Bubbles, crashes and information contagion in large-group asset market experiments

Hommes, Cars; Kopányi-Peuker, Anita; Sonnemans, Joep

doi:10.1007/s10683-020-09664-w

Cited by 38 publications

(31 citation statements)

References 52 publications

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“…The game was played as a second experiment after an unrelated asset pricing experiment (Hommes et al, 2018). Subjects were participating in a Learning-to-Forecast experiment, and they did not know beforehand that there would be a second experiment.…”

Section: Experimental Design and Proceduresmentioning

confidence: 99%

Yes, I'll Do it: A Large-Scale Experiment on the Volunteer's Dilemma

Kopányi-Peuker

2018

SSRN Journal

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In many real-life situations people face a simple decision whether to volunteer or not to provide some benefit for themselves and also for others. This research investigates the effects of the group size and the magnitude of the volunteering cost in a controlled large-scale laboratory experiment, where subjects play the volunteer's dilemma only once. The experiment varies group sizes ranging from groups of 3 to about 100, and 2 different cost/benefit ratios. Results show that high cost reduces volunteering probability only in the smallest groups, but not for other group sizes. Furthermore, non-monotonic group size effect is found on the individual volunteering decisions. These findings are not in line with the mixed-strategy Nash equilibrium prediction. Subjects volunteer more often in most treatments than the Nash prediction which benefits them on average compared to the Nash prediction.

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Section: Experimental Design and Proceduresmentioning

confidence: 99%

Yes, I'll Do it: A Large-Scale Experiment on the Volunteer's Dilemma

Kopányi-Peuker

2018

SSRN Journal

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“…For H = 1, the pricing equation 1corresponds to the price generating mechanism used in earlier LtF experiments (see, e.g., Hommes et al, 2005Hommes et al, , 2008Hommes et al, , 2020. The following results from these experiments are remarkably robust, although there are minor differences in the experimental design.…”

Section: Hypothesesmentioning

confidence: 84%

“…For example, inHommes et al (2005) stabilizing 'robot' traders are added to most groups. The number of participants per group varies across experiments from N = 6 inHommes et al (2005Hommes et al ( , 2008, to around 100 in some of the groups inHommes et al (2020).17 See the discussion ofFig. 2 below.18 In most LtF experiments, participants are only told that the first two prices are "likely" to lie between 0 and 100.…”

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confidence: 99%

Asset Price Volatility and Investment Horizons: An Experimental Investigation

2020

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We study the effects of the investment horizon on asset price volatility using a Learning to Forecast experiment. We find that, for short investment horizons, participants coordinate on self-fulfilling trend extrapolating predictions. Price deviations are then reinforced and amplified, possibly leading to large bubbles and crashes in asset prices. For longer investment horizons such bubbles do not emerge and price volatility tends to be lower. This is due to the fact that, for longer horizons, there is more dispersion in participants' forecasts, and participants extrapolate trends in past prices to a lesser extent. We also show that, independent of the investment horizon, if the initial history of asset prices is already relatively stable before participants start their prediction task, price volatility remains small, with prices close to their fundamental values for the duration of the experiment.

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“…2 For recent contributions using the learning-to-forecast paradigm to analyze financial asset markets, see Hommes et al (2005), Hommes et al (2008), Sonnemans and Tuinstra (2010), Hüsler et al (2013), Bao et al (2016), Bao et al (2017), Colasante et al (2017), Colasante et al (2018), Hennequin (2018), and Hommes et al (2018). Learning-to-forecast experiments have also been used in other environments, including goods markets (e.g., Sonnemans et al, 2004, Hommes et al, 2007, Bao et al, 2013 and macroeconomics (e.g., Pfajfar and Žakelj, 2014, Arifovic and Petersen, 2017, Cornand and M'baye, 2018.…”

Section: Introductionmentioning

confidence: 99%

Experience Does not Eliminate Bubbles: Experimental Evidence

Kopányi-Peuker

Weber

2018

SSRN Journal

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We study the role of experience in the formation of asset price bubbles. Therefore, we conduct two related experiments. One is a call market experiment in which participants trade assets with each other. The other is a learning-to-forecast experiment in which participants only forecast future prices, while the trade, which is based on these forecasts, is computerized. Each experiment comprises three treatments that vary the amount of information about the fundamental value that participants receive. Each market is repeated three times. In both experiments and in all treatments, we observe sizable bubbles. These bubbles do not disappear with experience. Our findings in the call market experiment stand in contrast to the literature. Our findings in the learningto-forecast experiment are novel. Interestingly, the shape of the bubbles is different between the two experiments. We observe flat bubbles in the call market experiment and boom-and-bust cycles in the learning-to-forecast experiment.

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Bubbles, crashes and information contagion in large-group asset market experiments

Cited by 38 publications

References 52 publications

Yes, I'll Do it: A Large-Scale Experiment on the Volunteer's Dilemma

Yes, I'll Do it: A Large-Scale Experiment on the Volunteer's Dilemma

Asset Price Volatility and Investment Horizons: An Experimental Investigation

Experience Does not Eliminate Bubbles: Experimental Evidence

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