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# A century of market returns
In the last post, I argued that leverage used early in an investing lifetime behaves differently from leverage applied later. Early risk spreads across the full compounding horizon, rather than stacking on top of an already large portfolio.
But does this idea hold up across real market history, including bad starts, boring decades, and outright disasters?
To answer that, I looked at nearly a century of data. Using my Bloomberg Terminal, I pulled S&P 500 total returns back to 1927 and simulated three different strategies across every possible 30-year window. Some investors start at great moments, others at terrible ones, most land somewhere in between.
Before examining outcomes, it’s worth grounding ourselves in the environment these strategies faced.
# The market backdrop investors actually lived through
*Chart* **1**\*: S&P 500 total return index, 1927 to present, logarithmic scale\*
This chart shows total US stock market returns over the last century, including dividends, crashes, wars, inflationary periods, and long stretches where nothing seems to happen.
Seen all at once, the trend looks clean and almost inevitable. Lived in real time, it was anything but.
This is the environment every strategy in this post is tested against—a full distribution of experiences spanning generations. The goal is not to beat this line in any given year, but to understand how different ways of taking risk change the range of outcomes over an entire investing lifetime.
# The three strategies being tested
All three strategies assume the same behavior: a fixed $1,000 monthly contribution into US equities through the S&P 500 over 30 years. The only difference is how market exposure is applied. There is no market timing or discretionary adjustments.
# Strategy 1: Plain DCA
This is the baseline. Each month, $1,000 is invested with no leverage. The portfolio grows passively over 30 years. If leverage does not improve outcomes relative to this, there is nothing interesting to discuss.
# Strategy 2: Portfolio rebalance leverage glidepath
This strategy starts with 2x market exposure and gradually reduces leverage to 1x over time. The entire portfolio is rebalanced monthly to maintain the target leverage. Early returns are amplified, and later, the portfolio behaves like a standard unlevered allocation.
This portfolio has a higher average leverage than a simple DCA, but ends with the same 1x leverage.
# Strategy 3: Contribution rebalance leverage glidepath
This strategy also starts with 2x exposure and ends at 1x, but leverage is applied only to new contributions. Existing portfolio capital is never deleveraged. Early contributions have higher exposure, and risk naturally declines as contributions become unlevered over time.
This strategy has the highest exposure to leverage, since the earliest investments will keep the leverage all the way through the end of the 30 year investing time horizon.
# A critical distinction
Both glidepaths front-load risk early, but they do so differently: one adjusts the entire portfolio, the other concentrates risk in early contributions. This distinction drives the differences in outcomes explored below.
# The best possible start date
Before looking at averages, it helps to see what “best case” looks like. Out of every 30-year window since 1927, the strongest outcome starts in March 1970. This investor benefited from long bull markets, disinflation, and a favorable early sequence of returns. This ends with almost the exact peak of the Dot Com Bubble.
No one can pick this start date in advance, but it provides an upper bound for what the strategies can achieve.
# What happens when everything goes right
*Chart 2: Portfolio value over time, March 1970 start*
All three strategies perform very well. Even plain DCA turns $360,000 of contributions into $3.2 million. The Full Portfolio Rebalance Strategy ends at $5.6 million, and the Contribution Leverage Strategy reaches $18.1 million.
The key takeaway is how leverage amplifies growth early, letting compounding do the rest. with an astonishing $18.1 million from $360,000 in total contributions
What matters more than the final number is *how* each strategy gets there.
# Drawdowns still exist, even in the best case
*Chart 3: Maximum drawdown, March 1970 start*
Even in the best 30-year window, none of the paths are completely smooth. Max drawdowns were roughly 30% for DCA, 53% for Full Rebalance, and 55% for Contribution Leverage.
Volatility is higher with leverage, but this is the cost of much larger final outcomes.
# Why the best case still matters
This section shows that when markets cooperate, leverage works as intended: it increases exposure when capital is small, allowing compounding to amplify results. If leverage only works in perfect conditions, it’s a bet, not a strategy.
# The worst possible start date
If leverage is going to fail, this is where it should fail. The weakest 30-year window starts in July 1952, spanning muted returns and deep drawdowns early in the investing lifecycle, ending just after the 1970s stagflation.
This start date tests whether a strategy can survive when the market simply does not cooperate.
# Portfolio outcomes under a difficult sequence
*Chart 4: Portfolio value over time, July 1952 start*
The plain DCA approach grows slowly, ending at $588,000 from $360,000 of contributions.
The Full Portfolio Rebalance ends at $825,000, and the Contribution Leverage strategy reaches $790,000.
By applying leverage to early contributions, the contribution strategy increases exposure when dollars are small, naturally reducing risk as the portfolio grows. Even in the worst start date, compounding still works in its favor.
# Drawdowns tell the real story
*Chart 5: Maximum drawdown, July 1952 start*
Maximum drawdowns were 43% for DCA, 54% for Full Rebalance, and 68% for Contribution Leverage.
Absolute losses were larger for leveraged portfolios, but much of the risk occurred when the portfolio was small. Average drawdowns remain closer to DCA levels, showing that early leverage concentrates risk in a way that is survivable over the long term.
