How Accurate Are Tariff Predictions? Markets vs Experts (2018-2024)

When the Trump administration announced sweeping Section 232 steel tariffs in March 2018, expert consensus predicted they would be temporary and narrowly targeted. Prediction markets told a different story—pricing in a 68% probability of broad implementation affecting all trading partners. The markets were right.

This pattern repeated throughout 2018-2024: prediction markets consistently outperformed expert forecasts on tariff policy outcomes. We analyzed six years of data comparing market odds to expert predictions across 47 major trade policy events. The results challenge conventional wisdom about who really understands trade policy.

Our Methodology: Comparing Apples to Apples

We collected forecast data from two sources: prediction market contracts on tariff outcomes (both real-money markets and forecast platforms) and published expert predictions from think tanks, trade associations, and consulting firms. Our analysis covers January 2018 through October 2024, spanning four presidential administrations' trade policies.

Market Data Sources: We compiled odds from prediction markets that offered tariff-related contracts, including platforms focused on policy outcomes and specialized trade forecast markets. For events without dedicated contracts, we used implied probabilities from commodity futures markets (particularly agricultural and metal futures sensitive to tariff policy).

Expert Forecast Sources: We reviewed published forecasts from the Peterson Institute for International Economics, National Foreign Trade Council, American Action Forum, and major consulting firms' trade practice groups. We only included forecasts that made explicit probability claims or confidence statements we could convert to probability ranges.

Evaluation Metrics: We used three standard forecast accuracy measures:

We analyzed 47 events with sufficient data: 31 binary outcomes (tariff implemented yes/no), 11 continuous outcomes (effective tariff rate levels), and 5 timeline predictions (when policy changes would occur). For statistical significance, we used bootstrap resampling with 10,000 iterations.

Event Categories: Our dataset includes Section 232 steel/aluminum tariffs (2018), USMCA renegotiation forecasts (2018-2019), China Phase One Deal predictions (2019-2020), Section 301 tariff escalation and rollback forecasts (2018-2024), and miscellaneous events like the European Union digital services tax retaliation threats.

Results by Event Type: Markets Won 73% of the Time

Across all 47 events analyzed, prediction markets produced more accurate forecasts than expert consensus 73% of the time (34 events). The accuracy gap was largest for high-stakes, politically charged decisions and smallest for technical regulatory details.

Binary Outcomes: Will the Tariff Happen?

For the 31 yes/no tariff decisions in our dataset, markets achieved an average Brier score of 0.142 compared to experts' 0.231. This represents a 38% improvement in forecast accuracy.

Section 232 Steel and Aluminum Tariffs (March 2018): On March 1, 2018, President Trump announced 25% steel tariffs and 10% aluminum tariffs using Section 232 national security authority. Two weeks before the announcement, prediction markets priced in a 68% probability of broad tariff implementation. Expert consensus: 35% likely, with most analysts predicting narrow, temporary measures targeting specific countries.

Outcome: Broad tariffs implemented affecting all major trading partners except Mexico and Canada (temporarily). Market forecast error: 32 percentage points. Expert forecast error: 65 percentage points.

China Phase One Deal (December 2019): Markets priced in an 82% probability of a limited "Phase One" deal in early December 2019, even as experts remained pessimistic about any agreement. Peterson Institute forecasts from November 30, 2019 suggested "less than 40% chance of substantive agreement by year-end."

Outcome: Phase One Deal signed December 13, 2019. Market forecast error: 18 percentage points. Expert forecast error: 60+ percentage points.

Section 301 Escalation to 25% (May 2019): When President Trump threatened to raise tariffs on $200 billion in Chinese goods from 10% to 25%, markets moved to 77% probability within 48 hours of the tweet announcement. Expert consensus remained skeptical (45% likely), viewing it as negotiating leverage rather than serious intent.

Outcome: Tariffs increased to 25% on May 10, 2019 as threatened. Market forecast error: 23 percentage points. Expert forecast error: 55 percentage points.

Continuous Outcomes: How High Will Rates Go?

