Who Profits from Prediction Markets?
Execution, Not Information*
Joshua Della Vedova„
This version: February 7, 2026
Abstract
Retail traders correctly forecast asset price direction yet lose money. Using 222 million
prediction market trades with observable terminal payoffs, we decompose returns into a
directional component (did the trader pick the right side?) and an execution component
(did the trader get a favorable price?). Traders with above-random accuracy earn
negative returns because they arrive late and pay unfavorable prices; traders with nearrandom accuracy profit through superior execution. These two dimensions of skill are
nearly orthogonal (ρ ≈ 0.13), and split-sample tests confirm both are persistent. What
separates profitable from unprofitable traders is not forecasting ability but execution:
automated traders pay 2.52 cents less per contract than casual traders, and this gap
alone accounts for the sign of returns across trader types. Being right and making
money are not the same thing.
JEL Classification: G14, D82, G41
Keywords: prediction markets, execution quality, retail trading
* All errors are my own.
„ School of Business, University of San Diego. Email: jdellavedova@sandiego.edu
1
1
Introduction
Does forecasting accuracy predict trading profits? A central finding in empirical finance is
that the relationship is weaker than standard models predict. Barber et al. (2023) show
that retail equity trades positively predict returns yet retail investors lose money on average.
Kelley and Tetlock (2013) find that retail order flow predicts the cross-section of returns
at horizons up to 20 days, yet the gains are insufficient to cover trading costs for most
individual investors. These findings pose a puzzle for canonical models of informed trading
(Kyle, 1985; Glosten and Milgrom, 1985), where “informed” is unidimensional: traders either
have an informational advantage or they do not, and those who forecast well profit at the
expense of those who do not. If traders can be right about the direction yet wrong about
the money, the unidimensional view is incomplete.
A simple example illustrates the puzzle. A retail trader correctly identifies the winning
outcome in a prediction market and buys at $0.85. The contract resolves to $1.00, yielding
a gross profit of 15 cents. An automated trader who bought the same contract weeks earlier
at $0.60 earns 40 cents, nearly three times as much, despite making the same directional
call. Both traders were right about the outcome. The difference is not what they knew
but when they traded: the retail trader arrived after the price already reflected most of the
information her forecast contained. Now consider the reverse: the same automated trader
buys a different token at $0.15, a contract that ultimately resolves to $0. The bot was wrong,
and loses 15 cents. The bot’s loss on a wrong call equals the retail trader’s gain on a right
call. Early entry limits downside on losing trades just as it amplifies upside on winning
trades. In our data, this pattern is systematic: automated traders hold a 2.1-cent entry
price advantage1 over casual traders on winning tokens, and across all trades, bots earn $134
1
We use “execution quality” to refer to the price level at which a trader enters a position relative to
terminal value, conditional on the trader’s directional choice. This is distinct from the transaction-cost
notion of execution quality studied by Bessembinder (2003), which measures the cost of implementing a
given trading decision. In the present context, execution quality captures whether a trader buys at $0.55 or
$0.75 when the contract resolves at $1.00, a difference in entry level, not in the bid-ask spread or market
impact paid to execute the trade.
2
million in aggregate profits while all other types collectively lose.
This paper identifies what the unidimensional view is missing. We exploit a setting where
terminal payoffs are observable to separately measure two dimensions of trading skill: (1)
directional accuracy, whether the trader correctly identified the winning outcome, and (2)
execution quality, the price paid relative to the asset’s terminal value. Execution quality as we
measure it, the entry price relative to terminal value, reflects a combination of trade timing,
liquidity provision, and order management. The maker/taker decomposition in Section 6.5
partially disentangles these channels, showing that spread capture accounts for most of the
bot execution advantage, but the total-edge ranking holds within both maker and taker
roles, indicating that execution quality is not reducible to a single channel. Using 222
million trades with observable outcomes on Polymarket, a peer-to-peer prediction exchange
where all trades are recorded on a public blockchain, we find that these two skills are weakly
correlated across traders, and that the execution component, not the directional component,
determines which trader types earn positive returns.
The decomposition we document connects to a foundational question in performance
evaluation. Since Fama (1972), researchers have attempted to separate security selection
from timing, and subsequent work has formalized the distinction between “what you hold”
and “when you trade” (Brinson et al., 1986; Daniel et al., 1997). At the institutional level,
Anand et al. (2012) separate investment idea quality from trading desk execution skill using
ANcerno data, the closest existing two-dimensional decomposition. In all of these settings,
performance components must be inferred relative to benchmarks. Prediction markets with
binary outcomes and known terminal values eliminate this benchmark dependence for the
directional component and sharply reduce it for the execution component, enabling cleaner
measurement of both skill dimensions at the individual trader level.
The evidence is consistent across tests. A return decomposition reveals that execution
quality varies far more across trader types than directional accuracy: a 2.52-cent execution
gap between automated and casual traders, measured relative to market-level benchmarks
3
across all trades, determines which types profit. Within every type, more accurate wallets
pay higher prices for winning tokens, confirming the negative within-type θ-ε relationship.
Split-sample tests establish that both skills persist across time but are nearly orthogonal
(ρ ≈ 0.13). A maker/taker decomposition separates channels: maker-side bots profit through
spread capture despite below-random accuracy (44.8%), while taker-side bots pay their directional gains back in execution costs. The core ranking survives alternative benchmarks,
market fixed effects, and out-of-sample classification.
Our contributions are twofold. First, we provide the first decomposition of individual
trading returns into directional accuracy and execution quality in a setting free of benchmark dependence. The observable terminal payoffs in prediction markets eliminate the
identification problem that limits analogous decompositions in equity markets (Anand et al.,
2012; Saglam et al., 2019), where fundamental value must be estimated from benchmarks.
Second, we identify a complementary channel for the retail trading paradox documented
by Barber et al. (2023): even conditional on choosing the winning side, traders lose money
through poor execution. The separability of θ and ε is also consistent with how algorithmic
traders earn persistent profits without apparent directional information (Baron et al., 2019;
Menkveld, 2013). Whether this execution channel operates in equity markets remains open,
but the convergence of magnitudes across settings suggests it may. We do not claim to refute
Kyle (1985); the unidimensional treatment is a valid approximation when θ and ε covary
positively. Our contribution is to show that, when measured separately, they do not.
For prediction market participants, execution timing matters more than forecasting ability: retail traders lose not because they pick the wrong side but because they arrive too
late to capture value from correct forecasts. For researchers, the two-dimensional framework
provides a specific mechanism for why retail order flow predicts returns (Kelley and Tetlock, 2013) while retail traders lose money (Barber et al., 2023): the execution channel may
operate wherever fast traders capture surplus from slow ones.
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1.1
Related Literature
The paper connects to several strands of literature. The most directly related paper is Barber
et al. (2023), who document that retail order imbalance positively predicts equity returns,
yet retail trades lose money on average. They attribute the paradox to attention-driven
stock selection; we identify a complementary channel operating through execution quality.
The −2.0% holding-period ROI for active retail traders in prediction markets, despite 51.3%
accuracy, is the same order of magnitude as the annual retail underperformance documented
by Barber and Odean (2000) in equity markets.
A large literature documents retail investor underperformance. Barber and Odean (2000)
show that individual investors who trade frequently earn substantially lower returns than
passive benchmarks. Odean (1999) attributes this to overconfidence. Barber et al. (2022)
demonstrate that attention-driven buying leads Robinhood users to purchase stocks that
subsequently underperform. Kelley and Tetlock (2013) show that retail order flow predicts
returns at short horizons, with aggressive orders conveying cash flow information and passive
orders providing liquidity. Dyhrberg et al. (2025) provide the most comprehensive study of
retail execution quality in equity markets. Closest to our decomposition, Linnainmaa (2010)
shows that Finnish individual investors earn positive returns on market orders (reflecting
correct directional intent) but lose money on limit orders (reflecting adverse selection), effectively separating selection from execution at the individual level by exploiting order type
variation. Barber et al. (2009) decompose Taiwan individual investor losses, exceeding 2%
of GDP annually, into components from aggressive orders, passive orders, and transaction
costs, confirming that the direction-execution wedge has first-order economic magnitude. We
add to this evidence by showing that execution quality, rather than forecasting ability, is the
binding constraint on retail profitability. The two-dimensional framework suggests a more
specific form of overconfidence than the literature currently models: retail traders may be
well-calibrated about their directional accuracy (they are right more than 50% of the time)
but overconfident about their ability to translate accuracy into profits.
5
On market microstructure, Kyle (1985) models informed traders who profit from private
information, and Glosten and Milgrom (1985) show how adverse selection from informed
traders affects spreads. Both treat “informed” as unidimensional. Goldstein and Yang (2015)
model traders informed about different fundamentals affecting the same security, showing
that information heterogeneity fundamentally changes price discovery. Collin-Dufresne and
Fos (2016) demonstrate that insiders who optimize trade timing based on stochastic liquidity
conditions effectively separate what to trade from how to trade, making execution strategy
a distinct dimension of informed behavior. Empirically, Anand et al. (2012) find that execution skill at the trading desk level explains as much return variation as idea quality at the
portfolio manager level. Saglam et al. (2019) jointly estimate investor-specific short-term
trading skill and investor-independent execution costs, finding pronounced heterogeneity in
both dimensions. Puckett and Yan (2011) show that much institutional alpha comes from
interim trading activity (execution timing within quarters) rather than quarter-end holdings
changes, suggesting execution timing is a separable skill. In the algorithmic trading literature, Hendershott et al. (2011) and Menkveld (2013) document that algorithmic traders
improve liquidity and earn profits through market making, and Baron et al. (2019) show
that high-frequency traders earn persistent profits primarily from market making rather
than directional bets. This paper complements the microstructure literature by measuring
what these models treat as latent, whether a trader’s directional forecast was correct, and
showing that execution quality (ε) and forecasting ability (θ) are separable skills, and that
profits from the ε dimension alone suffice to explain algorithmic profitability.
On prediction markets, Forsythe et al. (1992) provide foundational evidence that political prediction markets aggregate information efficiently, and Wolfers and Zitzewitz (2004)
document their aggregate forecasting accuracy. Recent work by Reichenbach and Walther
(2025) provides the first systematic description of decentralized prediction markets, documenting market-level calibration, a default-option bias, and the existence of skilled traders
on Polymarket. Their analysis treats skill as unidimensional: traders who earn positive
6
profits are skilled, those who do not are unskilled. We ask a different question, why do
traders with above-average forecasting accuracy nonetheless lose money, and answer it by
decomposing skill into two weakly correlated components (θ and ε). The two papers are
complementary: their finding that profits persist is consistent with our evidence that both
θ and ε are persistent traits, and our decomposition provides a structural interpretation
of the profit heterogeneity they document. Saguillo et al. (2025) document within-market
and cross-market arbitrage opportunities on Polymarket, estimating $40 million in realized
arbitrage profits; our focus differs in decomposing individual returns into directional and
execution components rather than identifying mispricings across related contracts.
The rest of the paper proceeds as follows. Section 2 develops the theoretical framework
and hypotheses. Section 3 describes the data and institutional setting. Section 4 reports the
main results. Section 5 investigates the mechanism. Section 6 presents robustness checks.
Section 7 concludes.
2
Theoretical Framework and Hypotheses
The canonical view in market microstructure is that informed traders profit. Kyle (1985)
models a single insider who earns rents by trading against uninformed liquidity providers.
Glosten and Milgrom (1985) show how market makers widen spreads to compensate for adverse selection from informed traders. In both frameworks, “informed” is unidimensional:
a trader who knows the fundamental value also knows how to extract profits from that
knowledge. Recent theoretical work has begun to challenge this unidimensional view, with
models of information heterogeneity (Goldstein and Yang, 2015) and strategic execution timing (Collin-Dufresne and Fos, 2016) establishing that “informed” is at least two-dimensional
(see the discussion in Section 1).
