Backtested results from 1930 follow compared to a benchmark of 50% int-term US Treasuries (IEF) and 50% US corporate bonds (LQD). 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.

Clearly, the purpose of Tactical Yield hasn’t been generating outsized returns. It’s simply been a smarter approach to bond exposure. It has generated bond-like returns, but at substantially less risk. To further demonstrate the strategy’s benefit, below we’ve also included the inverse strategy rules, which are in the market about the same amount of time.

Strategy premise:

We know that long-term returns for US Treasury funds are quite predictable. For a 10-year Treasury fund (ex. IEF), the initial 10-year yield has predicted about 86% of the total return over the subsequent 10 years. That % is even higher for shorter duration Treasuries.

There will be some deviation from the initial estimate based on how yields change over time. That’s a risk. Faber’s strategy essentially asks whether the “term premium” is sufficient to justify taking that risk. If it’s not, we should simply hold risk-free T-Bills.

Faber tested multiple variations of this concept. We are focusing on two assets: 10-year US Treasuries (represented by IEF) and US corporate bonds (LQD).

Strategy rules:

At the close on the last trading day of the month, measure two premiums:

• Term premium: Difference in yield between 10-year and 3-month US Treasuries
• Credit premium: Difference in yield between investment grade corporate bonds and 3-month US Treasuries

If the term premium is in the top half of history, measured up to that moment in time (no lookahead bias), allocate 50% of the portfolio to US Treasuries (IEF).

Likewise, if the credit premium is in the top half of history, measured up to that moment in time (no lookahead bias), allocate 50% of the portfolio to US corporate bonds (LQD).

Unallocated funds remain in cash. Hold all positions until the end of the following month. We’ve assumed the portfolio is rebalanced monthly, even if there is no change in position.

Sometimes simple is best:

This strategy makes intuitive sense.

It’s another approach to determining whether there is justification for longer duration bonds. Strategy developers usually use a trend-following approach, which has its benefits, but ignores potential risks that the trend doesn’t capture.

A more sophisticated approach would take into account how high/low current yields are (regardless of the premium over T-Bills), because as we know, point for point, changing yields have an outsized impact when starting yields are low.

An opportunity for overfitting?

The strategy considers yields up to that moment in time to determine how high or low each premium is. Does the start date of our data create an opportunity for overfitting? In other words, would these results have been different if our data had started from some other date? The short answer is no.

In the table below, we test versions of the strategy that compare premiums to the previous 30 and 50 years (rather than all data to that point in time). No significant difference in performance.

Nothing magical about the “top half”:

The strategy sets the premium cutoff at the “top half” of historical observations as of that moment in time. There’s nothing magical about that cutoff. In the table below, we test versions of the strategy setting the cutoff at between 30 and 70%.

The less strict the threshold (30 and 40%), the more the risk (volatility rises, duration of drawdowns increases, see UPI). The stricter the threshold (60 and 70%), the less the risk, but total return and risk-adjusted return metrics like Sharpe deteriorate. That matches what you would intuitively expect.

The 50% cutoff is a nice middle ground but isn’t set in stone. Investors could turn the dial up or down depending on the investment objective.

Outro:

More than anyone else, Meb Faber is responsible for popularizing Tactical Asset Allocation as a trading style, including many of the fundamental concepts used today. This is another of his simple but effective ideas.

We realize that for traditional buy & hold investors, TAA strategies that shift the entire portfolio multiple times throughout the year are a bit intense. There’s a degree of anxiety that comes with deviating from a diversified buy & hold portfolio. We get that.

But something like this strategy, which is grounded in common sense, trades infrequently, and never deviates very far from B&H returns, seems like an easy bare minimum approach to tactical investing.

That’s doubly true for Treasury funds, whose future returns are especially predictable. For long-term buy & hold’ish investors, when term spreads are super low (as they are now), why would we accept the interest rate risk of long duration Treasuries and not simply T-Bills and chill?

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: There is some discrepancy between our results and Meb’s very early in the test, especially around 1932. We chalk that up to different data sources and different start dates for measuring median premium values. Estimating yields so far in the past is inherently uncertain. We think both our test and Meb’s represent good faith efforts. Any differences between them is attributable to different data sources and/or noise.

The post Meb Faber’s “Tactical Yield”, Simple and Intuitive appeared first on Allocate Smartly.

Backtested results from 1991 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.

Members know that we are especially interested in novel asset allocation strategies (read why). Varadi’s strategy is definitely that – it’s unlike anything else we track – but a word of warning: this is an extremely volatile strategy, holding a single stock market sector at all times. We think investors should think of it as a unique risk asset, not a total portfolio solution.

How the strategy works:

You’ve likely seen a matrix like the following, breaking down market regimes by growth and inflation:

This is the “4 seasons” concept employed by a lot of strategies, most famously Harry Browne’s Permanent Portfolio and Ray Dalio’s All-Weather Portfolio.

Certain stock market sectors perform well during each regime, but how do we know which regime we are currently in in real-time?

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

But near-term forward inflation is trickier to model. Conventional measures are either backward-looking (ex. CPI) or longer-term in nature (ex. TIPS derived breakeven inflation rates). Varadi takes an alternate approach. He shows that certain sectors have a strong positive or negative relationship with expected inflation:

When inflation expectations are rising, Energy, Industrials, Financials and Materials tend to also rise. Conversely, Utilities, Health Care and Staples tend to rise when inflation expectations are falling.

Varadi leverages this idea to create an expected inflation indicator. He creates two hypothetical portfolios, one with sectors that have positive expected inflation beta and the other with sectors that have negative beta.

