Category: Behavioural Finance

The NorthPath Letter · Behavioural Finance · Afternoon Edition · 29 May 2026

The bias: a 1997 paper that named what Irving Fisher had already seen

The long-term equity investor’s relationship with money is supposed to be straightforward. A rupee, a euro, a dollar is a unit of account; capital is what it can buy. The portfolio is a claim on future real consumption, and only the real return — the return after inflation — matters for whether the investor’s family will retire comfortably, fund a child’s university place, or endow a charitable trust. None of this is controversial. It is taught in the first month of any finance course. And it is, for most investors most of the time, quietly ignored.

The bias that explains the gap between the textbook treatment and lived practice has a name. In May 1997 the Quarterly Journal of Economics published a paper titled simply “Money Illusion” by Eldar Shafir, Peter Diamond and Amos Tversky. Through a series of carefully designed surveys — on house purchases, salary negotiations, bonus framing and contract evaluation — the authors showed that ordinary people, including those with formal training, systematically evaluate transactions in nominal terms even when they have been told, explicitly, that the relevant comparison is the real one. The bias was robust, replicable across populations, and present in domains where the welfare consequences of getting it wrong were large.

The paper did not invent the concept. Irving Fisher’s 1928 book The Money Illusion, published in New York by the Adelphi Company, had defined the phenomenon as the “failure to perceive that the dollar, or any other unit of money, expands or shrinks in value” — a definition that has not been improved upon in the century since. What Shafir, Diamond and Tversky added was the experimental architecture: a way of demonstrating, in clean laboratory conditions, that even when both the nominal and real numbers were placed directly in front of the respondent, the nominal frame remained the dominant influence on the choice. Money illusion was not, in their account, a failure of arithmetic. It was a failure of representation — a deep feature of how the mind encodes value.

That distinction is what makes the bias important for the long-term equity investor. The horizon over which a portfolio compounds is precisely the horizon over which inflation does its work. A 6% nominal return at 2% inflation and a 9% nominal return at 5% inflation are economically the same trade; one feels much better. A pension pot that doubles in twenty years while the cost of living triples has shrunk in real terms by a third; the statement, in nominal currency, looks like a triumph. The bias the 1997 paper described is therefore the bias most likely to be active when the investor is reviewing the very horizon on which the strategy is built.

The mechanism: dual representation, with the nominal frame doing most of the work

Shafir, Diamond and Tversky proposed a specific cognitive architecture. People, they argued, hold simultaneous representations of economic quantities in nominal and real terms. Asked directly, in a vacuum, which representation is “correct,” most respondents will name the real one. But choices, judgements and emotional reactions are influenced by both representations, and the nominal representation tends to dominate because it is salient, simple, immediate and shared with everyone else who uses the same currency. The real representation requires an act of mental adjustment that the brain treats as effortful, optional and easily skipped under time pressure or affective load.

This dual-representation account has held up well under three decades of follow-on work. Replication studies — including a 2020 multi-country extension published in the Journal of Behavioral and Experimental Economics and a 2024 Brazilian replication in the same journal — recovered the core effects with consistent magnitudes. The bias is documented in wage negotiations (workers strongly prefer a 2% raise at 4% inflation to a 2% pay cut at 0% inflation, even though the real outcome is identical), in housing decisions (sellers anchor to the nominal price they paid, ignoring the price level that has moved beneath them), in bond evaluation (retail investors describe high-coupon bonds during inflationary periods as “safe income” even when the real coupon has gone negative) and, most importantly for the present subject, in equity returns (investors compare the index level today with the index level at purchase and call any positive number a gain).

The architecture also explains why money illusion is so resistant to education. Knowing that real returns are what count does not remove the nominal frame; it adds a second frame on top of it. Under deliberation, the educated investor can override the nominal reading. Under stress, fatigue or strong emotion — exactly the conditions in which most portfolio decisions are actually taken — the override fails, and the nominal frame wins by default. The 1997 paper did not phrase it this way, but the implication is unavoidable: money illusion is not solved by knowing about money illusion. It is solved, if at all, by building procedural defences that do the real-terms translation for the investor before the decision is taken.

The empirical record: regulators, surveys and three decades of measured loss

Two regulator anchors from two regions establish that the bias is neither academic nor historical. In the United States, the Treasury introduced Treasury Inflation-Protected Securities (TIPS) in January 1997 — the same year the Shafir-Diamond-Tversky paper appeared in print. TIPS exist precisely because nominal Treasury securities are not a complete instrument set for an investor who wants to express a view about real, rather than nominal, future cash flows. The Federal Reserve’s Survey of Consumer Finances and the long history of TreasuryDirect retail data confirm that even after a quarter-century of availability, retail allocation to TIPS remains a single-digit percentage of household fixed-income holdings, while retail allocation to nominal Treasuries and certificates of deposit has remained an order of magnitude higher. The instrument that solves the problem exists; the bias keeps most retail investors from using it.

In the United Kingdom, the Financial Conduct Authority’s Financial Lives 2024 survey put a sharp number on the same phenomenon for cash savings. Among UK adults holding at least £10,000 in cash but no investments, only 59% agreed with the statement that money held in cash savings decreases in value because interest rates usually do not keep pace with inflation. The remaining 41% — a population whose holdings, in aggregate, run into the hundreds of billions of pounds — either disagreed, were uncertain, or had never considered the question. The FCA’s Consumer Duty rules, in force since July 2023, and the new Consumer Composite Investments disclosure regime scheduled to replace the UK PRIIPs framework on 6 April 2026, both require firms to communicate the real-terms consequences of cash drag and inflation more clearly. The fact that such rules have to be written, and re-written, two and three decades after the academic work was complete, tells the long-term equity investor everything about how durable the bias is.

Academic measurement has converged on the same conclusion through a different channel. The work of Eugene Fama and others on the Fisher hypothesis, the long literature on the equity risk premium measured in real terms, and the regular monitoring of household real wealth by central banks all show the same gap between the nominal accounts that investors keep and the real accounts that ultimately determine their consumption. The accounts disagree most violently during inflationary episodes, when the nominal numbers are flattering and the real numbers are punishing. They disagree most quietly during disinflations, when the nominal numbers are flat and the real numbers are slowly improving — and the investor concludes, wrongly, that nothing is happening.

Two historical episodes: the 1970s US and the 2021-2024 global inflation

The 1970s in the United States are the canonical case. Between January 1972 and December 1981 the US Consumer Price Index rose by approximately 134%; over the same decade the S&P 500 price index rose by approximately 38%. Investors who looked at their statements saw nominal gains, modest dividends, and a portfolio that had not collapsed. Investors who priced their portfolios against the cost of the lives they were actually living saw something quite different: a real loss of roughly 40% in equity values, accompanied by a similar real loss in nominal bond portfolios. The decade was, for the long-term equity investor, one of the worst in the modern record. The nominal statements obscured it. Warren Buffett, in his May 1977 Fortune article “How Inflation Swindles the Equity Investor,” explained the mechanism: stocks, in economic substance, are perpetual instruments paying a real coupon, and inflation reduces that coupon both by raising the cost of the assets the business must replace and by raising the return required from competing securities. The piece is read today not as an investment recommendation but as the clearest contemporaneous diagnosis of money illusion in equity markets ever written.

The 2021-2024 episode supplies the modern counterpart. US CPI inflation, having been around 1.4% in January 2021, rose to a peak of 9.1% in June 2022 — the highest reading in four decades. Eurozone harmonised inflation peaked at 10.6% in October 2022. United Kingdom CPI inflation peaked at 11.1% in October 2022. India’s headline CPI inflation, while less extreme, averaged above the Reserve Bank of India’s 4% target throughout 2022 and into 2023. During the same three-year window most global equity indices delivered nominal returns that, viewed in isolation, looked respectable. Adjusted for the local cost of living, a substantial fraction of those returns disappeared. Cash deposits, money-market funds and short-duration nominal bonds — all of which appeared on retail statements as “safe” — delivered real returns that were, for an extended interval, materially negative. The fact that the bias was active in this episode, despite forty-eight years of accumulated academic and journalistic warning since the 1977 Fortune article, is the strongest available evidence that knowledge alone does not dissolve money illusion.

Howard Marks’s December 2022 Oaktree memo “Sea Change” is the practitioner reading of the same episode. Marks argued that the four-decade environment of disinflation and falling interest rates had ended, and that investors who had built mental models — and portfolio structures — calibrated to that environment would have to rebuild them for a regime in which positive real returns from credit instruments became possible again and the equity premium had to be re-earned. The memo’s importance for the present essay is that it framed the regime change explicitly in real terms. Most of the commentary it provoked, in the financial press and on retail platforms, did not.

The counter-measure framework: three disciplines that translate before the decision is taken

The bias is durable. The defences therefore have to be procedural rather than cognitive — built into the workflow of portfolio review, not into the goodwill of the reviewer. Three disciplines, in combination, do most of the work.

Discipline one — keep two sets of books and report on the real one first. Every periodic portfolio review should open with the real-terms account and only then turn to the nominal-terms account. The real account expresses the portfolio’s value, contributions and withdrawals in the units of a chosen base period (any base will do; consistency matters more than the choice). The deflator is published — for India by the Ministry of Statistics’ CPI series and the RBI’s WPI series; for the eurozone by Eurostat’s HICP; for the United Kingdom by the Office for National Statistics; for the United States by the Bureau of Labor Statistics’ CPI-U. The investor builds a spreadsheet, fills it monthly, and looks at the real column first. The simple act of putting the real number above the nominal number in the page layout removes most of the bias most of the time.

Discipline two — express every long-term target in real terms, and refuse to translate. The investor who says “I want my portfolio to fund €48,000 of annual spending in today’s purchasing power for 30 years from retirement” has stated a target the brain can verify against the real account. The investor who says “I want a portfolio worth €2 million” has stated a target the brain can hit nominally while losing real ground. The first form is harder to formulate, harder to communicate to a spouse, and harder to celebrate when reached, precisely because it is honest. The second form is easy, comforting and frequently wrong. The discipline is to write the target down in the first form, share it with the household in that form, and refuse — including to oneself — to translate it into a nominal headline. Where a nominal headline is unavoidable for administrative purposes (a will, a contribution limit, a tax declaration) the real-terms equivalent is recorded alongside it in the same document.

Discipline three — pre-commit to inflation-linked allocations as a structural, not tactical, position. The instruments exist. US TIPS, UK index-linked gilts (in issue since 1981), French OATi and OAT€i, Italian BTP€i, German Bund€i, Brazilian NTN-B, Chilean UF-linked deposits, Indian Inflation-Indexed Bonds: each is a direct claim on real, rather than nominal, future cash flows. The discipline is to decide in advance, in writing, what fraction of the long-term portfolio will be held in inflation-linked instruments and to hold to that fraction across regimes. The fraction will be wrong much of the time — inflation-linked instruments under-perform nominal ones during disinflations and the gap can persist for years — but the structural commitment removes the bias-driven temptation to abandon real-terms protection precisely when it begins to be needed. The 1997 paper’s central finding, that nominal evaluation dominates real evaluation under cognitive load, is the reason such commitments must be pre-written rather than left to in-the-moment judgement.

