In investment reporting, numbers do not speak for themselves. Metrics acquire meaning only when placed into a narrative that investors and allocators can understand and trust. Framing analytics results clearly and thoughtfully is essential to conveying the skill and intent behind a strategy.
Volatility provides a good example of this challenge.
Volatility is one of the most familiar risk metrics in finance but also one of the most misunderstood. High volatility often triggers concerns that a strategy is uncontrolled or reckless. Without explanation, numbers like annualized volatility above fifty percent or daily Value-at-Risk in double digits can overshadow the qualities that actually make the strategy attractive.
Yet in many cases, volatility is the direct outcome of a deliberate investment approach. High-conviction decisions, concentration in idiosyncratic positions, and active timing all contribute to volatility but also reflect skill and opportunity-seeking. If we stop at the headline figure, we miss what it tells us about the manager’s ability and intent.
One way to understand whether volatility reflects skill is to separate it into its upside and downside components. Does the portfolio experience large swings because it captures outsized gains, or because it suffers uncontrolled losses? When upside volatility dominates, it suggests that the risk is opportunistic and that the manager can exploit positive market movements.
For example, a portfolio with an upside volatility of nearly fifty percent and downside volatility less than half of that is signaling something important. It means that when the market offers opportunities, the manager captures them in size, and when the market turns against them, losses are controlled. High upside capture, especially when benchmarked against peers or indices, reinforces the view that the manager’s volatility is a byproduct of skillful, active positioning rather than randomness.
Framing volatility as a signal of skill instead of a flaw is not about hiding risk but about showing what kind of risk it is. Decomposing volatility into upside and downside components, and comparing those to benchmarks, helps make the case that the manager’s decisions are intentional and informed.
This post is the first in a series on turning portfolio analytics into allocator-ready narratives. Future parts will discuss how to frame drawdowns, payoff asymmetry, Value-at-Risk, and attribution to better communicate the skill and discipline behind a strategy.
If your metrics seem to accuse you of recklessness, perhaps they need a closer look. They may be telling a very different story.
