I often find myself reminiscing on my days at university, though rarely with the kind of nostalgia people like to romanticise.
One aspect I distinctly recall is diving into online research archives in an essay-induced panic. Hardly an intellectual pursuit when performed at 1am, but these were before the days AI could come to your rescue.
Ironically, I now find myself doing the same thing for fun - and for my job. The two can be synonymous if you’re lucky. In one of these late-night rabbit holes, I came across a study that caught my attention: Does Paying for Data Change Investment Decisions? (2026). As the title implies, the authors aimed to determine whether the act of paying for investment data vs receiving it for free, would alter how participants would value the information and therefore act upon it.
This becomes particularly important when we consider the last two decades in financial markets: an explosion in retail involvement alongside a rise in the availability of market data. Conventional wisdom holds that more information is always better – but is that actually the case?
The test
Participants were asked to complete a series of trading rounds, each beginning with $100 and a graph showing past half-hour stock returns and buy/sell order-flow imbalance (the net difference between buying and selling pressure). In some rounds the imbalance helped predict the next return, and in others it was pure noise.
Crucially, the experimental group of participants had to decide whether to pay $4 for access to the additional data. After they made their choice, the computer randomly determined whether the round was a paid round where their decision was binding, or a free round, where everyone received the data regardless of what they chose. Participants then judged whether the data was informative and chose how much to invest. By contrast, control participants always received the data for free.
I’ll spare readers from any further commentary on the thrilling world of experimental-design. Let’s look at the findings.
What can we take away from this?
The authors documented three main findings:
- Paying distorts beliefs: Participants who paid for the data were 11% more likely to believe the information was useful, even if that was objectively not the case.
- Paying impairs performance: Participants who paid for the data placed 31% more emphasis on it, and end up presenting with 14% larger forecast errors.
- Paying changes behaviour: Paying was found to trigger a sunk-cost effect where people invested 7% more than they overwise would, simply because they paid for the data. The burden fell disproportionately on the least sophisticated investors with lower financial literacy, who invested almost 15% more in the same situation.
Whilst this was a very study-specific simulation, I think it points to something much broader. This isn’t just a trader’s problem. Everyday investors are exposed to the same informational pressures, whether it’s a paid newsletter, a premium research platform or a guru behind a paywall. The mechanism can be identical. Once you’ve paid for information, you’re far more likely to believe it and consequently act on it, even if the signal is weak.
I believe it’s healthy to maintain a suspicion of anything that comes wrapped in an air of prestige and an excessive price tag. We’re all looking to carve out an investment edge, but sometimes the hunt for that edge can end up doing more harm than good. The more we invest in acquiring information, the more determined we become to believe it’s correct, even when the evidence points the other way.
Of course, I realise the irony here. I work for an organisation that collects a portion of revenue through producing research, insights and analysis. Here lies the beauty of editorial independence. Ultimately, our investment goals are best served by good judgement, not by the price tag attached to the data we consume. Whatever information you choose to rely on, make sure it has earnt its influence over your decisions.
A note: I’ve tried my best to condense a 40-page study into something that fits within the limits of human attention. If you’d prefer the unabridged version (footnotes, regressions and all), the full paper awaits here.
Simonelle Mody
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Curated by Simonelle Mody and Leisa Bell
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