Confidence and self-attribution bias in an artificial stock market

Abstract

Using an agent-based model we examine the dynamics of stock price fluctuations and their rates of return in an artificial financial market composed of fundamentalist and chartist agents with and without confidence. We find that chartist agents who are confident generate higher price and rate of return volatilities than those who are not. We also find that kurtosis and skewness are lower in our simulation study of agents who are not confident. We show that the stock price and confidence index—both generated by our model—are cointegrated and that stock price affects confidence index but confidence index does not affect stock price. We next compare the results of our model with the S&P 500 index and its respective stock market confidence index using cointegration and Granger tests. As in our model, we find that stock prices drive their respective confidence indices, but that the opposite relationship, i.e., the assumption that confidence indices drive stock prices, is not significant.

Anchoring Heuristics, Investor Sentiment and Stylized Facts in the Stock Market: An Agent Based Model

“…Agents can't change their strategy, so the number of agents following a specific strategy is predetermined ( [25] and [27]). The chart agents are divided based on their choice of analysis indicators used to arrive at their price expectations.…”

“…For the dividend variable, the current dividend ( t d ) paid by a risky asset (stock) in each cycle (time t) is calculated by an exogenous first order stochastic process AR(1) (similar to [5] [25] and [27]):…”

“…where 2 , , i t p d σ + : is the perceived variance of returns, considering the price and dividend volatility. The perceived variance of returns which is the same as the effective variance of fundamental traders ( 2 , , i t p d σ + ) is calculated as in [27]:…”

“…These agents also suffer from attribution bias/excess pessimism according to how accurate/mistaken their price expectation was. So, the objective is to improve the analysis developed about stock market through an ABM by building a market capable of simultaneously explaining three of the main stylized facts of the stock market, increas- Regarding the model of [27], the number of chart agents rises from 3 to 8 groups who form their expectations through technical analysis rules which are usually applied by trend following chart agents, who do not consider or make dividend predictions. The normality test, the standard deviation of returns and the excess kurtosis tests were applied to verify the effects of heterogeneity and chart agent sentiment regarding the generation and explanation of excess volatility and the heavy tail distribution of returns, we applied.…”

A heterogeneous artificial stock market model can benefit people against another financial crisis

“…They believe the extensively using of technical bits weakened the ability of forecasting and magnified the volatility of market price. Bertella et al [ 23 ] investigate the dynamics of stock price fluctuations and return in an artificial financial market which includes fundamentalist and chartist agents with and without confidence. They point out that that stock price significantly affects confidence index, but not vice versa.…”