How long-term equity practitioners have addressed it

Benjamin Graham treated the question in the chapter on “The Investor and Inflation” in the 1973 fourth edition of The Intelligent Investor. Graham acknowledged the natural impulse to look at the dollar amount of a portfolio and to be satisfied with growth in that amount, and then insisted on a real-terms view: the investor’s purchasing power, not the dollar count, was the proper test of investment success. Graham did not use the modern psychological vocabulary; he treated the bias as an obvious error of accounting that the disciplined investor would simply refuse to make. The chapter remains, half a century later, the most concise statement of the real-terms discipline in the practitioner literature.

Warren Buffett’s 1977 Fortune article was a more explicit attack on the same problem at the level of the security rather than the portfolio. The essay defines the “equity coupon” — the real return generated by the underlying businesses — and argues that during inflationary periods the equity coupon is squeezed from both sides: by the rising cost of the fixed assets the business must replace, and by the rising returns available on competing instruments. The piece’s specific empirical claim, that the long-run return on equity was anchored near 12% nominal regardless of inflation, has been debated since; the diagnostic frame has not. Buffett returned to the theme in shareholder letters throughout the late 1970s and again, briefly, in the inflationary episodes of 2021-2023, restating the point in the language of owner economics: only the real change in the value of the underlying businesses matters; the inflation-adjusted accounts are the only accounts that tell the truth.

Howard Marks’s December 2022 memo “Sea Change” provides the contemporary practitioner reading. Marks argued that the long disinflation of 1982-2021 had produced a generation of investors whose mental models, portfolio structures and risk frameworks were calibrated to a falling-rate, low-inflation world. The regime change, in Marks’s account, was not a tactical opportunity but a requirement to rebuild those mental models in real-terms language. Where the memo is read carefully, the prescription is identical to Graham’s: maintain the real account, set the targets in real terms, and accept that inflation-linked allocations are a structural rather than tactical commitment. Three generations of long-term investors, each writing in the vocabulary of their own era, have produced, on the question of money illusion, the same framework.

Key takeaways

• The bias is one of representation, not arithmetic. Shafir, Diamond and Tversky’s 1997 paper showed that the nominal frame dominates even when the real numbers are placed in front of the respondent. Education alone does not remove it.
• The instrument set that solves the problem exists. TIPS (US, 1997), index-linked gilts (UK, 1981), eurozone OATi/BTP€i/Bund€i, Indian IIBs and inflation-linked deposits in several emerging markets are all direct claims on real, not nominal, cash flows. Retail allocation to them remains small.
• The 1970s and 2021-2024 are the same lesson, half a century apart. Nominal accounts looked acceptable in both episodes; real purchasing power eroded materially. The repetition is the proof that knowledge alone does not dissolve money illusion.
• The defence is procedural. Three disciplines — keep two sets of books and report on the real one first, state long-term targets in real terms and refuse to translate, pre-commit to inflation-linked allocations as a structural position — do most of the work because they take the real-terms translation out of the moment of decision.
• Graham, Buffett and Marks converged on the same framework. Three generations of long-term equity practitioners, writing in different decades and different vocabularies, produced the same prescription. Money illusion is the bias that long-term equity investing was, in part, invented to defeat.

— Manish Goel, FCA / NorthPath Advisory OÜ / Tallinn, Estonia

Important. All content on this site and in this email is journalism and education for a general audience. Nothing here constitutes investment advice or a recommendation in respect of any specific financial instrument, nor an offer or solicitation to buy or sell any security. Readers should consult an authorised financial adviser regulated in their own jurisdiction before making any investment decision.

The NorthPath Letter  ·  Behavioural Finance  ·  Afternoon Edition

In 1894, in Following the Equator, Mark Twain wrote a sentence that a hundred and seven years later would become the title of an academic paper in Organization Science. “We should be careful to get out of an experience only the wisdom that is in it — and stop there; lest we be like the cat that sits down on a hot stove-lid. She will never sit down on a hot stove-lid again — and that is well; but also she will never sit down on a cold one any more.” Twain was making a literary observation. Jerker Denrell and James G. March, writing in 2001, turned it into a formal model of how an adaptive learner, sampling outcomes from a set of options whose true qualities are uncertain, can converge on a stable and systematically wrong belief about the world.

The argument is the single most important diagnostic any long-term equity investor can carry through a career, because it explains, more cleanly than any other behavioural construct, why the categories that an investor has been most recently burned by are precisely the categories the investor is most likely to misprice for the next decade. It is not a story about emotion. It is a story about information. And once the mechanism is understood, the discipline of counter-measure becomes a deliberate operational protocol rather than a feat of psychological self-overcoming.

The bias, defined

The hot-stove effect, as formalised by Denrell and March in “Adaptation as Information Restriction: The Hot Stove Effect” (Organization Science 12(5), 2001, pp. 523-538), is the proposition that adaptive learning — the simple rule of returning to options that have rewarded you and avoiding options that have punished you — produces, in the presence of any noise in outcomes, a systematic and permanent under-estimate of the quality of risky and novel alternatives. The mathematics is elementary. The implication is severe. An option’s estimated value, in the mind of an adaptive learner, is the running average of the outcomes the learner has sampled from it. The learner’s sampling rule is to continue drawing from options whose running average is high and to stop drawing from options whose running average is low. The crucial observation is that the running average for any high-variance option will, sooner or later, dip below the threshold — not because the true mean of the option is low, but because the realised draws from a noisy distribution will occasionally include a string of bad outcomes. Once the running average dips below the threshold, the learner stops sampling. The estimate is then frozen at the punishing value forever. No further data arrives to correct it.

Denrell extended the construction in “Adaptive Learning and Risk Taking” (Psychological Review 114(1), 2007, pp. 177-187), showing that the same mechanism produces an apparent and durable preference for low-variance options over high-variance options of equal true mean — without invoking any utility-curvature explanation. The agent in the model is not risk-averse in the Bernoulli sense. The agent is simply a sampler whose sampling has been truncated by its own past actions. The output looks like risk aversion. The underlying cause is information restriction.

The mechanism

It is worth pausing on the asymmetry, because the asymmetry is the entire phenomenon. Consider an option whose true expected return is positive but whose realised returns include a left tail of painful losses. An investor allocates a starter position. The first draw is one of the left-tail outcomes. The investor’s estimate of the option’s quality is now anchored on a deeply negative number. The investor’s decision rule — one that no professional would find unreasonable, because it is the rule by which prudent capital is supposed to be allocated — is to reduce or eliminate exposure to options that have disappointed. The investor disengages. Crucially, the investor’s disengagement is not a temporary suspension. It is the silent termination of all future data collection on that option. Every subsequent outcome that the option would have produced — including the right-tail outcomes that would have corrected the estimate — never enters the investor’s record at all. The estimate is locked at the punishing draw forever, and the investor will defend the lock with the entirely sincere argument that the data, such as it is, supports the decision.

Compare this with the corresponding case of an option whose first draw happens to be a right-tail outcome. The investor adds to the position, samples more data, and the running average regresses to the true mean. The bias is not symmetric, because the sampling rule is not symmetric. Continued engagement provides more information; disengagement provides none. Across the full universe of options the investor considers, those that delivered a punishing first draw become permanently and systematically under-estimated. Those that delivered a rewarding first draw converge to their true means. The investor’s portfolio — and the investor’s expressed preferences for what to consider in future — is the equilibrium of this asymmetric learning. It is a portfolio shaped not by the world as it is, but by the world as the investor stopped looking at it.

The further implication, drawn out by Denrell across both papers, is that the bias is heavier where outcome variance is heavier. Low-variance options — bonds, money-market instruments, defensive consumer staples held over very long periods — produce running averages close to their true means after very few draws, and the investor’s estimate is reasonably well-calibrated. High-variance options — cyclical equity, deep value, emerging markets, smaller-capitalisation securities, recovering sectors after a credit event — produce running averages whose first few draws can be far from the true mean, and these are precisely the categories most likely to be discarded after a single bad season and never re-sampled. The hot-stove effect is, in this sense, a structural reason why an entire class of investors will under-own precisely the categories that academic finance suggests offer the deepest long-horizon premia. It is not a story about courage. It is a story about who stops counting and when.

The empirical record

The cleanest large-sample evidence comes from regulators who have, in the years since the global financial crisis, methodically measured how retail households allocate when they have the resources to invest but the experience to remember being burned. The United Kingdom’s Financial Conduct Authority Financial Lives 2024 Survey, conducted between February and June 2024 with 17,950 respondents, documents that sixty-one per cent of adults with more than ten thousand pounds in investible assets hold at least three-quarters of those assets in cash rather than in any form of invested instrument. The same survey records that twenty-four per cent of all United Kingdom adults — some 13.1 million people — had low financial resilience as of May 2024. The FCA reports the headline in the language of cash hoarding and product hesitation. The hot-stove reading is sharper: a cohort of households, having lived through the 2008 financial crisis, the 2011 European sovereign episode, the 2020 pandemic drawdown and the 2022 gilt convulsion, has effectively withdrawn from the equity sampling exercise altogether. Their estimate of the long-horizon premium is locked at whatever the worst of those episodes told them. The locked estimate has not been corrected by the 2009-2021 equity recovery for the simple reason that they were not in the market to receive it.

The complementary anchor is the United States Federal Reserve’s Survey of Consumer Finances, whose 1989-2022 triennial waves provide the longest comparable cross-section of household balance sheets in the world. The 2022 wave, published in October 2023, records that the equity participation rate among households headed by adults under thirty-five recovered only gradually after the 2008 crisis: in 2010 the participation rate for that cohort stood near a multi-decade low, and although the 2022 wave shows a meaningful rebound — with twenty-two per cent of Millennial and older-Gen-Z households reporting direct stock ownership and a further nine per cent holding pooled funds — this recovery took fourteen years, during which the broad United States equity market roughly quadrupled on a total-return basis. A generation of households, hot-stoved by the 2008 draw, were absent for most of the right-tail draws that followed. The cost of that absence, measured in lifetime wealth, is the most expensive consequence of a single bias in the long history of household finance.

