Asymmetric information and learning by imitation in agent-based financial markets

Luca Gerotto, Paolo Pellizzari, Marco Tolotti

Risultato della ricerca: Contributo in libroChapter

Abstract

We describe an agent-based model of a market where traders exchange a risky asset whose returns can be partly predicted purchasing a costly signal. The decision to be informed (at a cost) or uninformed is taken by means of a simple learning by imitation mechanism that periodically occurs. The equilibrium is characterized describing the stationary distribution of the price and the fraction of the informed traders. We find that the number of agents who acquire the signal decreases with its cost and with agents’ risk aversion and, conversely, it increases with the signal-to-noise ratio and when learning is slow, as opposed to frequent. Moreover, price volatility appears to directly depend on the fraction of informed traders and, hence, some heteroskedasticity is observed when this fraction fluctuates.
Lingua originaleEnglish
Titolo della pubblicazione ospiteCommunications in Computer and Information Science
Pagine164-175
Numero di pagine12
Volume1047
DOI
Stato di pubblicazionePubblicato - 2019

Serie di pubblicazioni

NomeCOMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE

Keywords

  • Agent-based modeling
  • Bounded rationality
  • Information in financial markets

Fingerprint Entra nei temi di ricerca di 'Asymmetric information and learning by imitation in agent-based financial markets'. Insieme formano una fingerprint unica.

Cita questo