Markets, execution, and turning a signal into a trade that survives costs.
Autonomous research agents can now generate, code and backtest their own trading factors. This paper asks the question that matters more than any single discovery — can you trust the process that found it?
arXiv:2609.00731
A portfolio can look safe on a return basis and still become extraordinarily expensive to exit. Illiquidity-at-Risk gives that danger its own tail-risk number — and finds it spikes in ways continuous models miss entirely.
arXiv:2609.00943
A generative model can produce an implied volatility surface that looks statistically flawless and is still financially worthless. This paper maps the line between the two directly inside the model's latent space.
arXiv:2609.00332
A maturity-aware graph model turns a tiny forecasting edge into a smooth, near-market-neutral return stream from commodity calendar spreads — correlation with the S&P 500 of about minus two-hundredths.
arXiv:2606.25811
In fast markets a mid-price signal decays in microseconds, so latency is part of the strategy — not an implementation detail. "Best model" is meaningless without a time budget.
arXiv:2606.25986
Prediction is turning into a commodity. The value is migrating to everything around it — calibration, execution, reusable representations, and the decision a forecast is actually meant to serve.
6 papers
Point a pretrained time-series model at the stock market and it wins almost every contest. Look closer and the gaps are a fraction of a single bit — a ranking victory that carries no edge you could trade.
arXiv:2606.27100
Neural networks can beat the decades-old workhorses of bond forecasting — but this study insists on the harder test, asking whether a better forecast actually makes a better trade.
arXiv:2606.26815
A machine-learning system can be genuinely right about Bitcoin's next move and still lose almost everything — because the cost of acting on each small correct call exceeds the call itself.
arXiv:2606.00060