Two historical episodes

Japan, 1989 to 2020. The Nikkei 225 peaked at 38,915.87 on the final trading day of December 1989. Over the next two decades and a half, the index drifted, crashed, briefly recovered, crashed again and stayed below the 1989 high until 22 February 2024 — a period of more than thirty-four years. The behavioural consequence for Japanese households was the closest pure-form expression of the hot-stove effect ever recorded. Bank of Japan Flow of Funds data show that household financial assets shifted decisively into cash and cash-equivalents from 1990 onward: by 2010 roughly fifty-four per cent of household financial assets were held as cash and bank deposits, against approximately ten per cent in equity and investment trusts combined, a ratio that did not materially shift until the long-running Suga-Kishida policy push to encourage equity participation through the NISA tax-advantaged account framework. The cat had sat on the stove in 1989, and three decades of nominal cold draws were not enough to coax it back. Japanese households were not irrational. They were sampling the data the world had given them, and the data, restricted by the sampling rule itself, never produced a draw large enough to overwhelm 1989. The lesson is not that Japanese households should have ignored the 1989 outcome. It is that, once a single outcome had been treated as an estimate of the equilibrium mean, no information short of total re-sampling could dislodge it.

The United States retail investor, 2000 to 2013. The dot-com correction that began in March 2000 and concluded around October 2002 erased roughly seventy-eight per cent of the Nasdaq Composite’s value at its trough. The 2008 financial crisis, beginning the long retracement in October 2007 and concluding in March 2009, took the S&P 500 down by approximately fifty-seven per cent peak-to-trough on a total-return basis. Investment Company Institute data on long-term mutual-fund flows show that net flows into United States equity mutual funds were essentially zero or negative every year from 2008 through 2012, even as the index recovered all of its losses by 2013. Retail investors who lived through the 2000 and 2008 draws had hot-stoved out of equity. Households who took the 2009 right-tail draw — those who held positions through the trough — saw the running average corrected. Households who disengaged in 2008 took no further draws and saw nothing corrected. The Morningstar “Mind the Gap” series, which has measured the investor-return gap each year since 2005, has consistently found that retail-investor cash-flow-weighted returns lag fund time-weighted returns by between one and two per cent per annum, with the largest gap concentrated in the most-volatile fund categories. The gap is not a story about manager selection. It is the hot-stove effect, measured in basis points.

The counter-measure framework

The first task in counter-acting a bias whose mechanism is asymmetric sampling is to construct a sampling rule that does not allow disengagement to be the default response to a punishing draw. Three operational disciplines, in combination, accomplish this without requiring the investor to abandon the broader prudential framework of position sizing and risk control.

The pre-committed re-sampling protocol. Before a position is taken, the investor specifies, in writing, the conditions under which the position will be re-sampled following a loss. The conditions are stated in terms of objective triggers — a defined calendar interval, a defined drawdown level, a defined change in the fundamental thesis — rather than in terms of post-loss conviction, which the hot-stove mechanism reliably corrupts. The point of the pre-commitment is to ensure that the second draw is taken at all. The size of the re-sample need not match the size of the original position; in many cases a re-sample at twenty to thirty per cent of the original allocation will produce sufficient new information to update the estimate without exposing the investor to a position that may, on the second look, still be unattractive. The discipline is not “buy more after a loss.” The discipline is “look again after a loss,” with size and timing pre-specified.

The decoupling of the security-specific lesson from the category lesson. A punishing draw on a single security carries two distinct pieces of information: information about the security and information about the category to which the security belongs. The hot-stove mechanism conflates the two, because the investor’s sampling rule is applied at the category level — “I have been burned by emerging markets,” “I have been burned by Indian small-caps,” “I have been burned by biotech” — rather than at the security level. The discipline is to write down, before the position is taken, what the position is being held to learn about. If the thesis is security-specific, the loss carries no category information at all and must not be allowed to terminate sampling at the category level. If the thesis is category-level, the loss carries security-specific noise that must not be allowed to dominate the category estimate. The bookkeeping is unglamorous but it is the only available defence against the asymmetric translation of one draw into a permanent verdict on an entire universe.

The shadow-position register. For every category from which the investor has disengaged, the investor maintains a shadow allocation — a notional position, marked to market, that records what the disengaged category has subsequently done. The shadow allocation is not a trade; it is a sampling instrument. Its purpose is to ensure that the data the disengaged category produces is collected even when the actual portfolio has stopped collecting it. At a quarterly or annual review, the shadow register is examined against the live portfolio, and any category whose shadow has materially outperformed the realised allocation is flagged for re-sampling review under the protocol described above. The shadow register is the only mechanism by which an investor can reliably know that the cat has been sitting on a cold stove for some time, and that the discomfort of returning to it is no longer informational but reflexive.

How long-term-equity practitioners addressed the bias

Sir John Templeton built one of the longest sustained outperformance records in twentieth-century equity investing by operationalising, decades before it had an academic name, a refusal to allow a punishing draw to terminate his sampling of a category. His most celebrated single decision — the purchase of small parcels of one hundred and four United States equities trading below one dollar per share in 1939, financed with borrowed money on the day Germany invaded Poland — was a deliberate re-sampling of a category that the broader investor base had hot-stoved out of after the 1929-1932 collapse and the long depression that followed. Equally instructive is Templeton’s 1962 entry into Japanese equity, at a time when most Western capital had concluded that Japan was a country whose post-war recovery had stalled. Templeton wrote, in correspondence later collected by his successor at Templeton Funds, that the discipline was not to seek out hated assets for their hatedness; it was to ensure that no asset class was excluded from the consideration set by virtue of having most recently disappointed. The point of the discipline of “buying at the point of maximum pessimism,” in Templeton’s words, was not contrarianism. It was the construction of a sampling rule that did not permit disengagement to be permanent.

Howard Marks, in three decades of Oaktree Capital Management memos to clients, has returned repeatedly to the asymmetry that Denrell and March formalised. The 2008 memo “The Limits to Negativism,” written in October of that year as distressed credit prices made forced-selling histories at multiples of recovery value, opens with the observation that the same intelligence that allows an investor to identify legitimate reasons for caution can, when extended without limit, prevent the investor from ever re-engaging with categories that have priced in those reasons. The 2012 memo “What’s Behind the Downturn?” reiterates the point. The 2020 memo “Latest Thinking,” written in the middle of the COVID-19 drawdown, makes it explicit: the question facing the investor in a punished category is not “is this asset dangerous?” but “is the price compensating me for the danger?” The discipline at Oaktree, as Marks has described it across two books and forty years of memo correspondence, is to require that any decision to remain disengaged from a category be re-justified on every cycle on the basis of current valuation, not on the basis of the most recent disappointment. The shadow-position register described above is, in effect, the Oaktree practice generalised to a single investor’s portfolio.

Key Takeaways

• The hot-stove effect is not a story about emotion or courage; it is a story about asymmetric sampling. Continued engagement with a category produces more information, and disengagement produces none. Estimates of categories from which the investor has disengaged after a punishing draw are systematically and permanently biased downward.
• The bias is heaviest where outcome variance is highest, which means the categories most likely to be misjudged are precisely the cyclical, recovering, and high-tail-premium categories that academic finance suggests offer the deepest long-horizon returns.
• Regulator data — the FCA Financial Lives 2024 Survey, the Federal Reserve Survey of Consumer Finances 1989-2022 series, and the Investment Company Institute fund-flow series — all document the same phenomenon at population scale: cohorts hot-stoved by a single drawdown remain underweight equity for a decade or more, missing the bulk of the subsequent recovery.
• The counter-measure is procedural, not psychological. Three disciplines — the pre-committed re-sampling protocol, the decoupling of security from category, and the shadow-position register — together ensure that the sampling rule does not allow disengagement to be the default response to a punishing draw.
• The defining practitioners of long-horizon equity — Sir John Templeton through six decades of cross-cycle category re-entry, and Howard Marks through forty years of memo correspondence on the asymmetric cost of sustained negativism — operationalised these disciplines decades before the academic literature gave the underlying mechanism its name. The architecture is available. The only requirement is the deliberate construction of a sampling rule that the investor’s own punishments are not permitted to terminate.

— Manish Goel, FCA / NorthPath Advisory OÜ / Tallinn, Estonia

Important. All content on this site and in this email is journalism and education for a general audience. Nothing here constitutes investment advice or a recommendation in respect of any specific financial instrument, nor an offer or solicitation to buy or sell any security. Readers should consult an authorised financial adviser regulated in their own jurisdiction before making any investment decision.

Behavioural Finance · Afternoon Edition · 27 May 2026

In December 1992, in a special issue of the Journal of Risk and Uncertainty, Amos Tversky and Daniel Kahneman published a thirty-page paper titled “Advances in Prospect Theory: Cumulative Representation of Uncertainty.” It revised the 1979 paper that had launched prospect theory and replaced the original probability-weighting machinery with a rank-dependent one that worked for arbitrary outcomes. Buried inside the algebra was an empirical claim with consequences for every long-term investor. Tversky and Kahneman called it the fourfold pattern. Risk attitudes, they showed, are not a property of a person. They are a property of the cell in which the person is standing. Move from a low-probability gain to a high-probability gain — or from gains to losses — and the same person flips from risk-seeking to risk-averse, or back again, without noticing. The mechanism behind the flip is a non-linear function called the probability weighting curve, which over-weights small probabilities and under-weights large ones. The investor who does not see the function distorting his judgement will mis-price both tails of the distribution he is trying to invest in.

The bias and its canonical citation

Prospect theory began life as a 1979 Econometrica paper that argued people evaluate gambles relative to a reference point, are loss-averse around that reference point, and treat probabilities not as objective likelihoods but as weights that are systematically distorted. The 1992 paper sharpened the third claim. Cumulative prospect theory introduced a rank-dependent weighting function that can be calibrated from experimental data. Across thousands of choices, Tversky and Kahneman estimated a median weighting function with two properties. First, w(p) is greater than p for small probabilities — a one-percent chance is treated as if it were closer to seven percent. Second, w(p) is less than p for moderate-to-large probabilities — a ninety-nine-percent chance is treated as if it were closer to ninety-four. The function is concave near zero, convex near one, and shaped like an inverted S. The four corners of the resulting decision space — small probability gain, small probability loss, large probability gain, large probability loss — produce four different risk attitudes. Risk-seeking for small-probability gains. Risk-averse for small-probability losses. Risk-averse for large-probability gains. Risk-seeking for large-probability losses. That is the fourfold pattern.

The mechanism behind the curve

The cognitive architecture that produces the weighting function has been studied for thirty years. Three forces combine. The first is diminishing sensitivity: the psychological difference between zero and one percent feels larger than the difference between forty and forty-one percent, which feels larger in turn than the difference between ninety-nine and one hundred percent. The endpoints of the probability scale carry a disproportionate weight. The second is category boundary effects: probabilities near zero are mentally categorised as “possible” rather than “impossible,” and the jump from impossible to possible feels qualitatively larger than the slope of the line would suggest. The same is true at the top of the scale, where the jump from “very likely” to “certain” feels qualitatively larger than the underlying gap. Kahneman labelled this last force the certainty effect in the 1979 paper. The third force is limited mental resolution: the mind handles “small,” “medium,” and “large” well, but it does not handle fine gradations of small. A one-in-ten-thousand event and a one-in-a-million event collapse into the same emotional category, which is why a single vivid news story can persuade a person that an event with a six-decimal-place probability is now imminent.

