Purchased order flow refers to the practice of dealers or trading locales paying brokers for retail order flow. It is alleged that such agreements are used to 'cream skim' uninformed liquidity trades, leaving the information-based trades to established markets. We develop a test of this hypothesis, using a model of the stochastic process of trades. We then estimate the model for a sample of stocks known to be used in order purchase agreements that trade on the New York Stock Exchange (NYSE) and the Cincinnati Stock Exchange. Our main empirical result is that there is a significant difference in the information content of orders executed in New York and Cincinnati, and that this difference is consistent with cream-skimming.

Extending an empirical technique developed in Easley, Kiefer, and O'Hara (1996), (1997a), we examine different hypotheses about stock splits. In line with the trading range hypothesis, we find that stock splits attract uninformed traders. However, we also find that informed trading increases, resulting in no appreciable change in the information content of trades. Therefore, we do not find evidence consistent with the hypothesis that stock splits reduce information asymmetries. The optimal tick size hypothesis predicts that stock splits attract limit order trading and this enhances the execution quality of trades. While we find an increase in the number of executed limit orders, their effect is overshadowed by the increase in the costs of executing market orders due to the larger percentage spreads. On balance, the uninformed investors' overall trading costs rise after stock splits.

ABSTRACT A goal for stock exchanges is to increase participation by firms and investors. We show how specific features of the microstructure can reduce perceived ambiguity, and induce participation by both investors and issuers. We develop a model with sophisticated traders, who we view as expected utility maximizers with rational expectations, and unsophisticated traders, who we view as rational traders facing ambiguity about the payoffs to participating in the market. We show how designing markets to reduce ambiguity can benefit investors through greater liquidity, exchanges through greater volume, and issuing firms through a lower cost of capital.

ABSTRACT We investigate the role of information in affecting a firm's cost of capital. We show that differences in the composition of information between public and private information affect the cost of capital, with investors demanding a higher return to hold stocks with greater private information. This higher return arises because informed investors are better able to shift their portfolio to incorporate new information, and uninformed investors are thus disadvantaged. In equilibrium, the quantity and quality of information affect asset prices. We show firms can influence their cost of capital by choosing features like accounting treatments, analyst coverage, and market microstructure.

ABSTRACT This paper delineates the link between the existence of information, the timing of trades, and the stochastic process of prices. We show that time affects prices, with the time between trades affecting spreads. Because the absence of trades is correlated with volume, our model predicts a testable relation between spreads and normal and unexpected volume, and demonstrates how volume affects the speed of price adjustment. Our model also demonstrates how the transaction price series will be a biased representation of the true price process, with the variance being both overstated and heteroskedastic.

ABSTRACT This paper examines the effects of price‐contingent orders on security prices. We show that a market maker who knows the type and composition of trades will set larger spreads and adjust prices faster than if price‐contingent orders were not allowed. Because traders have rational expectations over the book, we demonstrate that uncertainty over order type reduces the variance of prices but with a corresponding loss in price informativeness. We also show that the sequence property of price‐contingent orders increases the probability of large price movements. This distinction between variance and episodic price volatility has important policy implications.

How best to discern trading intentions from market data? We examine the accuracy of three methods for classifying trade data: bulk volume classification (BVC), tick rule and aggregated tick rule. We develop a Bayesian model of inferring information from trade executions and show the conditions under which tick rules or bulk volume classification predominates. Empirically, we find that tick rule approaches and BVC are relatively good classifiers of the aggressor side of trading, but bulk volume classifications are better linked to proxies of information-based trading. Thus, BVC would appear to be a useful tool for discerning trading intentions from market data.

During the 2007–2009 financial crisis there was little or no trading in a variety of financial assets, even though bid and ask prices existed for many of these assets. We develop a model in which this illiquidity arises from uncertainty, and we argue that this new form of illiquidity makes bid and ask prices unsuitable as metrics for establishing “fair value” for these assets. We show how the extreme uncertainty that traders face can be characterized by incomplete preferences over portfolios, and we use Bewley's (2002) model of decision making under uncertainty to derive equilibrium quotes and the nonexistence of trading at these quotes. We then suggest alternatives for valuing assets in illiquid markets.

This paper provides an analysis of the asymptotic properties of consumption allocations in a stochastic general equilibrium model with heterogeneous consumers. In particular we investigate the market selection hypothesis, that markets favor traders with more accurate beliefs. We show that in any Pareto optimal allocation whether each consumer vanishes or survives is determined entirely by discount factors and beliefs. Since equilibrium allocations in economies with complete markets are Pareto optimal, our results characterize the limit behavior of these economies. We show that, all else equal, the market selects for consumers who use Bayesian learning with the truth in the support of their prior and selects among Bayesians according to the size of the their parameter space. Finally, we show that in economies with incomplete markets these conclusions may not hold. Payoff functions can matter for long run survival, and the market selection hypothesis fails.

We provide an axiomatic foundation for decision making in a complex environment. We do not assume that the decision maker has complete structural knowledge of the environment. Instead the agent knows the set of actions he can take, he formulates preferences directly on the actions, and chooses according to these preferences. On the basis of experience he modifies these preferences according to a systematic procedure. Our axioms are imposed on this procedure, rather than directly on the choice itself. The axioms consists of a group of natural structural restrictions and a group of independence axioms. Our main result is an axiomatic foundation for a set of simple adaptive learning procedures which include the replicator dynamic.