Invest1 distinct publisher3 min readUpdated
Since 1950 the S&P 500 has spent 93% of trading days below a prior peak, in an average 11% drawdown. Highs are the exception, and that should be said out loud before the next one breaks.
The Investor · Invest desk

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Ben Carlson has published a simple tally of how the S&P 500 has spent its time since 1950: nearly 1,400 new all-time highs, which works out to 7% of all trading days [1][2]. The useful half of that arithmetic is the other side of it, because 93% of trading days were spent somewhere below a prior peak [3], and that is the base rate any conversation about "taking something off the table at the top" should start from.
The shape of those 93% matters more than the headline. Averaged across every trading day of the past 75 years or so, an investor was sitting in an 11% drawdown [4]. On 56% of all trading days the market was 5% or more below its last high [5], and more than 40% of the time it was in a double-digit drawdown [6]. Subtract one from the other and the mild-discomfort band is thin: at most about 16% of trading days fall between 5% and 10% off the peak [1]. Roughly 44% of days sit within 5% of a high [2]. The distribution is barbelled. Markets are either near the record or a long way under it, and they do not spend much time in the polite middle where a client might feel like there is time to reposition calmly.
Then there is the measurement problem, which is where the behaviour gets expensive. Up days outnumber down days only 56% to 44% [7], and losses register about twice as hard as equivalent gains [8]. Score that as Carlson does and each daily glance carries an expected emotional payoff of about minus 0.32 units: 0.56 units of pleasure against 0.88 units of pain [4]. He traces the idea to Richard Thaler's myopic loss aversion, which he wrote up in Risk & Reward [9]. The nearly 1,400 highs and the 7% share imply something like 20,000 trading days in the sample [3], so the observation frequency an investor chooses is doing more work on their experience of returns than the returns are.
Carlson's own rules are unfashionably manual. He keeps a spreadsheet of IRAs, 401ks, a 529 and brokerage accounts, and updates the values once every six months, skipping the exercise entirely during a nasty bear market [11]. His stated rule is never to check portfolio value during a bear market unless a change actually needs to be made [10]. He flags brokerage accounts as the weak point, since they are easier to check daily than tax-deferred accounts and he knows people who look multiple times a day [12]. The blunt version, in his words: checking your investments does not make them go up faster, and monitoring performance more often will not improve your Sharpe ratio [13].
The practical consequence is that reporting cadence is a policy decision, and it is one that has to be made while things are pleasant. Carlson notes the market is currently trading at or near all-time highs [14], which by his own numbers is the unusual state [3]. Set the check-in interval, the drawdown script and the pre-agreed rebalancing triggers now. What to watch is whether the 5%-to-10% band gets used as a reaction window at all, given how few days historically sit there [1], and whether brokerage-account behaviour diverges from retirement-account behaviour the way Carlson describes [12]. That gap is the tell for who has an investment process and who has a habit.
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Ranked by verification strength, evidence, and original report placement.
There have been nearly 1,400 new all-time highs on the S&P 500 since 1950.
New all-time highs account for 7% of all S&P 500 trading days since 1950.
On 56% of all trading days, an investor would have been down 5% or more from all-time highs.
More than 40% of the time since 1950, the stock market has been in a double-digit drawdown.
Because 7% of trading days were new highs, 93% of the time the market was down from an all-time high.
Taking all trading days over the past 75 years or so, the average position was an 11% drawdown from the prior all-time high.
Evidence-backed comparisons of source perspectives and observed adoption signals. Read the methodology
Which Builder, Operator, and Investor concerns the observed source mix emphasized—not a truth score.
Evidence, demonstrated adoption, hype gap, incentives, and confidence are assessed independently, each on its own current evidence. How these are measured.
One self-published source, internally consistent but unreproduced
All quantitative claims trace to a single blog post that references a chart ('Here's the data') without publishing the series, methodology, index-return basis, or data provider. The figures are internally coherent, 7% new highs complements 93% below peak, 56% of days 5%+ down complements roughly 44% within 5%, and 56/44 up-down days matches the loss-aversion arithmetic, which supports moderate credibility. But nothing is independently corroborated, and the prescriptive core (monitoring frequency has no effect on returns or Sharpe Ratio; many people check brokerage accounts multiple times daily) is asserted from rhetoric and anecdote rather than data.
No adoption signal in supplied material
The cluster contains one opinion and analysis post. It reports no release, deployment, benchmark run, pricing or license change, or disclosed usage figures, and the only behavioral datapoints are the author's own semiannual spreadsheet routine and an anecdote about acquaintances checking brokerage accounts. That is insufficient to measure whether the framing or practice is being adopted by any population.
Mildly overstated: solid history, unevidenced prescription
The descriptive statistics are presented soberly, with an explicit 'assuming the future plays out like the past (not a guarantee obviously)' caveat, and the 93% and 11% figures are the strongest part of the piece. Overstatement enters at the prescriptive layer: the leap from a 2:1 loss-aversion scoring exercise to firm rules ('never check during a bear market', 'monitoring your performance regularly won't improve your Sharpe Ratio') claims a causal link between checking frequency and investment outcomes that the supplied evidence does not establish, and the acquaintance anecdote is used as if it were prevalence data. Gap is small and positive rather than large, because the numeric claims themselves are not inflated.
Visible self-promotion around a hold-and-look-less message
The post is author-owned media that promotes the author's own products alongside the advice: it cites his book Risk & Reward as the source of the loss-aversion argument, plugs the week's Animal Spirits video with a 'Subscribe to The Compound so you never miss an episode' call to action, and lists a books section and podcast book tour. The counsel to stop looking and stay invested is congruent with retaining audience and readership for long-horizon content. No compensation, ownership, or advisory-fee arrangement is disclosed in the supplied material, so the incentive is evident but not quantified, hence a mid-to-high rather than extreme score.
Moderate: coherent history, single unverified source, unmeasurable adoption
Confidence is limited by structure rather than by contradiction. Nothing in the cluster disputes the figures, and the statistics hang together arithmetically, which supports the central 93%-below-peak framing. But there is one publisher, one article, no published dataset, no adoption dimension to measure, and clear self-promotional incentive. The descriptive claims deserve provisional trust; the prescriptive claims about monitoring and outcomes should not be treated as established.
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1 article · August 14, 2026