Confidence and the Stock Market: An Agent-Based Approach

By Mario A. Bertella, F. R. Pires, Ling Feng, H. Eugene Stanley. 2014

92 citations recorded by the source corpus.

Using a behavioral finance approach we study the impact of behavioral bias. We construct an artificial market consisting of fundamentalists and chartists to model the decision-making process of various agents. The agents differ in their strategies for evaluating stock prices, and exhibit differing memory lengths and confidence levels. When we increase the heterogeneity of the strategies used by the agents, in particular the memory lengths, we observe excess volatility and kurtosis, in agreement with real market fluctuations—indicating that agents in real-world financial markets exhibit widely differing memory lengths. We incorporate the behavioral traits of adaptive confidence and observe a positive correlation between average confidence and return rate, indicating that market sentiment is an important driver in price fluctuations. The introduction of market confidence increases price volatility, reflecting the negative effect of irrationality in market behavior.

Subfields: Economics and Econometrics, Finance

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Philip S. Yu
2. Senzhang Wang

In Finance

1. Yuntao Yao
2. Philip S. Yu
3. B. Fang
4. Yunjia Zhang
5. Senzhang Wang
6. Xi Zhang

Citations in the corpus

• Improving stock market prediction via heterogeneous information fusion (2017, background): “There are also a few studies in the field of behavioral finance that discuss the effects of overconfidence in the decision-making process of investors [36, 37], but the impacts of social media on the overconfidence are still under exploration.”