human capital, risk assessment, weight, tangible, intangible, Toby Elwin, risk

Evaluating risk: financial models versus competency models, part 1

weight of tangible to intangible assets, assessments, Evaluating risk, financial models, competency models, Toby Elwin, blog
The disproportionate weight of tangible to intangible

We make models to get an idea, on a small-scale, of what might happen on a large-scale. Models help identify risk and attempt to predict outcomes. Many use models to then run scenarios or alternatives to identify what could or should be. Models then become a map for many management discussions as models provide options and a business without options is on borrowed time.

No model is, or ever will be, 100% accurate or predictive. Even with 100% of the information available, models can not predict the future, models can only guide business discussions and inform.

I like to advocate that models move our discussions from a personal (emotional) case to a business (rational) case.

One of the simplest reasons, other than the human condition called error, that models are not and will never be 100% accurate is time. Time is the most fundamental aspect to understand risk. And the further your time horizon, the greater amount of unknowns (variable, options, alternatives) added, the higher the risk.

Predicting what you might do tomorrow may be narrowed to, what you believe are, a finite set options. Your prediction for what you will do tomorrow is not a 100% guarantee to happen.


What about predicting what you will do 24 hours from now, one week, one month, six months, one year, five years — the further out the time horizon the larger the variables for model accuracy. In models, time is also called horizon or period.

The longer the timeline, the larger the amount of unknowns and unknowns are risk.

Competency Models Risk

An economic model attempts to abstract from complex human behavior in a way that sheds some insight into a particular facet of that behavior. Models and their build-outs inherently ignore important aspects of real-world behavior, models are truly as much an art as a science in their mathematical reasoning and interpretations. And as we continue let’s keep in mind how subjective art really is to the beholder.

The following are a small list of models you may have heard, built, or need:

  1. Business models:  try to identify whether an idea or innovation has economic value and draws on economics, finance, marketing, strategy, and operations
  2. Economic models:  [let’s not touch this one or we’ll begin to go into a far too abstract world of settled equilibrium, rational actors, reflexivity theory, and representative agents]
  3. Financial models:  try to identify how a business may react to alternative options or events and hopes to estimate the outcome of financial decisions before committing funds
  4. Competency models:  identify the unique combination of knowledge, ability, and skills needed to effectively perform a role and are used for selection, training and development, appraisal, and succession planning

Models are a way to understand the key decisions you may need to face and hedge the bets you’re placing. Ultimately investment risk is a risk for a team to deliver as much as it is a market or financial projection. Of the 4 models listed above, only 1 is predictive, the other 3 are based on luck, faith, and guess-work – none of which is confused with much of a business strategy.

What is risk? Risk is anything that can positively or negatively impact the plan; positive risk is opportunity. In financial terms risk is also called beta – we will delve into beta more in the next blog, but until then, valuation beta is a numeric measure of market or systemic risk.  Risk is comprised of 3 areas:

  1. known/knowns:  is there an instance or a comparable
  2. unknown/knowns:  this may happen to the lending or financial markets
  3. unknown/unknowns:  don’t know

Models identify risk and once risk is identified it is factored, or added, into the model’s evaluation. The act of building a model from research, data, assumptions, and knowledge provides a chance to test theories, draw conclusions, make modifications – before – undertaking a project or, let’s say, an acquisition. Being wrong is a great opportunity, not a problem. In an earlier blog about strategic planning I wrote:

We speculate from current-state pressure, demand, or pain and usually build hope. Hope is not a strategy.

Statistically, forecasting, probability, and profits each depend on informed judgments. All risk involves the objective facts and a subjective desire of what is to be gained, or lost. Both are essential and neither is sufficient. Compound the human challenge that we must wade through individual and collective wants, needs, values, bias, motivation, and other barriers of understanding, can we expect to plan anything with even a 50% likelihood of success?*

Valuation Trick-er-a-tion

Financial models are the lynchpin for investor decisions. However, it is shocking the amount of valuation models that are based on intangible factors as they are built and the intangible factors in their interpretations compared to supposedly tangible factors. In reality, market projections and discounted cash flows are fuzzy, flawed, and biased.

All valuation models are a greater leap of faith than competency models are for human capital valuation.

No risk assessment is accurate without a proper assessment of human capital. Just as financial capital intends to reflect underlying or expected enterprise value, competency models reflect the knowledge, ability, skills, and behaviors that reflect underlying enterprise human capital potential:

  • Valuation models:  mitigate present risk
  • Competency models:  mitigate future risk

The value of models, whether accurate or not, is in the effort to think through their inputs, the variables that go into the model, and the options to take based upon the model. Being right is nice, however, being wrong, may be more important.

A follow up blog will compare and contrast financial models to competency models and look at:

  1. real risk identification,
  2. risk management,
  3. risk mitigation, and
  4. the only way to mitigate the biggest risk variable to future results:  time

*Statistically, your strategy will fail

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