We report on six large-scale financial markets experiments that were designed to test two of the most basic propositions of modern asset pricing theory, namely, that the interaction between risk averse agents in a competitive market leads to equilibration, and that, in equilibrium, risk premia are solely determined by covariance with aggregate risk. We designed the experiments within the framework suggested by two theoretical models, namely, Arrow and Debreu’s complete-markets model, and the Sharpe-Lintner-Mossin Capital Asset Pricing Model (CAPM). This framework enabled us to measure how far our markets were from equilibrium at any point in time, thereby allowing us to gauge the success of the models. The distance measures do not require knowledge of the (uncontrollable) level and dispersion of risk aversion among subjects, and adjust for the impact of progressive trading on the eventual equilibrium. Unlike in our earlier, thin-markets experiments, we discovered swift convergence towards equilibrium prices of Arrow and Debreu’s model or the CAPM. This discovery is significant, because subjects always lacked the information to deliberately set asset prices using either model. Sometimes, however, the equilibrium was not found to be robust, with markets readily veering away, apparently as a result of deviations of subjective beliefs from objective probabilities. Still, we find evidence that this did not destroy the tendency for markets to equilibrate as predicted by the theory. In each experiment, we formally test and reject the hypothesis that prices are a random walk, in favor of stochastic convergence towards CAPM and Arrow Debreu equilibrium.

Strategic thinking, best-response, and mutual consistency (equilibrium) are three key modeling principles in noncooperative game theory. This paper relaxes mutual consistency to predict how players are likely to behave in one-shot games before they can learn to equilibrate. We introduce a one-parameter cognitive hierarchy (CH) model to predict behavior in one-shot games, and initial conditions in repeated games. The CH approach assumes that players use k steps of reasoning with frequency f(k). Zero-step players randomize. Players using k ( ≥ 1) steps best respond given partially rational expectations about what players doing 0 through k − 1 steps actually choose. A simple axiom which expresses the intuition that steps of thinking are increasingly constrained by working memory, implies that f(k) has a Poisson distribution (characterized by a mean number of thinking steps τ). The CH model converges to dominance-solvable equilibria when τ is large, predicts monotonic entry in binary entry games for τ < 1.25, and predicts effects of group size which are not predicted by Nash equilibrium. Best-fitting values of τ have an interquartile range of (.98,2.21) and a median of 1.55 across 60 experimental samples of matrix games, entry games and mixed-equilibrium games. The CH model also has economic value because subjects would have raised their earnings substantially if they had best-responded to model forecasts instead of making the choices they did. 1

Consider the postage stamp. As title to a future good (or, in this case, service) with monetary value, this humble object is essentially the same as a security. Its value, 37 cents, can be identiÞed with the present value of the service (delivery of a letter) to which its owner is entitled.

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Universidad de San Andrés in Buenos Aires for helpful comments. We are grateful to Urs Fischbacher for permission to use the z-Tree software; the Laboratory for the Study of Human Thought and Action at Virginia Tech where the experiment was run in October 2001; and the Swedish Competition Authority for financial support. 1 Believers in the latter perspective often invoke terms suggestive of folly or hysteria, like “mania, ” “panic, ” or (Alan Greenspan’s) “irrational exuberance, ” as in the titles

This paper tests a learning-based model of strategic teaching in repeated games with incomplete information. The repeated game has a long-run player whose type is unknown to a group of short-run players. The proposed model assumes a fraction of ‘short-run ’ players follow a one-parameter learning model (self-tuning EWA). In addition, some ‘long-run ’ players are myopic while others are sophisticated and rationally anticipate how short-run players adjust their actions over time and “teach” the short-run players to maximize their long-run payoffs. All players optimize noisily. The proposed model nests an agent-based quantal-response equilibrium (AQRE) and the standard equilibrium models as special cases. Using data from 28 experimental sessions of trust and entry repeated games, including 8 previously unpublished sessions, the model fits substantially better than chance and much better than standard equilibrium models. Estimates show that most of the long-run players are sophisticated, and short-run players become more sophisticated with experience. Key words: repeated games, self-tuning experience-weighted attraction learning, quantal response equilibrium

The bullwhip effect describes the tendency for the variance of orders in supply chains to increase as one moves upstream from consumer demand. Previous research attributes this phenomenon to both operational and behavioral causes. Operational causes are features of the institutional setting that lead rational agents to amplify changes in demand, while behavioral causes arise from suboptimal decision-making. This paper examines causes of the bullwhip through experiments with a serial supply chain, using the Beer Distribution Game. Unlike prior studies, we control all four commonly cited operational causes of the bullwhip, including uncertainty about customer demand. We eliminate demand uncertainty completely by making customer demand constant and known to all participants. Despite these controls, order amplification, instability, and supply line underweighting remain pervasive. We propose a new behavioral cause of the bullwhip, coordination risk, that arises when players place excessive orders to address the perceived risk that others will not behave optimally. We test two strategies to mitigate coordination risk: (1) holding additional on-hand inventory, and (2) creating common knowledge by informing participants of the optimal policy. Both strategies reduce, but not eliminate, the bullwhip effect. Holding excess inventory reduces order amplification by providing a buffer against the

This paper proposes an autoregressive regime-switching model of stock price dynamics in which the process creates pricing bubbles in one regime while error-correction prevails in the other. In the bubble regime the stock price depends negatively on inflation. In the error-correction regime it depends on the price-dividend-ratio. We find that the probability of regime-switch depends on exogenous inflation and lagged price. The model is consistent with Shleifer and Vishny’s theoretical noise trader and arbitrageur model and Modigliani’s inflation illusion phenomenon. The results emphasize the importance of inflation and the price-dividend-ratio when assessing investment risk.

www.elsevier.com/locate/jedc Price bubbles sans dividend anchors: Evidence from laboratory stock markets

Atleast in their personal lives, many economists recognize that they are surrounded by individuals who are less than fully rational. In their professional lives, however, economists often use models that examine the interactions of fully rational agents. To reduce the cognitive dissonance of this situation, many economists believe that interactions in markets will correct or offset individually anomalous behaviors. Although the reasons for this belief are often not spelled out clearly, several assertions are frequently put forward. For example, a first hypothesis states that if deviations from rationality are random, they will more or less cancel out at the aggregate level. Random deviations from rationality do occur in some situations (Bossaerts, Plott and Zame, 2003). However, the anomalies reported in the literature—like the biases under uncertainty reported in Kahneman, Slovic and Tversky (1982)—have a systematic pattern. Since these deviations from rationality are not random, they may plausibly affect the market equilibrium. A second common argument is that even if individuals are irrational at times, they will learn from their mistakes. While market experience can diminish anomalous behavior in some cases (List, 2003), a number of powerful individual anomalies, like the failure to update expectations in a Bayesian manner, are very robust to individual learning in markets (Camerer 1987, 1992; Ganguly, Kagel and Moser, 2000; Kluger and Wyatt, 2003). There is no general reason to believe that markets automatically render individual decisions more rational over time. A third powerful argument in favor of the irrelevance of individual anomalies