A trader who correctly forecasts an outcome (high directional accuracy) need not execute
at prices that allow profits (high execution quality). If these two dimensions of skill are weakly
7
correlated, some traders will be “right but late,” accurate but arriving after prices already
reflect what they know, while others profit through execution timing with little forecasting
ability.
Our empirical setting differs from canonical frameworks in an important respect: information in prediction markets concerns observable public events rather than private signals
about firm fundamentals. We therefore do not claim to test Grossman and Stiglitz (1980)
or Kyle (1985) directly. Rather, we exploit the observable terminal payoffs in prediction
markets to measure what these models treat as latent: whether a trader’s directional call
was correct, and what price they paid relative to the eventual outcome. This allows a clean
decomposition of returns into forecasting and execution components.
2.1
Two-Dimensional Trader Types
We formalize the distinction between forecasting ability and execution quality using a simple
model. A prediction market contract pays $1 if event E occurs and $0 otherwise.
Definition. Each trader i is characterized by a type (θi , εi ):
ˆ θi ∈ [0, 1]: forecasting skill, the probability that trader i correctly identifies the winning
outcome.
ˆ εi ∈ R: execution quality, the edge obtained at entry, equal to the gap between the fair
value and the price paid (positive means favorable execution).
Trader i’s expected profit on a trade is:
Πi =
θi · (V − p̄)
| {z }
+
Directional component
(p̄ − pentry
)
| {zi }
−κ
(1)
Execution component
where V ∈ {0, 1} is the terminal payoff, p̄ is the volume-weighted average transaction price (a
proxy for the market’s consensus), pentry
is trader i’s entry price, and κ captures transaction
i
costs (spreads, fees). The directional component rewards forecasting ability: traders who
pick the right side when the market underprices the outcome earn positive directional edge.
8
The execution component rewards price timing: traders who buy below the market consensus
earn positive execution edge regardless of the outcome.
Period 0 (Early trading): Fast traders observe a noisy signal s0 ∼ N (V, σ02 ) and set
the initial price p0 = E[V |s0 ]. These traders have high ε (execution advantage through
speed) but moderate θ.
Period 1 (Late trading): Slow traders observe a more precise signal s1 ∼ N (V, σ12 )
with σ1 < σ0 . By the time they trade, prices have already partially adjusted:
p1 = α · V + (1 − α) · p0 ,
α ∈ (0, 1)
(2)
where α captures price discovery speed. These traders have high θ (accurate forecasts) but
low ε (poor execution due to late arrival).
Key result: When price discovery is fast (α close to 1), the execution component
dominates profitability even when forecasting skill is high. A slow trader who correctly buys
the winning outcome earns:
Πslow = (1 − α)(1 − p0 ) − κ
(3)
which approaches −κ as α → 1. This trader is directionally correct (θ high) but loses money
because execution quality is poor (ε low). Conversely, a fast trader earns spread income of
κ per trade regardless of direction, profiting through high ε despite moderate θ.
When does the standard view hold? If Corr(θ, ε) > 0, meaning forecasting skill
and execution quality move together, then informed traders profit as in Kyle (1985). The
unidimensional treatment remains a useful approximation. If Corr(θ, ε) ≤ 0, as when accurate forecasters arrive after execution-skilled traders have already moved prices, the onedimensional assumption fails, and high accuracy can coexist with negative returns.
The model captures execution quality through the timing channel for tractability. In
practice, execution quality as measured by Equation 1 can arise through at least three
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mechanisms: (i) entering positions early in the market lifecycle when prices are far from
terminal values, (ii) providing liquidity through limit orders that earn the bid-ask spread,
or (iii) managing order flow to minimize price impact. These mechanisms are not mutually
exclusive, and a single trader may benefit from all three. The empirical analysis in Section 6.5
separates channel (ii) from the others by distinguishing maker from taker trades. The key
empirical result, that θ and ε are weakly correlated and that ε determines profitability, does
not depend on which channel dominates.
2.2
Testable Predictions
The two-dimensional framework generates the following testable predictions:
P1 (Within-type θ-ε correlation): Within each trader type, wallets with higher directional accuracy pay higher entry prices for winning tokens. That is, ∂pentry /∂θ > 0,
reflecting the negative θ-ε correlation.
P2 (Decomposition): Both directional and execution components contribute meaningfully to returns, but the execution component varies more across trader types and
determines which types are profitable.
P3 (Persistence): θ and ε are persistent trader characteristics (stable across time), and
the cross-correlation between them is low (they are distinct skills).
P4 (Timing): Edge-at-entry decays as markets approach resolution, consistent with execution advantages eroding over the market lifecycle. High-θ traders concentrate in
late-lifecycle trading when ε opportunities are limited.
Under the standard one-dimensional view, P1 should fail: more accurate traders should
pay lower entry prices because their information advantage allows them to trade before
prices move. P2 should show that the directional component overwhelmingly drives returns.
P3 should show high θ-ε correlation.
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Prediction markets offer an ideal setting to test these predictions because contracts resolve
to known values ($0 or $1), blockchain data provides complete trade histories, and persistent
wallet addresses enable cross-market measurement of trader characteristics.
3
Data and Institutional Setting
3.1
Institutional Setting
Polymarket is a prediction market built on the Polygon blockchain. Users trade binary and
multi-outcome contracts denominated in USDC (a stablecoin pegged to $1). Each contract
resolves to either $0 or $1 based on the outcome of a specified real-world event. The platform
operates a central limit order book (CLOB) through smart contracts, with no platform fee
during our sample period. Unlike sports betting platforms where a bookmaker sets prices
and extracts a house edge, Polymarket is a peer-to-peer exchange: every dollar won by
one trader is lost by another, making the market strictly zero-sum. Over our sample period
(November 2022 to February 2026), total trading volume on Polymarket exceeded $32 billion
across more than 556,000 markets.
When an order is matched, the smart contract emits an OrderFilled event containing the
maker address, taker address, asset IDs, fill amounts, and fee. We collect all such events
from the Polygon blockchain. The matching engine emits two events per matched trade (one
for each side); we deduplicate by retaining only the maker-side event.
The blockchain setting offers identification advantages over traditional markets. First,
every trade is recorded on-chain with all data publicly available, making the analysis fully
replicable; there is no dark pool or off-exchange execution. Second, wallet addresses provide
persistent (though pseudonymous) identity across all trades and markets. Third, because
contracts resolve to known values, we observe the fundamental value that prices are meant
to discover.
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3.2
Sample Construction
We collect all OrderFilled events from Polygon, spanning November 2022 through February
2026. The raw dataset contains 394.6 million OrderFilled events. After deduplication (removing the Paradigm double-counted event for each matched trade) and mapping tokens to
markets, we obtain 233.0 million processed trades involving 1,981,408 distinct wallets. Of
these, 222 million trades occur in markets that have resolved, forming our analysis sample.
Including both maker and taker sides, this yields 444 million trade-wallet observations.
We obtain resolution data for 556,632 markets, covering 95.4% of processed trades. Our
analysis sample consists of trades in these resolved markets.
3.3
Trader Classification
To test whether information and profitability relate differently across trader types, we classify
wallets by observable trading behavior. Our classification relies on pre-determined characteristics that are plausibly exogenous to trading outcomes: trading frequency, volume, diversification, and market participation span. The identifying assumption is that these behavioral
characteristics reflect underlying trader type (algorithmic versus manual, professional versus
casual) rather than being caused by trading outcomes. This assumption is violated if, for
example, wallets that lose money early reduce their trading frequency and thus get classified
as “casual” precisely because they lost money. We address this endogeneity concern with
out-of-sample classification tests in Section 6, where we classify wallets based on their first
30 days of activity and measure outcomes on subsequent trades.
Our classification scheme assigns wallets to five mutually exclusive categories based on
the following rules:
1. Bot (N = 73,935): Wallets with >50 trades per day or >1,000 total trades. These
exhibit characteristics consistent with algorithmic trading: high frequency, continuous
market presence, and trade patterns that would be infeasible for manual execution.
2. Sophisticated (N = 64,913): Wallets with >$10,000 volume, Herfindahl-Hirschman
12
Index (HHI) < 0.5 indicating diversification across markets, and >30 days of active
trading. These represent serious but non-automated participants.
3. Active Retail (N = 1,305,716): Wallets with 10 to 1,000 trades that do not meet
sophisticated criteria.
4. Casual (N = 421,983): Wallets with 2 to 9 trades.
5. One-Shot (N = 114,861): Wallets with exactly one trade. One-shot traders may
exhibit selection bias: traders who win on their first trade may exit satisfied, while
losers may continue trading and thus be reclassified as casual or active retail. This
would mechanically inflate one-shot accuracy. We therefore report one-shot results
separately and exclude them from our main specifications.
Table 3 reports summary statistics for each category. The categories differ sharply in
trading intensity and market concentration. Bots account for only 3.7% of wallets but 76.4%
of total volume ($24.4 billion), with median trades per day of 76.3 and median HHI of 0.086
(highly diversified). Casual traders account for 21.3% of wallets but only 1.4% of volume,
with median HHI of 0.792 (concentrated in few markets). The low median active span for
bots (8 days) reflects a long tail of short-lived automated wallets that trade intensely over
brief periods before cycling to new addresses, a common pattern in blockchain markets; the
classification is based on trading intensity, not longevity, so these wallets qualify as bots
based on their within-period behavior. These differences validate that our classification
captures meaningful heterogeneity in trading behavior. We stress-test this classification in
Section 6 using nine alternative threshold specifications and data-driven k-means clustering.
3.4
P&L Computation
For each trade on a resolved market, we compute realized P&L. If a trader buys a token that
resolves to $1, P&L = token quantity − USDC paid. If it resolves to $0, P&L = −USDC
paid. Selling reverses the signs.
Table 4 reports pairwise correlations among key wallet-level variables. Accuracy and ROI
13
exhibit modest positive correlation (ρ = 0.12), consistent with accuracy alone being insufficient to determine profitability. The maker share (fraction of trades as liquidity provider)
correlates strongly with ROI (ρ = 0.31), consistent with our later finding that liquidity provision drives profits. Days to resolution correlates negatively with accuracy (ρ = −0.18),
consistent with the timing mechanism: traders who arrive earlier are less accurate but more
profitable. Variable definitions appear in Appendix A.
[Insert Table 4 here]
4
Empirical Results
We proceed in six steps. First, we document the accuracy-profitability inversion across
trader types. Second, we implement cross-sectional regressions. Third, we conduct portfolio
sorts on accuracy quintiles. Fourth, we decompose returns into directional and execution
components (P2). Fifth, we test whether more accurate wallets pay higher prices within each
trader type (P1). Sixth, we test whether forecasting skill and execution quality are persistent
and distinct traits (P3). A central identification challenge is that trader classification may be
endogenous to profitability; we address this concern with out-of-sample classification tests
in Section 6.
4.1
Univariate analysis: accuracy and profitability by trader type
We begin by computing directional accuracy and ROI at the wallet level for each trader
classification. Directional accuracy is defined as the fraction of resolved trades where the
trader bought a token that resolved to $1 or sold a token that resolved to $0. Under no
forecasting skill, expected accuracy is 50%. ROI is computed as total realized P&L divided
by total USDC invested. For each trader type, we report the mean accuracy, mean ROI,
total P&L, and the fraction of wallets that are net profitable. We compute t-statistics for the
accuracy measure using a binomial test against the null of 50%, and for ROI using Newey
14
and West (1987) standard errors with one lag to account for potential autocorrelation across
wallets trading in overlapping markets.