The two portfolios would look as follows (click to zoom):

By dividing the positive expected inflation portfolio by the negative we create a “Sector-Implied Expected Inflation” indicator:

Varadi then breaks down how various stock market sectors perform during each market regime: growth up/down and inflation expectation up/down.

The standout performer during the “goldilocks” regime (growth up, inflation down) has clearly been technology (XLK). During reflation (growth up, inflation up) it has been energy (XLE).

During stagflation and deflation, the best performing sector has been less clear. Varadi doesn’t select the top performing sector, but rather draws on other data to land on the following sector ETFs to represent each market regime:

At the close of each trading day, the strategy determines the current market regime and buys the relevant ETF at the close. Positions are held until a change in regime. Even though the strategy could potentially trade on any trading day, it has averaged less than 6 position changes per year.

Geek note: We are tracking a modified version of Varadi’s original strategy. Varadi’s original traded much more frequently. That made sense in a hypothetical world without trading frictions (transaction costs + slippage), but we assume a modest 0.1% trading friction (0.2% round trip). Varadi worked with us to reduce turnover without materially changing the nature of the strategy.

A unique risk asset, not a total portfolio solution:

This should be an obvious statement, but just to be clear: this is not a total portfolio solution the way a conventional TAA strategy aims to be. It holds just a single stock market sector and will frequently experience large swings in performance, both positive and negative.

In 2025 it would have experienced a 24% drawdown (EOM) out of nowhere, but on the flip side, it is up 39% YTD as of the time we’re writing this. It’s a wildcard.

Over the long-term, it would have reduced broad market drawdowns, but would have also failed to outperform the broad market since 2008. To illustrate, below we show the relative performance of Varadi’s strategy versus the S&P 500 since 1991:

We view the strategy as a unique risk asset, which is meaningful in an era of rising correlation between risk assets. Monthly correlation to the S&P 500 stands at 0.60, and the average pairwise correlation to other strategies we track just 0.41. It may have value as a diversifier or as a partial replacement for equity exposure in a buy & hold portfolio.

Future research ideas:

Ideas to explore:

• Using other, more direct measures of expected inflation such as the breakeven inflation rate derived from TIPS vs Treasury yields.
• Beta-adjusting our positive and negative expected inflation portfolios.
  According to our analysis, the positive expected inflation portfolio has significantly higher beta to the broader market than the negative expected inflation portfolio. That means some of the “Sector-Implied Expected Inflation” is not “pure”; it’s driven by market beta. In our tests, performance would be improved by beta adjusting the inflation expectation portfolios.

Outro:

A big thank you to David Varadi for sharing this strategy and providing us with the opportunity to put it to the test. Readers who follow David know that he is a reliable source of “outside the box” ideas.

Ideas like this one are a major reason why we think a service like Allocate Smartly is important. These kinds of novel ideas are released into the wild, but too often get lost in the noise. There’s just too much information out there to keep track of it all. We’re all trying to take a drink from a firehose. We need an independent arbiter to gather these unique ideas and track them on an ongoing basis.

How will David’s strategy respond in the coming years to changing markets? Can the concepts presented here be expanded to improve other developers’ 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 David Varadi’s “Growth and Inflation Sector Timing”, a Wildcard Strategy appeared first on Allocate Smartly.

Over the last 15+ years, diversification (as opposed to market timing) has been a huge drag on Tactical Asset Allocation (TAA) performance, to the tune of 2.1% per year compared to the 60/40 benchmark. That “diversification drag” has been mostly due to US stock market dominance over almost all other asset classes over that period. TAA must use market timing to overcome this drag.

Of course, that’s not the whole story: (1) the benefit of diversification ebbs and flows and the next 15 years may be entirely different, and (2) diversification provides other benefits like reducing risk. But over the last 15 years, it’s been an important story.

Note: We track 100+ TAA strategies, so these results are broadly representative of TAA as a trading style.

In the graph above we show three equity curves since 1970. Green is the average return of the 100+ TAA strategies we track, and blue is the 60/40 benchmark (60% SPY/40% IEF, rebalanced monthly).

Orange represents a hypothetical portfolio that holds the average allocation of 100+ TAA strategies since inception, up to that point in time, rebalanced monthly. This is like removing the market timing from TAA and treating the average TAA allocation as a buy & hold portfolio.

We can then break down market timing versus diversification as follows:

• Market Timing = Average TAA Strategy / Average TAA Allocation
• Diversification = Average TAA Allocation / Benchmark
• Total Performance = Market Timing + Diversification

Diversification-only since 1970 looks as follows:

Through the 70’s and aughts, diversification provided a beneficial tailwind for TAA returns, mostly due to outperformance in alternative asset classes like gold and commodities.

Through all other decades, diversification has been a drag on return (but not necessarily risk-adjusted return, more on this later). Since August 2010, that drag has been 2.1% annualized.

TAA can still outperform through market timing (i.e. what TAA is holding at this moment compared to what it tends to hold), but it’s a headwind it must overcome.

A Global 60/40:

The US 60/40 benchmark is not the only benchmark in town. What if we instead used a global 60/40 benchmark represented by 60% ACWI, 20% IEF, 20% BWX, rebalanced monthly.

For the most part, excluding the 1980’s, TAA’s diversification has been neutral to slightly beneficial compared to the global 60/40. The global 60/40 has consistently been a much easier hurdle to beat due to ex-US underperformance.

If our primary interest was making strategies look good, we would benchmark to the global 60/40. In some ways it’s more relevant. But the US 60/40 is ubiquitous and best understood by investors, so we use it as our default (members: other benchmarks are available in the Compare Tool).

