Trump trades and the forecasting trap: why political betting reveals a costly investment truth
- Robin Powell
- 18 hours ago
- 12 min read
Updated: 12 minutes ago
Trump trades, or trading strategies betting on what the President may or may not do, sound like they should work. But here's the irony: investors who systematically bet Trump wouldn't back his words with actions earned S&P 500-level returns in 2025, while betting he would cost them 20%. That counterintuitive pattern reveals a forecasting failure destroying wealth across every market.
Imagine earning stock market returns in 2025 by following one counterintuitive rule. Every time a prediction market asked whether President Trump would take a specific action, you bet "no". Fire a cabinet secretary? No. Impose new tariffs? No. Sign that executive order? No.
The result? A 12% return, matching the S&P 500. Meanwhile, investors who consistently backed Trump to follow through lost 20% of their money.
This isn't a political observation. It's a probability problem extending far beyond one president's decision-making. The pattern revealed in Trump trades illuminates a fundamental truth about investing: we systematically overestimate our ability to predict what happens next, and that overconfidence costs us.
“We systematically overestimate our ability to know what’s coming — and that overconfidence costs us.”
The platform at the centre is Polymarket, a blockchain-based prediction market where users trade "shares" in real-world event outcomes. Think of it as a betting exchange for everything from politics and economics to pop culture and weather. You deposit digital currency, buy shares in outcomes you believe will occur, and collect payouts if you're right.
Bloomberg Opinion writers Carolyn Silverman and Timothy L. O'Brien analysed over 300 Trump-related markets on Polymarket between Inauguration Day and the end of September. What they discovered wasn't just interesting trivia about one politician. It was systematic evidence of a forecasting failure plaguing every corner of investing.
The Trump trades phenomenon: systematic overestimation
The Bloomberg analysis examined 300+ binary prediction markets focused on actions directly initiated by Trump. These weren't vague questions about whether he'd "talk about" something. They covered concrete actions: imposing tariffs, firing officials, signing executive orders, even details of his golf schedule. Each had a simple yes-or-no outcome. The analysis excluded short-term bets (those resolving within two weeks) to maintain statistical rigour.
The data revealed a consistent pattern. On the day each market opened, bettors assigned an average probability of 34% to Trump taking the specified action. But only 28% of those predicted events actually occurred.
That six-point gap represents what we might call the "forecasting premium that never materialises". It's systematic overconfidence baked into human prediction.
The financial consequences were stark. Always betting "no" delivered a 12% return over the period, matching the S&P 500's gain. Betting "yes" consistently cost investors 20% of their capital. Those who started betting against Trump in June saw returns climb to 19%.
This pattern held across every category analysed. Tariff and trade-related predictions showed the highest returns for betting "no". Domestic policy came second. Even where Trump followed through relatively frequently, markets still overestimated how often he'd act.
Here's the crucial nuance: Trump did take action 28% of the time. He's not inactive. The problem isn't that nothing happens. It's that our probability estimates are consistently inflated. We expect action at 34% frequency but get it at 28%. That gap, repeated across hundreds of decisions, creates exploitable inefficiency for those betting against our overconfidence.
As Silverman and O'Brien write: "Over-weighting personal biases or magical thinking can afflict every investor. It's also the bane of gamblers."
This observation cuts to the heart of the matter. This isn't about Trump's character or political ideology. It's about our prediction accuracy.
From politics to portfolios: why forecasting fails everywhere
The Trump trades pattern isn't isolated to politics. It's a microcosm of a much larger problem frustrating investors for generations: we cannot reliably predict what markets will do next, yet we persist in trying.
Market timing: even professionals fail systematically
When full-time professionals attempt market timing, the results are dismal. In 1975, Nobel Prize-winning economist William Sharpe demonstrated that a market timer must be accurate roughly 74% of the time just to break even against a passive buy-and-hold strategy at comparable risk. Subsequent research suggests the real threshold may be even higher.
That's extraordinarily demanding. Being right on three out of every four calls about market direction, and when to exit, and crucially when to re-enter.
How do professionals perform? Poorly.
