Financial decisions such as those involved in stock trading should, at least partly, be based on similar features as detecting trends in time-series data. When presented as a purely perceptual task, people’s accuracy in detecting trends is generally considered good, but real-world individual investors underperform the market globally. In a series of controlled experiments, we contrast financial decisions to perceptual ones, presenting participants with real-time evolving time series whilst manipulating the reward structure and the context. Our results show that participants’ decisions were not affected by trend direction in a classic perceptual decision-making scenario, whilst in a classic trading scenario they performed worse in both speed and accuracy during downward (i.e., bear markets) compared to upward trends (i.e., bull markets). In a final experiment where we carefully controlled the reward structure of both scenarios and the only relevant differentiating factor were the labels of the decisions, we did not find evidence for this difference between scenarios, but participants were slower in the trading scenario. Employing the Drift-Diffusion Model, we found evidence of lower efficiency in the classic trading, compared to the classic perceptual decision-making scenario. Our results provide much-needed insight into the cognitive basis of trading decisions and the general underperformance of real-world individual investors.