Diversification provides other benefits:

Of course, return isn’t the whole story. Diversification provides other benefits, namely reducing risk (drawdowns, volatility, path risk, etc.) and increasing risk-adjusted returns.

To illustrate, below we show the rolling 3-year max drawdown of the 60/40 benchmark (grey) versus our buy & hold “diversification-only” portfolio (orange) since 1970.

Note how diversification alone significantly pared down all major drawdowns except the Global Financial Crisis in 2007/2008 and the 2022 bear market.

Interestingly, TAA would have still done extremely well during the GFC and reasonably well in 2022, but that was all market timing. Diversification didn’t help much in those cases.

Outro:

The takeaway from all of the above is not “diversification is bad”. The opposite is true.

Even though, as tactical investors, we are adjusting our portfolios throughout the year, our actual investment horizon spans multiple decades. And over that long horizon, diversification is likely a net positive to the portfolio.

Having said that, it’s helpful to understand when the present market is increasing friction to our investment approach. Over the last 15+ years, diversification has acted as a headwind that TAA must overcome with market timing. It’s been a friction to the tune of 2.1% annually.

That won’t always be the case. It could be tomorrow or it could be another decade from now, but at some point, diversification will again provide a tailwind to TAA returns. In the meantime, we will stay the course and benefit from the risk reduction that diversification provides.

Lastly, note that all of the above applies to most diversified buy & hold portfolios as well. Popular B&H portfolios will be similar to the “diversification-only” results. Where TAA and B&H differ is in the additional market timing component that TAA provides.

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 Diversification Has Been a Huge Drag on TAA Performance for 15+ Years appeared first on Allocate Smartly.

TAA did reasonably well in 2025 and has done very well in these early days of 2026, relative to the ubiquitous 60/40 benchmark. How much of that is due to TAA correctly timing the market and how much is simply due to the types of assets TAA generally holds?

In the chart above we break down quarterly those two sources of outperformance: timing (blue) and assets TAA generally holds (green). The sum of the blue and green bars is total outperformance (gold). We’ll discuss methodology in a bit.

To reiterate, we’re measuring outperformance relative to the benchmark, not simply total return. So if total outperformance reads +1%, that means that TAA returned 1% more that quarter than the 60/40 benchmark, NOT that TAA returned 1%.

A quarterly breakdown:

• Q1 2025: “Generally held assets” outperformed (because they include less exposure to US stocks, which were weak). Timing was poor as TAA was too aggressively positioned entering into the market pullback in March. Net-net, still an outperforming quarter.
• Q2 2025: Both generally held assets and timing performed poorly. Stocks were the best place to be in Q2, and TAA was too conservatively positioned entering into the recovery. TAA often underperforms during short-lived blips like March/April (aka “whipsaw”).
• Q3 and Q4 2025: Generally held assets performed in line with the benchmark both quarters, but timing was good (overweighting gold and intl. assets was a big part of that), resulting in outperformance over the benchmark.
• YTD 2026: Both generally held assets and timing (overweighting everything but US stocks and bonds) have done well, resulting in strong outperformance so far in 2026.

In total, 57% of outperformance has been from generally held assets, and 43% from timing.

“Generally held assets” outperformance is fine, but timing outperformance is a must:

TAA strategies tend to more diversified than the 60/40 benchmark, with significant exposure to international asset classes and alternatives like gold and commodities. That has actually been a drag on performance over the last 15+ years due to the strong outperformance of the US market.

TAA should get “credit” for any outperformance that results from this diversification, but it’s important that it also produce outperformance from timing. If it doesn’t, we should just buy and hold these assets and save the headache, trading frictions and tax liability (when applicable).

Methodology:

Easy peasy…

Start with the average asset allocation of all TAA strategies over their entire history. Backtest this as a buy & hold strategy, rebalanced monthly. This is the performance of “assets generally held”.

Outperformance from assets generally held =
% return of assets generally held – % return of 60/40 benchmark

Outperformance from timing only =
% return of the average strategy we track – % return of assets generally held

Total outperformance =
% return of the average strategy we track – % return of 60/40 benchmark

Note: This data only includes individual strategies. We’ve ignored Meta Strategies.

Outro:

This was just a thought experiment. Is it actionable? Not really, but it’s interesting to suss out the true source of out/underperformance to know when TAA is timing the market well, and when it’s just benefiting from the tailwinds of diversification.

This may become a regular feature in the future.

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 Why TAA is Performing Well Now: Outperformance Attribution appeared first on Allocate Smartly.

The strategy has performed poorly relative to other strategies on the platform. You’ve turned down other stuff that was marginal like this, so I’m surprised it made the cut.

He’s right. Viewed in isolation, performance has been inferior. This is a good opportunity to talk about how our thinking has evolved on deciding what strategies to add to the platform.

Core TAA is saturated, so the hurdle is higher:

We track a lot of strategies that we would characterize as “core TAA”:

Start with a bunch of asset classes. Apply some flavor of trend-following/momentum and some weighting scheme to allocate among assets, and voila. There are classics like Faber’s GTAA 5 and Alpha Architect’s RAA, as well as newer choices like Hybrid Asset Allocation.

When we think of TAA, our first thought is this class of strategies. They differ in small ways and big, but they all leverage this same core idea.

For us to add to this list of strategies, the hurdle is higher. Members benefit little from adding another core strategy unless historical performance has been strong (and robust, more on this later).

A lower hurdle for novel strategies:

The more novel a strategy is, the lower we’re willing to set our hurdle. There are two reasons for that, one subjective and one quantitative.