Researchers John Graham and Campbell Harvey analyzed 237 investment newsletter strategies between 1980 and 1992. Each newsletter recommended a mix of equity and cash based on market timing predictions. The researchers found that only a small number of newsletters achieved higher average returns than a buy-and-hold portfolio constructed to have the same volatility. Less than a quarter of their market timing recommendations proved correct. Graham and Harvey's conclusion: 'There is no evidence that letters systematically increase equity weights before market rises or decrease weights before market declines.
Academic research goes further. In a 1996 study, Wayne Ferson and Rudi Schadt examined 67 mutual funds between 1968 and 1990 using conditional market timing models. They found evidence of "perverse" timing ability. Fund managers, on average, moved systematically in the wrong direction. The equally-weighted portfolio showed a negative timing coefficient with a t-statistic of -3.76, suggesting managers reduced exposure when they should have increased it, and vice versa.
But surely successful timing, if achieved, would generate substantial outperformance? Not necessarily. In 1995, fund manager Peter Lynch quantified the value of perfect foresight. Investing the same amount at the start of every year for 30 years from 1965 returned 11% annually. With magical powers to invest precisely at each year's lowest point? 11.7%. Just 0.7 percentage points more. Spectacularly unlucky timing (investing at yearly peaks) returned 10.6%. Only 0.4 percentage points less.
Even perfect timing adds trivial value.
If full-time professionals with instant information, powerful analytical tools, and careers on the line cannot time markets successfully, what chance do part-time political bettors have?
“If full-time professionals with instant information, powerful analytical tools, and careers on the line cannot time markets successfully, what chance do part-time political bettors have?”
Active management: forecasting in disguise
Active fund management is continuous forecasting. Every decision to overweight a sector, underweight a region, or favour growth over value represents a prediction about future relative performance.
The evidence on how well this works is now overwhelming.
Morningstar's mid-year 2025 Active/Passive Barometer found that only 33% of active strategies both survived and outperformed their passive peers over the 12 months to June 2025. Over ten years? Just 21% survived and beat their passive equivalents.
The picture varies by category, but not encouragingly. Only 14% of US large-cap active funds beat the S&P 500 over the past decade. Mid-cap and small-cap funds fared slightly better at 22-26%, but that still means roughly three-quarters failed.
Fee disparity explains much underperformance. Average active fund fees stood at 0.59% compared with 0.11% for passive funds. That 0.48 percentage point gap compounds relentlessly year after year.
S&P Dow Jones Indices SPIVA scorecards for mid-2025 confirm the pattern globally. Across nearly all categories and timeframes, two-thirds to 90% of active funds underperform their benchmarks over three-, five-, and ten-year periods. In Europe, 61% of equity funds and 59% of fixed-income funds underperformed in the first half of 2025 alone.
Perhaps most telling is the survivorship data. Only about half of active funds survive a ten-year stretch. Many merge or liquidate quietly, their failures conveniently erased from the historical record.
The pattern repeats with numbing consistency: high confidence, poor aggregate results, high costs.
The fundamental problem: you can't predict surprises
“By definition, you cannot systematically predict surprises.”
There's a deeper reason why forecasting fails so reliably. Markets price in expectations almost instantly. Thousands of professional investors, armed with sophisticated models and competing for advantage, ensure widely available information gets reflected in prices quickly.
Current prices already incorporate all known forecasts. They represent the collective best guess about future cash flows, growth rates, and risk premiums. To profit from forecasting, you'd need to predict not what everyone expects, but the surprises. The deviations from consensus nobody sees coming.
By definition, you cannot systematically predict surprises.
Think about major market-moving events of the past 25 years: the 11th September 2001 terrorist attacks, the 2008 financial crisis, the COVID-19 pandemic, the sudden collapse of Lehman Brothers, Brexit, Russia's invasion of Ukraine. How many investors saw these coming far enough in advance to profit?
The same principle applies to positive surprises: sudden technological breakthroughs, unexpected policy shifts, geopolitical breakthroughs. They move markets precisely because they weren't priced in.
This insight comes from the very top of the investing world. In a 1976 interview at age 81, Benjamin Graham (the "father of value investing" and Warren Buffett's mentor) reflected on six decades observing markets. His conclusion? "If I have noticed anything over these 60 years on Wall Street, it is that people do not succeed in forecasting what's going to happen to the stock market."
