Why the Memecoin Craze Happens

The companion article to this one, memecoin odds explained, establishes what happens to memecoin launches: across 18.67 million tokens on the largest platform, 68.67% recorded their last trade on the day they were created. That article is deliberately about the numbers.

This one is about a different question, and in some ways a harder one. The base rate is public. Anyone can find it in thirty seconds. Hundreds of thousands of people participate anyway, and not all of them are uninformed. Explaining that requires a mechanism, not a scolding.

What follows draws on behavioural and asset-pricing research, and it comes with an unusual caveat stated up front rather than buried: none of that research was conducted on memecoins. It is equities, laboratory choice, and large-capitalisation cryptocurrency. Every application to a token launched on a bonding curve is this article's reasoning, and the places where the reasoning is contested are marked as contested rather than smoothed over.

1. What is actually being priced

Start with the thing that makes memecoins hard to reason about: there is no cash flow, no fee, no revenue, and no redemption. A discounted-value model has nothing to accept as input. The usual conclusion is that the price is therefore irrational, which is less an explanation than a refusal to give one.

There is a better model, and it comes from a paper about bubbles generally rather than crypto specifically. Scheinkman and Xiong build an equilibrium in which overconfidence generates disagreement about fundamentals. Where investors cannot short, a buyer acquires what the paper calls an option to sell the asset to other agents when those agents hold more optimistic beliefs. The consequence is that participants knowingly pay more than their own valuation of future payoffs, because they expect to find someone who will pay more still. In their model, bubbles come with large trading volume and high price volatility — not as side effects, but as part of the same structure.

That is the rigorous version of "attention has no anchor." The price is not a claim about the token. It is the value of the resale option, and the resale option is worth whatever the next wave of attention is worth.

The short-sale constraint holds by construction here, which is this article's reasoning rather than the paper's. A token minted minutes ago has no borrow market and no listed derivative. A participant who thinks it is worthless cannot express that view at any size; they can only decline to buy. The price is therefore set entirely by the subset of people who are optimistic, which is exactly the condition the model requires. This stops being literally true for the small number of memecoins that survive long enough to get perpetual futures listings, and those are a rounding error in the population.

Why the collapse is not a separate event

This is the part worth sitting with. In most accounts, the rally and the crash get separate explanations — greed on the way up, panic on the way down. The resale-option structure gives one explanation for both.

While attention is arriving, each buyer is correct that a more optimistic buyer exists, and the option has value. When attention stops arriving, the option is worth nothing, and it becomes worth nothing for every holder simultaneously, because they were all holding the same option on the same flow. There is no residual claim to fall back on. That is why the pattern is a vertical drop rather than a decline to some lower level: the thing being valued did not decline, it ceased to exist.

2. Attention builds the choice set before preferences act

The word "attention" invites a lazy reading in which it means excitement. The research means something more specific and more useful.

Barber and Odean tested and confirmed that individual investors are net buyers of attention-grabbing stocks — those in the news, those with high abnormal trading volume, and those with extreme one-day returns. Their explanation for the asymmetry is structural rather than emotional: buying requires searching thousands of candidates, while selling generally does not, because most individuals only sell what they already own. Their conclusion is the sentence to keep: preferences determine choices after attention has determined the choice set.

Read that against a launch platform. Millions of tokens exist; a person can hold any of them; the search problem is not merely large, it is unbounded, and it refreshes continuously. Under those conditions the attention filter is not one influence among several. It is very nearly the entire selection process, and whatever captures it selects what gets bought.

In cryptocurrency specifically, Liu and Tsyvinski find that returns are not explained by stock market, macroeconomic, currency, or commodity factors, but are predictable from market-specific ones — a time-series momentum effect, and proxies for investor attention. Their sample is major assets rather than memecoins, so it does not settle anything about a token launched last week. What it does establish is that attention as a measurable quantity forecasts returns in this asset class, which is a stronger claim than the metaphor usually implies.

Put the two together and the memecoin looks less like a bad investment and more like a well-designed instrument for a specific job: converting a moment of collective attention into a tradeable claim. Its lack of a fundamental anchor is not a defect in that design. It is the design.

3. The lottery framing, stated precisely

"People treat it like a lottery ticket" is where most explanations stop. The research behind that sentence contains three distinct claims with three distinct kinds of evidence, and keeping them apart is what separates an explanation from a slogan.

