Video summary

Ben Carlson: Investing at All-Time Highs | Rational Reminder 412

Main summary

Key takeaways

Finance

Finance-focused summary: Investing near all-time highs

Macro / market context: investing near all-time highs

Ben Carlson argues investors should be “freaking out” about all-time highs—not in the sense of panic, but as a caution signal. He also notes evidence suggesting that returns from many historical “all-time high” start dates are not as bad as intuition predicts:

  • Only ~7% of trading days are outside all-time highs, implying most observations are “near/at highs.”
  • Returns tend to look better over ~1, 3, and 5 year horizons when starting from/all around all-time-high periods, consistent with bull markets lasting longer than expected.

Key takeaway: the next all-time high will happen, but it’s only one peak—and investor behavior often misreads probabilities after large recoveries.

Japan as an “outlier” case study (bubble, mean reversion, long-run conclusions)

Japan is used to address the “What about Japan?” argument against long-term investing.

Bubble characteristics

  • 1989 real estate bubble (Tokyo): Imperial Palace grounds in Tokyo (1989) were valued (by “claims”) at more than the entire Canadian real estate market.
  • Equities extremely expensive: stocks reportedly traded around ~100x earnings at the peak (vs. U.S. dot-com levels, in his framing).

After the peak

  • If you invested at the 1989 top, the next ~3 decades were described as poor/underwater (a “compressed” boom-and-bust).
  • Performance figures mentioned:
    • Japan (1970–1989): ~22% per year
    • Japanese small caps ~30% per year for two decades
    • Post-1990s long-run: about ~1–2% per year for Japanese stocks “since 1990” (described as terrible)
    • But blended over a very long horizon: ~8.7% annual returns over ~55 years, implying long-run equity behavior is broadly similar—albeit with painful multi-decade drawdowns.
  • Peak-to-new-high timeline: he says the Nikkei didn’t reach a new high until ~2024, after peaking around 1989.

Lessons emphasized

  • Diversification: Japan’s weakness didn’t prevent strong performance elsewhere.
  • Avoid avoidable “outsized risk” from country concentration.
  • Strategy diversification matters too: Japanese value/small-cap value may have held up better during the long weak period.
  • Reframing the objection: exceptions don’t invalidate rules—but they reinforce the need for risk management and diversification.

Major crash / lost-decade risk: Great Depression (severity + macro transmission)

Carlson highlights extreme downside and how policy and economic structure shaped outcomes:

  • U.S. stock market crash: about ~86% decline (also referenced as “stocks fell ~85%”).

Economic damage (explicit numbers)

  • Unemployment: ~20–25%
  • GDP: contracted by ~30%
  • Corporate profits: fell by ~70%
  • The depression-like conditions lasted until about ~1937, with improvement more tied to World War II.

Policy / “lender of last resort” angle

He argues today’s system makes an 80–90% stock collapse less likely because it has:

  • greater involvement,
  • more institutional knowledge,
  • stronger policy tools.

He also references the Fed’s evolving capacity relative to the Great Depression era and uses 2008 as evidence that faster government action + lower rates can help avoid repeating similar dynamics.

What tends to happen after crashes (performance regime shift)

Using rolling long-horizon evidence for the S&P 500:

  • Worst 30-year return starting from the Sept 1929 peak:
    • ~850% total return
    • ~almost 8% annual
  • After that crash, the market later produced the best 30-year return of the century-scale history:
    • roughly ~15–16% per year
  • “Worst to best” occurred in about ~3 years (as described).

General conclusion: after major drawdowns, expected returns improve, so crashes are often followed by stronger long-run compounding—even if the recovery is not immediate.

Risk management principles for stock portfolios

Two main approaches were highlighted:

  1. Diversification (closest thing to a “free lunch”)

    • He notes U.S. bonds did “pretty well” during the Great Depression relative to equities, and that liquidity and avoiding over-allocation to stocks mattered.

  2. Know your personal “blind spot”

    • Are you prone to panic?
    • Do you overreact to crashes?
    • Are you structurally too aggressive given how you may behave during stress?