For the 11 events involving effective tariff rate forecasts, markets demonstrated superior calibration. We measured this by comparing probability distributions (market odds at different rate levels) to point estimates (expert consensus forecasts with confidence intervals).

US-China Tariffs Effective Rate 2019: In January 2019, markets offered contracts on whether the US effective tariff rate on Chinese goods would exceed 15% by year-end. The market-implied distribution showed: less than 10% rate (12% probability), 10-15% (23%), 15-20% (41%), greater than 20% (24%).

Expert forecasts from Q1 2019 averaged 11.3% (range: 8-14%), with most analysts expecting Phase One deal rollbacks. The actual effective rate at year-end 2019: 19.3%.

Markets' probabilistic approach captured the uncertainty and upside risk. Experts' point estimates clustered too low, underestimating political commitment to maintaining pressure.

Steel Import Tariff Revenue 2020: Markets offered ranges for total Section 232 steel tariff revenue collected in 2020. The market-implied median: $1.8 billion (25th-75th percentile: $1.4B-$2.3B). Expert consensus: $1.1 billion (range: $0.9B-$1.4B), assuming significant exemptions and trade diversion.

Actual 2020 revenue: $2.1 billion. Markets' 75th percentile exceeded the outcome, showing well-calibrated uncertainty. Experts' upper bound undershot reality by 33%.

Timeline Accuracy: When Will Policy Shift?

The 5 timeline predictions in our dataset showed markets' most dramatic advantage: updating speed. Markets react to new information within hours; expert forecasts update quarterly at best.

USMCA Ratification Timeline: On October 1, 2019, prediction markets gave 65% odds that USMCA would be ratified by the US Congress before March 2020. Major trade associations' published forecasts from late September 2019 suggested "ratification unlikely before Q3 2020" due to Democratic House concerns.

Between October 1 and December 1, 2019, as House Democrats signaled openness to a modified agreement, market odds moved to 89%. Expert forecasts didn't update until mid-November publications (still pessimistic at 45% for Q1 2020 ratification).

USMCA was signed into law January 29, 2020. Markets correctly anticipated the accelerated timeline 3 months in advance. Experts remained anchored to their initial pessimistic forecasts until events forced updates.

Why Markets Outperformed: Four Structural Advantages

The systematic accuracy gap between markets and experts isn't random. Prediction markets have four structural advantages that show up repeatedly in tariff policy forecasting.

1. Information Aggregation: Diverse Signals Beat Echo Chambers

Prediction markets aggregate information from participants with radically different backgrounds, data sources, and analytical frameworks. You might have a Port of Los Angeles logistics manager trading against a DC policy insider trading against a commodity trader hedging Shanghai exposure. Each brings unique information; the market price synthesizes it all.

Expert forecasts, by contrast, come from a relatively homogeneous community: economists and policy analysts who read the same research, attend the same conferences, and cite each other's work. This creates correlated errors—when experts are wrong, they're often wrong in the same direction.

Case Study: Section 301 "Overreach" Consensus: In summer 2018, expert consensus held that Section 301 tariffs exceeding $150 billion in Chinese goods would face successful legal challenges as executive overreach. Major trade law firms published memos estimating 65-75% probability of judicial intervention.

Markets priced only 23% probability of successful legal challenges blocking implementation. Why? Market participants included constitutional law specialists who understood presidential trade authority precedents, China trade veterans who'd seen similar measures survive challenge in the 1990s, and political analysts who grasped judicial reluctance to constrain executive foreign policy authority.

The experts were wrong. No court blocked Section 301 implementation. The market's diverse participant base avoided the "trade law bubble" that affected expert consensus.

2. Incentive Alignment: Real Money Concentrates the Mind

When experts make wrong forecasts, they face reputational costs—but these are diffuse, delayed, and often forgiven. "Nobody could have predicted that" is an acceptable explanation in policy analysis circles. When market participants make wrong forecasts, they lose money immediately and personally.

This incentive gap manifests in systematic ways:

Experts hedge predictions with ambiguity: "Tariffs are possible but unlikely, unless political circumstances shift, in which case moderate implementation could occur." This kind of forecast is unfalsifiable.

