Risk Is Like Energy
The first in a series on practical learnings from building and running the quant infrastructure at Vertus. With billions at stake, in the most competitive market on earth.
The only thing that truly matters is sustainable, risk-adjusted returns.
Neither risk nor return can exist in a vacuum; they are always in a dependent relationship. As developers of quantitative trading models, our main goal is to maximize return over risk. Both of these variables have unique and immovable properties.
In the following series of blog posts, I will go over some of them, and let you see through my lense of building and running the quant infrastructure at Vertus. This document is meant to describe our philosophy and the underlying logic of why we do things and how we do them.
Risk is like energy. It can be transformed, but never destroyed.
Imagine a ball of Play-Doh, representing our risk laying on a plain, representing the universe of investable assets. We can smash the ball out as far as we want, the amount of Play-Doh on the table won't decrease, it will only be spread thinner. Diversification can only transform risk, not dissolve it. In practice, diversification indeed works best when spread as far as the yielding investable assets allow.
In most cases, these naive, broad diversification strategies outperform sophisticated discretionary pick-and-choose approaches. This is evident by the historical underperformance of actively managed funds.
When building diversification models for quantitative trading strategies, all four dimensions of diversification have to be utilized.
The most basic two-dimensional idea of diversification is the "What" and "How much"; our Play-Doh plain from earlier. While this encompasses single assets, such as stocks, commodities, crypto, etc., any vehicle that creates a tradeable equity curve should be taken into account (e.g. strategies, funds, derivatives).
The "When" dimension encompasses the timing of diversification decisions. We are already starting to fall outside of classical fund management at this point. To effectively time diversify decisions (as any allocation) we require a model that takes into account more than the inherent data of the underlying asset(s).
The "How" gets even more complex. It looks beyond underlying assets to different investment approaches. For example: say your underlying is a stock, do you enter using an option, outright buy shares or use other derivatives? If your underlying is a strategy, how and why does it enter a trade? Two strategies trading the same asset on the same day, and in the same direction can diversify your portfolio by controlling how they enter and exit positions.
While these points highlight the opportunities in complex diversification techniques, they also present the necessity to think multi-dimensionally. Your Play-Doh might be well spread across our 2D plain, but does it clump in a 4-dimensional space?
to be continued…