Confidence and self-attribution bias in an artificial stock market

• Published in: PloS one Vol. 12; no. 2; p. e0172258
• Main Authors: Bertella, Mario A, Pires, Felipe R, Rego, Henio H A, Silva, Jonathas N, Vodenska, Irena, Stanley, H Eugene
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 23-02-2017; Public Library of Science (PLoS)
• Subjects: Analysis; Attributional biases; Behavior; Bertella; Bias; Commerce; Computer and Information Sciences; Computer Simulation; Confidence; Decision Making; Econometrics; Economic models; Economics; Economics, Behavioral; Efficient market theory; Efficient markets; Humans; Hypotheses; Hypothesis testing; Investments; Kurtosis; Markets; Models, Economic; Physical Sciences; Portfolio management; Research and Analysis Methods; Securities markets; Simulation; Skewness; Social Sciences; Stock exchanges; Stock market indexes; Stock markets; Studies; Time series; Variation; Brazil; United States > US; Massachusetts
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0172258

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.