Loss Aversion in an Agent-Based Asset Pricing Model

A well-defined agent-based asset pricing model able to match the widely observed properties of financial time series is valuable for testing the implications of various biases associated with investors' behaviour. Extending one of the most successful models in capturing traders behaviour, we present a new behavioural agent-based asset pricing model. Specifically, we introduce a well-known behavioural bias in the model, loss aversion, and evaluate its implications. First, measuring how close the simulated time series are to its empirical counterparts, we show that the model with loss aversion better matches and explains the properties of real-world financial data, compared with the base model without the behavioural bias. Secondly, we assess the impact of different levels of loss aversion not only on the agents' switching mechanisms, but also on the properties of the time series generated by the model. We demonstrate how for different levels of the loss aversion parameter, the biased agents tend to be driven out of the market at different points in time. Since even the simplest strategies have been shown to survive the competition in an agent-based setting, we can link our findings with the behavioural finance literature, which states that investors' systematic biases lead to unexpected market behaviour, instabilities and systematic errors. Finally, we provide an in-depth analysis of the simulated time series and show the resulting dynamics replicate a rich set of the stylized facts including: absence of autocorrelation, heavy tails, volatility clustering and conditional heavy tails of returns, long memory of absolute returns, as well as volume-volatility relations, gain-loss asymmetry, power-law behaviour and long memory of volume.