysis begins in 1990.

(2) A reminder from our previous analyses: These results account for trading frictions (transaction costs + slippage) in the individual strategy backtests, but do not account for the additional friction of switching between strategies. That means that the actual results of taking such an active approach to selecting strategies would have been worse than what we’ve presented here.

(3) All data as of 04/30/2026.

The post Investing in “Distressed” TAA Strategies (Redux) appeared first on Allocate Smartly.

Let’s walk through a sample return contribution analysis using the most popular strategy on our platform: Financial Mentor’s Optimum3.

Return contribution by asset:

In the first chart we show each asset’s contribution to the overall strategy annual return of 14.5%. The chart shows that QQQ has been the biggest contributor at 3.1%, while trading friction has reduced return by about 0.7% per year.

You can click on any column (not in this article, but in the members area) to see that asset’s contribution by year. Below is the contribution from QQQ by year. Unsurprisingly, QQQ was most effective in the mid to late 1990’s.

Return contribution by asset category:

We also roll up results into broad categories. For instance, “US Equities” may include everything from the S&P 500 to individual stock market sectors. This sometimes makes it easier to interpret the data if a strategy trades many assets.

An example: it wasn’t clear from the first graph that international equities have been such a major contributor to strategy performance.

Return contribution by risk on/off:

Finally, we combine assets into the broadest possible categories: risk on and risk off. Risk on includes equities, real estate and high-yield bonds; risk off includes everything else. Not all assets fit neatly into these buckets, but this simplification provides a useful high-level view.

How you can use this data in your own analysis:

There isn’t a hard rule for what this data should look like. The usefulness of the data is going to depend on the strategy. Here’s a practical example.

Recall our recent analysis of Carlson’s Defense First. The strategy allocates, on average, about 10% of the portfolio to the US Dollar index (UUP), but UUP has contributed almost nothing to the strategy’s return. Factoring in real-world trading friction (transaction costs + slippage), we concluded that it might make sense to skip those positions and instead allocate to cash or short-term Treasuries.

That bears out in the Return Contribution analysis.

UUP contributed just 0.07% to annual return. Drilling down on the results by year we see that returns when holding UUP have been consistently inconsistent.

Again, the Return Contribution analysis confirms our conclusion that UUP has been an unproductive position, especially after accounting for trading friction (we understand it’s purpose is diversification, but a zero expectancy asset probably isn’t a good way to accomplish that).

This is just one simple example of the usefulness of the Return Contribution analysis. In the end, the purpose is really just understanding strategies better so that members can better judge the efficacy of the strategy and the assets traded.

New here?

We invite you to become a member for about a $1 a day, or take our platform for a test drive with a free membership. Put the industry’s best Tactical Asset Allocation strategies to the test, combine them into your own custom portfolio, and follow them in real-time. Learn more about what we do.

The post New Feature: Return Contribution Analysis appeared first on Allocate Smartly.

New here? Learn more: What is a Model Portfolio?

What are Withdrawal Rates?

The Safe Withdrawal Rate (SWR) measures the max amount that could have been withdrawn each year in retirement (with an annual adjustment for inflation) without running out of money over the worst retirement period. It’s the source of the well-known “4% withdrawal” rule in financial planning.

The Perpetual Withdrawal Rate (PWR) is a more conservative measure. Rather than a goal of not running out of money, the goal is to preserve the entire initial inflation-adjusted portfolio.

To clarify: We’ve always provided withdrawal rates for individual strategies (see the report).  We’ve now added that same capability to Model Portfolios as well.

Where to find Model Portfolio withdrawal rates:

You can find the SWR and PWR values at the bottom of the summary stats table.

For example, here are the results for a Model Portfolio split 50/50 between the two most popular strategies on our platform: Financial Mentor’s Optimum3 and Dr. Keller’s Hybrid Asset Allocation. Note the new stats shown at the bottom.

In the case of individual strategies, members can tweak the withdrawal rate analysis by changing retirement length and inflation assumptions, but in order to speed up Model Portfolio backtests, we only provide results based on a 30-year retirement/historical inflation.

We think that meets 99% of members’ needs. Results based on other assumptions tend to scale up or down in line with that baseline result. We may add a more intense withdrawal rate analysis in the future (like we do for individual strategies) depending on member demand.

Calculation note: Withdrawal Rates and the Compare Tool:

We also now provide SWR/PWR results on the Compare Tool for all strategies, Model Portfolios and benchmarks. An important calculation note:

The Compare Tool aligns strategies to their earliest common start date and shows stats based on that common start date. SWR/PWR is the one exception; we’ll always show the full sample SWR/PWR.

Why? Withdrawal rates are a unique statistic. They are based on the single worst n-year period in the test. Strategy A could dominate Strategy B by every other measure, but still have a lower SWR/PWR because of a single bad/unlucky series of n-year returns.

