The 10 Best Days Argument Is Half the Story

The 10 Best Days Argument Is Half the Story

Every few years, a market correction arrives and the passive investing community deploys a statistic: if you had missed the 10 best trading days in the S&P 500 over the past three decades, your annualized return would have collapsed from roughly 10% to around 4%. The implication is clear: stay invested, always, because those crucial days are unpredictable and the cost of missing them is severe.

The statistic is accurate. But it is incomplete. The same arithmetic applies in reverse, and the reverse case is considerably more interesting for anyone thinking carefully about risk management.

This article presents Quant Decoded's original analysis of the symmetric argument — what happens when you examine missing the worst days alongside the best — and then explores three research-backed tactical methods for tilting exposure away from the clusters where worst days concentrate.

The Classic Statistic and Its Mirror

The table below shows the S&P 500 total return outcomes across five scenarios from 1993 through 2025, a 32-year period covering five distinct bear markets and multiple volatility regimes.

Source: Quant Decoded Research (original analysis, 1993-2025). Total return index, dividends reinvested.

The headline result is striking: missing the 10 worst days more than triples the terminal wealth relative to buy and hold, producing $698,000 from a $10,000 investment versus $198,000. Missing the 20 worst days produces $1,840,000.

The final row contains the critical insight. When you miss both the 10 best and 10 worst days simultaneously, the annualized return barely changes — 10.8% versus 10.4%. The best and worst days largely offset each other in their return contribution. This means the buy-and-hold argument and its counterargument are symmetric in arithmetic, but the distributions of those extreme days are decidedly not symmetric in their location.

And location is everything.

Why Extreme Days Cluster

The passive investing argument assumes that you cannot predict when the best days occur, so you must remain fully invested to capture them. This assumption collapses once you examine where extreme days actually fall.

In the S&P 500 from 1993 through 2025:

• 7 of the 10 worst single days occurred when the index was already below its 200-day moving average
• 8 of the 10 best single days also occurred when the index was below its 200-day moving average

This clustering is not coincidental. It reflects the well-documented property of volatility persistence.

Engle (1982) introduced the ARCH model demonstrating that volatility in financial markets clusters in time: large moves, both positive and negative, tend to follow large moves. This research, which earned Engle the 2003 Nobel Prize, established the statistical foundation for understanding why extreme days do not occur at random throughout the calendar.

The data confirms this: the average VIX on the 20 worst single days since 1993 was 42.3. The average VIX on all other trading days was 16.1. Worst-day clusters are not distributed uniformly across market regimes; they concentrate overwhelmingly in high-volatility environments — specifically in bear markets and the turbulent bear market bounces that accompany them.

This explains the apparent paradox that 8 of the 10 best days also fell below the 200-day moving average. They are the violent upside reversals that characterize bear markets: the days when oversold conditions, short-covering, and policy announcements combine to produce enormous one-day gains. They happen in the same high-VIX, below-200MA environment as the worst days. Avoiding that regime reduces exposure to both, but the distribution of extreme moves in bear markets is negatively skewed: the downside days are more frequent and more extreme than the upside ones.

A regime signal cannot perfectly separate best days from worst days within a bear market. But it does reduce average exposure during the windows when worst-day clusters are most likely to extend. That is the practical value of the approach.

Three Tactical Methods

The following comparison table summarizes three research-backed methods for reducing exposure during worst-day clusters. All estimates use S&P 500 total return data from 1993 through 2025.

The max drawdown for buy-and-hold over 1993-2025 was -51%, occurring during the dot-com crash and the 2008-2009 financial crisis. All three methods reduced it substantially.

Method 1: The 200-Day Moving Average

The simplest regime signal is whether the index currently trades above or below its 200-day moving average. When the index closes below the 200-day MA, reduce or eliminate equity exposure; when it closes above, hold full equity.

The academic foundation for this approach was established by Faber (2007), who applied a 10-month moving average to S&P 500 monthly data from 1900 through 2006. His findings: the timing rule cut the maximum drawdown from 51% to 26%, improved the Sharpe ratio from 0.27 to 0.37, and reduced annualized return by only 30 basis points — from 10.0% to 9.7%. Critically, Faber used data predating the widespread awareness of moving average strategies, reducing the risk of in-sample optimization.

Clare, Seaton, Smith and Thomas (2017) extended this analysis across equities, bonds, and commodities across multiple countries and time periods. Their finding: moving average timing rules generate consistent risk-adjusted improvements that cannot be attributed to US equity data mining. The pattern holds across markets and asset classes.

In the Quant Decoded analysis, the 200-day MA rule applied to the S&P 500 from 1993 through 2025 reduced the maximum drawdown from -51% to -18% and improved the Sharpe ratio by 0.08 relative to buy-and-hold. The cost: approximately 4-6 position changes per year on average, and the underperformance visible in 2010-2021 when the strategy produced multiple false exit signals in a sustained low-volatility bull market.

Method 2: Volatility Targeting

Rather than a binary in/out signal, volatility targeting scales equity exposure continuously based on recent realized volatility. When realized volatility is high, reduce equity weight; when realized volatility is low, hold full or slightly above full weight. The target is a constant annualized volatility level — in this analysis, 10%.

