The Bank of England’s June Surprise
The Bank of England’s decision on Thursday, 19 June, delivered a twist that caught many forecasters off guard. The Monetary Policy Committee (MPC) held interest rates at 4.25% as universally expected, but the vote was 6–3 in favor of a hold – a more divided outcome than the consensus predicted.
In fact, a Reuters poll of economists had anticipated a 7–2 split, with only two members dissenting in favor of a cut. Instead, Deputy Governor Dave Ramsden broke ranks to join the two expected doves (Swati Dhingra and Jonathan Haskel, also known as “Alan Taylor” in some forecasts) in voting for a 25bps cut. This extra dissenting vote made the tone of the decision more dovish than most analysts had projected.
It’s a vivid reminder that, even when economists unanimously call the headline outcome correctly, the finer details can surprise – and those surprises can matter for markets.
The Surprise 6–3 Split: What Analysts Missed
In the run-up to the meeting, virtually every bank and economics shop predicted the Bank of England would hold rates at 4.25%, so on that score they were correct. However, they diverged on how the nine MPC members would vote. The dominant view was a 7–2 vote, expecting only the usual two dovish outliers to call for a cut.
Many previews named external members Dhingra and Taylor as the likely dissenters calling for a 25bp rate reduction. What actually transpired – a 6–3 vote – was outside the mainstream forecast. Only a couple of forecasters, notably Barclays and Morgan Stanley, had the conviction to predict that third dissenter. Barclays’ team, for instance, explicitly foresaw a 6–3 split, reasoning that Ramsden might join the doves given the recent softening in UK data. That contrarian call proved prescient.
Most others stuck with the safer bet of 7–2 and were left explaining why they underestimated the dovish tilt at the MPC.
Why did so many analysts miss that extra vote? Part of the answer is that predicting individual policymakers’ behavior is an inexact science. Central bankers can change their stance quickly in response to new data or risks. Ramsden, in this case, was considered a possible swing voter but not a certainty – many economists evidently assumed he would stay with the majority until August. It turns out the cumulative evidence of a cooling labor market and weak April GDP was enough to sway him early.
Even well-informed analysts don’t have a window into the MPC’s deliberations; they rely on reading tea leaves from speeches and data, which can lead to overconfidence in the consensus view. There’s also an element of herd behavior: sticking close to the pack (predicting 7–2 like everyone else) feels safer professionally than going out on a limb with 6–3 and being wrong.
The irony is that in this instance, the outliers like Barclays got it right, while the herd was wrong.
Forecasting: An Imperfect Art (See Also: Non-Farm Payrolls)
The BoE vote split fiasco is just one example of a broader truth – economic forecasting often falls flat on its face.
Look at the U.S. Non-Farm Payrolls (NFP) reports that come out each month. Banks and economists churn out estimates for the monthly jobs gains, and the range can be extremely wide. It’s not unusual to see one reputable institution predicting, say, 100,000 new jobs while another predicts 250,000 for the same month. And frequently the actual figure lands nowhere near the consensus.
For instance, in April the U.S. added 177,000 jobs, sharply above the ~130,000 median forecast that analysts had penciled in. In other words, the experts were off by a country mile – a surprise of nearly 50,000 jobs. Such surprises are more common than economists would like to admit. Whether it’s central bank decisions or key data releases, the range of predictions is often wide and the misses can be glaring.
Why are forecasts so often off-target? In part because economies are complex, adaptive systems, influenced by countless variables – many of which are unpredictable or even unknown.
Small wonder that the Bank of England’s own governor, Andrew Bailey, described the world as “highly unpredictable” when cautioning against confident rate projections. Models built on historical relationships can break down when the regime changes. We saw that during the pandemic and recent inflation surge, when even central banks had to admit their models failed to foresee the spike in prices.
Furthermore, data itself is noisy (revisions to job figures, for example, routinely shift the picture in hindsight). Analysts in banks do their best with the information at hand, but sometimes reality has other plans.
Traders’ Dilemma: Paralysis by Analysis
For traders – whether on a bank’s desk or managing a personal portfolio – these frequent forecasting errors are more than just academic. They can be the difference between profit and loss, or the cause of sleepless nights before a big event.
In theory, having lots of research should help a trader make better decisions. In practice, consuming too many divergent forecasts can lead to “analysis paralysis.” This is the state of being so overwhelmed by conflicting data and opinions that one struggles to make any decision at all.
