We develop a simple behavioral macro model to study interactions between the real
economy and the stock market. The real economy is represented by a Keynesian goods
market approach while the setup for the stock market includes heterogeneous speculators.
Using a mixture of analytical and numerical tools we find, for instance, that speculators may
create endogenous boom-bust dynamics in the stock market which, by spilling over into the
real economy, can cause lasting fluctuations in economic activity. However, fluctuations in
economic activity may, by shaping the firms’ fundamental values, also have an impact on
the dynamics of the stock market.
The paper proposes an elementary agent-based asset pricing model that, invoking the
two trader types of fundamentalists and chartists, comprises four features: (i) price determination
by excess demand; (ii) a herding mechanism that gives rise to a macroscopic
adjustment equation for the market fractions of the two groups; (iii) a rush towards fundamentalism
when the price misalignment becomes too large; and (iv) a stronger noise
component in the demand per chartist trader than in the demand per fundamentalist
trader, which implies a structural stochastic volatility in the returns. Combining analytical
and numerical methods, the interaction between these elements is studied in the
phase plane of the price and a majority index. In addition, the model is estimated by
the method of simulated moments, where the choice of the moments reflects the basic
stylized facts of the daily returns of a stock market index. A (parametric) bootstrap
procedure serves to set up an econometric test to evaluate the model’s goodness-of-fit,
which proves to be highly satisfactory. The bootstrap also makes sure that the estimated
structural parameters are well identified.
The unpredictability of returns counts as a stylized fact of financial markets. To reproduce this fact, modelers
usually implement noise terms − a method with several downsides. Above all, systematic patterns are not
eliminated but merely blurred. The present article introduces a model in which systematic patterns are removed
endogenously. This is achieved in a reality-oriented way: Intelligent traders are able to identify patterns and
exploit them. To identify and predict patterns, a very simple artificial neural network is used. As neural network
mimic the cognitive processes of the human brain, this method might be regarded as a quite accurate way of how
traders identify patterns and forecast prices in reality. The simulation experiments show that the artificial traders
exploit patterns effectively and thereby remove them, which ultimately leads to the unpredictability of prices.
Further results relate to the influence of pattern exploiters on market efficiency.
This article explores the influence of competitive conditions on the
evolutionary fitness of different risk preferences. As a practical example, the
professional competition between fund managers is considered. To explore how
different settings of competition parameters, the exclusion rate and the exclusion
interval, affect individual investment behavior, an evolutionary model based on a
genetic algorithm is developed. The simulation experiments indicate that the
influence of competitve conditions on investment behavior and attitudes towards risk
is significant. What is alarming is that intense competitive pressure generates riskseeking
behavior and undermines the predominance of the most skilled.