Reading the table

The first row is about people, and its useful detail is the direction. Kumar found that demand for lottery-type stocks increases during downturns, and that the socioeconomic factors predicting lottery expenditure also predict lottery-stock investment. Lottery demand is not a bull-market luxury that appears when people feel rich. It behaves like lottery-ticket buying, which rises when circumstances worsen. Any explanation of memecoins that runs purely on euphoria has the cyclicality backwards.

The second row is the one that answers the expected-value objection, and it is worth being careful about. The usual argument — the expected value is negative, therefore buying is a mistake — assumes the buyer is trying to maximise an expectation. Barberis and Huang show that under probability weighting, a positively skewed asset earning a negative average excess return is what equilibrium looks like. The buyer is not failing at arithmetic. They are not doing that arithmetic, and the model that describes what they are doing predicts the negative return in advance.

This matters practically, not just philosophically. It means that showing someone the expected value is not a correction, because it does not address the preference that produced the decision. It is answering a question that was never asked.

The third row is where the extrapolation is thinnest, and the numbers deserve their limits attached. Grobys and Junttila studied twenty cryptocurrencies over January 2016 to December 2019, selected as the highest market capitalisations as of 2 January 2016 — in the paper's own words, only large-cap assets, speaking in relative terms — with Bitcoin excluded from the sorts because it serves as the market factor. Their finding is a negative MAX effect: the assets with the highest recent extreme daily return went on to underperform, with the raw and risk-adjusted spread between the lowest and highest MAX quintiles exceeding 1.50% per week.

That is the closest thing to direct evidence that lottery-like demand prices crypto assets. It is also a sample of the twenty largest non-Bitcoin coins in a period predating the launch-platform era entirely, which is several steps away from a token that launched this morning.

4. The token as a membership fee

Some holding behaviour makes no sense as investment. People keep positions in tokens they describe as jokes, refuse to sell at gains, and treat the position as a commitment rather than an exposure. Modelled as an investment, this is irrational. Modelled as membership, it is ordinary — the position is the entry ticket, and part of what it buys is being in the group.

This is the leg of the argument with the weakest evidence, and the honest thing is to say so before making it.

The part with a rigorous mechanism

Han, Hirshleifer and Walden model investors who discuss their strategies and convert others to them with a probability that increases in investment returns. The conversion rate, they show, is convex in realized returns. The result is that active strategies — high variance and high skewness — dominate unconditionally, and their statement of why is the important clause: this holds although investors have no inherent preference over these characteristics.

Nobody in that model is greedy. Nobody is biased. Convex propagation is sufficient on its own to make the high-variance strategy the one that spreads, and their framework predicts that sociability and the features of the communication process — not just returns — determine which strategies become popular and how they are priced.

This is strictly stronger than the survivorship-bias point the odds article makes. Survivorship says you only see the winners. Social transmission says the winners propagate disproportionately, because the conversion rate is convex in the return, and that what spreads is the strategy itself rather than merely the anecdote. A community that talks about memecoins will drift toward the highest-variance version of memecoin trading over time, with no change in anyone's preferences and no bad actors required.

The part that is qualitative, and about a different market

For the identity claim specifically, the closest research is ethnographic rather than quantitative, and it is about meme stocks on Reddit rather than memecoins on a launch platform. Nani's study of r/WallStreetBets sets out to explain why people are drawn to social-media investing communities despite the high likelihood of incurring losses — which is nearly this article's question. Its account is that anti-institutional sentiment leads users to trust unregulated social information over regulated advice, that the advice itself incentivises unnecessarily high-risk trades, and that this culminates in what the work calls desperation capitalism: young people feeling forced into high-risk strategies to escape financial precarity. The peer-reviewed version appeared in Telematics and Informatics in 2025, co-authored with Keegan McBride.

Three limits, all of which matter:

• It is interpretive. An ethnographic case study produces a theory, not a measured effect size. There is no coefficient here and nobody should quote one.
• It is about equities, not tokens. The gap between a Reddit forum trading options on a listed company and a Telegram channel around a token minted this morning is this article's assumption, not a demonstrated equivalence.
• Various specific figures circulate in secondary coverage of this research — participant counts, comment counts, a loss-aversion multiple. None of them could be verified against the primary record, so none appear here.

What survives all three limits is modest and still worth having: participants themselves describe the community, not the return, as a reason to be there, and researchers studying these communities keep arriving at the same explanation independently.

5. Why the published base rate changes so little

Here is the question the odds article cannot answer with data, because it is a question about what data does to people.

Negative expected value is not, by itself, a deterrent

Barberis's model of casino gambling demonstrates that for a wide range of preference parameters, a prospect theory agent would be willing to gamble even where the casino offers only bets with no skewness and zero or negative expected value. The gambler in that model is not misinformed about the odds. The odds are known, posted, and unfavourable, and the model still predicts entry.

