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BUBBLES AND CRASHES: Experimental evidence of what causes financial market volatility

  • Published Date: October 2017

Bubbles and crashes are an endemic feature of financial markets, according to research by Cars Hommes and colleagues, published in the October 2017 issue of the Economic Journal. The results of their laboratory experiment – in which human participants play the roles of professional analysts and financial traders – demonstrate the potentially dangerous impact of ‘positive feedback’ on asset prices.

What causes financial bubbles? Since the 2007 financial crisis, this question has attracted renewed attention among academics and policy-makers.

The occurrence of bubbles challenges the ‘rational expectations’ paradigm in economics. Under rational expectations, individuals and organisations are assumed first, to be able to make unbiased forecasts of future asset prices; and second, to take optimal trading quantity decisions conditional on these forecasts.

Empirical tests of rational expectations (and thus of financial bubbles) face the difficulty of ‘testing two joint hypotheses’. When an asset price deviates from its rational fundamental value, it is difficult to assess whether this is caused by the market participants’ incorrect forecasts, or by their non-optimal trading quantity given their price forecast.

To study the causes of financial bubbles, this study involves a laboratory experiment with human subjects based on a simple experimental asset market. The rational fundamental asset price is known to the experimenter and it is therefore possible accurately to measure bubbles as deviations from the rational benchmark.

The participants in the experiment play the role of professional analysts or financial traders. The asset price is determined by a price adjustment process based on aggregation of all individual forecasts or trades. Three experimental treatments are designed:

• T1: Subjects play the role of analysts. They only provide a price forecast of the risky asset, while the optimal trading quantity is calculated automatically by a computer program based on their forecast.

• T2: Subjects play the role of traders. They submit a buy or sell trading quantity for the asset directly, without the help of a computer program, and they are not asked for an explicit price forecast.

• T3: Subjects play both the roles of analysts and traders. They first submit their price forecast, and then their trading quantity, without help from a computer.

The researchers find substantial deviations of the market price from its fundamental value in all treatments, but the deviations in T2 (on average 24.7%) and T3 (on average 36.0%) are larger than in T1 (on average 9.5%).

Mispricing is therefore a robust finding in speculative asset markets, and results from the joint forces of subjects’ failure to make unbiased price predictions and, to a larger degree, to their failure in calculating optimal quantity decisions given one’s own price forecast.

Figure 1 shows the price dynamics (main plots) and individual trading quantities (for T2 and T3, sub plots) in a typical market from each treatment:

The price dynamics (main plots) and individual trading quantities (for T2 and T3, sub plots) in three typical markets in T1 (left panel), T2 (middle panel) and T3 (right panel). Bubbles as deviations from the constant fundamental price 66 arise in all treatments. The largest bubbles arise in T2 and T3 due to non-optimal trading quantity decisions and temporary coordination on buying versus selling causing repeated bubbles and crashes.

Conclusions and policy implications

These experiments and other studies in behavioural finance show that financial bubbles and crashes are a robust and endemic feature of financial markets, due to their positive feedback feature.

This means that when average expectations go up, asset demand increases and as a result the asset price itself goes up. Individuals and organisations thus have (almost) self-fulfilling beliefs and learn to coordinate and follow the crowd and (almost) self-fulfilling bubbles and crashes emerge.

These insights from behavioural models are in sharp contrast with the benchmark rational model, where bubbles do not form and markets are efficient. It is important that models for macro-financial policy analysis take these behavioural features of complex financial systems into account, as stressed in a recent Science paper (Battiston et al, 2016).

Recent research by these authors (Bao and Hommes, 2017) on experimental housing markets shows that bubbles disappear when adding a sufficient amount of negative feedback (that is, more elastic housing supply). This gives policy insights for how to mitigate and stabilise financial bubbles by managing the positive feedback in complex financial systems.

ENDS


Notes for editors: ‘Bubble Formation and (In)efficient Markets in Learning-to-Forecast and -Optimise Experiments’ by Te Bao, Cars Hommes and Tomasz Makarewicz is published in the October 2017 issue of the Economic Journal.

Te Bao is at Nanyang Technological University and the University of Groningen. Cars Hommes is at the University of Amsterdam and Tinbergen Institute. Tomasz Makarewicz is at Bamberg University.

References:

Bao, T, and CH Hommes (2017) When Speculators Meet Constructors: Positive versus Negative Feedback in Experimental Housing Markets, University of Amsterdam Working Paper.

Battiston, S, J Doyne Farmer, A Flache, D Garlaschelli, AG Haldane, H Heesterbeek, CH Hommes, C Jaeger, R May and M Scheffer (2016)=, Complexity Theory and Financial Regulation, Science 351(6275): 818-819.

For further information: contact Romesh Vaitilingam on +44-7768-661095 (email: romesh@vaitilingam.com; Twitter: @econromesh); or Cars Hommes via email: c.h.hommes@uva.nl