Whether it's Trump's next policy announcement or next quarter's GDP, the pattern holds. We systematically overestimate our ability to know what's coming.
Why we keep trying: the psychological trap
If evidence against forecasting is overwhelming, why do millions of investors persist? The answer lies in deeply rooted psychological biases that make prediction irresistible.
The forecasting seduction
We're wired to assign higher probability to action than inaction. When someone with power announces an intention, we naturally expect follow-through. This action bias explains part of the Trump trades pattern: voters and bettors heard hundreds of promises and threats, overestimating how many would materialise.
The availability heuristic compounds this. Vivid, recent events dominate our probability estimates. A Trump tweet about tariffs, a Federal Reserve chairman's hint about rate changes, a CEO's confident earnings forecast. These concrete, memorable statements loom larger in our minds than the statistical reality that most don't translate into predicted outcomes.
Then there's hindsight bias. When a prediction comes true, we convince ourselves we "knew it all along". Selective memory reinforces the illusion of forecasting skill while conveniently forgetting dozens of failed predictions.
Media dynamics amplify these biases. Actions are newsworthy; non-actions invisible. When Trump imposed a tariff, headlines and analysis followed. When he didn't follow through on a threat, it simply disappeared from the news cycle. This asymmetry creates systematic bias in our mental models.
The trading costs
These tendencies have measurable financial consequences. Overconfidence leads directly to overtrading. Studies tracking individual investor accounts consistently find that more trading means worse performance. Research on investors using platforms like Robinhood found that the most frequently purchased stocks had subsequent negative returns of approximately 5% over the next month relative to the market.
Loss aversion adds another layer of cost. A pound lost feels approximately 2.5 times as painful as a pound gained feels pleasant. This asymmetry leads investors to hold losing positions too long (avoiding the pain of crystallising a loss) while selling winners too early (locking in the pleasure of a gain). The result? Poor timing that undermines returns.
Herd dynamics
We're social creatures, and our investment decisions reflect that. We look to others for validation, especially during uncertain times. This creates powerful herd dynamics where investment flows chase recent performance.
A classic example: the final quarter of 2000. Money flooded into high-tech "growth" funds just as valuations reached extremes. Over the subsequent two years, these funds suffered severe losses while unfashionable "value" funds (starved of inflows) produced positive returns. Investors who followed the herd paid a steep "selection penalty".
The flow-performance relationship persists despite overwhelming evidence that past performance doesn't predict future results. High inflows into successful funds often erode future alpha as managers struggle to deploy capital effectively at scale.
The illusion of control
Perhaps most fundamentally, forecasting and active trading create a feeling of control. During market turbulence or political uncertainty, doing something feels better than doing nothing. We want to believe our actions matter, that our research gives us an edge, that our timing will prove superior.
But it's an illusion. The "behaviour gap" (the difference between fund returns and actual investor returns) captures this reality. By jumping in and out based on forecasts and feelings, investors systematically underperform the very funds they invest in.
Even if markets were predictable (they're not), our psychology would sabotage us. Combine fundamental unpredictability with systematic behavioural biases and you get a compounding tax on wealth.
The evidence-based alternative
The good news? There's a better approach, backed by decades of evidence.
Policy over prophecy
Instead of trying to predict the unpredictable, successful investors focus on process. They build diversified portfolios aligned with their goals, risk capacity, and time horizon. They set systematic asset allocation policy based on expected long-term returns across asset classes. They rebalance with discipline, typically once or twice yearly, to maintain target weightings.
Critically, they ignore forecasts. Political, economic, or market-based. They recognise that attempting to act on predictions introduces costs (trading expenses, tax inefficiency, poor timing) outweighing any potential benefits.
Embrace uncertainty as feature, not bug
Markets are inherently unpredictable. That's not a flaw. It's what creates the risk premium rewarding long-term investors. If future returns were certain, there'd be no compensation for bearing risk.
