• Bob Van Art

Equilla Basic Concepts

Equilla’s statistical foundation goes back to 1952. In that year Dr. Harry Markowitz published his now renowned paper on portfolio selection. A number of disruptive concepts emerged from his work. Dr. Markowitz showed that it was possible to build an investment portfolio using only statistics. Even more important was the simple observation that the combined performance of the assets in a portfolio is more important than individual asset performance. Markowitz approached portfolio selection from the point of view of risk management. Simply stated, for any level of return that an investor seeks, there is an acceptable level of risk he is willing to take.

This ultimately led to the development of the asset class and the efficient frontier. Both are foundational in today’s investment management practices. It is common practice today to spread a portfolio over multiple asset classes to manage risk.

If we want to lower risk, we weight fixed income assets more heavily in our portfolio. This lowers the overall portfolio standard deviation and increases correlation and co-variance. If we want to increase risk exposure (thereby presumably increasing return) we would add some small cap assets or perhaps emerging market assets to our portfolio. This would increase portfolio standard deviation and reduce correlation and co-variance. You can think of these statistical measures as levers which control the balance of risk and reward. The relationship between risk and reward is multi-faceted and can be explained from both quantitative and qualitative points of view.

Quantitatively (that is statistically) risk and reward move together. An increase in the risk we are willing to assume results in an increase in the potential reward we will receive. Conversely decreasing risk typically decreases potential reward. This represents the quantitative link between the two. They are quantitatively correlated.

If we analyze the relationship on another plane we can consider the “qualitative” or “desirability” aspect of risk and reward. While risk and reward move together statistically, they are at opposite ends of the desirability scale; they are by definition antithetical. Risk is bad and reward is good. We can consider them qualitatively uncorrelated.

The antithetical relationship between risk and reward makes being a wealth manager difficult. Risk and reward are quantitatively correlated but qualitatively uncorrelated.

Since we can use this relationship in the form of asset classes to manage risk; does it not stand to reason that we should be able to use the same statistics to manage reward?

We manage risk by combining similar assets into classes and then allocating the portfolio against those classes. The antithetical qualitative linkage which exists between risk and reward suggests that to manage reward (the opposite end of the risk reward spectrum) we must consider the combination of dissimilar assets.

The question then becomes how can we recognize dissimilar assets as a unified group (similar to a class) for purposes of performance; managing return much as asset classes are used to regulate risk? This implies the construction of an entity that is functionally similar to an asset class, but statistically dissimilar; an entity which exists at the opposite end of the risk reward spectrum.

A Trade Group is the functional opposite of an Asset Class

Equilla addresses this dilemma with the development of a unique statistical bonding process called instantiation. The instantiation algorithm permits the Equilla to recognize assets from across different classes as a unique combined entity. We call this combination of statistically in-congruent assets a "trade group". Structurally a trade group exists as a small portfolio (3 or more assets). Logically a trade group is the antithetical of an asset class. It is, from a statistical point of view the opposite of an “Asset Class”. It is an “Anti-Class”.

We know that we control risk by spreading it over a combination of asset classes. Therefore we should be able to use a similar approach to affect the reward side of the performance continuum. Just as we spread risk across classes, we spread performance within the trade group. Equilla is built on a proven statistical foundation.

Equilla’s prime objective is not to outperform a market-based portfolio. Her prime objective is to accumulate assets. In the process of accumulating assets Equilla will eventually surpass and outperform a “Buy & Hold” portfolio because she will simply hold more shares. Outperforming a “Buy & Hold” portfolio is a by-product of asset accumulation. Equilla permits us to isolate and sell only a portion of the asset represented by asset price appreciation. We use the proceeds of the sale of the highly appreciated assets to purchase shares in underappreciated assets within the framework of the trade group. This is a concept called “performance harvesting”.

Equilla knows it is better to own more shares than fewer shares.

Over time the continual realization of appreciation and redeployment of the realized appreciation within the trade group actually adds shares to all of the assets in the group.

If you continually sell high and buy low you will accumulate shares.

What makes Equilla different from other computer models is Equilla’s orientation. Equilla does not use a list of rules for either selection or trading. There are no PE ratios, yield comparisons, ROI, betas, capture ratios, or any other typical financial metric used to determine trading or allocation. Equilla has no weighting or ranking system. Equilla does not attempt to rank assets by size, asset class, or even return.

Equilla does not attempt to predict what the market will do. We do not anticipate tomorrow. We look at what the market did yesterday and take advantage of that movement. Instead of hypothesizing on what might happen we capitalize on what did happen. Hindsight is 20/20 that’s what we count on.

Equilla is not predictive Equilla is responsive.

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