SPECULATIVE DYNAMICS WITH BOUNDED RATIONALITY LEARNING

We study a linear model for a future market characterized by the prese nce of different classes of traders. In the market there are three cla sses of traders: rational traders, feedback traders and fundamentalist traders. Each class of traders is described by a trading strategy and by an information set about the fundamental. The analysis is develope d under bounded rationality, rational traders forming expectations do not know the ''true'' model but believe in a misspecified model, The c onvergence of the learning activity to the Rational Expectations Equil ibria of the model is analyzed. Two different]earning mechanisms are s tudied: the Ordinary Least Squares algorithm and the Least Mean Square s algorithm, The main goal of the study is to analyze how the presence of different classes of traders in the market affects the robustness of the Rational Expectations Equilibria of the model with respect to b ounded rationality learning. Moreover we verify the claim that bubbles and erratic behavior in the stock price dynamics may arise because of learning non-convergence to Rational Expectations Equilibria. The res ults show that if the Ordinary Least Squares algorithm is used by the agents to update beliefs, convergence to one of the two Rational Expec tations Equilibria of the model is ensured only if there are positive feedback traders in the market. On the contrary, the Least Mean Square s algorithm guarantees convergence to the Rational Expectations Equili bria given an appropriate initial belief.