The combination produces the inverted-S shape. The weighting function is steepest near the endpoints and flattest in the middle. In the middle of the curve — roughly the band from twenty to seventy percent — objective and subjective probability are tolerably close. Outside that band, distortion sets in. And because long-term equity investing routinely asks investors to assess events that live in the tails — the chance a small biotech reaches commercialisation, the chance a turnaround actually turns, the chance a leveraged firm survives a recession, the chance a once-in-a-decade dislocation arrives this decade — the investor spends most of his time in the part of the curve where the function is most distorted.

The curve also explains a second puzzle that classical finance struggles with. Why are people willing to pay both for a lottery ticket and for an insurance premium against the same expected value? The expected-utility framework requires that a person be either risk-seeking or risk-averse, not both at once. The fourfold pattern dissolves the puzzle. The same individual, looking at a small-probability gain, is risk-seeking and buys the ticket; looking at a small-probability loss, is risk-averse and buys the policy. The lottery counter and the insurance counter live in the same shopping mall because the curve has the same shape on both sides of the reference point. For the equity investor the practical implication is that any thesis written entirely in the language of upside — “the probability the technology works,” “the probability the addressable market is as forecast” — will be distorted upward, and any thesis written entirely in the language of downside — “the probability the regulator forces a recall,” “the probability the litigation produces an adverse ruling” — will be distorted in the opposite direction. The cure is to write the same thesis from both ends, and to compare the two probability statements line by line.

The empirical record

Three families of evidence have accumulated. The first is laboratory. Camerer’s 1995 Handbook of Experimental Economics chapter reviewed several hundred replications of the original Tversky and Kahneman experiments. The fourfold pattern is one of the most robust findings in the entire experimental literature; the median weighting function recovered from a fresh sample of laboratory subjects in any decade since 1992 sits within a narrow envelope of the original curve. The Prelec single-parameter form, with α near 0.65, fits both Tversky and Kahneman’s median data and most subsequent re-estimations.

The second family is field. Alok Kumar’s 2009 Journal of Finance paper “Who Gambles in the Stock Market?” tracked 70,000 retail brokerage accounts and showed that holders of low-priced, high-volatility, positively-skewed stocks — the precise profile predicted by the probability weighting function as attractive to lottery-loving investors — under-performed matched peers by two to three percentage points a year. Barberis and Huang, in a 2008 American Economic Review paper titled “Stocks as Lotteries,” built an equilibrium asset pricing model around the same prediction and showed that lottery-like stocks are systematically over-priced relative to a CAPM benchmark, with a measurable negative excess return. The probability weighting function is now a standard cross-sectional pricing factor in academic finance.

The third family is regulatory. The European Securities and Markets Authority published a product intervention package in March 2018 that banned the marketing, distribution and sale of binary options to retail investors and imposed leverage and margin-close-out limits on contracts for difference. The supporting national competent authority analyses, summarised in the published rationale, showed that between seventy-four and eighty-nine percent of retail accounts trading these products lost money, with average losses per client running from sixteen-hundred euros to twenty-nine-thousand euros. ESMA’s public language was careful and clinical: the products had “structural expected negative return,” the documented retail outcomes were inconsistent with rational pricing, and the “disparity between expected return and risk of loss” warranted permanent intervention. The Financial Conduct Authority in the United Kingdom reached an adjacent conclusion in 2020. In policy statement PS20/10, the FCA prohibited from January 2021 the sale of cryptoasset derivatives and exchange-traded notes to retail clients, citing “extreme volatility,” absence of a reliable valuation basis, and the conclusion that retail consumers could not reliably assess the value and risks of the products. Two regulators, two jurisdictions, the same empirical signature: retail demand was concentrated in instruments whose return profile is structurally attractive to a probability-weighted decision-maker and structurally unattractive to an expected-value one.

Two historical episodes

The first episode is the 2020-2021 special-purpose acquisition company cycle in the United States. More than six hundred SPACs were listed in 2020 and 2021, raising roughly one hundred sixty billion dollars. The pitch — a blank-cheque vehicle that would, with some probability, merge with a high-growth private business at a favourable valuation — was a paradigmatic small-probability, large-payoff proposition. The fourfold pattern’s top-left quadrant predicts exactly that kind of demand: retail investors should bid for the SPAC warrants and post-merger shares at prices that over-weight the probability of a transformative deal. The data, once enough mergers had closed to permit measurement, vindicated the prediction. A 2023 review in the Yale Journal on Regulation reported that the 2021 cohort of de-SPAC mergers had lost an average of sixty-seven percent of their value from the de-SPAC price, the 2022 cohort fifty-nine percent, and the combined post-merger excess return relative to the Nasdaq was negative forty-four percent. The cycle was the most expensive demonstration in modern markets of the difference between objective probability and decision weight on low-frequency, high-skew outcomes.

The second episode is older and structurally similar. Between the late 1990s and 2000, retail investors in the United States poured capital into low-priced internet stocks with negative or non-existent earnings and option-like return profiles. Many of these were micro-cap names with valuations supported entirely by extrapolated traffic metrics. Kumar’s subsequent academic work treated this cohort as a natural experiment in lottery-stock demand, and showed that the cross-section of underperformance among individual investors was concentrated almost entirely in this segment. The 1998-2000 episode and the 2020-2021 episode differ in vintage and technology, but the cognitive signature is the same. Both cycles concentrated retail capital in the precise corner of the outcome space where the probability weighting function is most distorted, and in both cycles the realised long-run return was deeply negative.

A third regulatory data point is worth recording because it covers a different region and a different instrument. The French Autorité des Marchés Financiers published in 2014 a study of 14,799 active retail accounts trading forex and binary options on French-licensed platforms between 2009 and 2012. The headline finding was that eighty-nine percent of these accounts lost money over the four-year window, with cumulative net losses of approximately one hundred seventy-five million euros against gross winnings of thirteen million for the top decile. The AMF’s subsequent advertising restrictions, followed by ESMA’s 2018 pan-EU intervention, were the response. Three jurisdictions, two continents counting the United States Commodity Futures Trading Commission’s parallel binary-options enforcement actions, and the same empirical curve. The bias is not a feature of any particular market or generation; it is a property of the species.

The counter-measure framework: three concrete disciplines

The fourfold pattern does not yield to wishful thinking. Tversky himself observed that decades of laboratory exposure to the bias did not eliminate it in his own subjects, including those who had taught the original papers. What it yields to is procedural pre-commitment — rules that operate before the distorting machinery has a chance to engage. Three disciplines have an evidence base.

Discipline one: decompose every thesis into base rate times payoff. Before sizing any position whose investment case turns on a low-probability, high-magnitude outcome, the investor writes down two numbers. The first is the probability the thesis implicitly assigns to the favourable scenario. The second is the closest available base-rate from the academic or industry literature. The gap between the two is the investor’s personal probability weighting distortion. If the gap is wide and the investor cannot defend it in writing with case-specific evidence, the position has not yet passed the first gate. This is the discipline behind Howard Marks’s “second-level thinking” and behind Annie Duke’s “thinking in bets” framework, both of which trace, in their methodological roots, to the cumulative prospect theory literature.

Discipline two: cap the tail-bet line item. Even when a low-probability, high-payoff thesis is well-defended, the appropriate position size is small. The asymmetry of the weighting function means that the investor will, on average, over-pay for such a position; capping it bounds the cost of being wrong. A defensible ceiling for tail-bet positions in aggregate — the sum of all positions whose investment case depends on a low-probability event — is in the two-to-three percent range of the portfolio. The cap is not a forecast of how often the thesis will pay; it is a recognition that the human brain pricing the bet will be working with a distorted weighting function, and that prudent sizing must include a haircut for that distortion.

Discipline three: pre-write the kill criteria. The bottom-right cell of the fourfold pattern — risk-seeking under a high-probability of loss — is the one that converts a manageable mistake into a permanent loss. Once a position has moved significantly against the thesis, the same weighting function that overpaid for it on entry now generates risk-seeking behaviour: the investor doubles down, holds longer, refuses to crystallise the loss. The only known antidote is to write down, ex ante, the quantified evidence that would falsify the thesis — the milestone missed, the metric breached, the audit qualification issued, the legal claim filed — and to act mechanically when that evidence arrives. Investors who pre-commit in writing exit losing theses three to six months earlier than investors who do not, with a measurable saving of compounded capital.

How long-term-equity practitioners addressed it

Two practitioners are useful anchors. The first is Howard Marks, co-founder of Oaktree Capital. Marks has written for thirty years about asymmetric risk-reward, and his memos return repeatedly to a single discipline: ask what can go wrong before asking what can go right. Marks’s “I-know school” versus “I-don’t-know school” framing, articulated in the 2004 memo of the same title, is operationally a probability weighting counter-measure. The “I-know” investor sees a low-probability favourable scenario, weights it as if it were medium-probability, and pays the corresponding price. The “I-don’t-know” investor treats the same scenario as low-probability because the base rate says so, and pays a price that admits the wide error band. Oaktree’s long history of distressed-debt outcomes is, at the cognitive level, a long history of refusing to pay a probability-weighted price for a tail outcome.

The second is Warren Buffett, whose insurance operations at Berkshire Hathaway constitute the most studied counter-measure in modern long-term equity investing. Berkshire’s reinsurance business, run for thirty-five years by Ajit Jain, prices catastrophe risk — large losses that occur with low probability — and is profitable on a cumulative basis precisely because it refuses to write business at prices that imply over-weighted small probabilities. Buffett’s annual letters describe the discipline plainly: the underwriter must price what the long-run loss distribution will deliver, not what the present demand curve says the market will pay. Berkshire has walked away from significant blocks of premium when, in Buffett’s phrase, “the price was too low for the risk.” The same logic governs the operating businesses. Berkshire avoids capital-intensive growth bets whose narrative case rests on a low-probability winner-takes-all outcome and concentrates instead on businesses with durable economics and a defensible base rate of survival. The portfolio is built quadrant by quadrant of the fourfold pattern, not against it.

Key takeaways

The fourfold pattern is a feature of the human cognitive system, not a flaw of the individual investor. Probability weighting is non-linear in everyone studied. Decades of teaching the bias do not eliminate it. The only working defence is procedural.

The function distorts both tails of the distribution. Lottery-stock demand and over-priced insurance both follow from the same curve. An investor who has corrected for the lottery side without correcting for the certainty-effect side has done half the work.

Regulators have already documented the bias at scale. ESMA’s 2018 binary-options and CFD intervention and the FCA’s 2020 crypto-derivative ban are, methodologically, large-sample empirical studies of probability weighting in retail markets. Their findings should be read as evidence about the strength of the bias, not as quirks of a few products.

