Does Missing the Best Market Days Really Destroy Returns?

Research Goal

Test the popular claim that “missing the 10 best days” drastically reduces returns.

Questions

  • How often do the best/worst days occur close together?

  • Is buy‑and‑hold superior to timing strategies?

Introduction

One of the most frequently repeated statements in personal finance is:

“If you miss the 10 best days in the market, your returns collapse.”

It’s a powerful claim — and often used to justify passive investing and discourage market timing.
But is it actually true?
And more importantly: does it still hold in modern markets where volatility clusters and cycles have changed?

In this research note, we replicate the classic study using S&P 500 data from 1980 to 2026, identify the 10 best and 10 worst days, and analyse how missing those days affects long‑term cumulative returns.

All analysis is done in Python using free Yahoo Finance data.

Data & Methodology

Data Source

We use daily closing prices for the S&P 500 index (^GSPC) from January 1980 to January 2026, downloaded via the yfinance library.

Steps

  1. Load S&P 500 prices

  2. Compute daily percentage returns

  3. Identify:

    • the 10 best daily returns

    • the 10 worst daily returns

  4. Recompute cumulative returns for three scenarios:

    • Buy & Hold

    • Missing the 10 Best Days

    • Missing the 10 Worst Days

  5. Compare the paths and ending values.

The 10 Best Days (1980–2026)

The analysis identifies the following 10 strongest daily gains (sorted by date):

Date Return (%)
1987-10-1311.5800%
1987-10-219.0994%
2008-10-2810.7890%
2008-11-136.9213%
2008-11-246.4723%
2009-03-237.0758%
2020-03-139.2828%
2020-03-249.3823%
2020-04-067.0331%
2025-04-099.5154%

A striking observation:
Most of the best days occur during extreme market stress — 1987 crash aftermath, the 2008 financial crisis, and the COVID‑19 pandemic.

The 10 Worst Days

The results show:

Again, the worst days cluster around crises — exactly when the best days also occur.

This leads to an important insight:

The best and worst days often happen within the same weeks. Market timing is not a precision sport — you rarely avoid the worst days without also missing the best ones.

Key Findings

1. Missing the Best Days Is Disastrous

The analysis shows that excluding the 10 best days drops cumulative returns drastically.

This confirms the traditional message.

2. But Missing the Worst Days Is Even More Powerful

If an investor could avoid only the 10 worst days, the cumulative return nearly doubles versus buy & hold.

This raises an important nuance:

It’s not that the best days are magical — it's that volatility clusters.
You only get the best days when you also risk living through the worst ones.

3. Crisis periods dominate extreme returns

Both the best and worst days happen during:

  • 1987

  • 2008

  • COVID‑19 turbulence

  • 2025 volatility spike (based on your April 2025 datapoint)

Market timing during such periods is nearly impossible.

Interpretation: What Does This Mean for Investors?

Passive Investors

  • Staying invested is rational

  • Extreme days occur unpredictably and in clusters

  • Trying to sidestep volatility is risky

Active Traders

  • Trend‑following and regime‑switching models could theoretically avoid bad days

  • But discretionary timing is extremely difficult

  • Risk‑management matters more than predicting single days

Portfolio Managers

  • Extreme outliers justify the use of volatility filters, hedging, tail‑risk strategies, and diversification

  • Risk control > market timing

Conclusion

The data confirms the classic wisdom:

Missing just a handful of the best days devastates long‑term returns.

But your analysis also highlights a deeper insight:

The best and worst days come from the same storms.
If you avoid volatility, you avoid both.

This means that:

  • Buy & Hold remains a robust strategy

  • Market timing must be rule‑based, not emotional

  • Risk management and exposure control matter more than “picking days”

The myth is true — but the reason behind it is often misunderstood.

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Date Return (%)
1987-10-19-20.4669%
1987-10-26-8.2789%
1997-10-27-6.8687%
2008-09-29-8.0868%
2008-10-09-7.7617%
2008-10-15-9.0305%
2008-12-01-8.8925%
2020-03-09-7.5970%