ASSET PRICING WITH ADAPTIVE LEARNING

Abstract. We study the extent to which self-referential adaptive learning can explain stylized asset pricing facts in a general equilibrium framework. In particular, we analyze the e¤ects of recursive least squares and constant gain algorithms in a production economy and a Lucas type endowment economy. We \u85nd that recursive least squares learning has almost no e¤ects on asset price behavior, since the algorithm converges relatively fast to rational expectations. On the other hand, constant gain learning may contribute towards explaining the stock price and return volatility as well as the predictability of excess returns in the endowment economy. In the production economy, however, the e¤ects of constant gain learning are mitigated by the persistence induced by capital accumulation. We conclude that, contrary to popular belief, standard self-referential learning cannot fully resolve the asset pricing puzzles observed in the data