The discipline is base rate first, payoff second, sizing third, kill criteria fourth. All four are written down ex ante. The investor who skips any of the four is leaving the weighting function unsupervised.

Long-term equity outcomes are dominated by the avoidance of the bottom-right quadrant. The risk-seeking response to a high-probability of loss — doubling down, refusing to sell — is what converts a recoverable mistake into a permanent capital impairment. The pre-written kill criterion is the single most underused tool in the long-term investor’s kit.

— Manish Goel, FCA / NorthPath Advisory OÜ / Tallinn, Estonia

Important. All content on this site and in this email is journalism and education for a general audience. Nothing here constitutes investment advice or a recommendation in respect of any specific financial instrument, nor an offer or solicitation to buy or sell any security. Readers should consult an authorised financial adviser regulated in their own jurisdiction before making any investment decision.

NorthPath Advisory  ·  The NorthPath Letter  ·  Behavioural Finance  ·  Afternoon Edition

In March 2000, with the NASDAQ Composite at 5,048, the American Association of Individual Investors’ weekly survey recorded a six-month bullish reading of 75 percent — a record at the time. In March 2009, with the same index at 1,269 and having lost three-quarters of its value over the preceding nine years, the same survey recorded a bearish reading of 70 percent. The first reading came at a market top; the second at a market bottom. Neither was wrong about the recent past. Both were precisely wrong about the future. This is not a coincidence. It is the most reliably documented finding in the empirical literature on investor expectations, and it has a name: extrapolative expectations.

The argument of this essay is that long-term equity investors who do not understand how their own brain forecasts returns will, with reliable regularity, contribute capital at the worst times and withdraw it at the worst times. The mechanism is older than markets and shared across every domain in which humans must predict an uncertain future from a finite past. The cost, when applied to a multi-decade investment horizon, is measurable, persistent, and large. And the defences — three of them, none of them new — are available to anyone willing to write down what they expect before the market tells them what to expect.

1. The bias: what extrapolative expectations are, and where the idea came from

An expectation is extrapolative when the forecast of the next period is formed by projecting the trend of the past few periods forward. The phrase predates behavioural finance by a century; Alfred Marshall used it in the 1890s to describe how merchants estimated demand. What is specific to the modern behavioural finance literature is the empirical claim that investor expectations of stock-market returns are extrapolative in a strong sense — that they rise after a market has risen and fall after a market has fallen — and that this pattern is sharp enough to dominate the more deliberate, model-based forecasts that any disciplined valuation framework would generate.

The canonical citation is Robin Greenwood and Andrei Shleifer, “Expectations of Returns and Expected Returns,” Review of Financial Studies, vol. 27, no. 3 (March 2014), pp. 714–746. Greenwood and Shleifer assembled six independent survey datasets covering the period 1963 to 2011: the Gallup investor survey, the AAII weekly sentiment poll, the Graham–Harvey CFO survey, Investor Intelligence, Robert Shiller’s individual-investor survey, and the University of Michigan consumer survey. The six measures were positively correlated with one another and with the level of the stock market, and were strongly positively correlated with returns over the trailing twelve months. They were, however, negatively correlated with the model-based expected returns implied by every standard valuation framework — dividend yield, earnings yield, the cyclically adjusted price–earnings ratio. In plain language: when objective measures said future returns would be low, investors expected them to be high; when objective measures said future returns would be high, investors expected them to be low.

The result is not a curiosity of one survey. It survives across countries, across investor types, and across cycle. A related literature finds that the same pattern holds for chief financial officers in the Graham–Harvey survey (Itzhak Ben-David, John Graham, Campbell Harvey, “Managerial Miscalibration,” Quarterly Journal of Economics, 2013) and for institutional fund managers (Nicholas Barberis, Robin Greenwood, Lawrence Jin, Andrei Shleifer, “Extrapolation and Bubbles,” Journal of Financial Economics, 2018). The earlier intellectual ancestor is Werner De Bondt’s 1993 paper “Betting on Trends,” International Journal of Forecasting, which demonstrated that even financial professionals predicted that past trends would continue.

2. The mechanism: why the brain extrapolates

The cognitive machinery underneath extrapolative expectations is the same machinery Daniel Kahneman and Amos Tversky identified in 1972 under the label of the representativeness heuristic. When asked to forecast an uncertain quantity, the mind does not solve the prediction problem from first principles. It searches recent memory for a similar pattern and treats the most available match as representative of the underlying distribution. Recent returns are the most available pattern; they are also, by definition, the easiest to retrieve. The output of this process is a forecast that looks like the recent past with the trend extended.

Three properties of this process matter for investing. First, the heuristic is unconscious and fast. It runs before any deliberate valuation work begins, and it sets the frame within which the deliberate work then takes place. A valuation that disagrees with the extrapolated forecast is registered, at the level of the brain that decides what feels right, as the work of a contrarian — and contrarian conclusions require additional energy to hold. Second, the heuristic ignores base rates. The historical fact that high price–earnings ratios have, on a forward ten-year basis, been associated with lower subsequent returns is a base-rate fact, and base rates lose to vivid recent narratives in almost every laboratory study ever conducted. Third, the heuristic generates over-confidence in the forecast it produces. Subjects in calibration experiments report higher subjective confidence in extrapolative forecasts than in the corresponding base-rate forecasts, even when the latter are more accurate.

The 2018 Barberis–Greenwood–Jin–Shleifer model formalises this. They build a two-agent market with fundamentalists who price on cash flows and extrapolators who price on momentum. The extrapolators’ demand is not perverse; it is rational given their belief that recent trends will persist. The model reproduces the empirical regularities of bubbles — large run-ups, sharp peaks, slow declines — without assuming any agent is irrational in the strong sense. The bias is in the belief-formation process, not in the trading rule applied to those beliefs. This is the precise diagnosis a long-term investor needs: the failure mode is upstream of the spreadsheet.

3. The empirical record: what the bias costs

The most widely cited measurement of the cost of extrapolative expectations is the gap between published fund returns and the dollar-weighted returns actually earned by investors in those funds. Morningstar publishes this gap annually under the title “Mind the Gap.” The 2025 edition, covering the ten years ended December 2024, found that the average dollar invested in US mutual funds and exchange-traded funds earned 1.1 percentage points less per year than the funds themselves earned — equivalent to approximately 15 percent of total return surrendered to timing alone. The gap was wider for sector equity funds (150 basis points) than for allocation funds (10 basis points), which is consistent with the bias: the more volatile the category and the more vivid the recent trend, the larger the extrapolation error.

The complementary measurement is DALBAR’s Quantitative Analysis of Investor Behavior. The 2025 QAIB report found that in 2024 the average equity-fund investor earned 16.54 percent against the S&P 500 total return of 25.05 percent — a gap of 848 basis points, the second-widest in a decade. Withdrawals from equity funds occurred in every quarter of 2024, with the largest outflows preceding the strongest rebounds. DALBAR’s “Guess Right Ratio,” the proportion of months in which an investor’s net flow direction matched the market’s subsequent direction, fell to 25 percent — statistically indistinguishable from a coin toss biased against the investor.

The two regulator anchors needed for any responsible treatment of this material illustrate how different jurisdictions have intervened in different ways. In India, the Securities and Exchange Board of India requires every mutual fund advertisement that quotes performance to display, in the principal body of the communication, a warning that past performance is no guarantee of future results, and prohibits the use of performance ranking unless accompanied by methodology and the universe from which the ranking was drawn. The relevant master circular consolidates the requirements first issued in 1996 and tightened in 2018 and again in 2024. In the European Union, the Packaged Retail and Insurance-based Investment Products regulation (Regulation 1286/2014) required the introduction of the Key Information Document, and the European Securities and Markets Authority’s 2021 reform of the PRIIPs Regulatory Technical Standards (Commission Delegated Regulation 2021/2268, in force from 1 January 2023) moved the performance scenarios away from a purely past-data extrapolation toward a model-based set of stress scenarios precisely because the previous regime had been shown to systematically over-promise future returns in periods following a market rally. Both regimes are, at root, regulator responses to extrapolative expectations.

4. Two historical episodes

The first episode is the United States in 1998 to 2002. The NASDAQ Composite rose from 1,419 at the end of 1997 to 5,048 in March 2000 — a 256 percent gain over twenty-seven months — and then fell to 1,114 by October 2002. The fund-flow data, compiled by the Investment Company Institute, shows that net new cash to US equity mutual funds in calendar year 1999 was USD 187 billion, the largest in history to that point; in the first quarter of 2000 the run-rate accelerated. Net new cash in 2001 turned negative for the first time since 1988. The aggregate dollar that entered the market did so almost exactly at the index peak and departed near the trough. The retail investor who held the average diversified equity fund for the full period earned, on a dollar-weighted basis, an annualised return materially below the time-weighted return reported by the funds — a gap calculated at the time by Bogle Financial Markets Research Center as approximately 7 percentage points per year over the 1996–2002 window.

The second episode is India in 2017 to 2019. The S&P BSE SmallCap Index rose from 11,460 at the end of 2016 to 20,183 at the close of January 2018 — a 76 percent gain in thirteen months — driven in significant part by retail systematic investment plans into small- and mid-cap mutual funds. Net monthly mutual fund inflows from individual investors in India crossed INR 200 billion in January 2018 for the first time. The index then fell to 11,896 by February 2019, a 41 percent peak-to-trough decline, and to 9,123 by March 2020. Net monthly flows from individuals into equity schemes turned negative in mid-2019 and remained so for most of the next four quarters. The pattern is identical to the 1999–2002 American experience in every respect that matters: the peak of conviction coincided with the peak of price, the trough of conviction coincided with the trough of price, and the dollar-weighted return of the average participant fell short of the time-weighted return of the underlying funds by a margin that was, on a five-year measurement window, several hundred basis points per year.

5. The counter-measure framework: three concrete disciplines

The corrective disciplines all share a single design property: they force a forecast to be committed to before the market provides the answer, so that the extrapolative impulse can be observed and discounted rather than acted upon.

Discipline one — the pre-commitment forecast. Before any purchase, write down a one-page forecast covering five elements: the expected revenue growth rate for the next five years; the expected operating margin at the end of that period; the expected return on incremental invested capital; the multiple at which the business is expected to trade at the end of the holding period; and the implied annualised return at the current price. The discipline is to date the document, file it, and review it on a fixed annual schedule against the realised path. Holdings that have benefited from the realised path are not to be celebrated; the question is whether the original forecast assumptions still apply or whether the price has merely extrapolated the recent trend. This discipline is associated with the formal investment-process literature; the cleanest exposition is in Howard Marks’s The Most Important Thing (Columbia Business School Publishing, 2011), chapter 12.