Core investing idea: dealing with losses is fundamental. He references work attributed to Daniel Kahneman / Amos Tversky on loss aversion (losses “sting twice as bad” as gains feel good), and explains volatility clustering through behavior: losing money can trigger nightmares/visceral reactions, leading to panic in downturns.

Inflation: how to think about hedging (and what works long run)

Inflation is described as psychologically and behaviorally destabilizing (people become more angry; wages may not fully offset perceived price increases).

Inflation hedges discussed

Potential short-term hedges include:

  • gold
  • Bitcoin
  • energy stocks
  • precious metals
  • commodities

But he cautions these can underperform when inflation is not high.

“Best for most households” framework

He emphasizes that for most households, the best “hedge” is about long-term purchasing power via:

  • human capital / wage growth over time (hard to give universal advice),
  • controlling major household spending categories:
    • housing + transportation are ~50% of U.S. budgets (citing BLS),
  • using stocks as the long-term inflation hedge:
    • over ~100 years, stocks delivered roughly ~6–7% above inflation,
    • over shorter periods, stocks can falter vs inflation (example: 2022).

He reframes “hedging” as a standard-of-living over decades problem, not a narrow “next 12–36 months” problem.

Market timing: why it’s tempting and why it fails in practice

Timing is psychologically attractive (e.g., “get out at the top” / “buy at the bottom”), but he argues you must be:

  • right twice, and
  • many people either overstay or can’t re-enter after exiting.

Example (2008): a colleague sold around Sep 2008 (Lehman Brothers period) but didn’t get back in, then tried timing repeatedly afterward and was wrong each time.

Volatility vs risk (and why markets feel similar to “casino”)

  • Volatility clustering: during bear markets, volatility increases and large up/down days cluster close together (e.g., March 2020: ~8–9% down days alongside similarly large up days), making bottom timing extremely hard.
  • Volatility vs risk: Wall Street often quantifies risk numerically, but risk also has qualitative and emotional components.
  • Risk profile depends on:
    • Need (quantifiable: required return to meet goals),
    • Ability (net worth, savings rate, income),
    • Willingness (qualitative: emotional tolerance—hard to calculate).

Recessionary vs non-recessionary bear markets

He distinguishes two kinds of bear markets:

  • Non-recessionary: peak-to-trough around ~25%
  • Recessionary: around ~40%

He also notes declines can happen even with an economy that is still growing—because earnings and investor psychology can drive price drops independently of recession.

Stock market vs economy: relationship can diverge

Key points:

  • The stock market is not always the same thing as the economy.
  • Example: the U.S. stock market is ~40–50% tech (depending on definitions), while about ~50% of the economy is not tech; consumer services are also large.
  • The stock market is forward-looking and can adjust quickly; economic data is backward-looking and lags (he likens the economy to a battleship and the stock market to a speedboat).

He also addresses valuation/pundit narratives: even when predictions are wrong sometimes, the market keeps moving—so investors shouldn’t overreact to lagging macro data.

Portfolio construction: diversifying across geography, asset classes, strategies

Diversification is framed in three dimensions:

  1. Geographic (avoid single-country concentration such as Japan-only risk)
  2. Asset class (stocks/bonds/cash/real estate/alternatives)
  3. Strategy (e.g., small/mid caps, value, high quality, dividends)

He argues there’s no perfect portfolio ex ante: “perfect is the enemy of good.” Diversification can “tailor risks off the table” (reducing drawdown/“strikeout” risk) even if it caps some “home runs.”

Tactical investing / behavior warnings

He warns retail investors about casino-like products:

  • broker promotions for options trading,
  • meme stocks,
  • and behavioral framing via a stat: Americans spend more on lottery than on sporting events, books, video games, movies, and music combined.

If someone must speculate, he suggests sizing it as a small separate slice so core long-term holdings (e.g., target-date funds/index funds) remain untouched—though he notes it’s “not ideal.”

Explicit presenter/philosophy: “risk and reward” and when to change allocations

Ben emphasizes allocation changes should be driven by the risk/reward setup changing, not by predicting market moves—for example, changing allocations when an asset/fund is “not being paid enough risk.”