Imagine prepping for the BoE meeting: you read a dozen bank reports. Most say “no change, 7–2 vote,” one or two say “could be 6–3,” another even floats a wild scenario of a 5–2–2 vote split (yes, one firm actually entertained the idea of two members voting for a larger 50bp cut). By the end of it, your head is spinning. Do you position for a status-quo outcome? Do you hedge for a dovish surprise?
It’s easy to end up doing nothing – or doing something and second-guessing it immediately.
The same goes for trading the NFP release. With such a spread of payrolls estimates, a trader trying to parse every forecast might end up with no clear bias at all. One analyst says the labor market is slowing sharply, another sees resilience – and both back their claims with seemingly valid data.
If you absorb all of it uncritically, you might convince yourself of both bull and bear cases and freeze when the time comes to trade the number.
Paralysis by analysis is a well-known pitfall in trading psychology: too much information can be as dangerous as too little, if it sows doubt and hesitation.
Analysts vs. Traders: Different Games, Different Stakes
It’s also instructive to consider how an analyst’s incentives differ from a trader’s.
An economist or strategist at a big bank is paid to produce frequent analysis and forecasts – it’s literally their job to have a view on every major event. If they’re wrong this time, no one pulls them off the field; they’ll be back with a revised forecast next week or next month. Their currency is reputation and insight, not P&L.
A bit of boldness (like calling a 6–3 vote split when others say 7–2) might earn bragging rights if correct, but only minor blushes if wrong. After all, clients mainly remember the hits and tend to forgive the misses in a volatile environment. As the saying goes, “forecast with boldness; update with humility.”
A trader, on the other hand, lives and dies by decision-making and risk management. There’s real money on the line. If a trader bets on an outcome that doesn’t materialize – say, positioning for only two dissenters when in reality three show up, leading to a more dovish outcome and a bond rally – their portfolio will feel the pain immediately.
Traders can’t simply rationalize a bad call; they have to cut losses or adjust on the fly.
This fundamental difference means traders often approach forecasts with a skeptical eye. Rather than taking any single projection as gospel, savvy traders treat them as inputs for scenario planning. For example, knowing the consensus was 7–2, a trader might ask: “What if the vote is 6–3? How much rally in gilts (UK bonds) could that spark?”
By gaming out various outcomes (including the outlier scenarios), traders aim to be prepared for surprises rather than being blindsided by them.
Finally, traders often incorporate market-implied expectations alongside analyst forecasts. Market prices (such as OIS rates or futures) can reveal what the crowd is betting on. In June’s BoE case, market pricing before the meeting implied roughly a certain path of cuts by year-end, but not a full chance of an immediate cut.
If the outcome tilts more dovish (e.g. an extra dissenter), the market reacts by adjusting those odds – as happened when gilt yields dipped slightly on the 6–3 news before bouncing on other remarks.
The successful trader is the one who can filter the signal from the noise: using analysts’ research to inform, but not dictate, their strategy. Analysts provide the narrative and nuance, whereas traders must execute in real-time and know when to deviate from the script.
Cutting Through the Noise
The critical take-away for both retail and institutional market participants is this: don’t put blind faith in forecasts, no matter how many PhDs back them up. Economists from top banks will continue to disagree – often wildly – on things as fundamental as how nine people will vote, or how many jobs the economy created last month.
That doesn’t mean their analysis is useless. It can provide context, highlight key drivers, and outline plausible scenarios. But it does mean that uncertainty is the only certainty.
As traders and investors, we have to embrace the likelihood that reality will often defy the median expectation.
Rather than being paralyzed by a glut of analysis, it pays to simplify and focus on a few core scenarios and risk management plans. Treat forecasts as a guide to what might happen and what the consensus baseline is – not a guarantee.
Remember that even when virtually all economists agree on the direction of a decision (as they did with the BoE holding at 4.25%), the devil is in the details and surprises can lurk there.
In markets, it’s often those unexpected details that move prices. The Bank of England’s June meeting taught us that lesson yet again. So tip your hat to the few who called it right, but keep your powder dry: next time, the crowd could be wrong in a completely different way.
In the end, successful trading and investing require a balance – use analysis to inform your view, but be ready to act (or step aside) when the data hits the fan and the predictions go awry.
