Friday, February 5, 3:00 p.m.
Center for Social Complexity Suite
Research Hall, Third Floor

"Asset bubbles and complex adaptive systems - Can CAS solve the problem as to how and why bubbles exist?"

Matthew Oldham
Masters of Arts in Interdisciplinary Studies - CSS Concentration
George Mason University

Abstract: A common feature of financial markets since their advent has been the regular appearance of bubbles[1] and their subsequent collapse. The repercussions of these boom and bust cycles are severe, with over-investment and excessive trading occurring in the boom time, while the busts have on occasions lead to devastating financial crises and depressed real economies. One of the most recent occurrence of such an event saw the Dow Jones Industrial Average close at a record level on October 9, 2007 yet one year later the Dow dropped 21% in the first nine days of October 2008 and the world plunged into the Global Financial Crisis (GFC), which according to the IMF cost the global economy $USD11.9 trillion[2].

The mainstream doctrine has been to follow the Efficient Market Hypothesis (EMH) (Fama, 1970) - which states that market prices fully reflect all available information and as a consequence asset prices are unpredictable as they follow a random walk. This view has been further supported by (Malkiel, 1999) who demonstrated that prices on Wall St appear to move in a random fashion. However, the reality of continued episodes of boom and bust, and mounting statistical evidence (Bollerslev, Engle, & Nelson, 1994) provides strong evidence that in fact markets do not function in accordance with the EMH.

An alternate approach to the EMH is to consider financial markets as a complex system. Considering financial markets as a complex system is to accept that the outcomes in financial markets are the result of an emergent process based on the self-organized behavior of independently acting, self-motivated individuals (Farmer et al., 2012). The main attraction of utilizing a complex system framework is that it is able to generate extreme events and asset returns in line with what has been experienced in the real world as opposed to the theoretical solution put forward under the EMH framework..

The aim of this talk is to provide;
- further background of the gap between the EMH and reality - including the presence of power law returns with regard to share market returns;
- describe the findings of a basic systems dynamic model;
- demonstrate a model that replicates the finding of Vernon Smith's experimental bubble (Smith et al (1988)); and
- a historical replication of the South Sea Bubble.


Bollerslev, T., Engle, R. F., & Nelson, D. B. (1994). ARCH models. Handbook of Econometrics, 4, 2959–3038.

Farmer, J. D., Gallegati, M., Hommes, C., Kirman, A., Ormerod, P., Cincotti, S., … Helbing, D. (2012). A complex systems approach to constructing better models for managing financial markets and the economy. The European Physical Journal Special Topics, 214(1), 295–324.

Gupta, N., Hauser, R., & Johnson, N. F. (2005). Using artificial market models to forecast financial time-series.
Kindleberger, C. P., & Aliber, R. Z. (2011). Manias, panics and crashes: a history of financial crises. Palgrave Macmillan.

Malkiel, B. G. (1999). A random walk down Wall Street: including a life-cycle guide to personal investing. WW Norton & Company.

Newman, M. (2010). Networks: an introduction. Oxford University Press.

Smith, V. L., Suchanek, G. L., & Williams, A. W. (1988). Bubbles, crashes, and endogenous expectations in experimental spot asset markets. Econometrica: Journal of the Econometric Society, 1119–1151.

[1] A bubble can be defined as a period during which the market values of assets vastly exceeded reasonable assessments of their fundamental value. Alternatively, (Kindleberger & Aliber, 2011) state more generally that a bubble occurs when there is “an upward price movement over an extended range that then implodes”
[2] Retrieved from: