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Crowding is most likely an important factor in the deterioration of strategy performance, the increase of trading costs and the development of systemic risk. We study the imprints of \emph{crowding} on both anonymous market data and a large database of metaorders from institutional investors in the U.S. equity market. We propose direct metrics of crowding that capture the presence of investors contemporaneously tradi…
This paper is devoted to the important yet unexplored subject of crowding effects on market impact, that we call "co-impact". Our analysis is based on a large database of metaorders by institutional investors in the U.S. equity market. We find that the market chiefly reacts to the net order flow of ongoing metaorders, without individually distinguishing them. The joint co-impact of multiple contemporaneous metaorders…
Asset-pricing models typically condition on a fixed information set. This paper endogenises the market's conditioning architecture by allowing portfolios to choose representations whose induced exposures affect prices. Capital allocated across representations determines aggregate positions and the clearing premium, while price feedback changes representation value and, through causal certification, admissible represe…
How much capital a trading strategy can absorb before its edge disappears is a causal question about how much is deployed, but it is answered with observational proxies that rest on incompatible assumptions. We ask what experiment would answer it instead, and show that two features of the problem interact to constrain any answer. Deployed capital erodes the edge gradually, so a trial of fixed length measures less tha…
Investment herding, a phenomenon where households mimic the decisions of others rather than relying on their own analysis, has significant effects on financial markets and household behavior. Excessive investment herding may reduce investments and lead to a depletion of household consumption, which is called the crowding-out effect. While existing research has qualitatively examined the impact of investment herding o…
Crowding is widely regarded as one of the most important risk factors in designing portfolio strategies. In this paper, we analyze stock crowding using network analysis of fund holdings, which is used to compute crowding scores for stocks. These scores are used to construct costless long-short portfolios, computed in a distribution-free (model-free) way and without using any numerical optimization, with desirable pro…
This paper proposes a general model for synchronized crowding behavior. An order parameter is introduced to quantify the level of synchronization which is shown a function of percentage of agents in reactive state. Further, synchronization is shown to be driven by the most active agents with the highest volatility. A tipping point is identified when crowd becomes self-amplifying and unstable. By applying this model, …
We develop a methodology which replicates in great accuracy the FTSE Russell indexes reconstitutions, including the quarterly rebalancings due to new initial public offerings (IPOs). While using only data available in the CRSP US Stock database for our index reconstruction, we demonstrate the accuracy of this methodology by comparing it to the original Russell US indexes for the time period between 1989 to 2019. A py…
In this paper we formulate the now classical problem of optimal liquidation (or optimal trading) inside a Mean Field Game (MFG). This is a noticeable change since usually mathematical frameworks focus on one large trader in front of a "background noise" (or "mean field"). In standard frameworks, the interactions between the large trader and the price are a temporary and a permanent market impact terms, the latter inf…
We show that AI-driven investment strategies are inherently self-defeating at scale. As AI adoption rises, three mutually reinforcing channels -- signal crowding, performative signal erosion, and Red Queen competition -- compress excess returns. We derive the alpha half-life $h(φ) = \ln 2/[θ+ δ(φ)]$, where $θ$ is the natural mean-reversion rate and $δ(φ) = Nφρa/λ(φ)$ is the AI-accelerated decay component, which is co…
This paper proposes a parametric approach for stochastic modeling of limit order markets. The models are obtained by augmenting classical perfectly liquid market models by few additional risk factors that describe liquidity properties of the order book. The resulting models are easy to calibrate and to analyze using standard techniques for multivariate stochastic processes. Despite their simplicity, the models are ab…
A listed product that tries to match SG Trend-like returns with a small liquid futures set — cheap access, incomplete universe, visible crowding.
When too many trend books own the same contract the same way, entries get worse, exits gap, and ‘the CTA unwind’ becomes a flow event.
Crowding out is when public borrowing or spending raises rates or absorbs real resources so private investment or net exports fall, shrinking the net fiscal impulse.
Herding is correlated action because others are acting — information cascades, career risk, or indexation, not independent theses that happen to agree.
Short high short-interest names and long low short-interest names — crowding and borrow as a cross-sectional signal.
Crowding out is when public borrowing or spending raises rates or absorbs real resources so private investment or net exports fall, shrinking the net fiscal impulse.
A listed product that tries to match SG Trend-like returns with a small liquid futures set — cheap access, incomplete universe, visible crowding.
When too many trend books own the same contract the same way, entries get worse, exits gap, and ‘the CTA unwind’ becomes a flow event.
Short high short-interest names and long low short-interest names — crowding and borrow as a cross-sectional signal.