This paper tests the null hypothesis of no difference in the probability of a trade occurring at the ask using a new database containing intraday bid-ask quotes and transaction prices on both U.S. and Canadian Exchanges. We use LOGIT analysis to test the hypothesis across days of the week, price-stratified portfolios, and times of the day. We find systematic patterns in the probability of a trade at the ask resembling previously documented returns anomalies and conclude that the findings of previous weekend and intraday returns studies may be overstated. The significance of this conclusion substantially increases as one moves from the use of interday to intraday data.

Abstract Does house money exacerbate price bubbles? We compare house money asset market experiments with an earned money treatment where initial portfolios are constructed from a real effort task. Bubbles occur; however, trading volumes and earnings dispersion are significantly higher with house money. We investigate the role of cognitive ability in accounting for the differences in earnings distribution across treatments by using the cognitive reflection test (CRT). Low CRT subjects earned less than high CRT subjects. Low CRT subjects were net purchasers (sellers) of shares when the price was above (below) fundamental value. The opposite was true for high CRT subjects.

The paper reports on an experimental investigation of four methods of allocating public goods. The two basic processes studied are direct contribution and a public goods auction process. Both of these processes are studied with and without an additional unanimity feature. The results suggest that the auction process outperforms direct contribution. The effect of unanimity is to decrease the efficiency of both processes. Much of the paper is focused on an analysis of these results.

This article examines late trade reporting on the Nasdaq National Market System. A substantial number of trades are reported out-of-sequence on both absolute levels and relative to the combined centralized exchanges. We find minimal support for NASD permitted reasons for the late trade reporting. Evidence suggests that market makers could use late trade reporting to manage the release of information. This evidence is consistent with the hypothesis that the delayed reporting of trades is neither a random occurrence nor fully explainable by factors outside the market maker's control.

We report 28 new experiment sessions consisting of up to three experience levels to examine the robustness of learning and “error” elimination among participants in a laboratory asset market and its effect on price bubbles. Our answer to the title question is: “yes.” We impose a large increase in liquidity and dividend uncertainty to shock the environment of experienced subjects who have converged to equilibrium, and this treatment rekindles a bubble. However, in replications of that same challenging environment across three experience levels, we discover that the environment yields a rare residual tendency to bubble even in the third experience session. Therefore, a caveat must be placed on the effect of twice-experienced subjects in asset markets: in order for price bubbles to be extinguished, the environment in which the participants engage in exchange must be stationary and bounded by a range of parameters. Experience, including possible “error” elimination, is not robust to major new environment changes in determining the characteristics of a price bubble. (JEL C91, D83)

ABSTRACT Using laboratory experiments, we provide evidence on three factors influencing trader performance: fluid intelligence, cognitive reflection, and theory of mind (ToM). Fluid intelligence provides traders with computational skills necessary to draw a statistical inference. Cognitive reflection helps traders avoid behavioral biases and thereby extract signals from market orders and update their prior beliefs accordingly. ToM describes the degree to which traders correctly assess the informational content of orders. We show that cognitive reflection and ToM are complementary because traders benefit from understanding signals’ quality only if they are capable of processing these signals.