arXiv Machine Learning
The Axiomatic Trader: Latent Regularity, Information Budgets, and the Canonical Form of a Quantitative Investment System
Read the original on arXiv Machine Learning →The paper proposes five axioms that formalize the assumptions underlying systematic trading, each expressed through declared quantitative constants. It demonstrates that these axioms necessitate a five‑stage canonical structure for a quantitative investment system and proves that omitting any stage worsens performance under the axioms. Empirical tests on real market data show that the axioms hold, though certain declared constants (e.g., κ = 1 and an exponential decay assumption) are rejected.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.