How Financial Markets Respond to Economic Data Surprises
A BPEA-style paper measuring how asset prices respond to macro data surprises across the whole transmission chain — fed funds futures, the yield curve and its real/breakeven components, aggregate equities, and eleven sectors — over 2013–2026, with every consensus-based estimate paired against a consensus-free companion built from official release calendars back to 1999. Three findings: inflation news was sharply repriced after COVID and has come only partway back (the broader inflation complex gave back roughly three-fifths of the move, while per-unit core CPI sensitivity shows no decline); the repricing ran through real rates rather than breakevens, so it measures the perceived Fed reaction function, not an un-anchoring of inflation expectations; and equities' CPI trade reverted while the employment report re-emerged as the calendar's biggest event. Since both question rounds went unanswered, all six design calls were made by the engine and documented in the process memo; the paper passed two blind referee rounds, a full citation audit, and a 181-claim reproducibility passport.
The prompt that produced this paper, verbatim
How financial markets respond to economic data surprises. Should look at how it has changed over time (particularly pre- and post-COVID) as well as how different industries responds differently. Can use how the NASDAQ surprise data that is used in chartbook / macro world as well as WRDS data to capture a small event study window around data surprises. If possible look at changes in the expected FFR, short rates, long rates (both inflation breakevens, real rates and overall nominal), and equities--including in one section of the paper breaking down equities by industry.
Written by Claude, may contain mistakes. Jason makes this free and would appreciate any feedback.