Evidence-based investors design portfolios for ranges of outcomes rather than point forecasts. They acknowledge that all crystal balls are cloudy. They plan for dispersion in possible results, ensuring their financial plans can withstand various scenarios rather than betting everything on a single predicted future.
Minimise friction costs
Every pound spent on forecasting (whether through active management fees, adviser costs for tactical calls, or subscription services promising market insights) is a pound subtracted from returns. Every trade based on a prediction triggers transaction costs and potential tax consequences.
Passive strategies built on index funds capture market returns minus minimal costs. The evidence shows these costs matter enormously over time. A 0.48 percentage point annual fee differential (the current gap between average active and passive fund fees) compounds to roughly 13% of wealth over 30 years.
The equation is simple but powerful: time in the market beats timing the market.
The patience dividend
Perhaps the most compelling argument against forecast-based trading is the mathematics of missing the best days. Returns in equity markets are highly concentrated in a small number of days. Missing just the ten best trading days over a 20-year period can cut your returns nearly in half.
The problem? The best days often occur close to the worst days. They cluster during periods of high volatility when investor anxiety peaks. Attempting to dodge the bad days almost inevitably means missing the good ones too.
This isn't resignation. It's recognition. The evidence overwhelmingly favours staying invested in diversified, low-cost portfolios. Not because it's exciting or satisfying to our need for action, but because it consistently works.
Bringing it home: what Trump trades teach investors
The Trump trades phenomenon offers more than an amusing political footnote. It provides a concrete mental framework for evaluating all the forecasts bombarding us daily.
When you hear confident predictions (whether about Trump's next move, the Federal Reserve's interest rate path, which sectors will lead the market, or what China's GDP growth means for emerging market equities), remember the 34% versus 28% gap. Most confident statements don't materialise as predicted.
Silverman and O'Brien's advice applies universally: "Exhale. Keep paying attention, of course, but exhale. Patience is a virtue."
They note: "You're better off (financially and emotionally) betting that he won't do most of the stuff he says. And that might give you breathing room to focus on the things he says that really matter."
Replace "he" with "they" (the parade of market commentators, economic forecasters, and financial pundits) and the wisdom holds. Every guru, analyst, and newsletter writer follows the same pattern: lots of predictions, selective memory about hits, convenient forgetting of misses.
This doesn't mean ignoring everything. Some information matters. Some policy changes have real economic consequences. Trump did take consequential actions 28% of the time. The skill isn't in predicting which predictions will prove accurate (you can't do that reliably). The skill is recognising that you can't predict which is which, and building an investment approach that doesn't require you to.
Freedom from forecasting isn't giving up control. It's redirecting your energy from prediction (impossible) to process (effective). That's not passive acceptance. It's evidence-based strategy.
Conclusion
“The best investment strategy isn’t the one that predicts the future. It’s the one that doesn’t need to.”
Trump trades on Polymarket revealed something larger than one politician's follow-through rate. They exposed systematic overestimation extending across all financial forecasting. From investment newsletter writers to professional fund managers to individual investors, the pattern repeats: we expect action and certainty more often than reality delivers.
Decades of academic research, billions of pounds in aggregate underperformance, and countless studies of investor behaviour confirm the same lesson. Forecast-based investing (whether timing Trump's next tariff announcement or timing the market based on GDP predictions) fails reliably over time.
The temptation to predict is powerful. Stories are seductive. Forecasts create the illusion of control. Taking action feels better than patient discipline. We're wired to seek patterns, to feel we can influence outcomes, to believe this time will be different.
But evidence overwhelmingly favours a different approach: diversified portfolios, low costs, systematic rebalancing, disciplined patience. Not because it's exciting. Not because it satisfies our desire to outsmart the market. Because it works.
The costs of trying to predict compound against you: fees erode capital, poor timing creates behaviour gaps, overtrading generates friction, taxes multiply. The benefits of patient, diversified investing compound for you: returns accumulate, costs stay minimal, discipline prevents emotional mistakes.
Recognising you cannot predict the unpredictable isn't weakness or resignation. It's wisdom. It's what separates investors who let their portfolios work for them from gamblers who work against themselves.
The best investment strategy isn't the one that predicts the future. It's the one that doesn't need to.
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