Complexity Economics

Complexity-Based Early-Warnings in Defaulting Companies


The search for early-warning signs is one of the key issues for decision makers. The advantages of knowing in advance the evolution towards critical situations are obvious.

Complexity is a new and powerful indicator that quantifies the degree of sophistication and governability of a business and which impacts its Resistance to Shocks[1] (RtS). Both complexity and  RtS indicators establish a radically innovative means of anticipating crises. As of today, conventional methods have failed to identify such signals, especially with this level of details.

Through the Quantitative Complexity Management (QCM)[1]  any business which is undergoing severe stress or is on a path to collapse shows a rapid complexity fluctuations. A consistent and continuous reduction of complexity is a typical pre-alarm. If this occurs, then a decision maker, e.g. a trader, is able to put in place all those actions needed to avoid financial losses and/or take advantage of these situations e.g. via shorting.

Fact:  Changes in complexity indicate imminent crisis.

In the next paragraphs it will be shown how Complexity and Resistance to Shocks (RtS) of Balance Sheets are able to provide early-warning signals of an imminent crisis.

We have analysed the complexity of 3 listed companies whereby:

  • At the end of July 2016, two companies filed for bankruptcy and
  • Since August 2016, one company operates under judicial management

The bottom line is that these crises could have been be predicted with considerable anticipation.

Based on the measurement of complexity the first symptoms were as follows:

  • Swiber Holdings: from the 1st quarter 2014, i.e. two years before judicial management.
  • Halcon Resources Corp: since the 2nd quarter 2014, i.e. seven quarters prior to default
  • Atlas Resource Partners: from the 2nd quarter 2015, i.e. three quarters prior to default

The plots below compare the evolution of Balance Sheet complexity versus Total Assets of the three companies.

In all cases Total Assets of each Company were still growing, while situation of concealed crisis have been revealed by the corresponding complexity trend.

The consistent complexity decrease in all cases points to an increase of ‘decorrelation’ of the corresponding Balance Sheets. This means that the relationships between the various Balance Sheet entries are becoming more independent (i.e. the correlations are becoming weaker, pointing to a ‘less structured’ situation).  Consequently, the business is more ‘disordered’, hence difficult to control/predict and is therefore more exposed to unexpected events.

Let us examine the three cases:

Swiber Holdings  

The growth of the company’s Total Assets, between the 4th quarter 2013 and the 2nd quarter 2014, conceals a critical situation.

A complexity reduction of 20% of within just one quarter is a typical symptom that something is going wrong.  The compounded quarterly growth rate of complexity (-4,1%), double of that of Total Assets (-2,0%). Such complexity decrease is mainly due to growing disorder (lower correlations) in the Balance Sheet. Basically, the structure of the information within the balance sheet is becoming weaker.


Figure 1. Complexity and Total Asset evolution of Swiber Holdings (Q4 2013=100)

Halcon Resources Corp

The growth of Total Assets between the 4th quarter 2013 and the 4th quarter 2014, hides a critical situation highlighted by complexity reduction: -23% compared to a growth of  Total Assets of  20%.  As in the previous case, the Balance Sheet is becoming more ‘disordered’, pointing to a more fragile business.


Figure 2. Complexity and Total asset evolution of Halcon Resources Corp (Q4 2013=100)

Atlas Resource Partners

The small decline of its Total Assets, between the 4th quarter 2014 and the 1st quarter 2016 (-5%), hides a critical situation reflected by acomplexity reduction of -24%. The situation is becoming critical from the 2nd quarter 2015 onwards. The 24% of complexity reduction within a half-year period is a typical symptom of a failing/declining system.


Figure 3. Complexity vs Total assets evolution of Atlas Resource Partners (Q4 2014=100)


In conclusion, the monitoring of complexity provides businesses with new information of systemic nature which reflects the interplay of structure and disorder in Balance Sheets, and which may be expanded to include Cash Flow, Income Statements or Ratios. A sustained loss of complexity points to de-correlation which, in turn, reveals a growing level of disorder. This constitutes a formidable early-warning mechanism which classical Balance Sheet analysis will not reveal.


[1] QCM methodology and the related software tools allow to quantity the complexity and the RtS. The QCM approach complies  with UNI 11613 Business Complexity Assement guidelines. UNI 11613 furnishes guidelines as to how an organization may establish, monitor and put into practice the assessment of business complexity. The goal is to identify the critical complexity of a business and to identify and rank its drivers, with the objective of increasing the resilience of an organization. UNI, Ente Nazionale Italiano di Unificazione, performs regulatory activities in Italy.

[1] Resistance to Shocks (RtS), sometimes known as resilience, or ‘shock-worthiness’, measures the capacity to absorb shocks or destabilizing events, such as financial contagion, stock market collapses, market bubbles, natural disasters or geopolitical events. It provides an indication of how stable a company, portfolio or market is and how it will react to the said events.

Established originally in 2005 in the USA, Ontonix is a technology company headquartered in Como, Italy. The unusual technology and solutions developed by Ontonix focus on countering what most threatens safety, advanced products, critical infrastructures, or IT network security - the rapid growth of complexity. In 2007 the company received recognition by being selected as Gartner's Cool Vendor. What makes Ontonix different from all those companies and research centers who claim to manage complexity is that we have a complexity metric. This means that we MEASURE complexity. We detect anomalies in complex defense systems without using Machine Learning for one very good reason: our clients don’t have the luxury of multiple examples of failures necessary to teach software to recognize them. We identify anomalies without having seen them before. Sometimes, you must get it right the first and only time!

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