Measuring Risk

What’s the best way to evaluate risk? Learn from the past.

By Nader Museitif

Risk is a science. It can be calculated using events, trends, and probabilities. When we have a finite set of outcomes to a repeated event, based on historical readings, the future becomes somewhat less surprising if all constants hold. Statisticians do this all the time to predict things like weather, pandemic outbreaks, and traffic flows.

Risk also becomes an extremely subjective matter when cognitively processed by humans. On top of the factual layer, risk is a product of sentiment, emotion, and experience. The complexity of all these inputs forms a unique frame of mind that influences the processing of decisions and choices.

In an ideal objective system, what should come first? Well that varies widely; there’s the data cruncher and forecast modeling addict, there is the pure gut feeler, and there is the artist combining both sides and continuously refining a balance that adapts to different situations.

The topics of risk and mitigation flashed through my mind as I fled a burning skyscraper in Dubai last month. As I rushed down 60 flights of stairs, I got to question my whole risk rationale (or lack thereof). That risk self-critique even increased when I saw the building burning. Such critique can be remarkably diverse. I questioned the choice of building, apartment, exposure to wind, insurance decisions, and, as floors go buy, my physical preparedness for such a descent. My point is that our risk mentality changes as we get closer to certain loss events. As we face such events, we revisit our choices and identify the shortcomings we fell in. Many fundamentals and methodologies get shaken or sometimes validated.

But why should we wait for the loss outcome to hit before we change our mentality? And if we flip that argument to opportunities, are we missing on upsides around us because we do not estimate them (or anticipate them) well?

Ultimately, the question to ask is whether our assumptions about events and their occurrence actually maximize our losses and minimize our returns? Or are we calibrated to optimize both?

Answering this isn’t so difficult. It shows in successes, failures, returns, etc. If these are not up to the level, then a risk calibration might be one way to improve the status quo or avoid disasters. I’m not suggesting you hit the races and place a few bets. This is about getting into the data, understanding the trends, and applying a controlled gut feel. Experience is crucial and lessons learned are invaluable. Some start small, lose a little, but learn big. They come to the next round more prepared and vigilant of the risks, without losing sight of the opportunities.