This page explains the methodology behind every score and chart on the site: the data we used, how we scored it, the mistakes we corrected, and how much confidence we can place in the current results.
We obtained all of Trump's Truth Social posts since 2022—more than 35,000 posts—from CNN's mirror of the stiles/trump-truth-social-archive repository. The mirror updates every 5 minutes. For market data, we use two layers: SPY (S&P 500) for the broad market and Mag7 stocks (Apple, Microsoft, Nvidia, Alphabet, Amazon, Meta, Tesla) for company-level detail. We use daily and hourly prices from 2018 to the present. Section 3 explains which measure is appropriate and when.
Example: a major trade shock such as a "100% tariff" receives a −5; a personal statement such as "America is a great country" receives a 0; a trade deal announcement might receive anywhere from +3 to +5.
If a stock rose 2% after a post, did the post cause the move, or did the stock simply rise with the broader market? The standard approach is the abnormal return (AR) method. Subtract the index return from the stock return, and the remaining difference represents the movement specific to that stock:
stock_return − QQQ_return
The primary measure used in event studies in the finance literature.
This method is appropriate when a post concerns a specific company, such as Apple, Tesla, or a chipmaker. However, we found that the only topic through which Trump affects the market is tariffs. A tariff is a macro shock that affects the entire market at once. In that case, the broader-market movement being subtracted is the signal we are trying to measure. By definition, abnormal return washes out the macro shock.
We encountered this problem firsthand. The abnormal-return analysis found nothing. When we repeated the analysis using raw return, the tariff signal emerged.
That is why the site uses both measures. The main number on each card is the broad market's (SPY) raw return, which is the appropriate measure of a macro effect. Company-level Mag7 results use abnormal return and are meaningful only when a post concerns that specific company.
We compared six instruments side by side. The effect of the tariff signal on the magnitude of the next day's movement was notably clearer in broad indices (t-statistic, 2025-2026): VTI +4.11 · SPY +4.02 · DIA +3.97 · QQQ +3.47 · IWM +3.27 · Mag7 median +3.28. Although Mag7 had the largest coefficient, it had the lowest t-statistic because a seven-stock median is noisy: its baseline volatility is 1.13%, compared with SPY's 0.51%. In short, the seven-stock measure added clutter while providing weaker statistical evidence.
The time window also matters. If a post was published in the evening or at night in New York, while the market was closed, the reference point is the next open. If it was published while the market was open, the reference point is the previous close. This avoids measuring the aftermath of the reaction rather than the reaction itself.
War rhetoric did not move the market. Military and war-related posts produced a result close to zero, with t=+0.02 in the 339-day sample. The results were t=-0.98 for Iran-Russia-Israel, +0.25 for China, +0.08 for the Fed, -0.01 for energy, and -1.25 for the courtroom topic. The most plausible interpretation is that Trump produces military rhetoric so often that the market has become desensitized to it.
Post scores also do not predict the market's direction the next day. The correlation between the score and the next day's return has remained near zero in all five years: 2022 +0.015; 2023 +0.015; 2024 +0.003; 2025 +0.005; 2026 -0.044. The sign can change from year to year, and the result is the same for both abnormal and raw returns. We therefore abandoned the idea of a bot that makes directional predictions from posts.
Across the full dataset, we initially found “a 29% probability of a 2% or larger move following tariff days, compared with 15% on other days.” But in a walk-forward test using only the data available as of each day, the difference disappeared: 20.8% versus 20.1%. This was a classic in-sample illusion, so we do not present the initial result as a valid signal.
The only remaining asymmetry is in downside tail risk: the probability of a drop of more than 3% the next day is 8.3% following tariff days and 4.7% on other days. This difference is not sufficient for a directional prediction. It shows only that the risk of a large negative move is not evenly distributed.
In summary, across 27,564 posts, the general sentiment score does not produce directional predictions. War, geopolitics, and other prominent topics also fail to provide a reliable market signal. The only relationship that survives in the data is between intense tariff rhetoric and the magnitude of the next day's movement. The weakening of that relationship over time suggests that the market is adapting to repeated political threats.
Most posts are unrelated to the market. Only about 7% of the 27,564 scored posts receive a non-neutral score—that is, a score other than 0—because most posts contain genuinely political or personal content. This is not a bug. It likely reflects reality.
No directional relationship has been established—in fact, the evidence indicates that none exists. The sample is no longer small. Across 27,564 posts, there is no reliable relationship between the score and market direction. None of the numbers on the site should be treated as a predictive tool. They are a record of past results.
No weight has been incorporated into a trading bot. Our internal rule is simple: an unproven statistic cannot receive a high-confidence weight. It must first be tested on a separate holdout set. The monitoring service for the tariff-volatility relationship continuously measures its own predictive power. Because the relationship is currently in a “signal faded” state, the service takes no action.
Because this is a starting point, not a final claim. The goal is to accumulate enough data, measure it correctly, and assess honestly whether a meaningful relationship exists. If one emerges, that matters. If not, that is also a valuable result: "social media noise does not move the market as much as people think" is itself a finding.