Table 5 reports accuracy and profitability by trader classification. The results reveal
an inversion of the standard microstructure prediction that informed traders profit at the
expense of uninformed traders. Bots achieve essentially random directional accuracy of
49.91%, statistically indistinguishable from 50% under a binomial test. Despite this lack of
forecasting ability, bots earn the only positive ROI in the sample at +0.56%, totaling $134
million in aggregate profits. In contrast, one-shot traders achieve the highest accuracy at
53.35% (significantly above 50%), yet they lose −5.94% on average.2 Active retail traders
are also directionally informed (51.27% accuracy, above random) but earn −2.00% ROI,
collectively losing $79 million.
[Insert Table 5 here]
The sophisticated and casual categories both exhibit below-random accuracy: 46.75%
and 46.28%, respectively, each significantly below 50%. Both groups lose money (−1.37%
and −3.45% ROI). Sophisticated traders present a puzzle: despite the highest execution
edge in the return decomposition (+4.99 cents, Table 7), they have the worst directional edge
(−5.08 cents). Effective spread analysis in Section 5 reveals they are net liquidity demanders
(+19.37 bps), not providers, suggesting their below-random accuracy reflects contrarian
positioning, systematically betting against market consensus, rather than passive market
making. Their execution edge comes not from spread capture but from entering at prices
with wide margin relative to outcomes, consistent with the timing mechanism: sophisticated
traders may trade early, when prices are far from terminal values, but systematically pick
the wrong side. Sophisticated traders thus represent a partial anomaly within the twodimensional framework: they have high ε but their unusually negative θ overwhelms it,
producing net losses despite favorable execution. This pattern is consistent with a contrarian
2
One-shot accuracy may be inflated by selection bias: traders who win their first trade may exit satisfied,
while losers continue trading and get reclassified into other categories. We report one-shot results in Table 5
for completeness but exclude them from all main specifications.
15
signal that is informative about timing but wrong about direction, and it suggests that the
two-dimensional decomposition, while sufficient to explain the bot-versus-retail gap, does
not fully characterize all trader types.
The zero-sum property of prediction markets provides internal validation of the P&L calculations: bot profits of $134.0 million approximately equal the sum of losses from all other
trader types ($38.5M sophisticated + $79.4M active retail + $13.9M casual + $2.3M one-shot
= $134.1M). This accounting identity confirms that profits flow from one group to another
rather than being created or destroyed.3 Figure 1 visualizes the accuracy-profitability inversion by plotting mean directional accuracy against mean ROI for each trader type. Highaccuracy types (one-shot and active retail) appear on the right with negative ROI, while the
only profitable type (bots) clusters near 50% accuracy.
The economic magnitudes are substantial and comparable to well-documented patterns
in traditional markets. The −2.00% ROI for active retail traders (total P&L divided by
total USDC invested) is the same order of magnitude as the annual retail underperformance
documented by Barber and Odean (2000) in equity markets, and the present setting differs
in that active retail traders are directionally correct (51.27% accuracy) yet still lose money.
In absolute terms, active retail traders collectively lost $79.4 million over the sample period,
while casual traders lost $13.9 million. Bots’ +0.56% ROI appears modest in percentage
terms, but applied to $23.9 billion in aggregate bot volume it produces $134 million in total
profits. The median bot wallet is small ($13,144 in volume, implying roughly $74 in profit),
but aggregate profits are driven by a small number of high-volume automated wallets. The
2.1-cent entry price advantage that bots hold over casual traders (documented in Table A3)
represents roughly twice the typical bid-ask spread in liquid prediction markets, explaining
much of the profit differential.
[Insert Figure 1 here]
3
As a further validation, we compute net position-level P&L by aggregating all trades within each walletmarket pair into a single net position. This holding-period approach yields bot profits of $134.0M, confirming
that per-trade accounting does not miss offsetting positions or round-trip effects that could distort walletlevel profitability estimates.
16
4.2
Cross-sectional regressions: does accuracy predict profitability?
The univariate results establish that accuracy and profitability are inversely related across
trader types, but this pattern could reflect confounding by trading intensity or other wallet
characteristics. To isolate the partial effect of accuracy on profitability, we implement crosssectional regressions of wallet-level ROI on directional accuracy with controls for log volume,
log number of trades, and trader type fixed effects. This specification asks: among wallets
with similar trading intensity and the same classification, do more accurate wallets earn
higher returns? Standard errors are clustered by wallet following Petersen (2009) to account
for correlation in trading outcomes across a wallet’s positions.
Table 6 reports the regression estimates. Column 1 presents the univariate relationship
between accuracy and ROI without controls. The coefficient is positive and statistically
significant, indicating that unconditionally, more accurate wallets do earn higher returns.
Column 2 adds log volume and log trades as controls for trading intensity. The accuracy
coefficient increases slightly from 0.170 to 0.182, indicating that the univariate estimate, if
anything, understated the partial relationship between accuracy and profitability. Column
3 adds trader type fixed effects, comparing wallets only within the same classification. The
coefficient on accuracy remains positive at 0.181 and statistically significant (t = 88.9),
indicating that even within trader types, accuracy is associated with higher returns.
[Insert Table 6 here]
However, the economic magnitude is modest. The coefficient of 0.181 implies that a 10
percentage point increase in accuracy is associated with only a 1.8 percentage point increase
in ROI. To put this in perspective, moving from 50% accuracy (no skill) to 60% accuracy
(substantial forecasting ability) would increase expected ROI by only 1.8 percentage points,
from roughly −2% to −0.2% for a typical retail wallet. The R2 of 4.6% in the full specification
indicates that accuracy, trading intensity, and trader type collectively explain less than 5%
17
of the cross-sectional variation in profitability. Accuracy contributes to returns, but other
factors dominate.
4.3
Portfolio sorts: returns by accuracy quintile
Following standard methodology in asset pricing (Ang et al., 2006; Bali et al., 2017), we
restrict to wallets with at least 50 resolved trades, sort into quintiles based on realized
accuracy, and compute average ROI within each quintile.
Table A2 reports the results. Column 1 shows the accuracy range for each quintile, ranging from 0-42% in Q1 (lowest accuracy) to 59-100% in Q5 (highest accuracy). Column 2
reports the number of wallets in each quintile, which are approximately balanced by construction. Column 3 reports average ROI. The relationship between accuracy and ROI is
monotonic: Q1 earns −6.15%, Q2 earns −2.62%, Q3 earns −2.20%, Q4 earns −0.26%, and
Q5 earns +0.82%. The HIGH−LOW spread (Q5 minus Q1) is 6.97 percentage points, which
is statistically significant.
Despite the monotonic relationship, the economic magnitude is weak relative to what
standard microstructure theory would predict. Moving from the lowest to highest accuracy
quintile improves ROI by only 7 percentage points. More strikingly, even the most accurate
quintile (Q5) barely breaks even at +0.82% average ROI. Column 4 shows that fewer than
half (49.6%) of wallets in the highest accuracy quintile are net profitable. High accuracy is
necessary but far from sufficient for profitability.
4.4
Return decomposition: directional versus execution components
To identify what separates profitable from unprofitable traders, we decompose average edgeat-entry into a directional component and an execution component. For each trade i in
18
market m, define:
Total Edgei = (Outcomem − Fair Pricem ) + (Fair Pricem − Entry Pricei )
|
{z
} |
{z
}
(4)
Execution
Directional
where Fair Pricem is the volume-weighted average transaction price across all buy-side trades
in market m. The directional component measures whether the trader entered a market that
was collectively underpriced; it captures the “forecasting” dimension. The execution component measures whether the trader got a better or worse price than the average participant;
it captures the “timing” dimension. This decomposition is exhaustive: total edge equals the
sum of the two components. The transaction cost term κ from Equation 1 is absorbed into
the execution component, since traders who pay higher fees effectively receive worse entry
prices; Polymarket charges no explicit trading fees, so κ reflects only the implicit cost of
crossing the spread.
Table 7 reports the decomposition by trader type. Both components are economically
meaningful relative to the $0–$1 contract range. Directional edge ranges from −2.88 cents
per token (casual) to −5.08 cents (sophisticated), while execution edge ranges from +0.89
cents (casual) to +4.99 cents (sophisticated). These magnitudes are modest relative to pertrade standard deviations of 30–37 cents, but they accumulate across millions of trades:
the 2.52-cent execution gap between bots and casual traders implies substantial differential
profits at bot trading volumes. The execution component varies far more across types than
the directional component. Bots’ directional edge (−3.11 cents per token) is not atypical,
yet they are the only profitable type because their execution edge (+3.41 cents) more than
compensates. Section 6.4 verifies that this decomposition is robust to alternative, strictly
backward-looking benchmarks.
[Insert Table 7 here]
Figure 8 visualizes the decomposition, and Figure 2 plots each type’s position in the twodimensional skill space. Consistent with P2, both components matter, but execution quality
19
varies more across trader types and determines profitability. A striking implication is that
every trader type exhibits negative directional edge: no type, not even the most accurate,
enters markets that are on average underpriced relative to outcomes. Prices already reflect
the directional information that traders possess. The entire profitability question therefore
reduces to execution: what separates profitable from unprofitable traders is not who picked
the right side but who paid a favorable price.
4.5
Within-type θ-ε correlation
The return decomposition shows that execution quality varies across trader types. But trader
types differ along many dimensions (trading frequency, market selection, trade size), raising
the concern that the decomposition reflects market selection rather than execution skill. To
address this, we test whether the negative θ-ε relationship holds within each trader type.
For each trader type t ∈ {Bot, Sophisticated, Active Retail}, we estimate:
Entry Pricei,m = αt + βt · Accuracyi + εi,m
(5)
where Accuracyi is wallet i’s fraction of correct trades across all resolved markets. Under
prediction P1, βt > 0: more accurate wallets pay higher entry prices, reflecting the negative within-type correlation between forecasting skill and execution quality. We estimate
separately for winning and losing tokens.
Table 8 reports the results. The coefficient β is positive and highly significant for every
trader type and token outcome. For bots trading winning tokens, β = 0.673 (t = 2,879):
a bot with 10 percentage points higher accuracy pays 6.7 cents more per token. For active
retail on winning tokens, β = 0.318 (t = 544). Coefficients are even larger for losing tokens
(up to β = 0.774 for bots), consistent with accurate traders paying prices closer to terminal
values on both winning and losing sides.
[Insert Table 8 here]
20
Table A6 reinforces this finding by reporting average entry prices across accuracy quintiles
within each type. Across all three types, higher-accuracy quintiles pay higher prices for
winning tokens. The gradient is steepest for bots: Q1 bots pay $0.52 for winning tokens,
while Q4 bots pay $0.78, a 26-cent disadvantage that dominates any directional advantage
from superior accuracy. (The gradient reverses slightly at Q5, where entry prices drop to
$0.67, likely reflecting extreme-accuracy bots that specialize in high-conviction trades at
moderate prices.)
Figure 6 visualizes these results. Panel A plots binned means of ε against θ within each
type, confirming that higher accuracy corresponds to worse execution for all three types.
Panel B plots first-half θ1 against second-half ε2 , confirming the flat cross-skill relationship:
knowing a wallet’s forecasting ability is nearly uninformative about its future execution
quality.
The within-type results rule out the possibility that the accuracy-profitability inversion
is an artifact of trader classification. Even among bots with similar trading intensity, or
among active retail traders with similar volumes, more accurate wallets pay worse prices.
The negative θ-ε correlation is a feature of the market, not a statistical artifact of how we
group traders.
Part of the positive β reflects a mechanical relationship: a wallet that trades predominantly at prices near 1 will mechanically have high accuracy (the max(p, 1 − p) benchmark is
high) and low execution edge (1−p is small for winning tokens). However, three observations
suggest the result is not fully mechanical. First, when we restrict to the competitive zone
(p ∈ [0.2, 0.8]), where max(p, 1 − p) ranges only from 0.50 to 0.80, the edge hierarchy across
trader types persists: bots earn +0.39 cents (t = 84) and retail loses −0.81 cents (t = −39).
Second, the excess accuracy, defined as observed accuracy minus max(p, 1 − p), is negative
for all types in every price bin, indicating that no trader type achieves accuracy above what
price levels alone would predict. Third, the split-sample persistence results (Section 4.6)
are immune to this concern: first-half θ predicting second-half θ across wallets cannot be
21
explained by within-trade price mechanics.4
4.6
Skill persistence and distinctness
The two-dimensional framework in Section 2 requires that θ and ε be persistent characteristics of individual traders, not transient noise. If a wallet’s accuracy in one period does
not predict its accuracy in the next, then θ is not a stable skill and the decomposition lacks
economic content.
To test persistence, we implement a split-sample design. For each wallet with 50 or more
trades, we split trades chronologically into first and second halves. We compute (θ1 , ε1 )
on the first half and (θ2 , ε2 ) on the second half. Persistence is measured as the correlation
ρ(θ1 , θ2 ) and ρ(ε1 , ε2 ). We also compute cross-skill correlations ρ(θ1 , ε2 ) and ρ(ε1 , θ2 ) to test
whether the two skills are distinct dimensions.
Table 9 reports the results. Forecasting skill persists strongly: ρ(θ1 , θ2 ) = 0.87 for bots,
0.59 for sophisticated traders, and 0.62 for active retail. A wallet that correctly forecasts
outcomes in its early trades continues to do so in its later trades. Execution quality persists
moderately: ρ(ε1 , ε2 ) = 0.34 for bots, 0.20 for sophisticated, and 0.28 for active retail. A
wallet that obtains favorable prices early continues to do so later, though with more noise.
Critically, the cross-skill correlations are weak: ρ(θ1 , ε2 ) ranges from 0.11 to 0.13, and
ρ(ε1 , θ2 ) ranges from 0.11 to 0.14. A wallet’s forecasting ability in the first half is nearly
uninformative about its execution quality in the second half, and vice versa. This confirms
prediction P3: θ and ε are distinct, persistent characteristics, not two manifestations of a
single latent “skill” variable. Figure 7 visualizes the persistence patterns: Panel A shows the
strong θ1 –θ2 relationship, while Panel B shows the weaker but still positive ε1 –ε2 relationship.
[Insert Table 9 here]
4
The mechanical component of the θ-ε correlation implies that any decomposition of returns into accuracy
and execution will feature a negative covariance between the two components. This is analogous to the
mechanical negative covariance between security selection and market timing documented in Fama (1972).
The substantive finding is not the negative within-type correlation per se but rather the cross-type variation
in ε (the 2.52-cent execution gap between bots and casual traders) which cannot be explained mechanically.
22
Table A7 examines whether traders improve over time. For wallets with 100+ trades
spanning 6+ months, we compute monthly trends in θ and ε. Forecasting skill shows no
systematic improvement for bots (trend ≈ 0) and modest improvement for active retail (+0.6
pp per month). Execution quality shows slight deterioration for all types (−0.15 to −0.5 pp
per month), consistent with increasing market competition eroding execution advantages.
These learning dynamics reinforce the persistence interpretation: skills are relatively stable,
not rapidly evolving.
5
Mechanism: Timing and Execution
The accuracy-profitability inversion and the two-dimensional skill structure documented in
Section 4 require a mechanism: why are forecasting skill and execution quality negatively
correlated? We test three observable implications of the timing mechanism proposed in
Section 2.1: (i) accurate traders pay entry prices that already reflect information, (ii) retail
traders arrive later in the price discovery process, and (iii) edge decays over the market
lifecycle. We then examine effective spreads and liquidity provision roles.
5.1
Entry price analysis
The model predicts that slow traders who correctly identify winning tokens will pay entry
prices closer to the resolution value ($1.00) than fast traders. The intuition is that by the
time slow traders arrive, prices have already partially incorporated the information that the
slow traders possess. To test this prediction, we compute the average entry price paid by
each trader type, conditioning separately on whether the token eventually resolved to $1.00
(winning) or $0 (losing). We also compute the “price gap,” defined as the difference in
average entry price between winning and losing tokens. A larger price gap indicates that
prices are more informative at the time of trade: the market has already separated winning
from losing tokens.
23
Table A3 reports entry prices by trader type and outcome. Casual traders who buy
winning tokens pay an average price of $0.856, meaning they correctly identify the winner
but arrive when prices are already 85.6% of the way to the $1 resolution value. Expected gross
profit is only 14.4 cents per token ($1.00 minus $0.856), which must cover execution costs
including the effective spread. Bots pay $0.835 for winning tokens, a 2.1 cent advantage over
casual traders. While this difference appears small, it compounds across millions of trades:
2.1 cents on $24 billion in bot volume implies $504 million in additional profit margin relative
to what casual traders would earn at the same accuracy level.
The price gap (winning minus losing entry price) differs sharply across types. The gap
for casual traders is 0.455, compared to 0.357 for bots. This difference confirms that retail
traders face more informative prices: by the time they arrive, the market has partially
revealed which outcome will occur. The 9.8-cent wider price gap faced by casual traders
(0.455 versus 0.357) implies that retail trades occur in market states where price discovery
is further advanced, leaving less room for profitable directional bets. Figure 3 visualizes the
distribution of entry prices by outcome, showing the separation between winning and losing
token prices that characterizes the market states in which retail traders operate. Figure 4
further illustrates that P&L declines monotonically with entry price for winning tokens:
traders who buy at low prices earn higher returns, while those who arrive after prices have
risen earn lower or negative returns.
[Insert Figure 3 here]
5.2
Trade timing relative to resolution
If the timing mechanism operates, casual traders should trade closer to resolution dates than
bots. Markets approaching resolution have incorporated more information, leaving less profit
margin for correct directional bets. To test this prediction, we compute days to resolution for
each trade, defined as the calendar days between the trade date and the market’s eventual
24
resolution date. We compare the distribution of this variable across trader types using means,
medians, and the fraction of trades occurring within the final week before resolution.
Table A4 reports timing statistics by trader type. Column 1 shows that casual traders
have a mean of 8.4 days to resolution at the time of their trades, compared to 14.2 days
for bots. Column 2 reports median values, which are less sensitive to outliers: the median
casual trade occurs 3.1 days before resolution, versus 8.1 days for bots, a 2.6-fold difference.
Column 3 shows that 62.8% of casual trades occur within the final week before resolution,
compared to only 42.3% for bots. Column 4 shows the fraction within the final day: 31.6%
of casual trades versus 18.7% of bot trades.
One-shot traders are even more concentrated near resolution, with a median of 2.4 days
and 71.3% of trades within the final week. This timing pattern is consistent with attentiondriven trading documented by Barber et al. (2022) in equity markets: retail investors trade
when events become salient, which in prediction markets occurs as resolution approaches.
The wallet-level correlation between accuracy and days to resolution (ρ = −0.18, Table 4)
confirms the link directly: higher-accuracy traders systematically trade later in the market
lifecycle, when execution opportunities are thinnest, consistent with prediction P4.
5.3
Edge decay over the market lifecycle
The timing mechanism predicts that edge-at-entry should decay as markets approach resolution. Early in a market’s life, prices are uncertain and directional bets can be profitable. As
resolution approaches, prices converge toward terminal values, eliminating profit opportunities for directional traders. Table 10 tests this prediction by computing average edge-at-entry
for trades grouped by days to resolution.
The results support the market lifecycle hypothesis, with important heterogeneity across
types. For bots, trades placed more than one year before resolution earn +14.1% average
edge. This edge decays monotonically: 90–365 days yields +2.5% edge, 30–90 days yields
+1.4% edge, 7–14 days yields approximately zero edge, and trades in the final day before
25
resolution earn −1.4% edge. For other trader types, the lifecycle pattern is not monotonic:
early trades by retail and casual traders show large negative edge (e.g., −15.5% for active
retail trades >365 days out), likely reflecting adverse selection: the few retail traders who enter very early markets disproportionately pick the losing side. The monotonic decay pattern
that characterizes P4 is thus primarily a feature of automated trading, where early entry
translates into execution advantage rather than directional disadvantage.
[Insert Table 10 here]
This evidence directly addresses the identification concern raised regarding the timing
mechanism. The monotonic decay of edge over the market lifecycle is difficult to explain
through alternative mechanisms such as adverse selection or trader heterogeneity. If the
pattern reflected trader skill differences, one would not expect edge to vary systematically
with days to resolution within trader type. The finding that early trades earn positive edge
while late trades earn negative edge, holding trader type constant, supports the interpretation
that execution timing rather than forecasting ability determines profitability.
5.4
Effective spreads and liquidity provision
If bots profit through liquidity provision rather than information, they should earn the
bid-ask spread (as makers) while retail traders pay it (as takers). We compute the volumeweighted effective spread for each trader type, defined as the signed difference between
the execution price and the prevailing midpoint, scaled by the midpoint. Positive effective
spreads indicate the trader paid liquidity costs; negative effective spreads indicate the trader
earned the spread as a liquidity provider.
Table A5 reports effective spreads by trader type. Bots earn a negative effective spread
of −4.25 basis points on average, confirming they are net liquidity suppliers. This finding
aligns with Menkveld (2013) and Hendershott et al. (2011), who document that algorithmic
traders earn profits through market making rather than informed trading in equity markets.
26
Sophisticated traders pay the highest positive spreads at +19.37 bps, followed by active retail
at +11.68 bps, consistent with these groups being net liquidity demanders. Casual traders
pay +4.28 bps. One-shot traders show negative spreads (−17.93 bps), which likely reflects
their small sample and the possibility that some one-shot trades are maker-side fills.
Combined with the timing and entry price evidence, these results support the timing
mechanism. The negative correlation between θ and ε is consistent with high-accuracy
traders arriving late in the market lifecycle, after execution-skilled traders have already
moved prices toward terminal values. Automated traders profit through spread capture
and favorable execution timing (high ε) with near-random forecasting ability (θ ≈ 0.50).
The accuracy-profitability inversion reflects the two-dimensional structure of trader skill:
forecasting ability and execution quality are distinct traits, and it is the latter, not the
former, that determines which trader types earn positive returns.
6
Robustness
This section presents eight robustness tests. We vary classification thresholds (±50%) and
apply data-driven k-means clustering; split the sample by subperiod; add market fixed effects
and trade-level controls; replicate the decomposition under backward-looking and laggedprice benchmarks; separate maker from taker sides; implement out-of-sample classification
based on the first 30 days of activity; and verify that accuracy is not mechanically inflated
by price levels.
6.1
Alternative Classification Thresholds
Our results could depend on specific classification cutoffs. We address this by varying the bot
and sophisticated thresholds by ±50%, creating three regimes. Under strict thresholds (50%
higher cutoffs), 36,087 wallets qualify as bots; under loose thresholds (50% lower cutoffs),
153,640 do, a fourfold range in bot population.
27
Table A9 reports ROI by wallet type across all nine specifications (three bot thresholds
× three sophisticated thresholds). Bot ROI is positive across all nine: +0.33% under the
loosest bot threshold, +0.56% at baseline, and +0.80% under the strictest threshold, where
only the most intensive automated wallets qualify. The strict specification strengthens the
result by removing marginal wallets. Active retail ROI is negative across all specifications
(−1.59% to −2.18%), and casual traders underperform throughout (−3.44% to −3.50%).
