Turn-of-Month / Rebalancing Flow (probe): a killed candidate

Verdict: KILLED Stage: screen prototype
Identity: mechflow_tom

The calendar flow is real — SPY earns 5.65bp/day in the last-1-plus-first-3 window vs 3.96bp outside, and the footprint replicates on QQQ — but monetized standalone it sits in cash 76% of days: net Sharpe 0.27, below buy-and-hold SPY (0.58) and the screen bar, DSR 0.035 across 43 configs. The 60/40 month-end rebalance-fade variant decayed negative after 2018. A real effect that is an execution tilt, not a sleeve. KILLED as standalone. RE-EXAMINED 2026-08-03 because that first reason was BAD ARITHMETIC: a candidate need not beat the book to improve it, only clear own_SR > rho x S_b. Re-tested as a diversifier on ONE pre-registered config (no sweep, so no search penalty). The reframe was right about correlation — rho to the live book is +0.010, dropping the bar to +0.006 — and it still fails: own Sharpe -0.56 over the book's own window, and over 25 years / 6,309 sessions the full-history Sharpe of +0.278 carries a Newey-West t of only +1.42, with no decade reaching t=2 (best +1.32). A near-zero bar lowers what you must BEAT, never what you must PROVE. Adding it hurt the book at every weight (-0.03 to -0.15), and 87-96% of that harm came from the mean, not from variance. KILLED on the correct bar — this one is final.

Why it was worth testing

This died at the screen stage, before a full walk-forward was ever run. Screening exists so that ideas which cannot clear a coarse, cost-aware bar do not consume the far more expensive machinery behind it. A screen kill is a cheap kill, and it is published for the same reason as an expensive one: the trial was still spent, and it still raises the evidence bar for everything already in the book.

The result

Measure	Value
Screen net Sharpe	0.2700

What this does and does not say

It says this configuration, on this data, net of the costs we charge, did not clear the bar it pre-registered. It does not say the underlying economic effect does not exist, that no implementation of it works, or that someone with different data or different execution would reach the same conclusion. A null is evidence about a test, not a proof about a market.

It also does not say the trial was free. Every hypothesis tested raises the deflated-Sharpe hurdle for every sleeve already in the book, including the ones that survived. That is why the kill count is published beside the survivor count rather than behind it.