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The Safe Withdrawal Rate (SWR) measures the max amount that could have been withdrawn each year in retirement (with an annual adjustment for inflation) without running out of money over the worst retirement period.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.

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.

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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.