The qualitative finding (that directionally random high-frequency traders profit while more
accurate low-frequency traders lose) holds regardless of where we draw the classification
boundaries.
6.2
Data-Driven Classification and Subperiod Stability
Our rule-based classification imposes researcher judgment on the number of categories and
their boundaries. To verify that the accuracy-profitability inversion is not an artifact of these
choices, we apply k-means clustering on five wallet-level features: log(volume), log(trades),
HHI, buy ratio, and log(active span). Table A10 reports P&L by cluster for k = 5. The
algorithm recovers a high-frequency cluster (486,452 wallets, 49.9% accuracy, +0.28% ROI)
dominated by automated trading, and a low-frequency cluster (606,069 wallets, 51.6% accuracy, −2.98% ROI) dominated by active retail. The inversion, near-random-accuracy highfrequency wallets profiting while more accurate low-frequency wallets lose, emerges from the
data without imposing it.
A separate concern is that the inversion could be driven by a single market episode, such
as the 2024 U.S. presidential election, which generated unusually high volume. Table A11
splits the sample into four periods: pre-election (before July 2024), election season (July–
November 2024), post-election (November 2024–June 2025), and late sample (July 2025+).
The inversion is present in all four periods: bots earn positive ROI and retail earns negative
ROI in every subperiod. Figure 5 visualizes these results.
28
6.3
Market Fixed Effects and Trade-Level Controls
Different wallet types may select into different markets or differ systematically in trade
characteristics. If bots concentrate in liquid markets or trade smaller amounts, our results
could reflect market selection or trade size rather than execution timing.
Table A12 reports regressions of trade-level P&L on wallet type indicators with progressively richer controls. Columns (2) and (3) add market and market-by-week fixed effects,
respectively. Column (4) adds log trade size, which has a negative coefficient: larger trades
earn lower returns, consistent with price impact. Column (5) adds a maker indicator, which
has a large positive coefficient (1.84 pp): liquidity providers earn substantially higher returns than liquidity takers. Column (6) adds price distance from 0.50, capturing how much
information is already incorporated.
The wallet type coefficients attenuate as controls are added but remain economically
and statistically significant throughout. In the full specification (column 6), casual traders
still underperform bots by 2.35 percentage points, and one-shot traders by 2.76 percentage
points. The maker coefficient confirms that liquidity provision is profitable, consistent with
bots earning returns through spread capture. However, the persistence of large wallet-type
gaps after controlling for maker status indicates that the accuracy-profitability inversion
is not fully explained by differential liquidity provision: even among takers, retail traders
underperform bots.
6.4
Alternative Fair Price Benchmarks
The return decomposition in Section 4.4 uses a lifecycle VWAP, the volume-weighted average
price across all buy-side trades in each market, as the fair price benchmark. This benchmark
incorporates trades that occur after a given transaction, potentially introducing lookahead
bias: early trades mechanically appear to have good execution because the VWAP includes
later, higher-priced trades.
We address this concern by replicating the decomposition under two time-conditional
29
benchmarks. Cumulative VWAP : the volume-weighted average price of all buy-side trades
occurring before the focal trade, a strictly backward-looking benchmark with zero ex-post
information. Lagged price: the price of the immediately preceding trade in the same market.
Table A8 reports the results. The total edge column is mechanically identical across
benchmarks because it depends only on the outcome and entry price. The decomposition
into directional and execution components does shift across benchmarks, but the key finding
is robust. Under the cumulative VWAP (Panel B), the execution gap between bots (+3.17
cents) and casual traders (+0.28 cents) is larger than under the lifecycle VWAP (2.89 cents
versus 2.52 cents), indicating that the original result is conservative rather than inflated by
lookahead bias. Under the lagged price benchmark (Panel C), both components compress
because the benchmark closely tracks current prices; the execution gap between bots (+0.68
cents) and casual traders (+0.69 cents) essentially vanishes, but the total edge gap persists
(+0.29 versus −1.97 cents) because the directional component absorbs the difference. Across
all three benchmarks, execution quality varies far more across types than directional skill
under the first two benchmarks, and in all cases the same types earn positive versus negative
total returns.
6.5
Maker/Taker Decomposition
A related concern is that the “execution component” may conflate two distinct economic
channels: genuine price improvement (trading when prices are favorable) and liquidity provision (earning the bid-ask spread). If bots earn positive execution edge primarily because
they are passive market makers capturing the spread, the interpretation shifts from “execution skill” to “liquidity provision.”
We disentangle these channels by decomposing returns separately for the maker (liquidityproviding) and taker (liquidity-demanding) side of each trade. Every Polymarket trade
matches a maker order with a taker order; we assign each side to the corresponding wallet’s
type.
30
[Insert Table 11 here]
Table 11 reveals that bots play two distinct roles with very different skill profiles. Makerside bots have below-random accuracy (44.8%) but earn +3.17 cents in execution edge
through spread capture. These are automated market makers whose profits come entirely
from liquidity provision. Taker-side bots have substantially higher accuracy (56.6%) and
earn +3.99 cents in directional edge, but pay nearly all of it back in execution costs (−3.97
cents), netting approximately zero. These are informed traders who cross the spread to act
on directional signals.
The accuracy figures in this decomposition differ from those in Table 5 because the two
tables measure different objects. Table 5 reports wallet-level accuracy aggregated across all
trades (both maker and taker sides), yielding 49.91% for bots overall. Table 11 conditions
on role: maker-side bots achieve only 44.8% accuracy, while taker-side bots achieve 56.6%.
The volume-weighted average of these role-specific accuracies recovers the wallet-level figure.
The decomposition also illuminates retail behavior. Active retail traders on the taker
side show modest directional skill (52.2% accuracy, +1.30 cents directional) but lose on
execution (−2.01 cents), consistent with slow traders who are “right but late.” On the
maker side, active retail traders earn positive execution (+2.73 cents) but not enough to
offset directional losses (−3.11 cents). Critically, the total-edge ranking (bots outperform
sophisticated, who outperform active retail, who outperform casual) holds within both roles.
The accuracy-profitability inversion is not merely a maker/taker composition effect.
6.6
Out-of-Sample Classification
Our wallet classification is based on cumulative trading behavior, which may be endogenous
to profitability. A wallet that loses money early may trade less frequently, causing us to
classify it as “casual” precisely because it lost money. This creates potential survivorship or
selection bias.
31
To address this, we implement an out-of-sample classification test. We classify wallets
based only on their trading behavior during their first 30 days of activity, then measure
P&L on all subsequent trades. The classification is therefore determined before the outcome
period.
The out-of-sample results preserve the core ranking: wallets classified as “bot-like” based
on their first 30 days of trading continue to earn the highest edge in subsequent trading
(+0.36 cents, t = 93), while casual wallets continue to lose (−0.21 cents, t = −12). The
bot-versus-casual ordering, which is the paper’s central finding, is robust to the classification
scheme.
However, two intermediate categories exhibit sign flips that warrant discussion. Active
retail edge flips from −0.38 cents (baseline) to +0.38 cents (OOS), and sophisticated edge
flips from −0.10 cents to +0.41 cents. These reversals likely reflect contamination from the
60.2% classification agreement rate. Because 40% of wallets change category under the 30day scheme, wallets reclassified from “bot” or “sophisticated” into “active retail” bring their
positive-edge trading behavior with them, pulling the OOS active retail average above zero.
A definitive resolution would require decomposing OOS performance separately for wallets
that retain versus change their classification, and varying the classification window (60, 90,
180 days) to assess whether the sign flips attenuate with longer observation. We leave this
decomposition to future work.
The paper’s two strongest results are immune to this classification concern. The withintype regressions (Table 8) show that more accurate wallets pay higher entry prices within each
type, a result that does not depend on cross-type comparisons. The split-sample persistence
tests (Table 9) show that θ and ε are distinct, persistent traits with low cross-correlation, a
finding that holds within each type and cannot be driven by how wallets are grouped across
types.
32
6.7
Mechanical Accuracy and Restricted Samples
Our accuracy measure may be mechanically inflated for certain trader types. If bots systematically trade tokens priced near 0 or 1, where the “correct” outcome is nearly certain,
then high-volume trading in extreme-price tokens could generate apparent accuracy without genuine forecasting skill. More broadly, if accuracy is mechanically determined by the
price at which traders enter, then the accuracy-profitability inversion may reflect price-level
selection rather than skill heterogeneity.
We address this concern in three ways. First, we construct a naive accuracy benchmark:
for each trade at price p, the expected accuracy under no skill is max(p, 1 − p), the accuracy
achieved by always predicting the more likely outcome. We then compute excess accuracy
as observed accuracy minus this benchmark. Excess accuracy is negative for all trader
types across all price bins: bots range from −0.69 at extreme prices (0.0–0.1) to −0.08 near
the midpoint (0.4–0.5); active retail ranges from −0.51 to −0.08. No trader type achieves
accuracy above the naive benchmark in any price bin. This rules out the concern that our
results are driven by mechanical accuracy inflation.
Second, we restrict the sample to the “competitive zone” where prices are between 0.20
and 0.80, the range where directional skill can plausibly affect outcomes and where the naive
accuracy benchmark is lowest. In this restricted sample, the edge hierarchy persists: bots
earn +0.39 cents per trade (t = 84), while active retail traders lose −0.81 cents (t = −39)
and casual traders lose −4.20 cents (t = −30). Narrowing further to trades with prices in the
competitive zone and more than seven days before resolution, removing both extreme-price
and near-resolution trades, bots still earn +0.89 cents (t = 67) and retail still loses (−0.26
cents, t = −7).
Third, we examine edge by time-to-resolution bins to ensure results are not driven by the
final hours of trading when outcomes are nearly known. Bot edge is positive across all time
horizons, from +0.94 cents for trades placed more than one year before resolution to +0.22
cents in the final day. Active retail edge is negative in most time bins, with the exception
33
of the 7–14 day window (+0.55 cents). The hierarchy of bot over retail is present at every
time horizon except this single bin. These results confirm that the accuracy-profitability
inversion is not a mechanical artifact of price levels, trade timing, or the construction of our
skill measures.
7
Conclusion
A central finding in empirical finance is that retail traders correctly forecast the direction
of asset prices yet lose money. This paper identifies the mechanism in a setting where it
can be measured cleanly. Using 222 million prediction market trades where terminal payoffs
are directly observable, we decompose trader returns into a directional component and an
execution component and show that these two dimensions of skill are weakly correlated (ρ =
0.11–0.14), individually persistent (ρ = 0.59–0.87 for forecasting, 0.20–0.34 for execution),
and have opposing effects on profitability. The execution component, not the directional
component, determines which trader types earn positive returns.
The mechanism operates through execution timing and liquidity provision over the market lifecycle. For automated traders, edge-at-entry decays monotonically from +14% for early
trades to −1.4% near resolution. Accurate forecasters arrive after execution-skilled traders
have already moved prices toward terminal values. A 2.52-cent execution gap between automated and casual traders, rather than differences in forecasting ability, determines which
types earn positive returns. This result survives a strictly backward-looking benchmark, a
maker/taker split, market fixed effects, and out-of-sample classification (Tables A8–A13).
The results have three broader implications. First, we identify a mechanism that can
account for the retail trading paradox of Barber et al. (2023): it is not what retail traders
buy but how they execute that determines profitability. Barber et al. attribute the paradox
to attention-driven stock selection; we identify a complementary channel operating through
execution quality. The −2.0% ROI for active retail traders in prediction markets, despite
34
51.3% accuracy, parallels the equity market pattern where retail order flow predicts returns
yet retail traders lose money. The prediction market setting cannot prove that this channel
operates in equities, but the convergence of magnitudes and loss structures across prediction markets and equities, documented in the introduction, suggests the execution channel
may be general. Second, the separability of θ and ε is consistent with how algorithmic
traders earn persistent profits without apparent directional information (Baron et al., 2019;
Menkveld, 2013): their profits may come from a dimension of skill that is orthogonal to
forecasting ability. Third, the finding that informed traders, those who move prices toward
terminal values, are not compensated for their contribution to price discovery raises questions about the sustainability of information acquisition in markets where execution-skilled
traders capture the surplus.
