AI in trading is an authority-allocation problem: which components may infer and propose, which independent controls may authorise action, and how common models can turn local decisions into system-wide correlation.
Crowding is a covariance problem: common factor exposure, leverage constraints, and endogenous exits can make many strategies behave like one liquidation path.
Displayed depth is a contingent state, not guaranteed execution; adverse selection, queue priority, cancellation, and impact alter the book before the fill.
A market regularity becomes a tradable hypothesis only after data reconstruction, multiple-testing control, execution modelling, and live decay monitoring.
ANANNYE is a monthly research journal on markets, systems, and risk. Each issue centres on one research feature, written to make a single question precise: what is being measured, what mechanism may be at work, what evidence is available, and where the argument stops.
The journal is quantitative in method, but not mechanical in tone. Its focus is market microstructure, liquidity, volatility, execution, leverage, model risk, and the infrastructure through which decisions become prices. Artificial intelligence is treated as part of that infrastructure when it affects trading systems, governance, information flow, or operational risk.
The aim is a durable record of research judgement rather than a stream of commentary. Each issue pairs a clear thesis with technical framing, an original figure, primary references, and stated limitations.