That means that analyzing less data can only “improve” SWR/PWR, but that improvement is an illusion. We take the more conservative, pessimistic (and we’d argue, realistic) approach of always showing the full sample withdrawal rate.

A word of warning about withdrawal rates and short backtests:

The discussion above hints at something else members should bear in mind.

When you backtest your Model Portfolio, withdrawal rates are calculated based on your unique combination of strategies/assets. The shorter the backtest, the less conservative the withdrawal rate analysis will be. Withdrawal rates are based on the worst n-year period, and a shorter backtest means there are less n-year periods to consider.

Let’s say you test Model Portfolio A, which begins in 1970, and calculate an SWR of 5%. You then test Model Portfolio B, which begins in 1990, and calculate an SWR of 7%.

You should not naively assume that Portfolio B definitely has a higher withdrawal rate. It could simply be the lucky result of a much shorter backtest. We have to take into account backtest length when comparing any statistic, but it’s even more important when comparing withdrawal rates.

Methodology (for the geeks): How we calculate Withdrawal Rates

We use the approach described by William Bengen in his paper “Determining Withdrawal Rates Using Historical Data”.

We run separate simulations starting on every possible starting quarter in the backtest. When insufficient data exists to project forward n years, we “loop around” to the start of the sample, maintaining the sequence of returns. For a portfolio that begins in 1970, that would result in 200+ unique sequences. Withdrawal rates are based on the worst n year period in the test.

As always, use common sense:

Withdrawal rate analysis, like all investment analysis, involves a degree of uncertainty. The future is guaranteed to be different, and actual withdrawal rates could be significantly higher or lower than modeled results. It would be reckless to plan for retirement as if these values were chiseled in stone. When it comes to financial planning, investors should always err on the side of caution.

New here?

We invite you to become a member for about a $1 a day, or take our platform for a test drive with a free membership. Put the industry’s best Tactical Asset Allocation strategies to the test, combine them into your own custom portfolio, and follow them in real-time. Learn more about what we do.

The post New Feature: Model Portfolio Withdrawal Rates appeared first on Allocate Smartly.

We advocate combining multiple TAA strategies together into “Model Portfolios” to limit the risk of any single strategy underperforming. In our previous studies we selected strategies for our Model Portfolio based on recent return. In this study, each strategy in our portfolio is switched on and off based on trend-following – often called “surfing the equity curve”.

We track 100+ TAA strategies, making these findings broadly representative of TAA as a trading style.

Results from 1973:

We use a classic trend-following approach: comparing a shorter moving average (MA) to a longer one. We assume that the investor split the portfolio evenly across all 100+ strategies.
Each strategy is switched on or off independently. At the end of each month, if the strategy’s shorter X month MA was greater than its longer Y month MA, the investor allocated to that strategy; otherwise, that portion of the portfolio remained in cash.

We’ll show results for our hypothetical portfolios across four metrics, starting with annual return and Sharpe Ratio. For comparison, we also include the average strategy we track and 60/40 benchmark.

The longer the long MA, the more months the investor spent invested in each strategy. The average proportion of the portfolio invested ranged from little as 69% (1/2-month crossovers), up to 96% (2/36-month crossovers).

Unsurprisingly, because shorter MAs spent so little time invested, annual return suffered. Over the long-term, cash (T-Bills) will underperform nearly all assets. The Sharpe Ratio adjusts for that by looking at (excess) return per unit of risk, but here too we see shorter MAs underperform. Longer MAs were more effective, but all combinations still failed to outperform the average strategy.

A primary benefit of trend-following has been managing losses, so perhaps this is where we’ll see “surfing the equity curve” shine.

Below we look at two measures of loss: Max Drawdown and the Ulcer Performance Index (UPI). We believe Max Drawdown is of limited value; it’s capturing a single moment in time. UPI is our preferred measure because it considers return relative to both the depth and length of all drawdowns.

Nearly all combinations improved the portfolio’s max drawdown. That isn’t too surprising; cash (T-Bills) is a zero drawdown asset. What’s more relevant is that relative to return (i.e. UPI), all combinations still underperformed the average strategy except one (6/12-month crossovers).

Looking at the heatmap as a whole, we view the outperformance of that single 6/12-month combination as likely luck/noise.

What about “absolute momentum”?

Closely related to trend-following is “absolute momentum”. Here we run the same test, this time investing in each strategy if the strategy’s return over the last X months was > 0.

These results are all in line with our previous trend-following tests. No new information here.

Geek note: We also tested another common absolute momentum approach. Rather than comparing the strategy’s return over the last X months to zero, we compared it to the return on T-Bills. Such an approach would have spent even less time in the market, and further reduced annual return and improved max drawdown, but led to no improvement in risk-adjusted performance (Sharpe/UPI).

