Learning From Mistakes: An Interview With Dr. Vikram Singh on Extreme Regime Correlation Forecasting

The biggest lesson is that confidence in a particular approach can quickly become a liability when conditions change. Early in my career, I relied heavily on models that seemed robust, only to watch them fail when a new regime emerged. Now, I encourage myself and my team to treat every assumption as temporary and every success as provisional. This mindset is humbling, but it makes us more adaptable.

Over time, I have come to value open dialogue above technical precision. Our best insights have come from reviewing past failures as a group and being honest about what we missed. I regularly revisit meeting notes from years ago, searching for the seeds of current blind spots. This process is more about learning than about assigning blame, and it keeps us from repeating the same mistakes.

Context matters more than I used to think. The same model can perform very differently depending on macroeconomic conditions, policy changes, or even shifts in market sentiment. I make it a point to re-contextualize forecasts with every new cycle, never assuming the backdrop is fixed. This practice has helped me avoid some major errors in recent years.

Humility is essential. I have seen overconfidence sink even the most promising models. The best approach is to spiral back to old questions, examine them in light of current evidence, and invite critique from peers. Humility doesn’t guarantee accuracy, but it does foster resilience.