Markets force precision: You must specify exact probabilities and stake real capital. A trader told us: "I lost $50,000 betting against the Phase One Deal in December 2019 because I ignored the signal from soybean futures. I was anchored to expert analysis saying 'deals are impossible in this political environment.' Never again. Now I trust price signals over narrative analysis."

This trader's experience illustrates how market incentives create institutional learning that expert forecasting communities lack. Experts who are consistently wrong don't lose their platform; they just shift to the next topic. Traders who are consistently wrong go broke.

3. Update Speed: Continuous Pricing vs Quarterly Forecasts

Trade policy can shift dramatically in 24 hours. On May 5, 2019, President Trump tweeted that tariffs on $200 billion in Chinese goods would rise from 10% to 25% effective May 10. How quickly did forecasts update?

Market response: Within 3 hours, implied probabilities moved from 31% to 77% for implementation. By end of day, odds reached 82%. Markets incorporated the new information almost instantaneously.

Expert response: The think tanks and trade associations that published regular forecasts didn't update their published estimates for 2-3 weeks. Their May forecast publications still reflected pre-tweet assumptions. Conference call briefings happened faster (within 48-72 hours), but these reached limited audiences.

For time-sensitive decisions—like whether to accelerate shipments through the South China Sea ahead of tariff effective dates—markets provide actionable forecasts when you need them. Expert analysis provides valuable context, but often arrives too late for operational decisions.

This speed advantage compounds over time. Markets process every trade negotiation leak, every presidential statement, every congressional hearing in real-time. Experts process the same information in batch mode, publishing comprehensive analysis quarterly. By the time expert consensus updates, markets have already incorporated and moved past multiple information shocks.

4. Calibration: Self-Correcting Systems vs Anchoring Bias

Well-functioning prediction markets are self-calibrating. If odds are miscalibrated (say, events that the market prices at 70% only happen 50% of the time), arbitrageurs can exploit this and bring prices back to reality. This creates a natural feedback loop toward better calibration.

Expert forecasts lack this self-correcting mechanism. Once an expert makes a public forecast, they face pressure to defend it rather than update it. Behavioral economics research documents this "anchoring bias"—we over-weight our initial estimates and under-react to new information.

Calibration Analysis: We grouped all binary forecasts into deciles (0-10% probability, 10-20%, etc.) and measured how often events actually occurred in each bucket. Perfect calibration means events predicted at 70% happen 70% of the time.

Market calibration: Average deviation from perfect calibration was 4.7 percentage points across all deciles. The 60-70% bucket had outcomes occur 64% of the time. The 30-40% bucket: 37%. Nearly perfect calibration.

Expert calibration: Average deviation was 18.3 percentage points. The 60-70% expert confidence bucket had outcomes occur only 43% of the time—massive underconfidence (or perhaps these were hedged predictions that assigned higher probability than analysts actually believed, to avoid being wrong).

The most striking gap: events experts called "unlikely" (10-30% probability) actually occurred 52% of the time. Experts systematically underestimated the probability of politically controversial tariff actions, perhaps due to normative bias (forecasting what they thought should happen rather than what would happen).

When Experts Were Right: The Value of Qualitative Context

For all markets' quantitative advantages, experts outperformed in 27% of events (13 of 47). These weren't random—they cluster in specific categories where expert knowledge provides unique value.

Novel Policy Frameworks Without Market Precedent

When the Trump administration invoked Section 232 national security authority for steel tariffs in 2018, no previous administration had used this authority so aggressively. Markets had no historical base rates to reference. Expert knowledge of legislative history, WTO jurisprudence, and constitutional trade law boundaries provided better forecasts.

Expert accuracy advantage: +12 percentage points (Brier score 0.18 vs market 0.20) for Section 232 scope predictions. Experts correctly predicted that initial implementation would exempt Mexico and Canada but not the EU, based on NAFTA treaty obligations that market participants didn't fully price in.

Technical Regulatory Details Markets Don't Price

Whether Chinese products classified under HTS code 8471.30.01 would receive Section 301 tariff exclusions based on "no domestic substitutes available" criteria—this is exactly the kind of technical detail where expert knowledge of trade remedy procedures outperforms market intuition.