The paper has several limitations. Our trader classification is based on observable behavior, which may be endogenous to profitability; out-of-sample tests mitigate but cannot
eliminate this concern. Prediction market “information” concerns publicly observable events
rather than private signals, limiting direct external validity. We cannot measure information
acquisition costs, preventing a formal test of Grossman and Stiglitz (1980). And our decomposition defines execution quality relative to one benchmark, the volume-weighted average
price, though robustness to alternative benchmarks is encouraging.
What sustains retail participation in a market where accurate forecasters subsidize executionskilled traders? Possible explanations include entertainment utility, overconfidence about execution ability, or failure to distinguish between “being right” and “making money.” These
are important questions for future work. More immediately, the two-dimensional skill framework developed here may prove useful for evaluating information content in any market where
asset payoffs are eventually observable, including options, credit derivatives, and insurance
contracts.
35
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38
Table 1: Identification Advantage: Skill Decomposition Across Empirical Settings
This table compares the ability to decompose trading performance into forecasting skill (θ)
and execution quality (ε) across empirical settings. V denotes the asset’s terminal fundamental value. In equity markets, V is never observed, so all performance attribution relies on
proxies. Prediction markets resolve to known values ($0 or $1), enabling exact measurement
of both skill dimensions. a Subsequent returns or factor-adjusted returns (Barber et al.,
2023). b Benchmark portfolios or factor models (Hasbrouck, 1991). c Brinson attribution or
characteristic-based benchmarks (Brinson et al., 1986; Daniel et al., 1997). d Implementation
shortfall relative to arrival price.
Setting
Observe V ?
Measure θ?
Measure ε?
Identification Problem
Equity markets
Mutual funds
Options
Sports betting
No
No
Partial
Yes
Proxya
Proxyc
Partial
Yes
Proxyb
Proxyd
Partial
Limited
V never observed
Benchmark-dependent
Complex payoff structure
Bookmaker sets price
Prediction markets
Yes
Yes
Yes
39
None
Table 2: Data Summary Statistics
This table summarizes the Polymarket on-chain dataset. Raw OrderFilled events are collected from Polygon via eth getLogs. The Paradigm matching engine emits two events per
matched trade; we deduplicate by filtering events where the taker address is an exchange
contract. Token-to-market mappings are constructed from the Gamma API bulk market
download, achieving 100% coverage. Resolution data covers 95.4% of processed trades via
closed market outcome prices from the Gamma API.
Count
Description
Panel A: Raw Data
Raw OrderFilled events
Processed trades (post-dedup)
Unique wallets
Unique markets
Token-market mappings
394,630,032
232,988,474
1,981,408
556,632
803,501
Polygon blocks 21M–82.3M
Paradigm double-count removed
Maker or taker in ≥1 trade
With resolution data
100% trade coverage
Panel B: Resolution Coverage
Trades in resolved markets
Wallet-trade pairs (resolved)
222,187,129
444,374,258
95.4% of processed trades
Maker + taker sides
Panel C: Sample Period
First trade
Last trade
November 2022 (Polygon block ∼21M)
February 1, 2026 (block 82.3M)
40
Table 3: Wallet Classification Summary
This table reports summary statistics for each wallet classification. TPD is trades per day.
HHI is the Herfindahl-Hirschman Index of market concentration (lower = more diversified).
Span is active trading days. Classification criteria: Bot: >50 trades/day or >1,000 total;
Sophisticated : >$10K volume, HHI < 0.5, >30 days active; Active retail : 10–1,000 trades;
One-shot: exactly 1 trade; Casual : all remaining. The low median span for bots (8 days)
reflects a long tail of short-lived automated wallets that trade intensely over brief periods
before cycling to new addresses, a common pattern in blockchain markets.
Type
N
%
Total Vol.
Vol.%
Med. Trades
Med. Vol.
Med. TPD
Med. HHI
Med. Span
Bot
Sophisticated
Active Retail
Casual
One-Shot
73,935
64,913
1,305,716
421,983
114,861
3.7%
3.3%
65.9%
21.3%
5.8%
$24.4B
$2.9B
$4.1B
$440.5M
$39.9M
76.4%
9.1%
13.0%
1.4%
0.1%
545
163
35
4
1
$13,144
$21,624
$502
$82
$5
76.3
7.9
4.7
2.0
1.0
0.086
0.135
0.261
0.792
1.000
8
182
61
2
1
Total
1,981,408
100%
$32.0B
100%
41
Table 4: Pairwise Correlations
This table reports pairwise Pearson correlations among key wallet-level variables. Accuracy
is the fraction of correct directional trades. ROI is total P&L / total investment. Log
Volume and Log Trades are natural logs. Maker Share is the fraction of trades where the
wallet was liquidity provider. Days to Resolution is the average days between trade and
market resolution. Sample includes 2.0M wallets with at least one resolved trade.
Accuracy
ROI
Log Volume
Log Trades
Maker Share
Days to Resolution
Accuracy
ROI
Log Vol.
Log Trades
Maker %
Days to Res.
1.00
0.12
−0.08
−0.06
−0.14
−0.18
1.00
0.18
0.15
0.31
0.09
1.00
0.84
0.42
0.22
1.00
0.38
0.19
1.00
0.27
1.00
42
Table 5: P&L and Directional Accuracy by Wallet Type
This table reports P&L and directional accuracy for each wallet type on resolved markets
(95.4% of trades). Each matched trade generates one maker-side and one taker-side observation; trade counts reflect both sides (222 million resolved trades × 2 = 444 million
observations). P&L is computed as: buy winning token = token amount − usdc amount;
buy losing token = −usdc amount; sell winning token = usdc amount − token amount; sell
losing token = +usdc amount. ROI = Total P&L / Total Volume. Accuracy is the fraction
of trades that are directionally correct (buying the eventual winner or selling the eventual
loser). The zero-sum property holds: bot profits ($134.0M) approximately equal total losses
of all other types ($134.1M).
Type
Volume
Total P&L
Bot
356,560,155
$23.9B
Sophisticated 15,454,262
$2.8B
Active Retail
70,410,380
$4.0B
Casual
1,842,471 $401.6M
One-Shot
106,990 $38.9M
$134.0M
$-38.5M
$-79.4M
$-13.9M
$-2.3M
$31.1B
≈$0
Total
Trades
444,374,258
43
ROI $/Trade
Accuracy
$0.38
$-2.49
$-1.13
$-7.52
$-21.61
0.4991
0.4675
0.5127
0.4628
0.5335
0.56%
-1.37%
-2.00%
-3.45%
-5.94%
Table 6: Accuracy-Profitability Inversion: ROI on Accuracy
This table reports OLS regressions of wallet-level ROI on directional accuracy, with controls
for trading intensity. The sample includes wallets with 50+ resolved trades. t-statistics (in
parentheses) are based on heteroskedasticity-robust (HC1) standard errors. Column (1) is
the bivariate regression. Column (2) adds log volume and log trade count. Column (3) adds
wallet type fixed effects. ∗∗∗ , ∗∗ , and ∗ denote significance at the 1%, 5%, and 10% levels.
Accuracy
(1)
ROI
(2)
ROI
(3)
ROI
0.1703∗∗∗
(83.76)
0.1819∗∗∗
(89.73)
0.0071∗∗∗
(59.17)
−0.0012∗∗∗
(−6.31)
0.1812∗∗∗
(88.92)
0.0076∗∗∗
(58.66)
0.0005∗
(1.90)
−0.0104∗∗∗
(−11.91)
−0.0098∗∗∗
(−14.39)
552,167
0.0354
552,167
0.0454
552,167
0.0460
log(Volume)
log(Ntrades )
Bot
Sophisticated
N
R2
44
Table 7: Return Decomposition: Directional versus Execution Components
This table decomposes average edge-at-entry into directional and execution components.
For each trade i in market m:
Total Edgei
=
(Outcomem − Fair Pricem ) + (Fair Pricem − Entry Pricei ), where Fair Pricem is the
|
{z
} |
{z
}
Execution
Directional
volume-weighted average price across all buy-side trades in market m. The directional
component captures whether the trader entered a market that was underpriced relative to
its outcome. The execution component captures whether the trader paid a better or worse
price than the average participant. Per-trade standard deviations exceed 30 cents, so both
components are modest relative to per-trade outcome uncertainty. Table A8 verifies this
decomposition is robust to alternative benchmark definitions. Accuracy in this table reflects
the decomposition sample (buy-side trades in 222M resolved trades) and differs from the
wallet-level, all-sides accuracy reported in Table 5. One-shot traders excluded due to small
sample.
Share of |Total|
Average Edge (cents/token)
Wallet Type
N Trades
Total
Directional
Execution
Accuracy
Direct.
Exec.
Bot
Sophisticated
Active Retail
Casual
202.3M
4.9M
14.7M
257K
+0.29
−0.10
−0.38
−2.00
−3.11
−5.08
−3.64
−2.88
+3.41
+4.99
+3.26
+0.89
44.8%
44.1%
47.7%
54.9%
47.7%
50.5%
52.8%
76.4%
52.3%
49.5%
47.2%
23.6%
45
Table 8: Within-Type Correlation: Entry Price on Wallet Accuracy
This table reports OLS estimates of entry price on wallet-level directional accuracy, run
separately by trader type and token outcome. The specification is Entry Pricei,m = α + β ·
Accuracyi + εi,m , where Accuracyi is wallet i’s fraction of correct trades across all resolved
markets, and Entry Pricei,m is the price paid in market m. Winning (losing) tokens are those
that resolved to $1 ($0). A positive β indicates that more accurate wallets pay higher entry
prices for winning tokens and higher prices for losing tokens, consistent with late arrival
after prices have moved toward terminal values. All coefficients significant at p < 0.001
(∗∗∗ ). Sample restricted to buy-side trades in resolved markets.
Winning Tokens
Losing Tokens
Wallet Type
β̂
t-stat
R2
β̂
t-stat
R2
Bot
Sophisticated
Active Retail
0.673∗∗∗
0.446∗∗∗
0.318∗∗∗
2,879
498
544
0.115
0.137
0.076
0.774∗∗∗
0.559∗∗∗
0.390∗∗∗
4,831
669
736
0.208
0.195
0.109
46
Table 9: Skill Persistence: Split-Sample Correlations
This table reports split-sample persistence of forecasting skill (θ) and execution quality (ε).
For each wallet with 50+ trades, we split trades chronologically into first and second halves.
θ is directional accuracy (fraction correct); ε is average edge-at-entry (outcome minus price
paid). Own-skill persistence columns report the correlation of each skill between the first
and second half. Cross-skill columns report the correlation of one skill measured in the first
half with the other skill in the second half. All correlations significant at p < 0.001.
Own-Skill Persistence
Cross-Skill
Wallet Type
N Wallets
ρ(θ1 , θ2 )
ρ(ε1 , ε2 )
ρ(θ1 , ε2 )
ρ(ε1 , θ2 )
Bot
Sophisticated
Active Retail
45,420
23,547
63,862
0.870
0.587
0.619
0.339
0.203
0.276
0.106
0.119
0.128
0.110
0.121
0.139
47
Table 10: Average Edge by Days to Resolution
Edge is measured as 1{win} − p for buys and p − 1{win} for sells. Values are percentages.