He also stresses matching advice to personal circumstances and goals and criticizes “financial television” advice that doesn’t fit an individual’s situation.

Disclosures / disclaimers (verbatim themes)

The podcast disclaimer includes, in substance:

  • not an offer/solicitation to buy or sell securities,
  • information is believed truthful but not guaranteed accurate,
  • not investment/tax/legal advice (consult a professional advisor),
  • past performance not indicative of future results,
  • regulated entities in Canada and the U.S. are referenced,
  • discussed market indices are unmanaged and involve investment risk.

Tickers / assets / instruments mentioned

  • S&P 500 (crash and rolling return statistics)
  • Nikkei (Japan reference; new high ~2024 after ~1989 peak)
  • Japan / Japanese stocks, including small caps and value / small-cap value
  • Gold
  • Bitcoin
  • Commodities (general)
  • Energy stocks
  • Options (speculative instrument for retail investors)
  • Meme stocks
  • Lottery (behavioral proxy)

(No specific stock/ETF tickers were explicitly provided.)

Step-by-step / framework elements mentioned

  • Long-term investing framework: outliers like Japan don’t negate broad principles; adjust allocation only when risk/reward and personal situation change.
  • Household-oriented inflation approach:
    • focus on human capital / wage growth,
    • manage major spending (housing + transportation),
    • use stocks as the long-term hedge (not necessarily for the next 12–36 months).
  • Crisis risk management:
    • diversify,
    • identify your behavioral blind spot (panic tendency),
    • avoid timing; build a plan durable across environments.
  • Diversification decomposition: by geography, asset class, and strategy.

Key numbers / figures called out

  • All-time highs / timing returns
    • ~7% of trading days outside all-time highs
    • better subsequent returns over ~1, 3, and 5 years
  • Japan
    • ~100x earnings at the peak
    • Tokyo (1989) Imperial Palace grounds valued (claims) at more than the entire Canadian real estate market
    • ~22%/yr (1970–1989)
    • Japanese small caps ~30%/yr for two decades
    • ~1–2%/yr since ~1990
    • blended long-run: ~8.7% annual over ~55 years
    • Nikkei new high: ~2024 vs peak around 1989
    • Japan market cap as % of GDP: ~30% (1980) to ~150% (1989)
  • Great Depression
    • stock crash: ~86% (or ~85%)
    • unemployment: ~20–25%
    • GDP contraction: ~30%
    • corporate profits: ~70% decline
    • still ~20% unemployment by ~1937, improvement linked to WWII
  • Rolling returns (S&P 500)
    • worst 30-year window from Sept 1929 peak: ~850% total, ~almost 8%/yr
    • best 30-year after that crash: ~15–16%/yr
  • Bear markets
    • non-recessionary: ~25%
    • recessionary: ~40%
    • March 2020 example: ~8–9% up/down days (as described)
  • Inflation
    • stocks: ~6–7% over inflation over ~100 years
    • short-term example of underperformance: 2022
  • Household budgeting
    • housing + transportation ~50% of U.S. household budgets (BLS-referenced)
  • Speculation / timing
    • timing requires being right twice
    • 2008 example: colleague sold around Sep 2008; market fell another ~30% afterward

Presenters / sources (as named in subtitles)

  • Benjamin Felix (Chief Investment Officer, PWL Capital; co-host)
  • Dan Bordotti (Portfolio Manager at PWL Capital; co-host)
  • Ben Carlson (Director of Institutional Asset Management, Rholt Wealth Management; author)
  • “Producer Matt” (podcast producer; provided end disclaimer)

Additional sources referenced:

  • Jason Zweig (Wall Street Journal) referenced for an anecdote
  • loss aversion research attributed to Daniel Kahneman / Amos Tversky (subtitles show as “Daniel Conorman”)
  • Andrew W. Worth’s book “1929”
  • Benjamin Roth (book: “The Great Depression of Diary”)
  • Charles Perrow (“normal accidents” research)
  • BLS (U.S. Bureau of Labor Statistics)

Original video