Subjectively, it’s just more interesting for members to cast a wide net, tracking many different approaches, and understanding what is and isn’t working now.

Quantitatively, there’s value in combining dissimilar things. Just like investors should diversify across assets, they should diversify across strategies as well. This strategy zigs when that strategy zags, and presto, portfolio risk goes down and risk-adjusted return (ex. Sharpe Ratio) goes up.

“Average pairwise correlation”, or a strategy’s average correlation to all other strategies we track, is a simple measure of how novel a strategy is. All other things held equal, adding strategies with lower correlation to other strategies in a portfolio will provide better diversification.

At the bottom of this article we’ve shown the average pairwise correlation for all 100+ strategies we track (see the data). The five strategies with the highest average correlation (Metas excluded) are:

We would characterize all of these as “core TAA” strategies. As a rule, we believe investors should always diversify across multiple strategies, but if we were forced to invest in just a single strategy, these would be reasonable choices.

Conversely, the five strategies with the lowest average correlation are:

These are not “core TAA” strategies. We are not saying they are good or bad, but they are different, and thus, they may have value as diversifiers. At the very bottom of the list is the latest strategy added, Gold Cross-Asset Momentum. And that’s essentially why it was added, despite the marginal performance.

Lastly, note that there are other factors beyond correlation that might lead us to consider a strategy “novel”, like being especially tax efficient.

Stricter about basic robustness:

On a tangentially-related note…

In the past we’ve modelled a handful of strategies that we either identified as having some structural flaw (example) or concluded in a general sense were likely overfit to history (example).

We’ve often still added those strategies to the platform, assuming that our pessimistic analysis was sufficient. Our philosophy was that seeing everything, even questionable things, was better than being limited to a curated list.

In hindsight, that was the wrong approach. We track a lot of strategies, and it’s not reasonable to require new members to read through every long writeup when designing their portfolios. In the future, we plan to still discuss these strategies on our blog but either not add them to the platform or only add them after correcting for structural flaws.

Lesson learned. As a rule, we do not remove strategies from the platform, but we will take this stricter approach moving forward.

Impact on the Portfolio Optimizer and Meta Strategies:

One last consideration…

When we add strategies with low average correlation to the platform, those strategies are more likely to find their way into the optimizations that feed the Portfolio Optimizer and Meta Strategies. It’s an inherent part of portfolio optimization for all of the reasons previously discussed: when we combine dissimilar things, it lowers risk and improves risk-adjusted returns.

Should we account for this fact when adding these types of strategies to the platform? For example, should we consider how adding a strategy will impact the historical performance of Meta Strategies?

The short answer is no. As long as the strategy has value to someone (that’s the key part) we just do the work and let the chips fall where they may. To do otherwise is a recipe for overfitting. Further, 10 years from now, how many useful strategies would we have rejected because they would have negatively affected such-and-such optimization at that moment.

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 Member Note: Our Approach to Selecting Strategies for the Platform appeared first on Allocate Smartly.

Backtested results from 1970 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.

Dujava’s strategy is based on a well-studied link between gold and treasury yields. See Quantpedia’s article for a list of background research.

Strategy rules:

It doesn’t get simpler than this:

• At the close on the last trading day of the month, measure the 12-month total return of gold (represented by GLD) and 10-year US Treasuries (IEF).
• If the 12-month return of both GLD and IEF is positive, go long GLD at the close, otherwise move to cash.
• Hold position until the close of the following month.

When we say Dujava’s strategy is “simple” we don’t mean that as a criticism. All other things held equal, complexity increases the opportunity for overfitting. Strategies should only be as complex as they need to be.

Cross-Asset Momentum:

Like most assets, when gold is showing positive momentum, it has usually been followed by stronger future returns. If the strategy stopped there, it would still provide a significant benefit to simply buying and holding gold.

Dujava’s strategy adds an additional cross-asset momentum requirement. Gold returns have been even stronger when gold and treasury returns are both showing positive momentum.

To illustrate, below we’ve shown next month gold performance following months when gold and US Treasury momentum (as measured by 12m return) was either positive or negative, as well as when ignoring treasury momentum altogether.

The stats shown are annualized return, Sharpe Ratio and the # of months when the criteria was met.

The cross-asset requirement reduced exposure by 19% (77 / 405), cutting out a significant number of low performing months. Dujava’s data shows that this effect has been consistent over the last 50+ years. We have not shown those results here for brevity, but our data agrees.

Note: If you eyeball the first charts we presented with a skeptical eye, it appears that the advantage of the strategy versus buy & hold has waned since about 2002. Part of the reason for that is weaker return on cash over much of that period, meaning the strategy hasn’t been rewarded to the same degree when out of gold. If you look at returns only when in the market (like those presented immediately above), the strategy remains similarly effective.

Not a standalone solution:

This is obviously not a standalone portfolio solution. It’s easy to look at 50+ year results and think “yeah, I could have traded that”, it’s another thing to experience those results in real-time.

By its nature, gold runs hot and cold. When the going is good (like it is right now), it’s very good, but the strategy would have gone through long periods – up to two decades – of drastically underperforming the broader market. That means that, like all gold investments, it should be limited to a relatively small % of the total portfolio.

Alternatively, we discuss an outside-the-box use below: as an overlay.

Applying the strategy as an overlay:

We track 19 strategies with at least a 10% average allocation to gold (excluding this one). Let’s say we combined all 19 of those strategies into an equally-weighted portfolio.

