Asset Pricing Under Endogenous Expectation in an Artificial Stock Market

We propose a theory of asset pricing based on heterogeneous agents who continually adapt their expectations to the that these expectations aggregatively create. And we explore the implications of this theory computationally using our Santa Fe artificial stock market. Asset markets, we argue, have a recursive nature in that agents' expectations are formed on the basis of their anticipations of other agents' expectations, which precludes expectations being formed by deductive means. Instead traders continually hypothesize---continually explore---expectational models, buy or sell on the basis of those that perform best, and confirm or discard these according to their performance. Thus individual beliefs or expectations become endogenous to the market, and constantly compete within an ecology of others' beliefs or expectations. The ecology of beliefs coevolves over time. Computer experiments with this endogenous-expectations explain one of the more striking puzzles in finance: that traders often believe in such concepts as technical trading, market and bandwagon effects, while academic theorists believe in efficiency and a lack of speculative opportunities. Both views, we show, are correct, but within different regimes. Within a regime where investors explore alternative expectational models at a low rate, the settles into the rational-expectations equilibrium of the efficient-market literature. Within a regime where the rate of exploration of alternative expectations is higher, the self-organizes into a complex pattern. It acquires a rich psychology, technical trading emerges, temporary bubbles and crashes occur, and asset prices and trading volume show statistical features---in particular, GARCH behavior---characteristic of actual data.