Financial crises and risk management

author: Didier Sornette, Department of Management, Technology, and Economics, ETH Zurich
published: Oct. 17, 2008,   recorded: September 2008,   views: 41986


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The scientific study of complex systems has transformed a wide range of disciplines in recent years, enabling researchers in both the natural and social sciences to model and predict phenomena as diverse as the failure of materials, earthquakes, global warming, demographic patterns, and financial crises. In this talk, Didier Sornette describes a simple, powerful, and general theory of how, why, and when stock markets crash. Most attempts to explain market failures seek to pinpoint triggering mechanisms that occur hours, days, or weeks before the collapse. Sornette proposes a radically different view: the underlying cause can be sought months and even years before the abrupt, catastrophic event in the build-up of cooperative speculation, into an accelerating rise of the market price, otherwise known as a "bubble." This view implies the possibility of predicting such events and Sornette will describe the current status of predictions that he and his collaborators have made for events in various markets.

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Reviews and comments:

Comment1 Kumar, November 19, 2008 at 6:22 p.m.:

I think as long as the speaker does not right each step specially in mathematical statement, the user can not follow what he says and the flow. Every speaker must write. Otherwise he is lazy.

Comment2 scsamclark, December 27, 2008 at 2:39 p.m.:

The model proposed by Sornette on positive feedbacks and referenced in was not of a “Sornette and students” origin as stated by the lecturer.

The original model and the economic theoretical proposal that sustained the rule, including the idea that imitation may be rational, rather than a deviation to rationality, came from Gonçalves, see the following reference:

Sornette was way off, thinking that the phase transitions, to an organized state, should be slow, and that the herding was irrational (per noise trader model).

What Sornette and Zhou did do, was propose an extension to Gonçalves’ rule, and call it self-fulfilling Ising model (a name which IMO does not capture the main aspects of the above artificial financial market), solving a problem with the bimodal distribution (as reported by Sornette and Zhou), another solution to that problem was obtained by Gonçalves through the inclusion of other investor species like trend followers and speculators, following Farmer’s previous work:

Sornette seems to be assuming credit, for a whole conceptual network on endogenous economic dynamics that has been around for a while. Specifically, Gonçalves' proposal is conceptually linked to Farmer's proposal, Vaga's proposal, Simon's proposal, etc..

It is not at all the case that the financial literature hasn't addressed, before Sornette, many of the issues that Sornette is now addressing regarding endogenous price fluctuations, see the work developed at the SFI, and the work of the economic chaologists.
Sornette should really do his homework and reference properly the literature that has come before him, his work is not an island, nor does it stand alone without a bulk of intellectual production within economics and financial economics that has been around for a while now (for at least twenty years actually (twenty years is a lot of research!)).

As a final note, as far as I read both the works of Sornette and the works of Gonçalves as well as the works that have since been developed on the artificial financial market Netlogo models, by research teams other than Sornette’s, there is a failure by Sornette and Zhou in understanding several of the fundamental aspects of these models, in part, stemming from a failure to understand the concept of rationality.

Comment3 mussa cohen, September 13, 2009 at 10:07 p.m.:

Beside the TRILLIONS committed for all the multiple rescue plans, a necessary complementary strategic & logistic intervention become imperative to put some order in this crisis of total financial anarchy at all stages. We are all aware now that most figures and values, are unreliable, uncertain, and at best unmeasurable……….

Urgently required a new modern technologically advanced open Decision making Process of Management Information System (MIS) with live monitoring smart GRID +NETWORK supported by Multiplatform system with Multidimensional TOOLS to accurately reflect permanent live REALITIES.


Comment4 Mike Clayton, January 14, 2010 at 5:26 a.m.:

As a geology expert Sornette learned that quake prediction was similar to stock market prediction. Mandelbrot noted similarities in "statistics of extreme values" or fractal geometry or chaos theory for PHYSICAL processes, turbulence in ocean streams and cotton prices They all agree that the older efficient market models missed some long term memory effects, autocorrelation as some say, or in short term "herding" rather than independent data, and that any power law time series is unsustainable LONG TERM, thus bubbles are inevitable but unpredictable in detail. Many books and PhD theses on these economic and financial pricing models (and quake prediction). The problem is that ADVISORS selling IRA's use a much MILDER risk metric than that which would be predicted from the price variation distribution with FATTER TAILS than the normal bell curve. Sadly.

Comment5 igor waltritsch, June 11, 2010 at 1:34 a.m.:

Tottally stupid from a hedge fund manger from 1986 to 2002 said...I do not want to comment by my kid wd hav done better....having computers or math knowledge Its not knowledge


Comment6 Mike, September 8, 2010 at 12:07 a.m.:

Very interesting talk, but disappointing that there was no prediction of the largest bubble-crash since the great depression. Focusing on the Hang Seng because it exhibits nice repetitive behaviour is fine for the research world, but the real test of the model is whether or not it can be extended universally.

Also, "correct, but off on the timing" is an utterly pointless assertion as timing is everything in investing. A prediction is not a prediction without a suitable measure of timing. Period.

Last thought - assuming the final the model does lead to better bubble detection, what then? This additional knowledge adds to the positive feed-back effect, and changes the boundary conditions of the problem. The measuring system is altering the state of the measurand, and this will always be the case!

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