---
type: source
title: "Federal Reserve Study: Kalshi Prediction Markets Outperform Bloomberg Consensus for CPI Forecasting"
author: "Diercks, Katz, Wright — Federal Reserve Board (FEDS Paper)"
url: https://www.fool.com/investing/2026/03/16/federal-reserve-research-kalshi-prediction-markets/
date: 2026-03-16
domain: internet-finance
secondary_domains: []
format: article
status: processed
priority: medium
tags: [prediction-markets, kalshi, federal-reserve, cpi, accuracy, academic, markets-beat-consensus, macro-forecasting]
---

## Content

A Federal Reserve Board paper (authors: Diercks, Katz, Wright) published March 2026 evaluates the predictive accuracy of Kalshi prediction markets for macroeconomic indicators relative to Bloomberg consensus surveys.

**Key findings:**
1. Kalshi markets provided "statistically significant improvement" over Bloomberg consensus for headline CPI prediction
2. Kalshi markets were at parity with Bloomberg consensus for core CPI and unemployment
3. Kalshi perfectly matched the realized fed funds rate on the day before every FOMC meeting since 2022 — something neither Bloomberg consensus surveys nor interest rate futures consistently achieved

**Methodology:** The paper evaluates Kalshi markets across macroeconomic data releases (CPI, PCE, unemployment, FOMC rate decisions) comparing predictive accuracy to professional forecaster surveys (Bloomberg consensus) and financial instrument implied forecasts (futures markets).

**Context for this finding:**
- Kalshi received CFTC approval via $112M acquisition (referenced in Session 1 research journal)
- The Fed study was published contemporaneously with the CFTC ANPRM (March 16, 2026) — implicit regulators-studying-the-market signal
- Good Judgment Project superforecasters (no skin-in-the-game) also reportedly outperformed futures markets for Fed policy predictions by 66% (FT, July 2024)

**The complementary finding:** Both real-money prediction markets (Kalshi) and calibrated expert polls (GJP) outperform naive consensus on structured macroeconomic events. Neither definitively outperforms the other on this task type. This is consistent with the two-mechanism analysis: for structured macro-event prediction (binary outcomes, rapid resolution, publicly available information), both Mechanism A (calibration selection) and Mechanism B (information acquisition) are active but neither is the decisive advantage.

**What this does NOT address:** Financial selection (ICO quality, startup success, investment return prediction). Macro-event prediction (will CPI be above X) has structured resolution criteria. Investment selection (is this ICO worth investing in) does not.

## Agent Notes

**Why this matters:** A Federal Reserve paper showing Kalshi beats Bloomberg consensus is meaningful institutional validation of real-money prediction market accuracy — from a regulator's own research arm. This is the strongest institutional credibility signal for prediction markets since the Polymarket CFTC approval.

**What surprised me:** The perfect match on FOMC-day rates is striking. Professional forecasters with years of Fed-watching couldn't consistently match what Kalshi markets produced the day before FOMC meetings. This suggests financial incentives ARE generating information discovery and aggregation that polls can't match — even in the structured macro-event domain.

**What I expected but didn't find:** The paper apparently doesn't address prediction market accuracy for financial selection tasks. The Fed's interest is naturally in monetary policy and macroeconomic forecasting, not in investment quality evaluation. The domain gap in the literature continues.

**KB connections:**
- [[speculative markets aggregate information more accurately than expert consensus or voting systems]] — this is direct evidence supporting the claim in a real-money, regulated prediction market context
- Pairs with the Mellers two-mechanism analysis: this is Mechanism B evidence (financial stakes generating better information discovery) in a structured prediction domain; complements the Mellers Mechanism A finding in the geopolitical domain
- CFTC ANPRM context: The Fed's own research showing market accuracy improvement may influence CFTC's framework development — regulators studying the accuracy data as they design the rules

**Extraction hints:**
- ENRICHMENT: [[speculative markets aggregate information more accurately than expert consensus or voting systems]] — add Kalshi Fed study as supporting evidence with "structured macro-event prediction" scope qualifier
- POTENTIAL CLAIM: "Real-money prediction markets demonstrate measurable accuracy advantages over professional survey consensus in structured macroeconomic forecasting" — narrower but better-evidenced than the general claim

**Context:** This paper is from the Federal Reserve Board of Governors' Finance and Economics Discussion Series. Published March 2026, the same day as the CFTC ANPRM. The simultaneous release suggests the Fed and CFTC are coordinating on building an evidence base for prediction market regulation.

## Curator Notes

PRIMARY CONNECTION: [[speculative markets aggregate information more accurately than expert consensus or voting systems]]
WHY ARCHIVED: Federal Reserve institutional validation of real-money prediction market accuracy; complements the Mellers academic literature and rounds out the evidence base for Belief #1's grounding claims
EXTRACTION HINT: Archive as supporting evidence for the prediction markets accuracy claim, scoped to "structured macroeconomic event prediction." The FOMC-day perfect match finding is the most archivable specific claim. Note it doesn't address financial selection.
