Arslan Kussayin | 15.10.2025
Why the Market Becomes Unpredictable When Everyone Tries to Predict It
Financial markets are often described as puzzles that economists and investors tirelessly try to solve and predict . However, ironically, the more everyone tries to predict the future of the market, the more unpredictable the market becomes. Prices move not only because of external events, but also because of how people think the markets will shift. This self-repeating loop is deeply rooted in what makes markets both paradoxical and yet coherent.
At first glance, predicting market outcomes seems straightforward. Use economic models and insert data, rates, inflation, consumer spending and other key variables to forecast future patterns, trends and price shifts. However, these models heavily assume that everyone will act rationally so that their behaviour won’t change the very model itself. Though truthful, by the time a prediction or forecast becomes widely known, it causes the inverse effect almost always.
As an example, if an analyst announces a prediction on a particular stock’s rise, crowds of investors will rush to buy it to chase the trend, as a result driving up its price before the expected growth even occurs. The prediction itself becomes self-fulfilling in the short term; however makes the long-term future impossible to predict as prices no longer reflect fundamentals and economic models.
This is the theory that economist George Soros named “reflexivity”, which is the idea that market consumers’ predictions and actions will continuously influence the market in reality. That reflexivity creates constant feedback loops where beliefs that anticipate the future affect prices, and in turn affect beliefs yet again. And when enough consumers believe the market will change, it often does until a point of confidence flips, and the same effect of the reflexivity feedback loop causes an acceleration in a crash.
This reflexivity loop causes economists to face a major obstacle. The majority of traditional economic models, such as the “Efficient Market Hypothesis”, assume that all prices already use all available information, thus making it literally impossible to beat the market consistently. However, behavioural economics illustrates that investors are not perfectly rational; they frequently tend to implement irrational behaviour due to their human nature. The most frequent examples of this would be: herd behaviour, overconfidence, and fear often drive the markets further from the equilibrium. When all investors act on predictions, collective psychology replaces rational logic and amplifies the volatility and uncertainty of market outcomes.
To deal with this obstacle, economists use what's called probabilistic models along with stress testing rather than precise forecasts that have a higher risk of uncertainty. Instead of questioning “what will happen?” economists question “what could happen and with what likelihood?” Central banks, financial institutions and firms also heavily rely on scenario planning, simulating multiple possible futures to understand how markets might react to shocks through probabilistic models and stress tests.These economists' focus shifted from trying to make predictions to gaining resilience by not trying to know the future but rather preparing for its surprises or uncertainties. In the sense of financial markets, behavioural concepts such as weather systems, adaptive and deeply sensitive to human expectations and behaviours. Just as forecasting rain changes how people act and behave (e.g. carrying umbrellas, staying indoors), forecasting market trends changes how investors behave, which in turn changes the trend itself.
At the core, the irony is that prediction remains essential to finance, while also acknowledging that it can never be perfect and 100% certain. Investors and economists are forced to constantly walk on a fine line between understanding the market and influencing the market. The more cohesive and transparent global markets become, the more reflexive and unpredictable they grow.
To conclude, even the best economists are not the ones who predict the future most accurately, but those who understand why the market cannot be predicted at all and even still manage to build systems and models that can survive the uncertainty of a market.
At first glance, predicting market outcomes seems straightforward. Use economic models and insert data, rates, inflation, consumer spending and other key variables to forecast future patterns, trends and price shifts. However, these models heavily assume that everyone will act rationally so that their behaviour won’t change the very model itself. Though truthful, by the time a prediction or forecast becomes widely known, it causes the inverse effect almost always.
As an example, if an analyst announces a prediction on a particular stock’s rise, crowds of investors will rush to buy it to chase the trend, as a result driving up its price before the expected growth even occurs. The prediction itself becomes self-fulfilling in the short term; however makes the long-term future impossible to predict as prices no longer reflect fundamentals and economic models.
This is the theory that economist George Soros named “reflexivity”, which is the idea that market consumers’ predictions and actions will continuously influence the market in reality. That reflexivity creates constant feedback loops where beliefs that anticipate the future affect prices, and in turn affect beliefs yet again. And when enough consumers believe the market will change, it often does until a point of confidence flips, and the same effect of the reflexivity feedback loop causes an acceleration in a crash.
This reflexivity loop causes economists to face a major obstacle. The majority of traditional economic models, such as the “Efficient Market Hypothesis”, assume that all prices already use all available information, thus making it literally impossible to beat the market consistently. However, behavioural economics illustrates that investors are not perfectly rational; they frequently tend to implement irrational behaviour due to their human nature. The most frequent examples of this would be: herd behaviour, overconfidence, and fear often drive the markets further from the equilibrium. When all investors act on predictions, collective psychology replaces rational logic and amplifies the volatility and uncertainty of market outcomes.
To deal with this obstacle, economists use what's called probabilistic models along with stress testing rather than precise forecasts that have a higher risk of uncertainty. Instead of questioning “what will happen?” economists question “what could happen and with what likelihood?” Central banks, financial institutions and firms also heavily rely on scenario planning, simulating multiple possible futures to understand how markets might react to shocks through probabilistic models and stress tests.These economists' focus shifted from trying to make predictions to gaining resilience by not trying to know the future but rather preparing for its surprises or uncertainties. In the sense of financial markets, behavioural concepts such as weather systems, adaptive and deeply sensitive to human expectations and behaviours. Just as forecasting rain changes how people act and behave (e.g. carrying umbrellas, staying indoors), forecasting market trends changes how investors behave, which in turn changes the trend itself.
At the core, the irony is that prediction remains essential to finance, while also acknowledging that it can never be perfect and 100% certain. Investors and economists are forced to constantly walk on a fine line between understanding the market and influencing the market. The more cohesive and transparent global markets become, the more reflexive and unpredictable they grow.
To conclude, even the best economists are not the ones who predict the future most accurately, but those who understand why the market cannot be predicted at all and even still manage to build systems and models that can survive the uncertainty of a market.
