Stop chasing predictive certainty and start treating models as disposable tools. Adopting this framework prevents sunk-cost bias during 2026 volatility.
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In the high-stakes environment of global financial markets, the search for predictive certainty often leads to the industry’s most expensive mistakes. In his latest work, Useful Not True, author and strategist Derek Sivers challenges the fundamental premise of market forecasting, arguing that the future is not a destination to be discovered, but a construct of the imagination. For the professional trader, this distinction is not merely philosophical—it is a critical hedge against the cognitive biases that frequently derail portfolio performance.
Sivers posits that because the future does not exist, all data models, technical indicators, and macroeconomic projections are essentially imaginative exercises. By defining these tools as “useful” rather than “true,” investors can detach their ego from their positions, fostering a more agile approach to risk management. As the market enters a period of heightened volatility, the ability to distinguish between a helpful heuristic and an immutable truth has never been more vital.
Market participants are conditioned to view history as a blueprint for the future. However, as Sivers notes in his December 26, 2025, entry, the act of predicting is an act of storytelling. When an analyst projects a target price for a stock or a central bank’s next interest rate hike, they are not revealing a hidden reality; they are building a narrative based on historical patterns that may or may not hold relevance in the current cycle.
This perspective aligns with the core principles of Bayesian inference, which is increasingly popular among quantitative hedge funds. Instead of treating a forecast as a binary 'true' or 'false' outcome, successful traders treat every prediction as a probability distribution that must be constantly updated as new, real-time data arrives. When the market moves against a thesis, the 'useful not true' framework prevents the sunk-cost fallacy. If a model is no longer useful, it is discarded, regardless of how much time or capital was invested in its creation.
For institutional and retail traders alike, the psychological trap of 'being right' is the primary barrier to profitability. When a trader confuses a useful model with a universal truth, they often double down on losing positions, convinced that the market will eventually 'correct' to match their forecast.
By adopting Sivers’ mindset, traders can pivot toward a more objective execution strategy. This involves:
As we look ahead to the remainder of the fiscal year, the macro environment remains fraught with uncertainty. From shifting geopolitical alliances to the rapid evolution of algorithmic trading, the sheer volume of noise makes it easy to cling to rigid predictions for comfort. However, history suggests that those who remain flexible—who treat their models as disposable tools rather than sacred texts—consistently outperform those tethered to static narratives.
In essence, Sivers reminds us that the most successful market participants are not those who predict the future most accurately, but those who are best prepared for the fact that they cannot predict it at all. As market conditions evolve, the question for every trader should not be 'Is this prediction true?' but rather, 'Is this model still useful for my current objectives?' By focusing on utility over truth, traders can maintain the emotional discipline required to navigate the complexities of modern finance.
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