Below we’ve shown the results of our combined portfolio in two flavors. In the first (blue line), we simply trade our combined signal as-is. In the second (orange line), when our combined signal is calling for gold, we first refer to Dujava’s strategy. When both gold and UST momentum is positive, we trade the combined gold signal, otherwise we allocate that portion of the portfolio to cash.

Note: Results account for transaction costs.

Logarithmically-scaled. Click for linearly-scaled results.

There would have been a marginal improvement in results using the strategy as an overlay, with a slight decrease in risk and a slight increase in risk-adjusted performance.

Is the juice worth the squeeze? In other words, is it worth the extra hassle of checking the cross-asset momentum strategy before confirming any gold signal? We can’t answer that for individuals. We can only model historical results.

Two potential negatives of the overlay:

First, this would make the portfolio less tax efficient (which only matters if trading in a taxable account). And second, it adds a degree of complexity which implicitly raises the potential for overfitting. However, given the simplicity of the strategy, we think that’s a relatively small concern.

Outro:

A big thank you to the folks at Quantpedia for the constant stream of new and novel ideas.

We track 100+ strategies on this platform, so we’re surprised this is the first we’ve encountered that leverages this gold/treasury observation. We hope that the ideas presented here inspire developers to consider this concept in their own strategy design.

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 Gold Cross-Asset Momentum appeared first on Allocate Smartly.

All investment strategies go through rough patches. It’s the nature of taking risks in inherently unpredictable financial markets. One of the difficulties of investing is knowing when a rough patch is just a normal period of poor performance, and when it’s significant enough to warrant further concern. The Underperformer Watchlist is designed to assess that.

Being confident in your investment plan is paramount. Many investors find it difficult to trust strategies that have struggled in recent history, regardless of long-term performance. That’s okay. We track a lot of strategies, so if some small number are misbehaving, we think it’s reasonable to opt for other alternatives until performance is better understood.

What is the Underperformer Watchlist (UW):

The UW is not a list of strategies that have underperformed other strategies. That wouldn’t mean much by itself, and members can already determine that using the Strategy Screener.

The UW is a list of strategies that have underperformed their own expectations. In other words, strategies that have performed worse in recent years than one would expect based on their long-term historical performance.

Importantly, strategies on the UW are not necessarily bad strategies. We track 100+ strategies, and more than half of those would have appeared on the list at some point in history.

How strategies are selected for the Underperformer Watchlist:

We’ll discuss the math in a bit. For now, let’s show some broad concepts.

Remember, we’re concerned with a strategy’s performance relative to its own history, so as you’d expect, the following strategy would be flagged:

But the next one would not, despite performing identically in recent history:

In the first case, the loss was significantly worse than one would expect based on the earlier record, but in the second case, the loss was reasonably in line with historical norms.

Note however that the UW is not based on loss alone. It’s based on any significant deterioration in performance. That means the following strategy would also be flagged:

The extended period of flat performance was far enough outside of historical norms.

Also, performance is measured both in absolute terms and relative to a benchmark, so the following strategy would be flagged as well (blue = strategy, green = benchmark):

In absolute terms, the strategy continued to do what it had always done, but unlike the past, it underperformed its benchmark badly.

Strategies on the Underperformer Watchlist are not forever bad:

As mentioned previously, more than half of the 100+ strategies we track would have appeared on the watchlist at some point in history.

History is finite, but the future is infinite, and outliers are inevitable. That means that every strategy will eventually end up on the list, because at some point the strategy will behave worse than the finite range of past behavior. That could be tomorrow or it could be 100 years from now.

Appearing on the UW doesn’t in and of itself mean a strategy is bad, only that extra caution is warranted.

How we would use the Underperformer Watchlist:

The shotgun approach would be to avoid all strategies on the UW full stop. Fair enough. Members could build a diversified portfolio with strategies not on the list.

However, we think that there’s room for some discretion. Below are two real-world examples based on strategies currently on the list; one that is overfit and should probably be avoided, and another that is likely just going through a rough spell:

Dividend and Growth: When we first covered this strategy, we concluded that the strategy had a high probability of overfitting, making it less likely to perform as well in the future as it had historically. That proved truer than we expected, and the strategy has performed terribly since being added to the platform.

We expect an overfit strategy to remain on the report much longer than a more robust one. Overfit expectations are implicitly too optimistic, making it more likely the strategy will continue to underperform those too-optimistic expectations.

This is a prime example of a strategy that either should be avoided, or at the very least, come with an understanding that historical backtested results are not relevant to future out-of-sample results.

Risk Premium Value (RPV): This strategy has been 100% allocated to cash since mid-2023. That makes sense. It’s a valuation strategy, and by most metrics, financial markets have been significantly overvalued for a long time. Because the market has soared over that time and RPV has underperformed so badly, it has landed on the UW.

This is an example of a strategy we believe is reasonably robust; it’s simply out of favor. Markets can remain overvalued for a long time, RPV may still prove right in the end, and we think the strategy may still have a place in a diversified Model Portfolio.

Mean-reversion and “catching the falling knife”:

We previously published an analysis of investing in “distressed strategies”.

We showed that there has been some benefit to “catching the falling knife”. As strategies approach, and even exceed, their previous max drawdown, they have tended to generate above average returns in the short-term (next 1 to 12 months). How does that previous analysis jibe with the UW? Should we actually be embracing these UW strategies?

Both ideas are valid, they’re just relevant to different timeframes and have different purposes.

For investors looking for short-term opportunity and willing to white knuckle a high risk/high reward trade, then yes, our previous analysis stands. As strategies approach and exceed their previous max drawdowns, they have tended to generate above average returns in the short-term.

