Italy Beats Moody's.

Moody's is the largest of the three major rating agencies. It employs 7000 people worldwide and posted sales of 2.7 billion in 2012. Since rating agencies are under heavy fire from the beginning of the financial crisis - in January 2011, the Commission of Inquiry Financial Crisis U.S. Senate stated that "The three rating agencies have been instrumental in triggering the financial collapse" - we have decided to calculate their ratings. In particular, we chose Moody's because it is the largest credit rating agency and also because it is perhaps the one that has downgraded Italian debt more aggressively than others. Obviously, Moody's is publicly traded and therefore subject to the dynamics of the markets as all listed companies.

In Moody's rating we did not assess the financial performance of the company or its ability to meet its financial obligations and even its probability of default. In other words, we have not performed a calculation of the conventional rating, but instead have we focused on another key feature for those who live in turbulent times: resilience, i.e. the ability to company to resist and survive sudden and extreme events (natural disasters, failures of large companies or banks, financial contagion, etc..). Since the global economy is constantly exposed to extreme events and turbulence, which will become not only more intense but also more frequent, resilience becomes a feature of an economy or a business that could make the difference between survival or its collapse.

For the analysis we used the quarterly information that Moody's publishes on its website. In particular, we used the following items:

1. Net income
2. Depreciation and amortization
3. Weighted average shares outstanding Basic
4. Provision for income taxes
5. Total expenses
6. Operating
7. Income before provision for income taxes
8. Revenue
9. Selling general and administrative
10. Operating income
11. Earnings per share Basic
12. Diluted
13. Diluted
14. Non-operating income (expense) net
15. Interest income (expense) net
16. Restructuring
17. Other non-operating income (expense) net
18. Net income attributable to Moody's
19. Net income attributable to non-controlling interests
20. Gain on sale of building

The Resilience Rating is as follows:

(see interactive Moody's Business Structure Map here).


The Resilience Rating of 72% is, on the scale of conventional ratings, equivalent to BBB-, one step from class BB +, the first of speculative ratings.

Given that Italy has often been targeted by Moody's we wanted to compare the resilience of both. Using macroeconomic data published by Eurostat, we obtain the following Resilience Rating:

(see interactive Business Structure Map of Italy here).




A Resilience Rating of over 75% places Italy two steps above Moody's, i.e. at the level of BBB +.

The result immediately raises the question: shouldn't he who has the power to judge others be the first to set a good example? Would you trust a coughing cardiologist as he smokes while recording your electrocardiogram?

 

How is the Eurozone Doing? Still Extremely Fragile.

As EUROSTAT publishes new data, we update our quarterly analyses of the Complexity and Resilience of the Eurozone. The situation as of Q4 2012 is as follows:

Complexity. A growing economy becomes necessarily more complex (see black curve below). However, at the same time it is important to stay away from the so-called critical complexity (red curve). Before the crisis has crippled the global economy things are proceeding relatively low although the two curves were already quite close. Since complexity has peaked in early 2008 there has been a persistent reduction of complexity, equivalent to the loss and destruction of what has been created in the past. In mid-2011 the situation has stabilised but still dangerously close to critical complexity. In other words, the situation is that of extreme fragility. This means that the system is not in the condition to absorb shocks or contagion without major consequences. Moreover, there is no clear signal of recovery apart from the mild growth of complexity in the second half of 2012.

 

It is interesting is to see complexity for the core 15 EU member states and the 12 which have joined later (for Croatia there is insufficient data to incorporate it in the analysis). It appears that the group of 15 (red curve below) are indeed on a road to mild and sustained recovery. The remaining 12 nations are still on a downward path with indications of stabilisation.

However, what counts is the system as a whole. The resilience (robustness) of the EU27 system is indicated below. There is a mild upward trend but the value of resilience is below 50% which reflects extreme fragility. Certainly the system does not contain triple-A components, as the Rating agencies claim.

Based on the above plots one can infer how the crisis has so far destroyed approximately ten years of growth. And it's not over yet. The oscillatory character of the curves over the past 12-18 months suggests a state of prolonged stagnation. The next 3-4 quarters will show for sure.


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You can run the above analysis yourself here: www.rate-a-business.com

In a Globally Crippled Economy, Can There Be AAA-rated Countries?

According to S&P, the following countries have been rated AAA (see complete list of country ratings here):

United Kingdom

Australia

Canada

Denmark

Finland

Germany

Hong Kong

Liechtenstein

Luxembourg

Netherlands

Norway

Singapore

Sweden

Switzerland

Nine of the above countries are from Europe, the area of the globe that has been hit the hardest by recession and public debt issues.

Because of globalisation we are all on the same boat - every economy is connected to (almost) every other economy. This is what is meant by interdependency. The global economy forms a densely connected network through which information travels at the speed of the Internet. So, if the economy is a global mess - we're actually talking of a meltdown, which sounds pretty dramatic - can there exist triple-A rated economies? In theory yes. In theory anything is possible, nothing is impossible. But that of course depends on the theory.

How can a system that is severly crippled, impregnated with trillions and trillions of derivatives and toxic financial products, of which nobody knows the total amount in circulation (some say 10 times, some say 15 times the World's GDP - the uncertainty is high enough to reflect the severity of the problem.) contain so many large triple-A economies. Does that really make sense? In a state of metastacizing economic crisis how can this be explained?

The problem is quite simple really. Credit Rating Agencies are rating the wrong thing. They rate the Probability of Default (PoD) of a country (or a corporation). Instead, they should be rating other  more relevant characteristics of an economy, such as its resilience and complexity. Resilience (fragility) has nothing in common with performance. You can perform extremely well, and think you're like this:

but in reality you're like this:

Wouldn't you want to know? Isn't survival a nice reflection of success? More soon.