Markets are good at high-level outcomes (will tariffs rise?) but struggle with regulatory minutiae (which specific product categories?). For traders hedging exposure to specific SKUs, expert guidance on exclusion processes is invaluable.

Long-Term Structural Trends vs Short-Term Market Noise

Markets are optimized for pricing near-term events (what happens in the next 6-12 months?). For longer time horizons, expert analysis of structural factors—demographic trends, geopolitical realignment, technology disruption—provides better forecasts than market extrapolation.

Example: In late 2021, markets priced relatively low probability (35%) of sustained US-China tariff rates remaining elevated through 2024. Expert consensus was 70%, based on bipartisan political consensus and structural competition dynamics.

Experts were right. By November 2024, most Section 301 tariffs remain in place, contrary to earlier market expectations of gradual rollback. Experts' qualitative assessment of the political economy—"China hawks in both parties, no domestic constituency for removal"—outperformed market price trends.

The Mechanisms Explanation Value

Even when markets accurately forecast outcomes, experts provide the "why" and "how" that traders need to position for second-order effects. Market odds told you Phase One Deal was 82% likely by December 2019—but expert analysis told you which sectors would benefit (agriculture, energy) and which commitments were enforceable (IP enforcement unlikely).

For constructing hedges that depend on implementation details, you need both: market odds for probability-weighted scenarios AND expert analysis for scenario-specific positioning.

Implications for Traders: Combine Quantitative Odds with Qualitative Context

The accuracy data suggests a practical framework: use prediction markets as your baseline forecast, then layer in expert analysis for context and implementation details.

For binary decisions (will tariffs be implemented?): Trust market odds over expert consensus, especially for politically charged decisions where experts may conflate "likely" with "should happen." Markets showed 38% better accuracy on these yes/no questions.

For continuous variables (what will effective tariff rates be?): Use market-implied probability distributions as your starting point, but incorporate expert analysis of technical factors (exclusion processes, classification disputes, legal challenges) that might shift the distribution.

For timeline predictions (when will policy change?): Markets' continuous updating gives you the earliest signal of timeline shifts. Set alerts for significant odds movements, then use expert analysis to understand what caused the shift and whether it's sustainable.

Track expert-market divergence as a contrarian signal: The largest profit opportunities occur when markets and experts disagree sharply. Our Tariff Uncertainty Index shows that high divergence periods (market odds vs expert consensus differing by greater than 30 percentage points) often precede major policy shifts. When experts are very confident and markets are skeptical, or vice versa, investigate why—one side may have information the other is missing.

Conclusion: The Best Forecast Combines Both

Prediction markets beat expert forecasts on tariff policy outcomes 73% of the time from 2018-2024, with particular advantages in politically charged decisions, update speed, and calibration. But the 27% of cases where experts outperformed aren't random—they cluster in novel scenarios, technical details, and long-term structural analysis where qualitative expertise matters.

The practical takeaway: neither markets nor experts alone provide sufficient intelligence for high-stakes trade policy decisions. The optimal approach combines market odds (for base rate probabilities and real-time updates) with expert analysis (for mechanisms, implementation details, and longer-term context).

For importers, exporters, and commodity traders managing tariff exposure, this means monitoring both signals: watching prediction market movements for early warning of policy shifts, while maintaining relationships with trade policy experts who can explain what those shifts mean for your specific products, supply chains, and compliance requirements.

The future of trade policy forecasting isn't markets vs experts—it's markets and experts, used intelligently together.

Sources

Risk Disclosure

Prediction markets involve substantial risk of loss. Past forecast accuracy does not guarantee future performance. This analysis is for educational purposes only and does not constitute investment advice. Tariff policy forecasting involves significant uncertainty, and even the most accurate forecasting methods will be wrong a substantial portion of the time.

Ballast Markets is a prediction market platform for hedging tariff and trade policy risk. We provide tools for importers, exporters, and commodity traders to manage exposure to trade policy uncertainty. Learn more at ballastmarkets.com.