The UW however is longer-term in nature. Moving onto and off of the list will usually happen over the course of multiple years or even a decade plus. This is more relevant to investors creating a portfolio to set and forget for the long-term.

Again, both ideas are valid. One is shorter-term and the other longer. One is a high risk/high reward play. The other is about long-term portfolio design.

The math for the nerds:

The UW is created based on Interquartile Ranges (IQR).

We first calculate rolling 12, 18, 24, 30 and 36 month returns for each strategy (hereafter, n = # of months), both in absolute terms and relative to a benchmark.

That benchmark is unique to each strategy, based on the strategy’s average asset allocation up to that moment in history. We do this so that we can isolate the impact of timing, as opposed to an unlucky asset universe. For example, we wouldn’t want to judge a bond-only strategy against a stock market benchmark.

We then flag current n month returns that are more than 1.5 IQR below Quartile 1, as measured n months prior. This is a common threshold in data analysis for identifying outliers.

Strategies remain on the UW until their current n month return exceeds the Quartile 1 return from n months prior. Quartile 1 represents returns that are below average, but still well within historical norms.

We list the n month lookback (from 12 to 36 months) that caused the strategy to be added to the UW. Strategies can move to a longer lookback if underperformance persists.

Longer lookbacks are harder to recover from than shorter lookbacks. In other words, a strategy flagged for its 36-month return will likely remain on the report much longer than one flagged for its 12-month return.

How the Underperformer Watchlist will affect the platform:

For now, the UW will affect the member’s platform in three ways:

• The Underperformer Watchlist report itself. You will find the report under the “Strategies” menu. The report is updated monthly, after the last trading day of the month.
• On the list of All Strategies, we’ve added a icon next to each flagged strategy. This is just a simple, visual clue that the strategy is on the watchlist.
• On the Portfolio Optimizer, members now have the option to exclude UW strategies from their optimizations. It’s an “all in” or “all out” decision. Pro members still have the ability to pick and choose.

Members, check out the Underperformer Watchlist now. Questions? Comments? Contact us.

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: The Underperformer Watchlist appeared first on Allocate Smartly.

Backtested results from 1971 follow. Results are net of transaction costs – see backtest assumptions. Learn about what we do and follow 90+ asset allocation strategies like this one in near real-time.

Logarithmically-scaled. Click for linearly-scaled results.

We show results in two flavors. In orange, we show the strategy as designed. And for comparison, in green we show the inverse strategy, i.e. holding the 11 countries with the lowest 6-month return.

Our benchmark is the All-Country World Index (ETF: ACWI). It’s not a perfect match for the 22 countries traded by the strategy, but it’s close enough for the purpose of this analysis.

At first glance, the strategy looks effective. Over the entire test, the strategy has significantly outperformed the benchmark and the inverse strategy, both in terms of return and risk-adjusted return (Sharpe and UPI).

Why isn’t the benchmark ACWI closer to the center of the two versions of the strategy? After all, at all times one of the two strategy versions is holding nearly all of the countries in ACWI.

The main reason is that ACWI is a cap-weighted index. For example, the US currently makes up 63% of holdings, while New Zealand makes up just 0.05%. This strategy strips away that cap weighting. Allocation to each country is equal. The fact that even the inverse strategy almost matches the benchmark shows that much of the strategy’s outperformance has come from this equal weighting (and not momentum) ^(*).

Why the “Lazy Man’s Momentum Strategy” is a non-starter:

Below we’ve shown the relative performance of the strategy versus the inverse strategy. Put another way, this is the strategy equity curve from the first chart above (orange line) divided by the inverse equity curve (green line).

When relative performance is rising, the strategy is outperforming, and when it’s falling, the inverse strategy is outperforming. When it’s flat, it means the strategy is no more predictive than a coin flip.

These results paint a much different picture than our earlier 30,000 foot view results. Since mid-2000, relative performance has been flat, meaning the strategy has been no more predictive of future returns than its inverse. It’s always difficult to know when a strategy has become ineffective, but we’re confident that 25 years is long enough to call it.

This isn’t a debunking of momentum in general. Absolute momentum has done very well since 2000. So, for example, these results might have been very different if the strategy rules were instead “pick the 11 country indices with the highest 6-month return, but if the 6-month return was negative, allocate that portion of the portfolio to cash.”

However, pure relative momentum as it’s defined here (selecting the highest momentum, regardless of whether it is negative), has not been effective at trading country indices over the last 25 years.

This illustrates the importance of not just looking at long-term summary stats, which can be skewed by success early in the backtest, but also how performance has evolved over time.

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.

* * *

Geek notes:

(1) This strategy only trades twice per year, at the end of Dec and Jun. That’s an obvious opportunity for overfitting. What if we instead traded just one month later, at the end of Jan and Jul? It would change the entire track record, despite having no practical significance.

In response we tested a “tranched” version of the strategy, trading 1/6 of the portfolio at the end of Dec and Jun, 1/6 at the end of Jan and Jul, etc. We didn’t show the results here because they are essentially identical; our concerns were unfounded. In the real world, we would still prefer a tranched approach, but it didn’t lead to a significant difference in historical results.

(2) As mentioned, much of the strategy’s outperformance came from the equal-weighting of country indices. Equal-weighting (as opposed to cap-weighting) worked well over most of the period tested, but note that equal-weighting would have been a terrible idea since 2008. That’s mostly due to the outperformance of the US, especially relative to European countries, which make up most of the 22 country indices covered by this strategy.

