We present a simple agent-based model to study how the proximate triggering factor of a crash or a rally might relate to its fundamental mechanism, and vice versa. Our agents form opinions and invest , based on three sources of information, (i) public information, i.e. news, (ii) information from their "friendship" network , promoting imitation and (iii) private information. Agents use Bayesian learning to adapt their strategy according to the past relevance of the three sources of information  . We find that rallies and crashes occur as amplifications of random lucky or unlucky streak of news, due to the feedback of  these news on the agents' strategies into collective transient herding regimes. These ingredients provide a simple mechanism for the excess volatility documented in financial markets. Paradoxically, it is the attempt for investors to learn the level of relevance of the news on the price formation which leads to a dramatic amplification of the price volatility due to their collective search for the "truth" . A positive feedback loop is created by the two dominating mechanisms ( Bayesian learning and imitation) which, by reinforcing each other, result in rallies and crashes. The model offers a simple reconciliation of the two opposite (herding versus fundamental) proposals for the origin of crashes within a single framework and justifies the existence of two populations in the distribution of returns, exemplifying the concept that rallies and crashes are qualitatively different from the rest of the price moves.
We present a simple agent-based model to study the development of a bubble and the consequential crash and investigate how their proximate triggering factor might relate to their fundamental mechanism, and vice versa. Our agents invest according to their opinion on future price movements, which is based on three sources of information, (i) public information, i.e. news, (ii) information from their "friendship" network and (iii) private information. Our bounded rational agents continuously adapt their trading strategy to the current market regime by weighting each of these sources of information in their trading decision according to its recent predicting performance . We find that bubbles originate from a random lucky streak of positive news, which, due to a feedback mechanism of these news on the agents' strategies develop into a transient collective herding regime. After this self-amplified exuberance, the price has reached an unsustainable high value, being corrected by a crash, which brings the price even below its fundamental value. These ingredients provide a simple mechanism for the excess volatility documented in financial markets. Paradoxically, it is the attempt for investors to adapt to the current market regime which leads to a dramatic amplification of the price volatility . A positive feedback loop is created by the two dominating mechanisms ( adaptation and imitation) which, by reinforcing each other, result in bubbles and crashes. The model offers a simple reconciliation of the two opposite (herding versus fundamental) proposals for the origin of crashes within a single framework and justifies the existence of two populations in the distribution of returns, exemplifying the concept that crashes are qualitatively different from the rest of the price moves.
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how the proximate triggering factor of a crash or a rally
the development of a bubble and the consequential crash and investigate how their proximate triggering factor
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its
their
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form opinions and invest ,
invest according to their opinion on future price movements, which is
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, promoting imitation
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Agents use Bayesian learning to adapt their strategy according to the past relevance of the three
Our bounded rational agents continuously adapt their trading strategy to the current market regime by weighting each of these
A
in their trading decision according to its recent predicting performance
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rallies and crashes occur as amplifications of random lucky or unlucky streak of news, due to the feedback
bubbles originate from a random lucky streak
A
positive news, which, due to a feedback mechanism of
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into collective transient herding regimes.
develop into a transient collective herding regime. After this self-amplified exuberance, the price has reached an unsustainable high value, being corrected by a crash, which brings the price even below its fundamental value.
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learn the level of relevance of the news on the price formation
adapt to the current market regime
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due to their collective search for the "truth"
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Bayesian learning
adaptation
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rallies
bubbles
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rallies and