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A model of information flows and confirmatory bias in financial markets

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Abstract

An agent-based artificial market is developed to investigate the impact of confirmatory bias on volatility and kurtosis in one-period returns. Sentiment investors (similar to chartists) trade based on their assessment of future prices and the views of connected neighbours. Confirmatory bias reduces volatility and kurtosis, as new information becomes biased towards their previous decision thereby reducing trading activity. However, when the trading volume of the fundamental investor is low, confirmatory bias increases the levels of kurtosis in return suggesting that while overall trading activity of the sentiment investors falls, it becomes more coordinated.

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Notes

  1. For example, an investor who has experienced a protracted and deep correction may develop a negative view on the market long after the end of the correct.

  2. Typically, the SoW variable is exogenous. This is the case for many models used in the social learning literature. This paper looks to use the models and methods developed in the social learning literature to examine what happens if sentiment investors source their information from each other in addition to central sources such as past prices. To do this, the SoW is determined endogenously in a way that is consistent with fundamental trader, chartist models that emerged from Day and Huang (1990) and Lux (1998), and consistent with the papers outlined in the paragraph above. Bowden and McDonald (2008) and Bowden (2014) consider the impact on aggregate opinion dynamics in the presence of an exogenously set SoW within the framework of the social network used in this model.

  3. The impact of changing the time horizon of sentiment investors was analysed in Bowden (2012). The key result was that the relationships between network structure and levels of volatility and kurtosis held for all lengths of time horizon between 1 and 50 periods.

  4. The probabilities associated with other possible combinations of \(\bar{{x}}_{i,t} \), \(\bar{{x}}_{i,t-1} \) and the SoW are determined using the same method.

  5. This is consistent with definitions used in finance.

  6. The values for drift and variance are chosen to reflect average daily returns of stocks. See, for example, Campbell et al. (2001) or Wei and Zhang (2006).

  7. The parameter values used are the same as used in simulating Figs. 2 and 3.

  8. To confirm that, demand from sentiment investors falls as confirmatory bias \(({\omega })\) increases Fig. 5 was reproduced with \(T_\mathrm{F} = 1\). This figure has been omitted from this paper but can be obtained from the author upon request.

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Authors and Affiliations

  1. Faculty of Business and Law, Swinburne University of Technology, Melbourne, Australia

    Mark P. Bowden

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  1. Mark P. Bowden
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Correspondence to Mark P. Bowden.

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Bowden, M.P. A model of information flows and confirmatory bias in financial markets. Decisions Econ Finan 38, 197–215 (2015). https://doi.org/10.1007/s10203-015-0164-y

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