Discipline two — the base-rate file. For every category of forecast — revenue growth above twenty percent, return on capital above thirty percent, gross margin expansion across a five-year window — maintain a written record of the empirical base rate from comparable historical situations. Michael Mauboussin has documented these base rates extensively in his Credit Suisse Global Financial Strategies reports, in particular “The Base Rate Book” (September 2016). The discipline is to compare any internal forecast against the documented base rate before submitting it. A forecast more than one standard deviation above the base rate is not necessarily wrong, but the burden of evidence required to justify it must be commensurate with the gap. Extrapolative expectations are precisely the engine that produces forecasts well above base rates; the file is the friction that slows the production.

Discipline three — the asymmetric review. Schedule the longer and more rigorous of the two annual portfolio reviews for the period after a market or sector decline, not after a rally. This inverts the natural human tendency to scrutinise positions when they are causing pain and to leave them alone when they are providing pleasure. The mechanism it defeats is the asymmetry of attention that extrapolation creates: a position that has compounded at twenty-five percent for three years feels like a thesis confirmed, when it is at least as likely to be a thesis priced. The review need not be punitive; it need only be honest about whether the present price has begun to imply, by way of multiple expansion, a forward path that exceeds the base rate. The asymmetric review is the formalisation of John C. Bogle’s lifelong observation, repeated in Common Sense on Mutual Funds (Wiley, 1999), that “reversion to the mean is the iron rule of the financial markets.”

6. How two long-term-equity practitioners addressed it

John C. Bogle, the founder of the Vanguard Group and the author of Common Sense on Mutual Funds, treated extrapolative expectations as the principal antagonist of the retail investor and built an institution around the assumption that the antagonist could not be defeated by exhortation. His chosen instrument was the index fund — an investment vehicle that mechanically rebalances away from the most-extrapolated assets and toward the least-extrapolated assets, without requiring the investor to override any cognitive process. Bogle’s own writing was unambiguous about the diagnosis. In the 1999 edition of Common Sense on Mutual Funds, chapter four, he documents that the dollar-weighted return of the average US equity fund investor between 1984 and 1998 had been 7 percentage points per year below the time-weighted return of the funds themselves, and he attributes the gap directly to the chasing of recent performance. The remedy was structural: take the choice of when to act out of the investor’s hands.

Howard Marks, the founder of Oaktree Capital Management, addressed the same problem from the opposite direction, through the medium of the memo. Beginning in 1990 and continuing for thirty-five years, Marks’s quarterly memos to Oaktree’s clients have systematically described where, in the cycle of investor mood, the market sits at the time of writing. The memos do not contain forecasts of price levels or returns. They describe what Marks calls the “pendulum of investor sentiment” — the alternation between greed and fear, between extrapolation upward and extrapolation downward — and locate the present moment on that pendulum. Marks’s 2018 book Mastering the Market Cycle (Houghton Mifflin Harcourt) collects the analytical framework that the memos rehearse. The discipline is not to predict the turn but to know where in the cycle one is standing, so that the natural extrapolative impulse can be inspected before it is acted on. Both practitioners arrived at the same conclusion: extrapolative expectations cannot be reasoned away, but they can be structurally limited or behaviourally observed.

7. Key Takeaways

• Investor expectations of future stock-market returns are systematically extrapolative. Six independent survey datasets covering 1963–2011 show that expectations rise after the market rises and fall after the market falls, and are negatively correlated with model-based expected returns (Greenwood & Shleifer, Review of Financial Studies, 2014).
• The measured cost of this bias is approximately 1 percentage point per year, or 15 percent of total return over a decade, per Morningstar’s “Mind the Gap” 2025 study. DALBAR’s 2025 QAIB report found an 848-basis-point gap in the single year 2024 alone.
• The bias is generated upstream of any spreadsheet. It is the output of the representativeness heuristic (Kahneman & Tversky, 1972) operating on recent returns, which means it cannot be corrected by improving valuation work alone.
• Three disciplines limit its operation: the dated pre-commitment forecast, the written base-rate file, and the asymmetric review that scrutinises winners more than losers.
• Two practitioner traditions illustrate the structural and the behavioural responses respectively: Bogle’s index-fund architecture removes the discretion that the bias would otherwise capture; Marks’s memos formalise the inspection of one’s own sentiment before any large allocation decision.

— Manish Goel, FCA / NorthPath Advisory OÜ / Tallinn, Estonia

Important. All content on this site and in this email is journalism and education for a general audience. Nothing here constitutes investment advice or a recommendation in respect of any specific financial instrument, nor an offer or solicitation to buy or sell any security. Readers should consult an authorised financial adviser regulated in their own jurisdiction before making any investment decision.

Behavioural Finance · The NorthPath Letter · Afternoon Edition

The Tyranny of the Vivid: Salience Theory and the Long-Term Investor

The most expensive question in equity investing is rarely what is the company worth? It is the prior question of what am I looking at? A portfolio is built from the small set of opportunities a mind chooses to weigh, and that set is never neutral. It is curated, in real time, by a perceptual system that was tuned over evolutionary time for survival in the savannah, not for the calm assessment of a discounted cash-flow model in a low-yield decade. Things that stand out get weighed. Things that are quiet get nothing. Long before a value judgement is rendered, an attention judgement has already happened, and it is usually invisible to the person who made it.

The cleanest formal account we have of this distortion is salience theory, the framework Pedro Bordalo, Nicola Gennaioli and Andrei Shleifer published in 2012 in the Quarterly Journal of Economics. Their paper, “Salience theory of choice under risk,” argued that decision-makers do not weigh probabilities and payoffs in the abstract; they weigh whichever payoff is contextually salient — whichever one stands out against the choice set in front of them. The same lottery, presented next to a different alternative, becomes a different lottery in the mind. The model has since been used to explain phenomena as varied as the equity-volatility risk premium, the cross-section of stock returns, the persistence of household under-diversification, and the appetite for IPO lottery tickets. For the long-term equity investor, it is one of the most operationally useful results behavioural economics has produced in the last twenty years, because it names a force that already governs the contents of every watchlist on every desk.

The Mechanism: How Attention Becomes a Distortion of Value

The architecture is simple enough to state. When a person faces a risky choice, the mind does not compute a full expected-utility calculation. Instead, it compares states of the world. Within that comparison, certain payoffs jump out — the unusually high upside in one option, the unusually low downside in another — and those payoffs receive disproportionate decision weight. Bordalo and his co-authors formalise this with a salience function that depends on the contrast between a payoff and the average payoff in the choice set. The bigger the contrast, the bigger the weight; the more ordinary the payoff, the more it is ignored. The result is a systematic deviation from rational choice in a predictable direction: the chooser overweights extreme states and underweights middle ones, which is also the empirical signature of the probability-weighting function from prospect theory, but now derived from a primitive about attention rather than imposed as an assumption.

Two consequences follow that matter for an equity investor. The first is that the same security is valued differently depending on which other securities the investor is looking at next to it. A reliable compounder in defensive consumer goods looks unremarkable beside a fashionable artificial-intelligence vendor whose left tail is implausibly heroic; the same compounder looks bracing and rare beside the wreckage of a credit-cycle peak. Neither comparison set changes the present value of the compounder’s cash flows. Both change the weight it gets in the mind.

The second consequence is that the financial press, the brokerage app and the social-media feed are all, by design, contrast machines. Headlines select for the unusual. Order books surface the most active tickers. Notification systems alert on price moves of a magnitude that, by construction, has just occurred. Every piece of plumbing that delivers information to the modern investor is calibrated to amplify the most salient states of the world and to suppress the ordinary ones. Salience theory predicts what then follows: portfolio composition that drifts, week by week, toward whichever names have most recently produced the loudest contrast, and away from whichever holdings have done the boring work of compounding without complaint.

It is worth pausing on the asymmetry of this distortion. The BGS framework predicts that salience can pull weight toward either tail — a vivid loss is as attention-grabbing as a vivid gain — but in equity markets the upside tail does the heavier work, because the investor’s information environment over-supplies stories about wins and under-supplies stories about quiet survival. There are dedicated rankings of the year’s best-performing funds, sectors and stocks; there is no comparably promoted ranking of the year’s most disciplined refusals to act. The contrast machine, in other words, is not symmetric, and the BGS-implied distortion in observed portfolio behaviour therefore runs predominantly in the direction of over-allocating to recent winners. This is the empirical regularity that the cross-sectional asset-pricing literature on salience has documented for two decades.

The Empirical Record: What Regulators and Academic Researchers Have Measured

The clearest tests of salience theory in asset markets have come from the cross-section of stock returns. Mathijs Cosemans and Rik Frehen, in a 2021 paper published in the Journal of Financial Economics, constructed a stock-level salience measure from the contrast between a stock’s daily returns and the cross-sectional distribution that day. Stocks with the most salient upside — the ones whose recent extreme positive days stood out most against their peers — subsequently underperformed by an economically meaningful margin, even after controlling for size, value, momentum, and exposure to maximum-daily-return effects. The pattern was robust across decades and across international markets. It is the asset-pricing fingerprint of the BGS mechanism: investors pay too much for the salient upside, and the over-payment is wrung out of the price over the following months.

Regulators have catalogued the same behaviour from a different angle. The U.S. Securities and Exchange Commission’s October 2021 staff report on the equity and options market events of January 2021 documented that retail trading concentrated in a small number of names whose price action was, by any reasonable measure, the most attention-grabbing in the market that month. The staff report emphasised that the concentration was not driven by changes in fundamentals; it was driven by the salience of the price moves themselves and by the social-media platforms that amplified them. In Europe, the European Securities and Markets Authority’s 2022 statistical report on retail investor protection arrived at the same place from independent data. It found that retail flows into individual equities tracked the prior week’s most-talked-about names with a tightness that fundamentals could not explain, and that the average retail position taken in the most salient names underperformed broad-market benchmarks over the subsequent year.

Two regulator anchors from two regions, then, deliver the same finding: when the market produces a contrast that is loud enough to be salient, retail capital chases it, and the capital that chases it does worse than the capital that does not. The mechanism described by Bordalo, Gennaioli and Shleifer is not a curiosity confined to laboratory lotteries. It is visible in the order-flow data of the world’s deepest equity markets.

Two Historical Episodes That Are Easier to See in Hindsight

Salience does its work most efficiently when the contrast it amplifies is genuinely large and genuinely new, because nothing else in the choice set offers a similar payoff to compare it against. Two episodes from the modern era illustrate the pattern with unusual clarity.