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Complexity Maps Get a Facelift.

Ontonix has announced today the release of version 6.0 of its flagship software system OntoSpace. The full Press Release is available here.

One of the salient new features is the new display of Business Structure Maps, illustrated below. One may notice that now each node of the map has different dimensions. These are computed based on the Complexity profile, i.e. size is function of the node's importance (footprint) on the entire system. This allows to focus immediately on the important issues.

This is what the above map looked like in the previous version released in 2010.

But there is more. In very large cases, things become difficult to grasp (this happen not only with OntoSpace but in life in general). Consider, for example, the Business Structure Map of the EU (each group of nodes, depicted either in blue or red - alternating colours are used to enables users to distinguish the various groups - corresponds to a country).

Not very clear is it? In fact, the map has 632 nodes which are interconnected by 41671 rules! How do you go about analysing that? Well, you can't. For this reason OntoSpace v6.0 supports the so-called Meta-maps, which are obtained from the above map by grouping all variables into "meta-nodes" and also by condensing all the interactions between meta-nodes into only one link. The result looks like this:

This is of course much more clear. A Meta-map is a nice way to represent a system of systems whereby each node is a system with various nodes (variables). More on OntoSpace v6.0 soon.


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Rating the Rating Agencies. And those who control them.

Who rates the Rating Agencies? Who rates those that award triple-A ratings to companies that fail the day after or to junk bonds and toxic financial products that lead to global economy meltdown? The answer: nobody. Who controls them? Huge investment funds, such as BlackRock, for example. If you control a rating agency and if you control publicly listed companies the circle is closed. An excellent book on the subject is "The Lords of Ratings" by P. Gila and M. Miscali.

Ontonix provides quarterly ratings of the resilience of corporations, banks, national economies and systems thereof. We also rate rating agencies. One in particular: Moody's. Here is their latest resilience rating:

 

Which is equivalent to BBB-

And here is the rating of one of the investment funds that controls Moody's, BlackRock:

They get a resilience rating of 83%, which corresponds to an AA. Surprising? Not really.


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A Structured Look at Cellular Automatons

From the Wikipedia: A cellular automaton is a discrete model studied in computability theory, mathematics, physics, complexity science, theoretical biology and microstructure modelling. It consists of a regular grid of cells, each in one of a finite number of states, such as "On" and "Off" (in contrast to a coupled map lattice). The grid can be in any finite number of dimensions. For each cell, a set of cells called its neighbourhood (usually including the cell itself) is defined relative to the specified cell. For example, the neighbourhood of a cell might be defined as the set of cells a distance of 2 or less from the cell. An initial state (time t=0) is selected by assigning a state for each cell. A new generation is created (advancing t by 1), according to some fixed rule (generally, a mathematical function) that determines the new state of each cell in terms of the current state of the cell and the states of the cells in its neighbourhood. For example, the rule might be that the cell is "On" in the next generation if exactly two of the cells in the neighbourhood are "On" in the current generation, otherwise the cell is "Off" in the next generation. Typically, the rule for updating the state of cells is the same for each cell and does not change over time, and is applied to the whole grid simultaneously, though exceptions are known.

We have measured the complexity and extracted the complexity map of a few cellular automatons which may be found here and are illustrated in the image below:

While  humans are good at recognising patterns and structure, rapid classification of patterns in terms of their complexity is not easy. For example, which is more complex in the above figure, Rule 250 or Rule 190? The answer is below.


Rule 30

Rule 54

Rule 62

Rule 90

Rule 190

Rule 250

It appears that Rule 250 Automaton is the most complex of all (C = 186.25) , while the one with the lowest complexity is Rule 90 (C = 64.31). Not very intuitive, is it?  Intuition is given only to him who has undergone long preparation to receive it (L. Pasteur).






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Correlation, Regression and how to Destroy Information.

(The above image is from an article by Felix Salomon - 23/2/2009).

When a continuous domain is transferred onto another continuous domain, the process is called transformation. 

When a discrete domain is transferred onto another discrete domain, the process is called mapping. 

But when a discrete domain is transferred onto a continuous domain, what is the process called? Not clear, but in such a process information is destroyed. Regression is an example. Discrete (often expensive to get) data is used to build a function that fits the data, after which the data is gently removed and life continues on the smooth and differentiable function (or surface) to the delight of mathematicians. Typically,  democratic-flavoured approaches such as Least Squares are adopted to perpetrate the crime.

The reason we call Least Squares (and other related methods) "democratic" (in democracy everyone gets one vote, even assassins who get re-inserted into society, just as respectful hard-working and law-observing citizens) is that every point contributes to the construction of the mentioned best-fit function in equal measure. In other words, data points sitting in a cluster are treated equally with dispersed points. All that matters is the vertical distance from the sought best-fit function.

Finally, we have the icing on the cake: correlation. Look at the figure below, depicting two sets of points lying along a straight line.

The regression model is the same in each case. The correlations too! But how can that be? These two cases correspond to two totally different situations. The physics needed to distribute points evenly is not the same which makes them cluster into two groups. And yet in both cases stats yields a 100% correlation coefficient without distinguishing between two evidently different situations. What's more, in the void between the two clusters one cannot use the regression model just like that.  Assuming continuity a-priori can come at a heavy price.

Clearly this is a very simple example. The point, however, is that not many individuals out there are curious enough to look a bit deeper into data (yes, even visually!) and ask basic questions when using statistics or other methods.

By the way, "regression" is defined (Merriam Webster Dictionary) as "trend or shift to a lower or less perfect state". Indeed, when you kill information - replacing the original data with a best-fit line - this is all you can expect.

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