Seeking Alpha

In short, "Alpha", as used in financial circles, is a risk-adusted measure of the active return on an investment…In order to consider relative performance often the return of a benchmark is subtracted.

For more about alpha and its mathematics I recommend this article of Keplerian Finance.

Alpha, "average" performer  and efficiency

I want to extend the understanding of alpha to "performance relative to a benchmark".  Providers want a kind of alpha related to an "average" performer and efficiency…to answer questions like: can we beat the (competitors in a) market?…do we produce more efficient?…

In non financial sectors it's much more difficult to understand risk-adjustment and the many other factors and consequently it's difficult to calculate an alpha…but many quant innovations deal with the search of something like it…it may be a set of information that gives more insight about the positions of products, services…in market segments.

However, quant innovations seeking alpha are thrilling…they need to deal with uncertainty, the duality of the known and unknown, progressive problems…stressors…and offer what-if (across scenario) analytics…optionality….

The two lives of market participants

Market participants live in an external and an internal world. The market and their work. Quant innovation makers' want to serve them in segments to perform better (make better products with better processes or do better marketing).

But quant innovation makers are market participants themselves and The Quant Innovation Mesh is a tool that helps them seeking alpha. Will our quant innovation work and sell well?

To understand, whether it will our point of view is that of the key actions and actors.

External and internal types

Remember that the external life of an innovation is influenced by standards, rules, state of the art, expectations…the what (the effectiveness related to requirements). Outside forces, like "physical products must become programmable" drive markets.

The internal life does not only represent the how, but also a compass…where will the innovation go?…what big questions will it answer?...what barriers will it overcome?…what will the actors get beyond the innovation's fit for purpose?

That's why I'm interested in

External Purpose Type
External Value at Use 
Internal Purpose Type 
Internal Value at Use

and the details related to them.

UnRisk CM again

I've filled in The Quant Innovation Mesh scheme here and after further thinking, I may change this a little..later.

However, the point of view is fund management, although with a pointed external view the CEO's is important, because she's paying penalties when violating the rules.

So, the drivers of the external world of risk management of investment and capital management firms are regulators and auditors. This is what UnRisk CM is certified for. It exposes risky horrors and helps managing them. 
This has nothing to do with any alpha estimations related to funds…It's in compliance with by managing the obligatory tasks…

But the object of desire of the firms go beyond model validation, VaR calculation, back and stress tests…it's seeking alpha and does what Aaron Brown calls "red-blooded risk management".

There are many ways to optimize risk to "beat the market" the regulatory framework it could look like this
  • estimate x%,  n day VaRs each day before trading…
  • compare actual daily P&L against VaR prediction…
  • test for the correct number and distribution of VaR breaks…
  • collect data within the limit of the reliable VaR…
  • investigate days when you lose more then VaR...calculate expected shortfalls…
  • analyse tail risk  further…by, say, power law analysis
  • analyze other risk factors (a kind of Var "cube")
  • try to determine the optimal Kelly bet base on informative data within the VaR limit…
This is very simplified, but more than the regulatory bodies require. UnRisk CM does not only calculate tons of useful numbers automatically, but also provide high-level programming and hundreds of useful functions helping to evaluate this results further…

Play games or define game rules?

And this is a very general requirement. Quant innovations are especially attractive if they are two-sided in the sense that they analyze and control what is but also what-if and what could be. In short, does the innovation help playing the competitive game or does it even help to define game business scenario analytics.

In general, a quant innovation that helps conserving capital, setting the stage for its application field, building skills and leveraging technology has a principle potential for attractiveness…seeking alpha is exciting.

Such an innovation is compelling, because it serves both sides of marketer and maker lives. But, be aware, in detail, in their possible win/lose outcomes, they're not identical.

External and internal values

Red-blooded is different from cold-blooded (fulfilling regulatory requirements) risk management. So, the external value may be positive…the deal is in a regulation-compliant stage, but what-if analysis has shown some possible dark clouds and the internal worldview of trading says "stop!"…

UnRisk CM wouldn't work if it just supported compliance with regulation…but there are more difficulties...

Fund managers usually start with an illusion (possible alpha) and they may run through stages of confusion, and even disillusion, but using UnRisk CM it's not blind belief…Risk managers are skeptics they may start with disillusion and may run through clearness and positive views due to justified belief.

BTW, Arming David, means that UnRisk CM does support quant work in a way that doesn't require rocket science skills. And this includes quant work that's not directly related to risk management.

Remember, what I want: understand whether a quant innovation is not only economically feasible, but attractive, compelling…Consequently, my diagnosis tool The Quant Innovation Mesh adds more emotional criteria than other methodologies I've seen and used in my life as an evaluator and reviewer of innovation projects for the European Commission.

It was inspired by( and I couldn't have done it without the details in) Shawn Cone's great blog the Story Grid.

I've spent now many days developing, carefully choosing attributes and methods, backtesting…but it's still improvable. However, its clear about the positive payoff through better decisions in projects I'm working for.

And I'll continue applying, (hopefully) improving and describing it…for the benefit of Storydoers...