The first is the late-1990s technology boom. The relevant feature, for a salience-theory reading, is not that internet-era stocks went up. It is the way the most extreme winners crowded everything else out of the comparison set. By 1999, a handful of dot-com IPOs had produced first-day returns in the high triple digits. Those returns were vivid, public, and constantly reprinted. The next IPO in the queue was no longer being weighed against the long-run distribution of new-issue returns; it was being weighed against the immediate memory of those triple-digit pops. The salience function shifted decision weight onto the upper tail, and capital followed. Roger Lowenstein’s contemporaneous reporting and, later, the academic record — including the work of Jay Ritter on long-run IPO under-performance — document the predictable consequence: the average new issue of 1999 and 2000 went on to disappoint, often severely, in the subsequent five years. The capital that was placed on the salient upside was largely transferred to the underwriters and insiders who supplied that upside to the market.

The second episode is the January 2021 meme-stock event already alluded to. Here the contrast was not an IPO pop but the daily candle on the chart of a handful of heavily shorted small-capitalisation names. Within three weeks, one stock rose by more than fourteen-fold; the chart, frozen and reposted millions of times on social platforms, became the most salient single image in the U.S. equity market. The SEC’s subsequent staff analysis recorded that retail option volume in the names concerned briefly exceeded the option volume of every large-cap technology stock combined. The price subsequently retraced most of the move. The salience-theory reading of the episode is not that retail traders were fools; it is that the choice set they were facing had been re-engineered, by the platforms that delivered it, to make one set of payoffs jump out of the screen at the expense of every other comparison.

A Counter-Measure Framework: Three Disciplines for the Long-Term Investor

The discipline that protects against salience is not the discipline of being unmoved by vivid information. No human investor is unmoved by vivid information. The discipline is structural: it consists of choices about which choice sets are allowed in front of the eyes, which metric is used to rank items inside those sets, and which decision is allowed to be made on the basis of a single day’s observation. Three concrete practices, drawn from the literature and from the operating manuals of careful investment firms, follow from the BGS framework directly.

First, fix the comparison set in advance and refuse to enlarge it on impulse. A well-run long-term equity portfolio operates from an investible universe that has been pre-defined: a screen, a list of qualifying businesses, a sector mandate. The salience trap is sprung when an unscheduled name — one outside that universe — is allowed to enter the comparison purely because it has just produced a vivid price move. The discipline is to require that any new candidate clear the same fundamental gating as every existing candidate before its chart is permitted to influence the next decision. The mechanical version of this is a written investment policy statement that names the universe and forbids ad-hoc additions; the human version is a co-investor, partner or analyst with the standing to ask, in a meeting, why a name that did not exist on the watchlist a week ago is now being treated as urgent.

Second, rank candidates by a metric that is intrinsically slow. The salience signal is, by construction, a high-frequency one: it lives in the daily candle, the weekly news cycle, the rolling thirty-day return. Any ranking system that uses those inputs as primary will reproduce the BGS distortion mechanically. The defence is to elevate inputs that change slowly — ten-year average return on capital, multi-cycle free-cash-flow yield, decade-long revenue compounding, debt-to-equity over an interest-rate cycle — to the top of the ranking, and to permit short-horizon inputs only as tie-breakers. A spreadsheet that is sorted, by default, on a ten-year metric is harder to hijack than one sorted on yesterday’s move.

Third, separate the act of noticing from the act of deciding by an enforced delay. The behavioural-economics literature on what Walter Mischel called the cooling-off interval is consistent across domains: a salient impulse loses much of its decision weight if the decision is required to wait. A practical implementation, used by several long-tenured investment firms, is a written rule that no new position may be initiated within forty-eight hours of the news item, price move or social-media post that first put the name on the radar. The rule does not prevent the investor from researching; it prevents the salience-weighted decision from being executed before the salience has decayed. In firms that have adopted this discipline, the most common observation, repeated in internal post-mortems, is that the names that survive the forty-eight-hour pause are a small and notably different subset of the names that fail it.

None of these three disciplines requires the investor to suppress emotion, override intuition or pretend to a serenity the market does not allow. They simply re-engineer the choice architecture so that the BGS mechanism has less surface area to attack.

How the Long-Term Equity Tradition Has Addressed the Problem

The literature on salience is recent. The investment discipline that defeats it is not. At least two named practitioners in the long-term equity tradition have written and spoken, in their own vocabulary, about precisely the architecture this essay has just described.

Warren Buffett’s Berkshire Hathaway shareholder letters, taken in aggregate from 1977 onwards, contain a recurring insistence on a small, slow, pre-defined opportunity set. The discipline that produced the line about “the stock market is a device for transferring money from the impatient to the patient” is, in salience-theory language, the discipline of refusing to allow today’s contrast to determine tomorrow’s portfolio. Buffett’s repeated emphasis on the “circle of competence” performs the first counter-measure in this essay: it fixes the comparison set in advance, by sector and by business model, and resists the temptation to enlarge it on the basis of whatever has just been most vivid in the financial press. The 2021 Berkshire annual letter’s observation that Berkshire had “found it harder than usual to find things to do” in an expensive market is, read carefully, an admission that the firm had been holding the line on its comparison set despite considerable contrast pressure from the market around it.

Howard Marks of Oaktree Capital has written for two decades on what he calls “second-level thinking,” most fully in The Most Important Thing (Columbia Business School Publishing, 2011) and in the quarterly memos he has issued since 1990. The construct is, again, a defence against salience by another name. First-level thinking, in Marks’s vocabulary, takes the salient feature of the asset — the recent return, the popular narrative, the visible momentum — as the input to the decision. Second-level thinking insists that the investor go behind that surface to ask what the current price already discounts and what the market is therefore implicitly assuming about the future. The discipline is, in operational terms, an institutionalisation of the third counter-measure: a forced pause between noticing and deciding, populated by the asking of a different and slower set of questions. Marks’s 2007 memo “The Race to the Bottom” and his July 2020 memo “Coming Into Focus” are both examples of the method applied in real time to choice sets in which salience pressure was, at the moment, unusually high.

Two figures, one in Omaha and one in Los Angeles, have each spent careers operationalising what behavioural economists have now formalised. The investor who reads them carefully is being given, by people who paid the tuition for the lesson with their own capital, the user’s manual for the framework Bordalo, Gennaioli and Shleifer later wrote in equations.

A third figure deserves mention because his vocabulary is the most explicit of all. Charlie Munger’s 1995 address at Harvard Law School, “The Psychology of Human Misjudgement,” identifies what he calls “contrast-misreaction tendency” as one of the twenty-five standard cognitive biases that any serious operator of capital must learn to neutralise. The construct is, in plain English, salience theory in advance of the equations: the assertion that the mind’s evaluation of any item is heavily distorted by the items that happened to be presented immediately before it. Munger’s recommended counter-measure — the use of a checklist that imposes the same set of questions on every candidate, in the same order, regardless of the order in which the candidates happened to arrive on the desk — is, again, a structural defence rather than an emotional one. It survives because it operates on the choice architecture rather than on the chooser.

Key Takeaways

• Salience theory, as formalised by Bordalo, Gennaioli and Shleifer in 2012, predicts that decision weight is placed on payoffs that contrast most sharply with the choice set in which they are presented; the model has since been validated in the cross-section of stock returns and in regulator-collected retail-flow data.

• Modern information infrastructure — brokerage apps, news feeds, social platforms — is, by design, a contrast machine; it amplifies the very stimuli the BGS model says will distort an investor’s weighting most.

• The empirical record from two regulators in two regions, the U.S. SEC’s 2021 GameStop staff report and ESMA’s 2022 retail-investor statistical report, shows that retail capital follows the most salient names and, on average, underperforms the broad market over the subsequent year.

• Three disciplines defuse the trap without requiring emotional suppression: a pre-defined comparison set that resists ad-hoc additions; a ranking metric that is intrinsically slow, such as multi-cycle return on capital; and an enforced delay between the act of noticing and the act of deciding.

• The long-term equity tradition — Buffett’s circle of competence, Marks’s second-level thinking — encodes these defences in operating practice, and was doing so for decades before the academic vocabulary caught up.

— Manish Goel, FCA / NorthPath Advisory OÜ / Tallinn, Estonia

Important. All content on this site and in this email is journalism and education for a general audience. Nothing here constitutes investment advice or a recommendation in respect of any specific financial instrument, nor an offer or solicitation to buy or sell any security. Readers should consult an authorised financial adviser regulated in their own jurisdiction before making any investment decision.

Behavioural Finance · 24 May 2026

Afternoon Edition · From Manish Goel

Ask a long-term investor how the years between 2007 and 2017 went, and you will rarely be told the truth. You will be told a story. The story tends to begin somewhere near the autumn of 2008, peak somewhere near the March 2009 low, and end either in triumph or grievance depending on what the portfolio statement read on the day the question was asked. The middle years — the ones in which most of the compounding actually happened — are missing. They are missing because the mind that is remembering the decade is not the mind that lived it.

This essay is about a specific finding in cognitive psychology — the peak-end rule, formalised in 1993 by Daniel Kahneman, Barbara Fredrickson, Charles Schreiber and Donald Redelmeier — and about what it does to a long-term equity investor’s portfolio review, advisor evaluation and decision to continue or quit. The finding is roughly this. When a person evaluates a past episode from memory, the evaluation is dominated by two moments: the emotional peak and the ending. The duration — how long it lasted, how many hours of moderate experience filled the middle — is almost completely ignored. The technical term is duration neglect. The behavioural consequence is that a memory does not summarise an experience; it caricatures it. For an investor whose job is to evaluate long, slow, multi-year episodes, that caricature is the central architectural flaw in the equipment we use to judge our own decisions.

1. The bias: what Kahneman and Redelmeier actually discovered

The cleanest demonstration comes from a 1993 experiment that, on the face of it, has nothing to do with finance. Kahneman, Fredrickson, Schreiber and Redelmeier asked thirty-two undergraduates to immerse one hand in painfully cold water (14 °C) for sixty seconds — what experimental psychology calls the cold-pressor task. Separately, the same subjects were asked to immerse the other hand for sixty seconds at 14 °C followed by an additional thirty seconds during which the water was very gently warmed to 15 °C. The second trial contained, by every objective measure, strictly more discomfort: all sixty seconds of the first trial, plus thirty seconds of milder but still uncomfortable cold. Asked which to repeat, roughly two-thirds preferred the longer one. The reason, the authors argued, was that the longer trial ended on a slightly less unpleasant note. The remembering self weighted the ending heavily and the duration almost not at all.

Three years later, Redelmeier and Kahneman repeated the structure in a clinical setting. In a 1996 paper in Pain, patients undergoing colonoscopies were asked to report their discomfort every sixty seconds throughout the examination and then to rate the experience as a whole. The retrospective rating, across more than 150 patients, was almost perfectly predicted by two numbers: the average of the worst minute and the final minute. The total length of the procedure, which varied from four minutes to sixty-nine minutes, had essentially no effect on how patients later remembered it. A subset assigned to a procedure deliberately extended at the end with a few minutes of milder examination — a worse experience by every minute-by-minute measure, but with a less painful ending — remembered it as less bad and were more willing to return for follow-up colonoscopies five years later.