Earlier entry (more days to resolution) generally yields higher edge. One-shot traders have
insufficient observations beyond 365 days (shown as –).
Trader Type
0–1d
1–3d
3–7d
7–14d
14–30d
30–90d
90–365d
>365d
Bot
Sophisticated
Active Retail
Casual
One-Shot
-1.36
0.26
1.37
5.00
13.25
-0.22
-1.36
0.94
5.95
24.40
-0.12
0.89
1.58
11.44
56.97
0.00
1.80
-0.00
10.13
55.24
0.55
2.23
-2.10
6.43
-4.05
1.36
4.14
0.39
1.81
-8.20
2.54
0.04
-7.50
-11.93
28.10
14.07
-9.08
-15.53
-22.08
–
48
Table 11: Return Decomposition by Maker/Taker Role
This table decomposes returns separately for maker (liquidity-providing) and taker (liquiditydemanding) sides using the cumulative VWAP benchmark. Each trade has one maker and
one taker; we assign each side to the corresponding wallet’s type. Panel A reports maker-side
results. Panel B reports taker-side results.
Average Edge (cents/token)
Role
Wallet Type
N Trades
Total
Directional
Execution
Panel A: Maker (liquidity provider)
Bot
202.0M +0.29
Sophisticated
4.9M −0.11
Active Retail
14.7M −0.38
Casual
255K −1.97
−2.88
−4.94
−3.11
−2.26
+3.17
+4.83
+2.73
+0.28
44.8%
44.1%
47.7%
55.0%
Panel B: Taker (liquidity demander)
Bot
154.1M +0.02
Sophisticated
10.5M −0.93
Active Retail
55.6M −0.71
Casual
1.6M −3.31
+3.99
−2.56
+1.30
−4.67
−3.97
+1.63
−2.01
+1.36
56.6%
48.0%
52.2%
44.9%
49
Accuracy
Accuracy vs. Profitability by Wallet Type
Return on Investment (%)
0.0%
-1.0%
Bot
Sophisticated
Active Retail
Casual
One-Shot
-2.0%
Grossman-Stiglitz
Paradox Region
-3.0%
-4.0%
-5.0%
-6.0%
46%
47%
48%
49%
50%
51%
Directional Accuracy (%)
52%
53%
Figure 1: Accuracy versus ROI by Trader Type
This figure plots mean directional accuracy (x-axis) against mean ROI (y-axis) for each of the five trader
classifications. Marker size is proportional to log total volume. The accuracy-profitability inversion is visible:
high-accuracy types (active retail, one-shot) appear on the right with negative ROI, while the only profitable
type (bots) clusters near 50% accuracy. The shaded region marks where traders are directionally correct yet
unprofitable. The dashed vertical line marks 50% accuracy (no skill); the dashed horizontal line marks 0%
ROI (breakeven).
50
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Figure 2: Two Dimensions of Trading Skill
This figure plots directional edge (x-axis) against execution edge (y-axis) from the return decomposition
(Table 7), using the buy-side VWAP benchmark. The dotted diagonal marks zero total edge (directional +
execution = 0); points above the line are net profitable. All types have negative directional edge, but they
differ sharply in execution edge. Bots sit near the zero-total-edge line despite the worst accuracy (44.8%
in the decomposition sample), while casual traders fall well below it despite the highest accuracy (54.9%).
Accuracy labels reflect buy-side trades in the decomposition sample; all-sides wallet-level accuracies appear
in Table 5.
51
Entry Price (Buying Winner)
1.0
0.8
Entry Price Distribution When Buying Eventual Winner
Bot
Sophisticated
Active Retail
Casual
0.6
0.4
0.2
0.0
p10
p20
p30
p40
p50
Percentile
p60
p70
p80
p90
Figure 3: Entry Price Distributions by Outcome
This figure shows the distribution of entry prices for tokens that resolve to $1 (winning, solid line) versus
$0 (losing, dashed line). Retail traders who buy winning tokens pay prices concentrated near $0.85–$0.90,
leaving limited profit margin. The separation between winning and losing price distributions illustrates the
information already incorporated into prices by the time retail traders arrive.
52
Performance Decomposition by Entry Price
Panel A: ROI by Entry Price
40
20
80
Accuracy (%)
0
ROI (%)
100
Bot
Sophisticated
Active Retail
Casual
20
40
60
40
20
60
80
Panel B: Accuracy by Entry Price
Bot
Sophisticated
Active Retail
Casual
0
0.2
0.4
0.6
Entry Price Bucket (midpoint)
0.8
0.2
0.4
0.6
Entry Price Bucket (midpoint)
0.8
Figure 4: P&L by Entry Price
This figure shows average P&L as a function of entry price for winning tokens. Traders who buy at low
prices (left) earn higher returns; traders who buy at high prices (right) earn lower or negative returns. The
downward slope illustrates the “right but late” mechanism: directionally correct trades become unprofitable
when entry prices are too close to the resolution value.
53
ROI by Wallet Type Across Subperiods
2
Bot
Sophisticated
Active Retail
Casual
0
ROI (%)
2
4
6
8
10
pre
election
election
season
post
election
Period
late
2025
plus
Figure 5: ROI by Trader Type Across Subperiods
This figure shows average ROI by trader type across four subperiods: pre-election (before July 2024), election
season (July–November 2024), post-election (November 2024–June 2025), and late sample (July 2025+). The
accuracy-profitability inversion persists across all periods, confirming the finding is not driven by a single
event or time period.
54
B. Cross-Skill: Does Not Predict
Bot
Sophisticated
Active Retail
Execution Quality ( , cents)
7.5
5.0
2.5
0.0
2.5
5.0
7.5
0.0
0.2
0.4
0.6
Forecasting Skill ( )
Second-Half Execution Quality ( 2, cents)
A. Within-Type: Higher Accuracy, Worse Execution
0.8
Bot
Sophisticated
Active Retail
75
50
25
0
25
50
75
0.0
0.2
0.4
0.6
0.8
First-Half Forecasting Skill ( 1)
1.0
Figure 6: Two-Dimensional Skill Structure: θ versus ε
Panel A plots binned means of execution quality (ε, cents) against forecasting skill (θ) within each trader type.
Higher-accuracy wallets receive worse execution (lower ε), as predicted by the two-dimensional model. Panel
B plots first-half forecasting skill (θ1 ) against second-half execution quality (ε2 ) for a random subsample.
The flat regression lines confirm low cross-skill correlation (ρ = 0.11–0.14): knowing a wallet’s forecasting
ability is nearly uninformative about its future execution quality. Sample includes 132,829 wallets with 50+
trades.
55
Second-Half
0.8
A. Forecasting Skill Persistence
45-degree line
Bot ( =0.34)
Sophisticated ( =0.20)
Active Retail ( =0.28)
20
0.6
0.4
0.2
0.0
0.0
B. Execution Quality Persistence
30
45-degree line
Bot ( =0.87)
Sophisticated ( =0.59)
Active Retail ( =0.62)
Second-Half (cents)
1.0
10
0
10
20
0.2
0.4
0.6
First-Half
0.8
30
1.0
30
20
10
0
10
First-Half (cents)
20
30
Figure 7: Skill Persistence: Split-Sample Correlations
Panel A plots second-half forecasting skill (θ2 ) against first-half forecasting skill (θ1 ) for each wallet, with
binned means overlaid. Forecasting skill persists strongly: ρ = 0.87 for bots, 0.59 for sophisticated, 0.62 for
active retail. Panel B repeats the exercise for execution quality (ε). Execution quality persists moderately:
ρ = 0.34 for bots, 0.20 for sophisticated, 0.28 for active retail. In both panels, the dashed line is the 45degree line and solid lines are binned conditional means by trader type. For each wallet, trades are split
chronologically into first and second halves. Sample: wallets with 50+ trades.
56
Return Decomposition: Directional vs. Execution Components
Edge at Entry (cents per token)
4
2
+0.29c
0
-0.10c
-0.38c
-2.00c
2
4
Directional component
Execution component
Net total
edge
Acc: 44.8%
Acc: 44.1%
Acc: 47.7%
Acc: 54.9%
Bot
Sophisticated
Active Retail
Casual
Figure 8: Return Decomposition by Trader Type
This figure decomposes average edge-at-entry into directional (pink) and execution (blue) components by
trader type. Black dots indicate net total edge. All types have negative directional edge (−2.9 to −5.1
cents per token), reflecting that entry prices overstate fair value on average. What separates profitable from
unprofitable types is execution quality: bots achieve +3.41 cents of execution edge per token, while casual
traders achieve only +0.89 cents. This 2.52-cent execution gap determines which types earn positive total
returns. Buy-side accuracy labels at the bottom show that the most accurate types in this decomposition
sample (casual, 54.9%) have the worst net edge, while the least accurate (bots, 44.8%) are the only profitable
group. These figures reflect buy-side accuracy in resolved markets and differ from the all-sides accuracy
reported in Table 5.
57
A
Variable Definitions
Table A1: Variable Definitions
B
Variable
Definition
Directional Accuracy
Fraction of resolved trades that are correct: buying tokens
that resolve to $1, or selling tokens that resolve to $0.
Under no skill, expected accuracy is 50%.
ROI
Return on investment: total P&L divided by total USDC invested.
Entry Price
Price paid per token at trade execution, computed as
USDC amount / token quantity for buy trades.
Days to Resolution
Calendar days between the trade date and the market’s
resolution date.
Maker
Indicator equal to 1 if the wallet was the liquidity provider
(passive side) in the trade, 0 if liquidity taker (active side).
Price Distance
Absolute distance of the trade price from 0.50: |p − 0.5|.
HHI
Herfindahl-Hirschman Index of market concentration across
a wallet’s trades. Lower values indicate more diversification.
Forecasting Skill (θ)
Wallet-level directional accuracy: fraction of resolved
trades where the trader was on the correct side.
Execution Quality (ε)
Average edge-at-entry: E[1{win} − p]
across a wallet’s buy-side resolved trades.
Directional Edge
Outcome minus fair price: the component of edge attributable
to entering a market where the consensus underpriced the outcome.
Execution Edge
Fair price minus entry price: the component of edge attributable
to paying a better (or worse) price than the market average.
Fair Price
Volume-weighted average transaction price across all buy-side
trades in a market. Serves as the benchmark for decomposition.
Additional Tables
58
Table A2: P&L by Accuracy Quintile
This table reports P&L by accuracy quintile for wallets with 50+ resolved trades.
Quintile
Accuracy Range
N Wallets
Avg ROI
Total P&L
% Profitable
Q1 (Low)
Q2
Q3
Q4
Q5 (High)
0–42%
42–49%
49–51%
51–59%
59–100%
110,586
111,232
109,681
110,321
110,347
−6.15%
−2.62%
−2.20%
−0.26%
+0.82%
−$295M
−$13M
+$32M
+$84M
+$246M
22.3%
27.0%
24.9%
41.4%
49.6%
59
Table A3: Entry Price Conditional on Outcome by Wallet Type
This table reports the average entry price by wallet type, conditional on buying the eventual
winning or losing outcome token. “Avg Price|Win” is the volume-weighted average price
paid for tokens that ultimately resolve to $1. A higher price indicates the trader arrived
later in the price discovery process. The Gap column is the difference between winning and
losing entry prices.
Type
N (Winner)
Avg Price|Win
N (Loser)
Avg Price|Lose
Gap
Bot
Sophisticated
Active Retail
Casual
One-Shot
81,100,238
3,015,993
11,311,643
273,791
25,472
0.8350
0.8100
0.8438
0.8561
0.6342
103,916,415
3,825,704
18,208,293
488,890
20,690
0.4776
0.4723
0.4136
0.4012
0.4030
0.3574
0.3377
0.4302
0.4549
0.2312
60
Table A4: Days to Resolution by Wallet Type
This table reports the timing of trades relative to market resolution. Days to resolution
is computed as the calendar difference between the trade date and the market’s resolution
date.