Reminder: These results do not account for all trading frictions:

A reminder from our previous analyses: these results account for trading frictions (transaction costs + slippage) in the individual strategy backtests, but do not account for the additional friction of switching the strategies on/off. That means that actual results would have been worse than what we’ve presented here.

Debunking the idea of “surfing the equity curve”:

We’ve seen many analyses showing that applying some trend-following approach as an “overlay” to such-and-such strategy would have improved performance. We generally view those analyses as overfitting, and the results in this article demonstrate why.

If a trend-following overlay can’t be applied broadly to all strategies across a consistent set of parameters (as we’ve done here), and instead relies on one set of trend-following parameters specifically tuned to one particular strategy, then the benefit of that one set of parameters applied to that one specific strategy is almost certainly a result of overfitting.

While it’s true that we haven’t tested every possible approach to trend-following, if none of these simple tried-and-true approaches show promise, it’s hard to put faith in a more complex (and likely overfit) one.

Outro:

Our advice for selecting strategies remains the same:

Select a broad range of diverse, robust, high-quality strategies. Consider avoiding strategies that have significantly underperformed their own historically-derived expectations until performance is better understood (see the Underperformer Watchlist), but beyond those extreme cases, do not consider recent performance in strategy selection.

As mentioned, this is the third article in a series. Be on the lookout for additional tests in the coming weeks in search of an effective approach for selecting strategies based on recent performance.

New here?

We invite you to become a member for about a $1 a day, or take our platform for a test drive with a free membership. Put the industry’s best tactical asset allocation strategies to the test, combine them into your own custom portfolio, and follow them in real-time. Learn more about what we do.

The post “Surfing the Equity Curve”: Using Trend-Following to Switch Strategies On and Off appeared first on Allocate Smartly.

We advocate combining multiple TAA strategies together into “Model Portfolios” to limit the risk of any single strategy underperforming. In our previous study we selected strategies for our portfolio with the highest recent return. In this study, we select strategies with the highest recent volatility-adjusted return, aka “Sharpe Ratio”.

We track 100+ TAA strategies, making these findings broadly representative of TAA as a trading style.

Results from 1973:

Sharpe Ratio = (return – risk-free rate) / volatility.

We assume that at the end of each month the investor looked at the Sharpe Ratio over the last X months of all 100+ strategies we track. The investor selected the top Y strategies and traded those Y strategies for the following month (equally-weighted).

We’ll show results for our hypothetical portfolios across four metrics, starting with annualized return. For comparison, we also include the average strategy we track, as well as the 60/40 benchmark.

Recall from our previous study that selecting strategies with the highest return tended to select riskier strategies, and thus all combinations of lookbacks and portfolio sizes produced significantly higher returns than the average strategy.

That holds somewhat true here as well. Because the Sharpe Ratio includes a “risk-free hurdle” (T-Bills), it also rewards higher returning strategies, independent of risk. Later we’ll look at removing this risk-free hurdle.

What we really want to understand is how this approach performs on a risk-adjusted basis. Next, we look at the Sharpe Ratio of our hypothetical portfolios.

No combination of lookbacks and portfolio sizes significantly outperformed the average strategy; at least to a degree that makes this approach worthwhile.

There was a stark difference between 12+ month lookbacks and shorter lookbacks. This was true across all four metrics. Selecting strategies based on their Sharpe Ratio over less than 12 months has been particularly ineffective.

Next, we look at two measures of loss: Max Drawdown and the Ulcer Performance Index (UPI). We believe Max Drawdown is of limited value; it’s capturing a single moment in time. UPI is our preferred measure of drawdown-adjusted return because it considers return relative to both the depth and length of all drawdowns.

We were surprised by the high UPI produced with a 12-month lookback, even by smaller portfolios. The 3 strategy/12-month lookback portfolio produced a UPI of 3.59, compared to 3.05 for the average strategy.

That might appear enticing, but we chalk it up to mostly luck. The benefit of selecting strategies this way has been inconsistent over time and has been ineffective since 2011 (not shown for brevity). In a future installment of this series, we’ll share a modified approach that has been more consistently effective.

Reminder: These results do not account for all trading frictions:

A reminder from our previous analysis: these results account for trading frictions (transaction costs + slippage) in the individual strategy backtests, but do not account for the additional friction of switching between strategies. That means that actual results would have been worse than what we’ve presented here.

At the end of this multipart series, we may take the most promising approaches to strategy selection and apply this additional analytical step.

Bonus data: Removing the risk-free hurdle

As previously mentioned, when we select strategies with the highest recent Sharpe Ratio, we implicitly select for riskier strategies, because the Sharpe Ratio includes a risk-free hurdle (T-Bills).

