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We propose an empirically motivated financial market model in which speculators rely on trend-following, contrarian and fundamental trading rules to determine their orders. Speculators' probabilistic rule-selection behavior -the only type of randomness in our model- depends on past and future performance indicators. For a large number of speculators, the model's intrinsic noise vanishes and its dynamics is driven by an analytically tractable nonlinear map. An in-depth investigation into this map provides the key to understanding how the model functions. Since our model is able to match a number of important stylized facts concerning financial markets, it may be regarded as validated.

Within the seminal asset-pricing model by Brock and Hommes (1998), heterogeneous boundedly rational agents choose between a fixed number of expectation rules to forecast asset prices. However, agents’ heterogeneity is limited in the sense that they typically switch between a representative technical and a representative fundamental expectation rule. Here we generalize their framework by considering that all agents follow their own time-varying technical and fundamental expectation rules. Estimating our model using the method of simulated moments reveals that it is able to explain the statistical properties of the daily behavior of the S& P500 quite well. Moreover, our analysis reveals that heterogeneity is not only a realistic model property but clearly helps to explain the intricate dynamics of financial markets.

We develop a partial equilibrium model in which firms can locate in two separate regions. A firm’s decision where to locate in a given period depends on the regions’ relative profitability. If firms react strongly to the regions’ relative profitability, their market switching behavior generates unstable dynamics. If the goal of policy makers is to stabilize these dynamics they can do so by introducing profit taxes that reduce the regions’ relative profitability. While stability can already be obtained by imposing profit taxes in one of the two regions, total welfare is maximized if policy makers coordinate their tax setting behavior across regions. However, policy makers only interested in welfare in their own region may have the incentive to decrease their profit tax below this level, thereby attracting more firms and increasing tax revenues, at the cost of instability in both regions.

After showing that the distribution of the S& P 500’s distortion, i. e. the log difference between its real stock market index and its real fundamental value, is bimodal, we demonstrate that agent-based financial market models may explain this puzzling observation. Within these models, speculators apply technical and fundamental analysis to predict asset prices. Since destabilizing technical trading dominates the market near the fundamental value, asset prices tend to be either overvalued or undervalued. Interestingly, the bimodality of the distribution of the S& P 500’s distortion confirms an implicit prediction of a number of seminal agent-based financial market models.

We propose a novel agent-based financial market framework in which speculators usually follow their own individual technical and fundamental trading rules to determine their orders. However, there are also sunspot-initiated periods in which their trading behavior is correlated. We are able to convert our (very) simple large-scale agent-based model into a simple small-scale agent-based model and show that our framework is able to produce bubbles and crashes, excess volatility, fattailed return distributions, serially uncorrelated returns and volatility clustering. While lasting volatility outbursts occur if the mass of speculators switches to technical analysis, extreme price changes emerge if sunspots coordinate temporarily the behavior of speculators.

We propose a simple agent-based financial market model in which speculators follow a linear mix of technical and fundamental trading rules to determine their orders. Volatility clustering arises in our model due to speculators’ herding behavior. In case of heightened uncertainty, speculators observe other speculators’ actions more closely. Since speculators’ trading behavior then becomes less heterogeneous, the market maker faces a less balanced excess demand and consequently adjusts prices more strongly. Estimating our model using the method of simulated moments reveals that it is able to explain a number of stylized facts of financial markets quite well.

The seminal cobweb model by Brock and Hommes reveals that fixed-point dynamics may turn into increasingly complex dynamics as firms switch more quickly between competing expectation rules. While policy-makers may be able to manage such rational routes to randomness by imposing a proportional profit tax, the stability-ensuring tax rate may cause a very high tax burden for firms. Using a mix of analytical and numerical tools, we show that a rather small profit-dependent lump-sum tax may even be sufficient to take away the competitive edge of cheap destabilizing expectation rules, thereby contributing to market stability.

In order to demonstrate that nonlinear tax systems may have surprising and potentially undesirable side effects, we develop an evolutionary market entry model in which firms decide on the basis of past profit opportunities whether or not to enter a competitive market. Our main focus is on the case of a proportional tax on positive profits. Such a piecewise-linear tax scheme induces a kink in the profit functions of firms’ strategies, and may lead to abrupt changes in a market’s dynamics, coexisting attractors and hysteresis problems. Since these phenomena can also be observed in more general models, a proper understanding of their basic mechanism may be helpful to explain the intricate behavior of many economic systems.

Within the seminal cobweb model of Brock and Hommes, firms adapt their price expectations by a profit-based switching behavior between free naïve expectations and costly rational expectations. Brock and Hommes demonstrate that fixed-point dynamics may turn into increasingly complex dynamics as the firms’ intensity of choice increases. We show that policy-makers are able to manage rational routes to randomness by adjusting profit taxes. As suggested by our analytical and numerical analysis, policy-makers should increase (decrease) profit taxes if destabilizing expectations generate higher (lower) profits than stabilizing expectations to alter the composition of applied expectation rules and thereby to promote market stability. Our results are not restricted to cobweb models: a huge body of literature demonstrates that rational routes to randomness may emerge in many different markets.

We develop a simple agent-based financial market model in which heterogeneous speculators apply technical and fundamental analysis to trade in two different stock markets. Speculators’ strategy/market selections are repeated at each time step and depend on predisposition effects, herding behavior and market circumstances. Simulations reveal that our model is able to explain a number of nontrivial statistical properties of and between international stock markets, including bubbles and crashes, fat-tailed return distributions, volatility clustering, persistent trading volume, coevolving stock prices and cross-correlated volatilities. Against this background, our model may be deemed to have been validated.