Wallet Type
Mean Days
Median Days
% Within 7 Days
% Within 1 Day
Bot
Sophisticated
Active Retail
Casual
One-Shot
14.2
18.6
11.8
8.4
6.2
8.1
11.4
5.2
3.1
2.4
42.3%
35.2%
51.4%
62.8%
71.3%
18.7%
12.1%
24.3%
31.6%
38.2%
61
Table A5: Effective Spreads by Wallet Type
This table reports the volume-weighted effective spread by wallet type. The spread is computed as 2 × direction × (price − midpoint), where midpoint is estimated from surrounding
trades. Negative values indicate the wallet type is on average earning the spread (as maker);
positive values indicate paying the spread (as taker).
Type
N Trades
Vol-Weighted Spread
Total Volume
Bot
356,490,376
Sophisticated 15,462,333
Active Retail
70,469,200
Casual
1,843,505
One-Shot
107,102
-4.25 bps
19.37 bps
11.68 bps
4.28 bps
-17.93 bps
$23.9B
$2.8B
$4.0B
$401.3M
$38.9M
62
Table A6: Entry Prices by Accuracy Quintile and Trader Type
This table reports average entry prices by accuracy quintile within each trader type, for
buy-side trades on winning tokens. Accuracy quintiles are defined within each trader type.
Higher entry prices on winning tokens indicate worse execution (less room for profit between
entry price and $1 resolution).
Average Entry Price
Accuracy Quintile
Bot
Sophisticated
Active Retail
Q1 (Lowest)
Q2
Q3
Q4
Q5 (Highest)
0.521
0.624
0.679
0.780
0.673
0.557
0.613
0.654
0.688
0.793
0.748
0.687
0.659
0.703
0.751
63
Table A7: Learning Dynamics: Monthly Trends in θ and ε
This table reports monthly trends in forecasting skill and execution quality for wallets with
100+ trades spanning 6+ months. Trend/mo is the average slope of a wallet-level regression
of monthly θ (or ε) on calendar month. % Improving is the fraction of wallets with a positive
trend.
Forecasting Skill (θ)
Execution Quality (ε)
Wallet Type
N
Trend/mo
Std
% Impr.
Trend/mo
Std
% Impr.
Bot
Sophisticated
Active Retail
5,712
2,096
1,934
≈0
−0.005
+0.006
0.038
0.057
0.052
49.7%
46.9%
53.8%
−0.0015
−0.005
−0.003
0.023
0.037
0.031
47.4%
45.6%
44.0%
64
Table A8: Return Decomposition: Robustness to Alternative Fair Price Benchmarks
This table replicates the return decomposition from Table 7 under alternative fair price
benchmarks. Panel A uses the lifecycle VWAP (volume-weighted average price across all buyside trades in the market), which may contain lookahead bias. Panel B uses the cumulative
VWAP computed from all buy-side trades preceding each observation, a strictly backwardlooking benchmark with no ex-post information. Panel C uses the previous trade price
(lagged price). The total edge column is mechanically identical across panels because it
depends only on the outcome and entry price. Sample: 222M resolved trades.
Average Edge (cents/token)
Benchmark
Wallet Type
Total
Directional
Execution
Accuracy
Panel A: Lifecycle VWAP (original benchmark)
Bot
+0.29
−3.11
Sophisticated −0.10
−5.08
Active Retail −0.38
−3.64
Casual
−2.00
−2.88
+3.41
+4.99
+3.26
+0.89
44.8%
44.1%
47.7%
54.9%
Panel B: Cumulative VWAP (backward-looking only)
Bot
+0.29
−2.88
Sophisticated −0.11
−4.94
Active Retail −0.38
−3.11
Casual
−1.97
−2.26
+3.17
+4.83
+2.73
+0.28
44.8%
44.1%
47.7%
55.0%
Panel C: Lagged price (previous trade price)
Bot
+0.29
−0.39
Sophisticated −0.11
−1.23
Active Retail −0.38
−1.49
Casual
−1.97
−2.66
+0.68
+1.12
+1.10
+0.69
44.8%
44.1%
47.7%
55.0%
65
Table A9: Robustness: ROI Across Alternative Classification Thresholds
This table reports ROI by wallet type across nine classification specifications (3 bot thresholds × 3 sophisticated thresholds). Bot thresholds: B1 (>20/day or >500), B2 (>50/day or
>1,000, baseline), B3 (>100/day or >2,000). Sophisticated thresholds: S1 (>$5K, HHI<0.7,
>14 days), S2 (>$10K, HHI<0.5, >30 days, baseline), S3 (>$50K, HHI<0.3, >60 days).
Specification
B1 S1
B1 S2
B1 S3
B2 S1
B2 S2
B2 S3
B3 S1
B3 S2
B3 S3
Bot
Sophisticated
Active Retail
Casual
One-Shot
0.33%
0.33%
0.33%
0.56%
0.56%
0.56%
0.80%
0.80%
0.80%
-1.31%
-1.14%
-0.80%
-1.60%
-1.37%
-1.03%
-1.76%
-1.39%
-1.17%
-1.71%
-1.68%
-1.59%
-1.95%
-2.00%
-1.90%
-1.91%
-2.18%
-2.03%
-3.50%
-3.45%
-3.44%
-3.50%
-3.45%
-3.44%
-3.50%
-3.45%
-3.44%
-5.94%
-5.94%
-5.94%
-5.94%
-5.94%
-5.94%
-5.94%
-5.94%
-5.94%
66
Table A10: P&L by Data-Driven Cluster (k-means, k = 5)
This table reports P&L by cluster from k-means clustering (k = 5) on log volume, log trades,
HHI, buy ratio, and log active span. Features are standardized before clustering. “Dominant
Type” is the most common threshold-based wallet type within each cluster.
Cluster
0
1
2
3
4
N Wallets
Trades
Volume
ROI
Accuracy
Dominant Type
224,567
606,069
325,779
338,541
486,452
930,399
15,836,593
2,267,252
15,217,550
410,122,464
$224.3M
$276.4M
$193.1M
$3.3B
$27.1B
-3.49%
-2.98%
-5.87%
-1.43%
0.28%
0.4769
0.5158
0.4413
0.5254
0.4988
Casual
Active Retail
Casual
Active Retail
Active Retail
67
Table A11: P&L by Wallet Type Across Subperiods
This table reports P&L and accuracy by wallet type across four subperiods.
Period
Type
Trades
Volume
P&L
ROI
Accuracy
Pre-Election (2022–May 2024)
Bot
Sophisticated
Active Retail
Casual
One-Shot
1,021,736
136,828
266,738
30,973
3,207
$266.7M
$39.5M
$38.9M
$5.1M
$469K
$1.7M
$734K
$-1.8M
$-504K
$-111K
0.64%
1.86%
-4.66%
-9.83%
-23.67%
0.5206
0.4661
0.4489
0.4157
0.4546
Election Season (Jun–Nov 2024)
Bot
Sophisticated
Active Retail
Casual
One-Shot
25,389,421
2,778,783
14,673,139
310,149
35,138
$4.7B
$528.3M
$1.2B
$60.5M
$6.2M
$58.2M
$-14.8M
$-36.0M
$-5.9M
$-1.5M
1.24%
-2.80%
-3.00%
-9.83%
-23.69%
0.5075
0.4619
0.4954
0.4312
0.6213
Post-Election (Dec 2024–Jun 2025)
Bot
Sophisticated
Active Retail
Casual
One-Shot
54,692,595
3,636,454
24,437,847
523,107
27,571
$5.2B
$814.0M
$1.0B
$117.7M
$14.5M
$24.0M
$-7.9M
$-14.7M
$-1.6M
$191K
0.46%
-0.97%
-1.42%
-1.38%
1.31%
0.4929
0.4470
0.5243
0.4738
0.5916
Late 2025+ (Jul 2025–)
Bot
Sophisticated
Active Retail
Casual
One-Shot
275,456,403
8,902,197
31,032,656
978,242
41,074
$13.8B
$1.4B
$1.7B
$218.3M
$17.7M
$50.2M
$-16.5M
$-27.0M
$-5.8M
$-929K
0.36%
-1.16%
-1.59%
-2.65%
-5.23%
0.4995
0.4777
0.5123
0.4685
0.4256
68
Table A12: P&L Regressions with Market Fixed Effects and Trade-Level Controls
This table reports OLS regressions of trade-level ROI (in percentage points) on wallet type
indicators and trade-level controls. Bot is the omitted category. Log(Trade Size) is the
natural log of USDC trade value. Maker equals 1 if the wallet was the liquidity provider
(maker) in the trade. Price Distance from 0.5 is |p − 0.5|, measuring how far the price
has moved from maximum uncertainty. Standard errors clustered by wallet in parentheses.
∗∗∗
p<0.01.
(1)
No FE
(2)
Mkt FE
(3)
Mkt×Wk
(4)
+Size
(5)
+Maker
(6)
Full
Bot (reference)
–
–
–
–
–
–
Sophisticated
−1.93∗∗∗
(0.12)
−1.12∗∗∗
(0.04)
−3.30∗∗∗
(0.08)
−3.85∗∗∗
(0.15)
−1.78∗∗∗
(0.11)
−1.04∗∗∗
(0.04)
−3.12∗∗∗
(0.08)
−3.64∗∗∗
(0.14)
−1.52∗∗∗
(0.14)
−0.89∗∗∗
(0.05)
−2.87∗∗∗
(0.10)
−3.41∗∗∗
(0.17)
−1.41∗∗∗
(0.13)
−0.82∗∗∗
(0.05)
−2.71∗∗∗
(0.09)
−3.18∗∗∗
(0.16)
−1.18∗∗∗
(0.12)
−0.71∗∗∗
(0.05)
−2.48∗∗∗
(0.09)
−2.92∗∗∗
(0.15)
−1.08∗∗∗
(0.12)
−0.64∗∗∗
(0.05)
−2.35∗∗∗
(0.09)
−2.76∗∗∗
(0.15)
−0.42∗∗∗
(0.02)
−0.38∗∗∗
(0.02)
1.84∗∗∗
(0.03)
−0.35∗∗∗
(0.02)
1.76∗∗∗
(0.03)
−2.15∗∗∗
(0.08)
Yes
Yes
444M
0.181
Yes
Yes
444M
0.203
Yes
Yes
444M
0.215
Active Retail
Casual
One-Shot
Log(Trade Size)
Maker
Price Dist. from 0.5
Market FE
Week FE
Observations
R2
No
No
444M
0.008
Yes
No
444M
0.142
Yes
Yes
444M
0.168
69
Table A13: Out-of-Sample Classification Test
This table reports an out-of-sample classification test. Wallets are classified based on trading
behavior during their first 30 days of activity only. Performance is measured on all subsequent trades (OOS). The baseline columns reproduce the full-sample classification results
for comparison. The sample includes 646,278 of 1,981,408 wallets; the remainder have no
trades after their first 30 days (primarily one-shot and casual traders whose entire history
falls within the classification window). One-shot wallets are excluded because the 30-day
scheme cannot distinguish them from casual traders.
OOS Performance
Baseline Performance
Type (First 30 Days)
N Wallets
Edge (cents)
t-stat
Edge (cents)
t-stat
Bot
Sophisticated
Active retail
Casual
32,396
24,366
413,309
176,207
+0.36
+0.41
+0.38
−0.21
93.1
38.7
52.0
−11.7
+0.29
−0.10
−0.38
−2.00
112.9
−6.2
−48.9
−30.1
70