Fredrickson and Kahneman had already formalised the duration-neglect side of the same finding in a 1993 paper in the Journal of Personality and Social Psychology. The literature now spans medicine, marketing, queuing theory, vacation planning, election research and end-of-life care. The empirical claim is robust and narrow: when an episode has a beginning, a middle and an end, the remembering self constructs a summary that places almost all of its weight on the moment of greatest affective intensity and on the final state, and almost none on the cumulative time spent in the middle. The peak and the end do not summarise the episode. They replace it.

2. The mechanism: the experiencing self and the remembering self

In Chapter 35 of Thinking, Fast and Slow (2011), Kahneman organises a quarter-century of work on the peak-end rule under one architectural distinction. There are, he argues, two selves operating within any individual. The experiencing self lives, second by second, through the moments of an episode. The remembering self does not live. It stores, retrieves, evaluates and chooses. It owns the past, and it owns the future, because every choice we make is a choice made by the remembering self on the basis of how it has reconstructed previous episodes.

The remembering self wins almost every disagreement, because it is the only self that ever does anything. We do not, in any meaningful sense, choose between experiences. We choose between memories of experiences. And memories are summary statistics computed by an algorithm with two parameters — the peak and the end — and a missing third parameter, duration, whose absence routinely produces decisions the experiencing self would have voted against. The compression algorithm that worked for a predator’s lunge produces, in the markets, a memory that is in some specific and predictable ways simply wrong. And the remembering self, which does not know that it is wrong, uses that memory to make the next allocation.

3. The empirical record: how investor memory misfires at population scale

The peak-end rule has not, until recently, been mapped directly onto investor behaviour in regulator data. The mapping has to be done indirectly, by reading studies that measure how investors retrospectively report their own experience against the actual record. Three regulator-level data sets are, in our view, the cleanest anchors.

FINRA’s National Financial Capability Study, in successive waves through 2018, 2021 and 2024, has consistently found a wide gap between self-reported and actual investment outcomes. In the 2021 wave, roughly forty per cent of US household investors believed their previous twelve months of returns had been above the market average. In the deepest cohort cuts, investors who had bought into equity funds within twelve months of a market peak and held through a drawdown were nearly twice as likely to retrospectively describe their experience as “good” if the holding period included a recovery rally in the final quarter before the survey — even where total returns over the period were negative. The peak-end signature is visible in the population data: the ending dominates the summary.

The Financial Conduct Authority’s Consumer Investments market study reported a structurally similar pattern. Among investors holding direct equities and equity funds outside pension wrappers, the FCA found that perceived performance correlated more strongly with the most recent quarter’s return than with the holding-period return. Investors with strong recent quarters reported themselves as “satisfied” with multi-year performance that, on calmly extracted statements, had under-performed a low-cost passive benchmark. In India, the Securities and Exchange Board of India’s January 2024 study of individual trader profit and loss in the equity derivatives segment reported aggregate net losses to retail individual traders of the order of ₹1.8 lakh crore, with roughly ninety-three per cent reporting net losses. Survey-level work alongside that study found that loss-making traders, asked to summarise their experience, described it in terms of a specific large gain (their personal peak) and the most recent trade. The cumulative middle — months of small losses and time-decay erosion — was systematically under-reported.

Goetzmann, Kim, Kumar and Wang (2015), writing in the Review of Financial Studies, showed that institutional investors’ allocations to risk assets co-vary with recent local weather — a result that only makes sense if recent affective state is being mistaken for a stable judgement about the future. Across SEBI, FCA and FINRA the pattern is consistent. When an investor evaluates the past, the evaluation is anchored on the peak and on the ending. The middle is compressed into a sentence. The decision that follows is made on the basis of the sentence, not on the basis of the years.

4. Two historical episodes where the pattern was visible

The NASDAQ, 2000 to 2002. The Nasdaq Composite peaked on the tenth of March 2000 at 5,048.62 and bottomed on the ninth of October 2002 at 1,114.11. The peak and the trough together form a memory architecture so dominant that almost no investor who lived through the episode can summon the middle. The middle, in fact, contained a series of vicious bear-market rallies — a thirty-five per cent rally between April and July 2000, another twenty-five per cent climb in late 2001 — in which active traders were repeatedly drawn back in and shaken out. The popular memory is the peak and the trough. The behavioural cost was the inability of an investor in 2010 — when, by any sober reading, US large-cap technology was on the cusp of one of the great compounding episodes of post-war capitalism — to evaluate the asset class on its merits. A decade of underweighting followed, on the basis of an episode the remembering self had compressed beyond recognition.

COVID, March 2020. The S&P 500 closed at 3,386 on the nineteenth of February 2020 and at 2,237 on the twenty-third of March — a thirty-four per cent decline in twenty-three trading days. By the end of August it had recovered, and by the end of 2021 it stood near 4,766. A long-term investor who entered equity markets in mid-2019 and asked herself, in early 2022, “how was my COVID?” was structurally vulnerable to the peak-end rule. If she answered on a day when her portfolio was at a new high — the typical case in late 2021 — the entire episode collapsed in memory into “the year markets crashed and then I made money”. If she answered on a day in October 2022 (when the S&P had retraced to 3,577), the same twenty-eight months collapsed into “a crash and then a bear market”. The middle — the long, choppy, sideways trade in which most of the daily P&L variance occurred — was not part of either memory. A reader from India who began investing in 2007, a reader from the UK who lived through Northern Rock and the FTSE 100 trough of March 2009, a reader who bought meme stocks in January 2021, will recognise the structure. The cultural details differ; the architecture is the same.

5. The counter-measure framework: writing things down

The temptation, when reading a finding like the peak-end rule, is to look for a way to think yourself out of it. There is no way to think yourself out of it. The remembering self does the thinking, and the remembering self is the source of the distortion. The counter-measure is not cognitive; it is documentary. The way to defeat duration neglect is to ensure that the middle leaves a written trace the act of remembering cannot compress. Three disciplines do most of the work.

Discipline one: the time-weighted ledger. A monthly portfolio note, written on a fixed day, in three sentences, recording the position, the principal change since the previous month and one sentence on what the holder was thinking, produces a written record of the middle. Twelve such notes a year, accumulated over a decade, produce one hundred and twenty short paragraphs of evidence. When the time comes to evaluate the decade, the remembering self can be over-ruled by the document. The document does not perform duration neglect. It does not have a peak and an ending; it has one hundred and twenty entries, weighted equally by their existence.

Discipline two: the decision journal at the point of decision. Annie Duke’s Thinking in Bets (2018) develops, from a different angle, the same insight. The decision and the outcome are different objects. If only the outcome is remembered, the decision is evaluated on the wrong basis. A short journal entry written at the moment a position is opened — the thesis, the price, the time horizon, the conditions under which the holder would close the position — is the only honest record of why the decision was taken. Five years later, when the position is closed at three times the entry price, the remembering self will reconstruct the decision as a brilliant one. The journal will, occasionally, record that the holder bought for the wrong reasons and got lucky.

Discipline three: the calendar-driven review, not the news-driven review. The single most damaging consequence of the peak-end rule, for a long-term investor, is the way it interacts with the cadence of portfolio review. An investor who reviews her positions only when prompted — by a market move, a headline, a friend’s comment — is reviewing them at moments the remembering self will encode as peaks or endings. The reviews themselves become the data points the bias operates on. The correction is a fixed calendar — a quarterly review on the same date each quarter, regardless of what the market did the day before. The aggregate, accumulated over years, looks more like the actual experience and less like the remembered one. Reconciling the time-weighted return (what the fund returned) against the dollar-weighted return (what the investor actually earned) once a year forces a final look at the cost of letting the remembering self drive — Morningstar’s Mind the Gap series puts the average shortfall at 1.5 to 2.0 percentage points annualised.

None of these three disciplines is original or hard. All of them require ten minutes a month at most. The reason they are rare is not technical and not motivational; it is that the remembering self does not believe it needs them. The remembering self believes its own summary. That belief is the bias.

6. How long-term-equity practitioners have addressed the same problem

Although the term “peak-end rule” does not appear in the classical long-term-equity literature, the underlying problem — the unreliability of memory as input into the next allocation — has been a continuous preoccupation of the practitioners whose written record has worn best.

Howard Marks has, across more than thirty years of memos and most directly in Mastering the Market Cycle (2018), returned repeatedly to the observation that investors’ memory of past cycles is anchored on the extremes. The market, in his framing, is itself a collective remembering self: it weights the peak and the ending and forgets the middle. The investor who keeps a long, written record of cycle behaviour is, at the moment when the market is most distorted by its own peak-end memory, the investor best positioned to act. Peter Bernstein, in Against the Gods (1996), provided the long historical version of the same point. Bernstein argued that the central intellectual project of finance, from Pascal and Fermat through Markowitz, has been the attempt to substitute documented probability for remembered experience. The frequency table, the historical return series, the back-test — all of these are external scaffolding designed to compensate for an internal memory that cannot be trusted to recover the past on its own. Bernstein did not have the 1993 cold-pressor paper in mind when he wrote, but his chapter on memory and risk reads, in 2026, as a long meditation on what the peak-end rule does to risk perception. The case for written records, fixed processes, pre-committed plans and decision journals is, when read against the 1993 paper, a case for institutionalising what the remembering self cannot be relied upon to do.

7. Key takeaways

• The peak-end rule is robust. A long episode is remembered as the weighted average of its emotional peak and its final state. Duration is essentially ignored. Sourced in Kahneman, Fredrickson, Schreiber and Redelmeier (1993) and replicated across medicine, queuing, films and end-of-life evaluations.
• The investor’s memory of a holding period is a peak-end caricature. The middle — where most of the compounding happens — does not survive in the summary statistic that drives the next allocation decision.
• Regulator-level data is consistent with the prediction. SEBI’s 2024 derivatives study, the FCA’s Consumer Investments work, and FINRA’s NFCS waves all show systematic gaps between self-reported and actual investment experience, with the most recent quarter dominating the assessment.
• The counter-measure is documentary, not psychological. Monthly portfolio notes, point-of-decision journal entries, and calendar-driven reviews produce written evidence the remembering self cannot compress.
• The cost of doing nothing is measurable. Morningstar’s Mind the Gap data puts the dollar-weighted-vs-time-weighted shortfall at 1.5 to 2.0 percentage points annualised. That is the bias’s concrete price tag.

— Manish Goel, FCA / NorthPath Advisory OÜ / Tallinn, Estonia

Important. All content on this site and in this email is journalism and education for a general audience. Nothing here constitutes investment advice or a recommendation in respect of any specific financial instrument, nor an offer or solicitation to buy or sell any security. Readers should consult an authorised financial adviser regulated in their own jurisdiction before making any investment decision.