The concept of rating is known to the wide public since the onset of the global economic crisis that has erupted in 2008. Ratings are essentially opinions about publicly listed companies and in which one wishes to invest. Rating agencies are institutions who emit such opinions. However, because the major three rating agencies are said to have been "enablers of the financial crisis" and because the performance of ratings has been abysmal, they have earned a bad reputation - various governments, such as those of Italy, Australia and the USA, just to give three examples, are suing them - and the concept of rating has today a generally negative connotation.
But what does a rating really denote? A rating attempts to measure the Probability of Default (PoD) of a business. The idea is simple. You purchase the obligations of a company which needs to raise cash. The company promises to return your money back with interest. The idea of the rating is to measure the probability of not getting the investment back. It's as simple as that. However, ratings have show abysmal performance in the past few years - and this is one reason why people trust them less - not to mention that they may be manipulated easily by those who calculate them. In fact, rating agencies defend themselves by claiming that their ratings are opinions, not science (given the huge amounts of capital that revolve around ratings, they should be science, not opinions!).
But the biggest problem with ratings originates form their very heart. The key to a rating is probability, one of the least understood and elusive concepts in mathematics and physics. A probability (of a future event) has no physical meaning when applied to a single corporation or an individual. It is one thing to state that out of, say, 1000 corporations from a certain market segment, 5 have defaulted over a period of three years, it is another to claim that corporation X has a Probability of Default of, say, 0.025%. Such as statement is meaningless. It has no foundation in science because our science doesn't allow us to make predictions. And ratings are futile attempts at making predictions. A sophisticated form of circle-squaring. It cannot be done. Besides, ratings have been conceived in a totally different economic context. Today, the World is turbulent, the economy is globalized and dominated by shocks and uncertainty. One cannot use laminar flow models to simulate turbulent flow in fluids, for example. Specific turbulence models need to be used in such cases.
What we claim in this short article is that the concept of rating should be overhauled, starting right at the base, at the very roots of the entire construct. What we need, in particular, is to depart from the concept of Probability of Default and move on to something different, something a bit more rational and scientific. One such quantity is resilience, the ability to resist shocks and impacts and shocks are the hallmark of a turbulent economy. While resilience is a property of systems, a PoD is not. Resilience is a physical property and it can be measured. Initially developed by mechanical engineers to characterize materials, the concept of resilience may be easily extended to a wide variety of systems: corporations, banks, markets, countries, portfolios, societies, traffic systems, the human body.
The idea, therefore, is to rate systems (corporations) based on their resilience, not on their Probability of Default.
All that is needed to measure the resilience of a system is a set of observable outputs which it produces. In the case of a corporation it can be quarterly Financial Statements, such as Cash Flow. In the case of air traffic one may use the output of an airport radar which scans airspace at a certain frequency. Data, in other words. It all hinges on data. We need data to make decisions and the quality of our decisions depends on the quality of the data itself.
If we use data that is unreliable or that has fragile structure, our decisions will be equally unreliable and fragile.
So, the idea is to actually rate the data that represents a given system. This opens infinite possibilities. With a measure of resilience we can attach a quality tag to each piece of data we use to make decisions, to manage systems, to run corporations, to drive an economy.
Resilience is measured on a scale from 0% to 100%. A low value reflects a system which is unable to survive turbulence and which is, for all practical purposes, unstable and which can easily deliver unpleasant behavior. Unexpectedly. In other words,
resilience allows us to extend the concept of rating to all sorts of systems.
Rating a system, therefore, is equivalent to rating the resilience of the data which it produces and which we use to manage and control it. Many of the systems that we are confronted with in our daily activities generate data which is collected according to specific norms and protocols. We have compiled a number of Analysis Templates for the following systems/businesses/data:
- Small Medium Enterprise
- Financial Institution Ratios
- Retail Bank
- Retail Bank Branch
- Balance Sheet
- Cash Flow
- Income Statement
- Industry Ratios
- Common Stock
- Real Estate Residential Sector
- Real Estate Office Sector
- Real Estate Hotel Sector
- Country Financial Risk
For example, the Analysis Template necessary to obtain a resilience rating of a Small/Medium Enterprise looks like this:
More templates may be found at our resilience rating portal which allows users to analyze and rate any kind of business.
Resilience is related intimately to structure. Structure reflects information, interdependency and functionality. Data which is structured conveys more information than data which is chaotic. An example of structure is shown here. Simply move the mouse pointer over the map nodes and links. The example in question illustrates the structure of the financials of a Small/Medium Enterprise.
An engineering example is shown here where we rate a power generation plant.
The bottom line is that resilience rating can be extended to cover not just corporations but also generic systems. Most importantly, however, the concept may be applied to actually rating the quality of our decisions. Decisions based on data having fragile structure increase risks which are compounded by the increasing turbulence and complexity of our economy.