What if we removed that hurdle, and simply selected strategies based on return relative to volatility? The results across our 4 metrics (click to zoom):

Removing the risk-free hurdle will tend to select strategies that are more conservative than the average strategy. That’s because it favors strategies that hold assets that perform more consistently month-to-month, like US Treasuries.

All other observations, however, hold.

Outro:

In short, selecting strategies based on recent Sharpe Ratio has been a better approach than selecting strategies based on recent return, but it has still been suboptimal. In a future post we’ll share a modified version that has been more effective.

Our advice for selecting strategies remains the same:

Select a broad range of diverse, robust, high-quality strategies. Consider avoiding strategies that have significantly underperformed their own historical expectations until performance is better understood (see the Underperformer Watchlist), but beyond those extreme cases, do not consider recent performance in strategy selection.

As mentioned, this is the second article in a multipart series. Be on the lookout for additional tests in the coming weeks.

New here?

We invite you to become a member for about a $1 a day, or take our platform for a test drive with a free membership. Put the industry’s best tactical asset allocation strategies to the test, combine them into your own custom portfolio, and follow them in real-time. Learn more about what we do.

The post Selecting TAA Strategies Based on Recent Performance, Part 2: Recent Sharpe Ratio appeared first on Allocate Smartly.

We are proponents of combining multiple TAA strategies together into what we call Model Portfolios to limit the risk of any single strategy going of the rails. In this study we ask, what if, each month, we selected strategies for our portfolio that had performed best in recent history.

We track 100+ TAA strategies, making these findings broadly representative of TAA as a trading style.

Results from 1973:

We assume that at the end of each month the investor looked at the return of all 100+ strategies we track over the last X months. The investor then selected the top Y strategies and traded those Y strategies for the following month (equally-weighted).

We show results across four metrics, starting with annualized return. For comparison, we also include a portfolio of all strategies we track (the “average strategy”), as well as the 60/40 benchmark.

All combinations of lookbacks and portfolio sizes significantly outperformed the average strategy in terms of pure return. That should be unsurprising. We track a wide range of strategies from conservative to aggressive, but selecting the top recent performers (as well as bottom recent performers) will tend to select the riskiest strategies.

What we really want to understand is risk-adjusted return. Next, we look at the Sharpe Ratio.

As the number of strategies selected increases, the Sharpe Ratio approaches the average strategy, but no combination significantly outperforms the average strategy.

Put another way, at some point, as the number of strategies selected increases, we reach a sufficiently diversified portfolio to match average strategy results, but selecting strategies based on recent performance doesn’t appear to add additional value.

Next, we look at two measures of loss: Max Drawdown and the Ulcer Performance Index (UPI). We believe Max Drawdown is of limited value; it’s capturing a single point in time. UPI is our preferred measure of drawdown-adjusted return as it considers return relative to both the depth and length of all drawdowns.

These results tell a similar story. In terms of minimizing drawdown, selecting the 1 to 2 top recent performers has been terrible. No combination has significantly outperformed the average strategy.

It’s interesting that across 3 out of 4 metrics, results deteriorate badly with a 3 to 6 month lookback. It appears that selecting strategies that have outperformed over the last 3 to 6 months (solely because they’ve outperformed) has been an especially bad idea.

A fly in the ointment:

At longer lookbacks and larger portfolio sizes (i.e. the bottom right of each table), these results are essentially in line with the average TAA strategy on a risk-adjusted basis (Sharpe and UPI). There has been no significant advantage, but also no significant disadvantage to selecting strategies based on recent performance.

However, there’s an important consideration we didn’t account for: trading friction (transaction costs + slippage). As always, we accounted for trading friction in our individual strategy backtests, but for simplicity, we didn’t account for the additional friction of switching between strategies.

This means that actual results would have been worse than what we’ve presented here. That really puts a nail in the coffin of the idea of chasing recent performance when selecting strategies.

Our advice for selecting strategies remains the same:

Select a broad range of diverse, robust, high-quality strategies. Consider avoiding strategies that have significantly underperformed their own historical norms (see the Underperformer Watchlist) until performance is better understood, but beyond those extreme cases, recent performance shouldn’t be a factor in strategy selection.

Outro:

As mentioned, this is the first of a multipart series. Be on the lookout for additional tests in the coming weeks, like selecting strategies with the highest recent volatility-adjusted return, and conversely, selecting underperforming strategies.

New here?

We invite you to become a member for about a $1 a day, or take our platform for a test drive with a free membership. Put the industry’s best tactical asset allocation strategies to the test, combine them into your own custom portfolio, and follow them in real-time. Learn more about what we do.

The post Selecting TAA Strategies Based on Recent Performance (Part 1) appeared first on Allocate Smartly.