The post Challenging the “Lazy Man’s Momentum Strategy” appeared first on Allocate Smartly.

Backtested results from 1961 follow. Results are net of transaction costs – see backtest assumptions. Learn about what we do and follow 90+ asset allocation strategies like this one in near real-time.

Logarithmically-scaled. Click for linearly-scaled results.

Like our test of the original GGC, our results differ significantly from the author’s. We use earliest-vintage OECD CLI data to minimize lookahead bias. More on this later.

Strategy rules tested:

The author provided the helpful flow chart to the right laying out the strategy rules (click to zoom).

• At the close on the last trading day of the month, calculate a “Diffusion Index” based on OECD CLI data from the previous month-end (the 1-month lag is due to the delay in reporting). The Diffusion Index measures the % of countries whose CLI value rose month-over-month.
  If the Diffusion Index value is > 50%, the strategy is risk on, otherwise it is risk off. The original GGC was based only on this first step ⁽¹⁾. GGC Enhanced takes the additional steps below to determine the specific risk on/off asset to hold.
• If risk on, measure the 12-month return of US versus international stocks (represented by SPY and IEFA). 12-month return is one of the most common measures of momentum.
  If the 12m return of SPY > IEFA, allocate 100% of the portfolio to SPY, otherwise IEFA.
• If risk off, measure the 12-month return of US aggregate bonds (AGG) versus short-term US Treasuries (BIL).
  If the 12m return of AGG > BIL, allocate 100% of the portfolio to AGG, otherwise cash.
• All positions are executed at the market close. Hold all positions until the last trading day of the following month.

From end-of-month to mid-month:

OECD CLI data for a given month is released in the first 1-2 weeks of the following month (see future release dates). The original GGC strategy took advantage of this by executing trades on the 15th calendar day, rather than waiting until the next month-end.

We’ve opted to track a second version of GGC Enhanced that does the same. Members will note that there are two versions of GGC Enhanced in the members area: Monthly and Mid-Month.

Strategy results for this mid-month version versus other versions of GGC follow. The mid-month results begin in 1973 due to data limitations, so the results below only cover the overlapping period.

Logarithmically-scaled. Click for linearly-scaled results.

On paper, trading at mid-month has outperformed trading at end-of-month, but most of that outperformance came during a brief period in the 1980’s and the two versions have tracked each other closely since.

Geek note: For multiple reasons, this mid-month version is not the same as the monthly version traded on a mid-month alternate trading day. This is a geeky discussion that would only appeal to a small % of our audience. Interested readers should contact us. Suffice to say, if seeking a mid-month trade, this mid-month version is likely better than the monthly version traded on a mid-month alt. trading day.

The importance of vintaged economic data:

Many types of economic data are initially released at one value, and then later revised. That means that a backtest based on the data as it looks today may not accurately reflect positions that would have been taken in real-time. This is especially true for OECD CLI data, because the entire data series since inception is affected by the addition of each new monthly data point.

For all economic data on this platform, we use the earliest data point available (aka, the “earliest vintage”) ⁽²⁾. In our test of the original GGC, we showed the impact of using this earliest vintage data. It was significant.

We did not go through that same process for GGC Enhanced. Comparing the author’s results to our own, we see some negative impact but not significantly so. That should be considered a small feather in the cap for GGC Enhanced that it held up well on unseen data.

Our take on GGC Enhanced:

Despite the additional complexity, GGC Enhanced is driven by the same economic data as the original, and our take on both strategies are similar.

Because GGC trades on such a unique data set, it has delivered returns with relatively low correlation to other strategies we track. All things being equal, that’s a good thing. When we combine dissimilar strategies (into what we call Model Portfolios) we smooth overall portfolio performance, because this strategy will often zig when that strategy zags.

If we were to select just a single strategy or a very small number of strategies, we would prefer the Enhanced version, because it takes into account long-term momentum, a fundamental market force that has worked for basically as long as financial markets have existed (see “Core Idea #1”).

However, if we were building a diversified portfolio of strategies, we might prefer the original version. Many of the strategies we track already have exposure to momentum. If the purpose of GGC is to smooth out performance, it may be more useful to have a “purer” signal with lower correlation to other strategies in the portfolio.

Members will note that this bears out in the Portfolio Optimizer. Despite inferior performance on paper, the original GGC still appears in more optimized portfolios.

A big thank you to Grzegorz Link for the opportunity to put his strategy to the test. Grzegorz is one of the good guys. He is a source of novel ideas, but is always self-deprecatory about the limits of backtesting and the risks of overfitting. We highly recommend you follow the good things he’s doing.

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:

• The original GGC signaled risk on when the Diffusion Index was greater than or equal to (>=) 0.5. This enhanced version uses greater than (>). That difference was not intentional by the author, but we’ve followed suit in our results to stay consistent with his. This only affects historical results as there are currently an odd number of countries in the index, but that could change in the future.
• Not only is each country’s entire data series revised when each monthly data point added, but the list of countries included in the CLI has periodically changed over time. We vintage the monthly data points for each country, but we cannot properly vintage the way in which the constituents countries have changed prior to 12/2021. Long story short: our vintaging for OECD CLI data removes some of the lookahead bias, but it is imperfect, and a degree to uncertainty still exists.

The post Using the OECD Composite Leading Indicator + Momentum to Time the Market appeared first on Allocate Smartly.

Strategy results from 1971 follow. Results are net of transaction costs – see backtest assumptions. Learn about what we do and follow 90+ asset allocation strategies like this one in near real-time.

Logarithmically-scaled. Click for linearly-scaled results.