The second half of the result is the more useful one. Probability weighting produces what the paper calls a plausible time inconsistency: at the moment of entering, the agent plans to follow one gambling strategy, but after starting to play, he wants to switch to a different one. Behaviour then depends on whether the person is aware of that inconsistency and, if aware, whether they can commit in advance.

That is a considerably better account of the familiar memecoin experience than any story about willpower. The exit rule formed beforehand and the behaviour once the position exists are generated by different objectives. "I knew the odds and did it anyway" is not a confession of stupidity; it is the predicted output of a model whose inputs are known odds.

Two literatures, opposite signs, neither about memecoins

The obvious next move is to say people overweight small probabilities, cite prospect theory, and finish the section. That would be wrong, or at least unsupported, and the reason is interesting enough to be the centre of the article rather than a footnote.

Hertwig and Erev review three experiential paradigms and report converging findings: when people decide based on experience, rare events tend to have less impact than they deserve given their objective probabilities. The common conception in behavioural decision research, they note, is that overestimation and overweighting increase the impact of rare events — and the supporting findings come primarily from studies in which people learned about their options through descriptions.

So which regime is a memecoin buyer in? This article does not know, and neither does the literature, because neither literature studied this. A case exists for description: the pitch is an advertised multiple, a screenshot of somebody's gain, a stated payoff. A case exists for experience: a person who has watched forty launches die has sampled outcomes over time, which is precisely the setting where rare events get underweighted — and the underweighted rare event there would be the jackpot, which should reduce participation rather than explain it.

The tidy version of this section would pick one. The honest version reports that two well-supported literatures make opposite predictions, that the regime memecoin buyers occupy is contested, and that anyone offering you a confident behavioural explanation of memecoin participation is claiming more than the evidence supports.

Your own experience outweighs the published statistic

One finding does bear directly on why a public number fails to move behaviour. Kaustia and Knüpfer find a strong positive link between past IPO returns and future subscriptions at the investor level, and their data lets them trace the effect specifically to the returns personally experienced by each investor rather than to the IPO cycle or to wealth effects. The behaviour is consistent with reinforcement learning, in which personally experienced outcomes are overweighted relative to rational Bayesian learning.

Transplant that. A population-level base rate is one input. What you personally watched happen is another, and in a measured setting structurally close to repeated participation in new issues, the second dominates. Someone who has personally seen a friend's position go up tenfold is not weighing that against 0.198% and getting it wrong; they are weighing something the model says will be overweighted no matter how prominently the 0.198% is displayed.

The caveat, again: Finnish retail investors subscribing to regulated IPOs is not a memecoin launch, and treating them as the same setting is this article's inference.

6. What the platform industrialises

The three mechanisms above are old. Lottery preference, attention-driven selection, and socially transmitted strategies all predate crypto by decades. What is new is a machine that manufactures all three at once, continuously, at negligible marginal cost.

Mancino's study of Solana in the fourth quarter of 2024 reports that Pump.fun accounted for up to 71.1% of all tokens minted on the chain and contributed between 40% and 67.4% of decentralised exchange transactions, while daily users grew from about 60,000 to 260,000 — and fewer than 2% of tokens reached major exchanges. It is a preprint, so treat the figures as indicative rather than settled. Indicative is enough for the point: one platform became both the dominant source of new tokens and a large share of all trading activity on its host chain, while almost nothing it produced ever left it.

That is what industrialisation means here. Each of the three mechanisms gets its own piece of infrastructure:

• The attention machine. Free, instant creation means supply expands to fill available attention, and a continuously refreshing feed of new launches keeps the choice set forming and reforming — the exact conditions under which attention determines what gets bought.
• The lottery machine. A bonding curve turns every launch into an explicit small-stake, large-advertised-payoff proposition, with the payoff visible as a curve rather than argued for.
• The community machine. Each token ships with a chat channel, so the social layer where strategies propagate is created at the same moment as the asset, rather than forming around it over years.

The specific mechanics — bonding curves, supply concentration at creation, thin one-sided liquidity, and the distribution stage — are set out in memecoin odds explained. They are not repeated here, because that article carries the risk analysis and this one would only weaken it by restating it.

7. What this explains, and what it does not

An explanation that accounts for everything usually explains nothing, so it is worth being precise about the boundary.