The theoretical basis for this approach draws on Moreira and Muir (2017), published in the Journal of Finance. They showed that volatility-managed portfolios — which scale exposure inversely to the prior month's realized variance — improve Sharpe ratios across market, value, momentum, and profitability factors. The mechanism is the GARCH effect identified by Engle: realized variance is highly persistent. When volatility is elevated, it tends to remain elevated, meaning that scaling down during high-vol regimes mechanically reduces exposure before worst-day clusters extend.

In the Quant Decoded analysis, the 10% volatility target applied to the S&P 500 from 1993 through 2025 produced the largest Sharpe improvement of the three methods (+0.12) and reduced maximum drawdown from -51% to -19%. It requires continuous position adjustment as realized volatility changes — approximately daily in practice — which creates higher implementation complexity than the 200-day MA signal.

The key cost: volatility targeting reduces exposure during high-volatility recoveries, not only high-volatility declines. In November 2020, when markets surged following vaccine trial announcements, realized volatility remained elevated from the preceding months of turbulence. A volatility-targeting strategy would have carried reduced equity exposure during that sharp rally, missing a significant portion of the gains.

Method 3: VIX Regime Filter

The third approach uses the VIX index as a regime signal. When the prior week's VIX close exceeds 25, reduce equity exposure by 50%; when it falls below 25, hold full equity. The threshold of 25 is approximately the 82nd percentile of VIX observations since 1993 — historically about 18% of trading days.

Quant Decoded's analysis found that 72% of the S&P 500's 30 worst single days since 1993 occurred during periods when VIX was above 25 at the prior week's close. The VIX regime filter is therefore less precise than volatility targeting but more directly calibrated to the worst-day clustering phenomenon. In the comparison analysis, it reduced maximum drawdown from -51% to -22% and improved the Sharpe ratio by 0.05.

The key limitation: VIX is a lagging indicator of sorts for sudden shocks. Approximately 28% of worst days occur when prior VIX was below 25 — these are genuine surprises, like the onset of a crisis before markets have priced the fear. On 2022's worst single days, for example, VIX had already spiked to elevated levels before the largest declines, which meant the signal would have triggered; but for an event like the first days of the COVID selloff in late February 2020, VIX moved from calm to elevated in the same rapid window as the market decline.

The Honest Costs

No evidence-based summary of tactical allocation methods is complete without accounting for what each approach sacrifices.

All three strategies underperformed buy-and-hold in cumulative 2010-2021 returns. This was the longest sustained low-volatility bull market in the sample period: 11 years in which valuations expanded, central banks provided continuous accommodation, and realized volatility remained suppressed except for brief episodes. In such an environment, any approach that reduces equity exposure — whether triggered by MA crossings, elevated realized volatility, or high VIX — generates drag without a compensating benefit. The 200-day MA strategy in particular produced 3-4 false whipsaws per year in the sideways periods of 2011, 2015-2016, and early 2018.

The 200-day MA approach also carries a tax consideration in non-retirement accounts. Each exit from equities constitutes a taxable event, converting unrealized gains into realized ones. In a tax-efficient buy-and-hold framework, those same gains would have compounded untaxed for decades. The tax drag from periodic MA-triggered exits can be substantial for high-net-worth investors in taxable accounts.

Volatility targeting has an additional behavioral cost. During sharp rallies after volatile periods — the exact moments when passive investors celebrate their discipline — a volatility-targeting investor holds reduced equity. The November 2020 vaccine rally example illustrates this: a 10% vol-target strategy carrying 50% equity exposure into that week would have captured only half the gains. The strategy is mechanically right about risk ex ante but can feel decisively wrong ex post during powerful recoveries.

The VIX filter's noisiness creates its own version of the problem. VIX can remain above 25 for extended periods during volatile but ultimately bullish regimes — early 2009, mid-2020, and stretches of 2022 all saw extended VIX > 25 periods during which the market ultimately rallied substantially from depressed levels. A half-equity position through those rallies captures only partial upside.

Practical Takeaways

The evidence across 1993-2025 suggests several probabilistic conclusions about worst-day exposure and these tactical methods.

Investors who hold equity primarily in tax-advantaged retirement accounts and can tolerate 3-5 position changes per year tend to have better risk-adjusted outcomes with the 200-day MA rule than with buy-and-hold during high-volatility market regimes. The Faber evidence extends this finding back to 1900, which reduces concerns about overfitting to the post-1993 sample.

Investors with short-term risk tolerance constraints — institutions with drawdown-linked capital requirements, or individuals near retirement — tend to see the largest practical benefit from volatility targeting, as the continuous scaling prevents the abrupt large drawdowns that are most disruptive to financial plans. The Moreira and Muir evidence that the improvement persists across multiple factors, not just market beta, reduces the concern that this is a data-mined artifact of US equities.

Long-term passive investors who are willing to hold through drawdowns of 40-50% and who have long investment horizons tend to find the operational and tax costs of these methods outweigh the risk-adjusted benefits, particularly in periods like 2010-2021 when the costs are most visible and the benefits are absent.

The symmetric argument at the heart of this analysis is not an argument for active market timing in the traditional sense. It is an argument that the "10 best days" framing is incomplete without its mirror — and that the clustering of extreme days in identifiable regimes is a documented empirical phenomenon, not a prediction. Whether to act on that phenomenon involves costs that deserve explicit accounting alongside the potential benefits.