A variation of the Rothschild-Stiglitz' equilibrium is examined in the context of competitive lending under adverse selection. The predictions of the model are tested in an experimental market setting. If equilibrium exists, the loan contracts offered and taken should separate projects by quality. When equilibrium exists, the experiments confirm the theory. The entrepreneurs with high-risk projects take bigger loans and pay higher credit spreads than those with low-risk projects. When equilibrium does not exist, which happens exactly when the candidate equilibrium does not provide a Pareto-optimal allocation, in half of the sessions loan trading stabilizes around the candidate equilibrium pair. In the other half, however, markets never settle down. This finding has important implications. When lenders can offer menus of contracts, as is usually the case in reality, the outcome may not be the zero-profit separating contracts of the standard model. Worse, fitting the standard model to field data may lead to serious biases in estimated parameters while falsely accepting the model's main restriction (separation).

Microstructure noise in security prices biases the results of empirical asset pricing specifications, particularly when security-level explanatory variables are cross-sectionally correlated with the amount of noise. We focus on tests of whether measures of illiquidity, which are likely to be correlated with the noise, are priced in the cross-section of stock returns, and show a significant upward bias in estimated return premiums for an array of illiquidity measures in Center for Research in Security Prices (CRSP) monthly return data. The upward bias is larger when illiquid securities are included in the sample, but persists even for NYSE/Amex stocks after decimalization. We introduce a methodological correction to eliminate the biases that simply involves weighted least squares (WLS) rather than ordinary least squares (OLS) estimation, and find evidence of smaller, but still significant, return premiums for illiquidity after implementing the correction.

Temporary deviations of trade prices from fundamental values impart bias to estimates of mean returns to individual securities, to differences in mean returns across portfolios, and to parameters estimated in return regressions. We consider a number of corrections, and show them to be effective under reasonable assumptions. In an application to the Center for Research in Security Prices monthly returns, the corrections indicate significant biases in uncorrected return premium estimates associated with an array of firm characteristics. The bias can be large in economic terms, for example, equal to 50% or more of the corrected estimate for firm size and share price.

We present results from an experiment where participants have access to automated trading algorithms, which they may deploy at will while still trading manually. Treatments differ in whether robots must not be halted, deployment is compulsory, or robots can be halted and replaced at will. We hypothesize that robot trading would reduce mispricing, and that the effect would be more pronounced as commitment degree increases. Yet, compared to manual trading only, we observe equally large and frequent mispricing and, in early trading, significantly higher bid–ask spreads and more frequent flash crashes/price surges. Participants earn more, provided they combine robot and manual trading. Compared to evidence from archival data, we find significantly higher use of liquidity-taking robots. We attribute this to the inability, in the field, to identify the presence of liquidity takers when they happen not to trade.

We study the Lucas asset pricing model in a controlled setting. Participants trade two long‐lived securities in a continuous open‐book system. The experimental design emulates the stationary, infinite‐horizon setting of the model and incentivizes participants to smooth consumption across periods. Consistent with the model, prices align with consumption betas and comove with aggregate dividends, particularly so when risk premia are higher. Trading significantly increases consumption smoothing compared to autarky. Nevertheless, as in field markets, prices are excessively volatile. The noise corrupts traditional generalized method of moment tests. Choices display substantial heterogeneity, with no subject representative for pricing.

We present a theory and experimental evidence on pricing and portfolio choices under asymmetric reasoning. We show that under asymmetric reasoning, prices do not reflect all (types of) reasoning. Some agents who observe prices that cannot be reconciled with their reasoning switch from perceiving the environment as risky to perceiving it as ambiguous. If they are ambiguity-averse, these agents become price-insensitive. Results from an experiment show that, consistent with the theory, (i) without aggregate risk, mispricing decreases as the fraction of price-sensitive agents increases; and (ii) with aggregate risk, price-insensitive agents trade to more balanced portfolios.