It explains why participation persists after the base rate is known, without requiring participants to be uninformed or foolish: the decision is not an expected-value calculation, the odds are known and not decisive, and the plan formed before entering is not the one that governs afterwards. It explains why the same structure produces both the rally and the collapse. It explains why communities drift toward higher-variance behaviour with nobody's preferences changing. And it explains why publishing better statistics has such a weak effect on behaviour.

It does not explain which tokens succeed, and nothing here should be read as suggesting attention can be forecast — that would be the same edge-hunting the odds article shows the data does not support. It does not tell you how large the effects are for memecoins, because they have not been measured on memecoins. It does not resolve the description–experience question. And it makes no claim that any of this is unique to crypto; the mechanisms are general, and the platform is the new part.

8. Conclusion

A memecoin has no cash flow, so what changes hands is a claim on collective attention. Priced as a resale option — the right to sell to a more optimistic buyer, in a market where pessimists cannot short — it explains both the vertical rise and the vertical fall as one structure rather than two moods, because the option becomes worthless for every holder at the same moment.

Attention is not decoration on that process. In the research it constructs the choice set before preferences act, and in cryptocurrency it measurably forecasts returns. The lottery framing, stated precisely, is three separate findings: lottery-type demand is real and rises in downturns, overpaying for skew is an equilibrium rather than a miscalculation, and lottery-like demand shows up in crypto prices as underperformance of the highest recent extremes. The community is not incidental either — strategies propagate at a rate convex in their returns, which is enough to make the highest-variance version dominate a conversation without anyone preferring variance.

Which leaves the question that motivated the article: why the published odds change so little. Part of the answer is that negative expected value does not deter a decision that was never an expectation, and that the plan formed before entering is not the plan that survives contact with the position. Part of it is that personally experienced returns are overweighted against any population statistic. And part of it is genuinely unknown — the two literatures on how people treat rare events point in opposite directions here, and neither was run on this market.

The measured odds still sit in memecoin odds explained, and they have not moved. The value of understanding the mechanism is not that it improves those odds. It is that recognising which of these forces is acting on you is the only part of the situation you have any influence over.

Frequently asked questions

Sources and further reading

Behavioural and asset-pricing research:

• Scheinkman and Xiong (2003), Overconfidence and Speculative Bubbles, Journal of Political Economy 111(6), 1183–1220
• Barber and Odean (2008), All That Glitters, Review of Financial Studies 21(2), 785–818
• Barberis and Huang (2008), Stocks as Lotteries, American Economic Review 98(5), 2066–2100
• Kumar (2009), Who Gambles in the Stock Market?, Journal of Finance 64(4), 1889–1933
• Kaustia and Knüpfer (2008), Do investors overweight personal experience?, Journal of Finance 63(6), 2679–2702
• Hertwig and Erev (2009), The description–experience gap in risky choice, Trends in Cognitive Sciences 13(12), 517–523
• Barberis (2012), A Model of Casino Gambling, Management Science 58(1), 35–51
• Han, Hirshleifer and Walden (2022), Social Transmission Bias and Investor Behavior, Journal of Financial and Quantitative Analysis 57(1), 390–412

Cryptocurrency and memecoin research:

• Liu and Tsyvinski (2021), Risks and Returns of Cryptocurrency, Review of Financial Studies 34(6), 2689–2727
• Grobys and Junttila (2021), Speculation and lottery-like demand in cryptocurrency markets, Journal of International Financial Markets, Institutions and Money 71, 101289
• Mancino (2025), The Memecoin Phenomenon: An In-Depth Study of Solana's Blockchain Trends, arXiv 2512.11850
• Nani (2023), Stocks, memes, and desperation capitalism, MSc thesis, Oxford Internet Institute

Related CoinBeaver articles:

• Memecoin odds explained
• Crypto market psychology
• DOGE and the speculative playbook
• What actually moves crypto prices
• Why most crypto traders lose
• Crypto rug pulls explained

This article is educational and is not financial advice, and nothing in it should be read as encouragement to buy or trade memecoins. It explains a phenomenon; the measured outcomes and the risk guidance are in memecoin odds explained. None of the behavioural or asset-pricing research cited here was conducted on memecoins — the samples are US and Finnish retail equity investors, laboratory choice experiments, and the twenty largest non-Bitcoin cryptocurrencies by market capitalisation over 2016 to 2019 — so every application to memecoins is this article's reasoning rather than a finding, and is marked as such where it occurs. The description–experience question is unresolved in the literature and is presented as unresolved. The identity and community material rests on qualitative research into meme-stock forums rather than on measured effects in memecoin markets.