# Why this section matters
Anyone can design a strategy that looks good when markets cooperate. The real test is survival. In the worst historical start date, the contribution leverage approach does not eliminate pain, but it keeps it manageable, allowing compounding to continue over decades.
# Looking at every possible 30-year investing lifetime
The best and worst start dates show extremes, but real investors cannot pick when they start. To see the full picture, I examined every 30-year window since 1927.
Each month represents a new investing lifetime, creating a rolling series of outcomes shaped by different sequences of returns.
# Returns across time by start year
*Chart 6: Rolling 30-year returns by start date*
In this chart, we see the DCA end portfolio values, Full Rebalance excess returns to DCA, and Contribution Rebalance excess return to both Full Rebalance and DCA.
A few patterns stand out:
* Leveraged strategies outperform unlevered DCA across every start date.
* Both the contribution leverage and full rebalance strategies heavily outperform the DCA strategy over most time frames, but the higher leverage at the end of the investment horizon leads to the contribution strategy having much higher peak returns.
* Even in muted periods, the leveraged strategies start from a higher baseline, outcomes are lifted across the board.
# Why this view matters
Single back tests tell stories, but rolling windows reveal structural advantage. They strip out hindsight and show how leverage changes outcomes across all historical sequences.
The takeaway is not that leverage always wins, it is that *how* leverage is applied shapes both its benefits and risks.
# From timelines to distributions
Rolling return charts show trends over time, but most investors experience outcomes as a range, not a single line. The more useful question is:
*If I invest for 30 years, what kind of outcome am I likely to end up with?*
# Ending portfolio value by percentile
*Chart 7: End portfolio value percentiles across all 30-year windows*
*\*100 percentile Contribution Rebalance Strategy is actually over $18 million, I just had to cap the chart so the other strategies were visible*
This chart shows the full distribution of ending portfolio values for each strategy. Each point represents a real 30-year investing experience; the only difference is the start date.
Key takeaways:
* At every percentile, both leveraged strategies produce higher ending values than plain DCA, meaning the entire distribution shifted upward
* The left tail improves: the worst outcomes under contribution leverage are meaningfully better than the worst DCA outcomes.
* The right tail also benefits: contribution leverage captures the largest upside by keeping early contributions highly exposed.
# Why this changes the leverage conversation
Most debates focus on extremes, best case or blow-up. This chart shows a subtler point: when applied early and tapered over time, leverage can improve both typical and poor outcomes. It’s not a gamble, it’s a form of diversification.
# Worst, average, and best historical returns
*Chart 8: Worst, average, and best 30-year returns by strategy*
*\*Best Return for Contribution rebalance is actually around 4,500%, but I had to limit the chart to 2,000% so the others are visible.*
This chart compresses the full distribution into three reference points for each strategy: the worst, average, and best 30-year outcomes.
Key points:
* Both leveraged strategies raise average returns relative to plain DCA.
* Contribution leverage improves the worst outcomes compared with unlevered DCA, showing it raises the floor while also boosting upside.
* Portfolio leverage glidepath raises averages and best outcomes, but its worst-case result is closer to DCA due to rebalancing path dependency.
# What this summary hides and what it reveals
This chart does not show how often each outcome occurs or the journey along the way. But it highlights a central insight: leverage, applied early and tapered over time, can raise both the floor and ceiling of long-term outcomes.
The final piece is whether an investor could realistically endure the inevitable drawdowns.
# Returns are optional. Drawdowns are not.
Most investors do not abandon a strategy because of low average returns, they abandon it because the path feels unbearable. Drawdowns are the lived experience of risk and determine whether compounding gets the time it needs to work. So after looking at ending outcomes, the final question is simple:
What did these strategies *feel* like to hold?
# Average and maximum drawdowns
*Chart 9: Average drawdown and maximum drawdown by strategy*
Maximum drawdown shows the single worst peak-to-trough loss in any 30-year window, while average drawdown reflects what investors typically experienced. Both matter, but differently.
Key points:
* Leveraged strategies increase drawdowns, as expected.
* Maximum drawdowns are concentrated early when the portfolio is small and leverage is high, so absolute losses are less daunting than percentages suggest.
* Average drawdowns remain closer to the unlevered experience, showing most risk occurs when capital is limited.
This aligns with earlier findings: losses happen when they are cheapest, gains compound when they are most valuable.
# Why this matters more than returns
Success is not determined by the best historical outcome but by whether a human can stick with the strategy through inevitable discomfort. Contribution leverage shifts risk in time rather than simply increasing it, enabling higher long-term returns without making the journey intolerable.
# One final note
The original purpose of this experiment was to justify my own investing methodology, which I may get into in a future post.
More importantly, it shows the tangible benefit of taking more risk and using leverage early in an investing career, when time allows compounding to work.
Across nearly a century of S&P 500 history, there has never been a 30-year period where leverage did *not* produce higher returns than a simple DCA approach, including worst starting points, rolling windows, and all percentiles.
I’m not saying everyone should be in leveraged investments. But for a long-term, diversified portfolio, there is a clear advantage to taking more risk early and gradually reducing it. Front-loading exposure reshapes the path of risk: losses happen when the portfolio is small, gains compound when it is large, and outcomes become higher and more predictable.
**Front-loading risk early doesn’t make investing riskier, it makes it smarter over the long run.**
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