Defense First would have produced benchmark-like returns after ~1980, but its focus on defensive assets means it would have generally done a better job weathering market downturns. Importantly, Defense First has exhibited relatively low correlation to other tactical strategies, potentially making it a useful diversifier when combined with other strategies in a Model Portfolio.

Defense First turns conventional Tactical Asset Allocation (TAA) on its head. A common tactical approach, shared by many strategies we track, is using risk asset momentum (ex. stock momentum) to guide the risk posture of the entire portfolio. When risk asset momentum is positive, the portfolio tilts towards risk, otherwise towards defensive assets.

Defense First instead uses defensive assets to guide the risk posture of the portfolio, only holding risk assets when defensive assets are not exhibiting strong momentum.

Strategy rules tested:

• At the close on the last trading day of the month, measure the momentum of four defensive assets: long-term US Treasuries (represented by TLT), gold (GLD), commodities (PDBC) and the US dollar index (UUP), plus a risk-free rate represented by 13-week US T-bills (BIL).
  Momentum is measured as the average of each asset’s 1, 3, 6 and 12-month dividend-adjusted % return.
• Rank the four defensive assets from highest to lowest momentum, with portfolio allocation to each as follows: 40% (top rank), 30%, 20% and 10%.
• If a defensive asset’s momentum is less than that of T-bills, allocate that portion of the portfolio to US stocks (SPY).
  This is a form of “dual momentum”. Assets must exhibit positive momentum, with higher momentum assets allocated a larger % of the portfolio.
• Hold all positions until the end of the following month. Rebalance monthly regardless of whether there is a change in position.

Does negative momentum in defensive assets predict positive stock returns? No, but yes.

Obviously, if we’re using defensive asset momentum to determine when to hold US stocks, we should consider whether defensive assets perform that task well. If not, then we’re blindly dumping the portfolio to an inherently risky asset.

First, we’ll test stock market performance (SPY) in the month following positive or negative excess momentum for each defensive asset (by “excess momentum” we simply mean the asset’s momentum relative to T-bill momentum):

Note: We looked at more than simply annualized return in our analysis, but we’re only showing this single stat to keep things simple.

Broadly speaking, when viewed individually like this, defensive assets do a poor job predicting next month stock market returns.

Commodities have been predictive, but gold and the US dollar have not, and US Treasuries have actually been negatively predictive (i.e. stocks are stronger following strong excess treasury momentum, but we already know that).

But that’s not the whole story.

Remember, if just one of these assets were exhibiting negative excess momentum, the strategy’s allocation to stocks would be just 10%, a pittance. What we really want to measure is stock market performance following weak excess momentum across multiple defensive assets.

These results show that:

• When 2 defensive assets agree, stock returns have been middling. That’s okay though; exposure to stocks is only 30%, so this is still a relatively defensive portfolio.
• When 3+ defensive assets agree, the signal has been predictive of outsized returns. At this point, exposure to stocks is high enough (60%+) to have a significant impact on the portfolio.
• We did not show results specifically for all 4 defensive assets agreeing, because the number of historical instances was too small to be significant.

In short, yes, when viewed as a whole and not individually, negative excess momentum in defensive assets has been reasonably effective at predicting strong future stock market returns. Having said that, we still have some reluctance (see below).

Thoughts on strategy design: something good, something bad

First, something good:

The stats presented in the previous section show that this strategy was primed for overfitting to the past. Carlson could have boosted historical results by using different rules for each defensive asset, especially Treasuries versus commodities. We appreciate that he’s kept the ruleset simple and consistent, and more likely to be robust to an unknown future.

Something we would consider changing:

The strategy does not require stock momentum to be positive. It blindly dumps the portfolio to stocks when defensive momentum is negative, regardless of what stocks are doing at that moment.

This all feels like the “dumping to bonds” flaw in reverse. For the uninitiated: some strategies are designed to blindly dump to long-duration bonds when the strategy shifts to defense. That worked for most of the market’s history but failed miserably in 2022 when risk assets and bonds fell simultaneously to a degree not seen in 100 years. This feels the same, but in reverse (read more).

We would consider a rule like “stock exposure cannot exceed X% when stock momentum is negative, unallocated funds remain in cash”. In our tests, there would have been little impact on long-term performance, but the portfolio would be more robust to an uncertain future.

Lastly, consider holding cash, not the US Dollar (UUP):

This is the first strategy we track that holds the US Dollar index (UUP). We followed suit in our results, but here’s an important caveat: We would strongly consider holding cash whenever the strategy is signaling a position in UUP.

Currencies tend not to move much month to month. Our results assume trading costs (transactions fees + slippage) of 0.1% per trade, 0.2% round-trip.

The strategy’s average allocation to UUP at any given time would have been ~10%. But despite being a significant holding, UUP would have contributed very little to overall return even before accounting for trading costs – just 0.06% annually.

After accounting for trading costs, the position in UUP is an almost guaranteed loser. Again, it’s simply a function of how little currencies move relative to our trading cost assumption.

Even if in your real-world trading environment transaction fees are zero, you still have slippage to contend with, as well as the opportunity cost of not earning a risk free return on cash.

Outro:

Defense First is a unique take on conventional TAA. As readers know, we seek out unique ideas like this, because combining dissimilar strategies helps to control portfolio volatility and risk.

A big thank you to Thomas Carlson for authoring this paper and giving us the chance to put it to the test. Carlson is a long-time member to Allocate Smartly and has provided valuable feedback to us over the years. This was his first published paper and we’re looking forward to more from him in the future.

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 Carlson’s “Defense First” appeared first on Allocate Smartly.