Investor sentiment in an artificial limit order market

Lijian Wei, Lei Shi

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Abstract

This paper examines the under/overreaction effect driven by sentiment belief in an artificial limit order market when agents are risk averse and arrive in the market with different time horizons. We employ agent-based modeling to build up an artificial stock market with order book and model a type of sentiment belief display over/underreaction by following a Bayesian learning scheme with a Markov regime switching between conservative bias and representative bias. Simulations show that when compared with classic noise belief without learning, sentiment belief gives rise to short-term intraday return predictability. In particular, under/overreaction trading strategies are profitable under sentiment beliefs, but not under noise belief. Moreover, we find that sentiment belief leads to significantly lower volatility, lower bid-ask spread, and larger order book depth near the best quotes but lower trading volume when compared with noise belief.
Original languageEnglish
Article number8581793
Pages (from-to)1-10
Number of pages10
JournalComplexity
Volume2020
DOIs
Publication statusPublished - 30 Jun 2020

Bibliographical note

Copyright © 2020 Lijian Wei and Lei Shi. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

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