ABSTRACT This paper models transaction costs as the rents that a monopolistic market maker extracts from impatient investors who trade via limit orders. We show that limit orders are American options. The limit prices inducing immediate execution of the order are functionally equivalent to bid and ask prices and can be solved for various transaction sizes to characterize the market maker's entire supply curve. We find considerable empirical support for the model's predictions in the cross‐section of NYSE firms. The model produces unbiased, out‐of‐sample forecasts of abnormal returns for firms added to the S&P 500 index.

Consider two agents who learn the value of an unknown parameter by observing a sequence of private signals. The signals are independent and identically distributed across time but not necessarily across agents. We show that when each agent's signal space is finite, the agents will commonly learn the value of the parameter, that is, that the true value of the parameter will become approximate common knowledge. The essential step in this argument is to express the expectation of one agent's signals, conditional on those of the other agent, in terms of a Markov chain. This allows us to invoke a contraction mapping principle ensuring that if one agent's signals are close to those expected under a particular value of the parameter, then that agent expects the other agent's signals to be even closer to those expected under the parameter value. In contrast, if the agents' observations come from a countably infinite signal space, then this contraction mapping property fails. We show by example that common learning can fail in this case.

Traditional autocorrelation and variance ratio tests are based on serial uncorrelatedness rather than martingale difference. As such, they do not capture potential nonlinearity-in-mean, which could lead to misleading inferences in favor of the martingale hypothesis. This paper employs various parametric and nonparametric nonlinear models as well as several model comparison criteria to examine the potential martingale behavior of Euro exchange rates in the context of out-of-sample forecasts. The overall evidence indicates that, while martingale behavior cannot be rejected for Euro exchange rates with major currencies such as the Japanese yen, British pound, and US dollar, there is nonlinear predictability in terms of economic criteria with respect to several smaller currencies.

We predict and find that accounting restatements that adversely affect shareholder wealth at the restating firm also induce share price declines among non-restating firms in the same industry. These share price declines are unrelated to changes in analysts' earnings forecasts, but instead seem to reflect investors' accounting quality concerns. Peer firms with high industry-adjusted accruals experience a more pronounced share price decline than do low-accrual firms. This accounting contagion effect is concentrated among revenue restatements by relatively large firms in the industry. We also find that investors impose a larger penalty on the stock prices of peer firms with high earnings and high accruals when peer and restating firms use the same external auditor. Our results are consistent with the notion that some accounting restatements cause investors to reassess the financial statement information previously released by non-restating firms.

ABSTRACT We study an institutional investment problem in which a centralized decision maker, the Chief Investment Officer (CIO), for example, employs multiple asset managers to implement investment strategies in separate asset classes. The CIO allocates capital to the managers who, in turn, allocate these funds to the assets in their asset class. This two‐step investment process causes several misalignments of objectives between the CIO and his managers and can lead to large utility costs for the CIO. We focus on (1) loss of diversification, (2) unobservable managerial appetite for risk, and (3) different investment horizons. We derive an optimal unconditional linear performance benchmark and show that this benchmark can be used to better align incentives within the firm. We find that the CIO's uncertainty about the managers' risk appetites increases both the costs of decentralized investment management and the value of an optimally designed benchmark.

This paper investigates the effect of internal control deficiencies and their remediation on accrual quality. We first document that firms reporting internal control deficiencies have lower quality accruals as measured by accrual noise and absolute abnormal accruals relative to firms not reporting internal control problems. Second, we find that firms that report internal control deficiencies have significantly larger positive and larger negative abnormal accruals relative to control firms. This finding suggests internal control weaknesses are more likely to lead to unintentional errors that add noise to accruals than intentional misstatements that bias earnings upward. Third, we document that firms whose auditors confirm remediation of previously reported internal control deficiencies exhibit an increase in accrual quality relative to firms that do not remediate their control problems. Finally, we find firms that receive different internal control audit opinions in successive years exhibit changes in accrual quality consistent with changes in internal control quality. Collectively, our cross-sectional and intertemporal change tests provide strong evidence that the quality of internal control affects the quality of accruals.

Currently, a growing literature is emerging on estimating the impact of exogenous shocks using the difference-in-difference (DD) tech? nique. Essentially, this technique compares the impact of an unexpected event in a particu? lar locale (called the treatment/experimental group) to a location or set of locations (called a control group) similar to the experimental group in all respects except for the shock itself. One challenge many DD studies face is how to choose the control group, and there is now a growing literature on this (Joshua A. Angrist and Alan B. Krueger 1999; Jeffrey D. Kubik and John R. Moran 2003; and Alberto Abadie, Alexis Diamond, and Jens Hainmueller 2007). Another challenge is whether one can general? ize one's results based on a single experimental group, as is typical for most DD analysis. This paper adopts a generalized-difference-in-dif ference (GDD) technique outlined in Ariel R. Belasen and Solomon W. Polachek (forthcom? ing) to examine the impact of hurricanes on the labor market. This technique incorporates many experimental as well as many control groups, and as such this approach addresses a number of shortcomings in current DD analyses. We find that earnings of the average worker in a Florida county rise over 4 percent within the first quarter of being hit by a major Category Four or Five hur? ricane relative to counties not hit, and rise about Wa percent for workers in Florida counties hit by less major Category One to Three hurricanes. Concomitantly, employment falls between Wi and 5 percent depending on hurricane strength. On the other hand, the effects of hurricanes on neighboring counties have the opposite effects, moving earnings down between 3 and 4 percent in the quarter the hurricane struck. To better examine the specific shocks, we also observe sectoral employment shifts. Finally, we conduct a time-series analysis and find that, over time, there is somewhat of a cobweb, with earnings and employment rising and falling each quarter over a two-year time period.

The utility of homeownership as a household wealth-building vehicle has long been recognized. In recent years, homeownership has been promoted as an important strategy for improving the financial situation of lowand moderateincome households. However, this strategy does not come without its risks, as homeownership exposes households to potential troubles along multiple dimensions. This paper highlights the conditions under which a homeownership strategy is likely to be effective. A key contribution is its significant focus on the risks of homeownership, which are assessed by studying the distribution of foreclosure across neighborhoods. According to the Current Population Survey (CPS), between 1994 and 2006, homeownership rates among households in the first and second income quartiles increased by 11.1 and 12.9 percent, respectively. This exceeded the 10.3 percent increase observed for the general population and was due in part to several factors. First, income, education, and wealth for lowand moderate-income households all increased significantly over this period (Arthur B. Kennickell 2006), which increased the accesAssets And Credit Among Low-inCome HouseHoLds †

We design experiments to jointly elicit risk and time preferences for the adult Danish population. Since subjects are generally risk averse, we find that joint elicitation provides estimates of discount rates that are significantly lower than those found in previous studies and more in line with what would be considered as a priori reasonable rates. The statistical specification relies on a theoretical framework that involves a latent trade-off between long-run optimization and short-run temptation. Estimation of this specification is undertaken using structural, maximum likelihood methods. Our main results based on exponential discounting are robust to alternative specifications such as hyperbolic discounting. These results have direct implications for attempts to elicit time preferences, as well as debates over the appropriate domain of the utility function when characterizing risk aversion and time consistency.

In this paper we develop two nonparametric tests of treatment effect heterogeneity. The first test is for the null hypothesis that the treatment has a zero average effect for all subpopulations defined by covariates. The second test is for the null hypothesis that the average effect conditional on the covariates is identical for all subpopulations, that is, that there is no heterogeneity in average treatment effects by covariates. We derive tests that are straightforward to implement and illustrate the use of these tests on data from two sets of experimental evaluations of the effects of welfare-to-work programs.