Communication impacting financial markets
Europhysics Letters
ANDERSEN Jorgen Vitting - Université Paris 1 Panthéon-Sorbonne (Author)
VRONTOS Ioannis - Athens University of Economics and Business (Author)
DELLAPORTAS Petros - Athens University of Economics and Business (Author)
GALAM Serge - Centre de recherches politiques de Sciences Po (Author)
1 - 8 p.
Econophysics, Sociophysics, Financial market
Since the attribution of the Nobel prize in 2002 to Kahneman for prospect theory, be- havioral finance has become an increasingly important subfield of finance. However the main parts of behavioral finance, prospect theory included, understand financial markets through individual investment behavior. Behavioral finance thereby ignores any interaction between participants. We introduce a socio-financial model (Vitting Andersen J. and Nowak A., An Introduction to Socio-Finance (Springer, Berlin) 2013) that studies the impact of communication on the pricing in financial markets. Considering the simplest possible case where each market participant has either a positive (bullish) or negative (bearish) sentiment with respect to the market, we model the evolution of the sentiment in the population due to communication in subgroups of different sizes. Nonlinear feedback effects between the market performance and changes in sentiments are taken into account by assuming that the market performance is dependent on changes in sentiments (e.g., a large sudden positive change in bullishness would lead to more buying). The market per- formance in turn has an impact on the sentiment through the transition probabilities to change an opinion in a group of a given size. The idea is that if for example the market has observed a recent downturn, it will be easier for even a bearish minority to convince a bullish majority to change opinion compared to the case where the meeting takes place in a bullish upturn of the market. Within the framework of our proposed model, financial markets stylized facts such as volatility clustering and extreme events may be perceived as arising due to abrupt sentiment changes via ongoing communication of the market participants. The model introduces a new volatility mea- sure which is apt of capturing volatility clustering and from maximum-likelihood analysis we are able to apply the model to real data and give additional long term insight into where a market is heading.