Knowledge that Transforms

This study examines the stock-price reactions to analyst forecast revisions around earnings announcements to test whether preannouncement forecasts reflect analysts' private information or piggybacking on confounding events and news. We find that management earnings forecasts influence the timing and precision of analyst forecasts. More importantly, evidence suggests that prior studies' finding of weaker (stronger) stock-price responses to forecast revisions in the period immediately after (before) the prior-quarter earnings announcement disappears once management earnings forecasts are controlled for. To the extent that management earnings forecasts are public disclosures, our results suggest that the importance of analysts' information discovery role documented in prior studies is likely to be overstated.

We introduce an auction design framework for large markets with hundreds of items and complex bidder preferences. Such markets typically lead to computationally hard allocation problems. Our new framework consists of compact bid languages for sealed-bid auctions and methods to compute second-price rules such as the Vickrey–Clarke–Groves or bidder-optimal, core-selecting payment rules when the optimality of the allocation problem cannot be guaranteed. To demonstrate the efficacy of the approach for a specific, complex market, we introduce a compact bidding language for TV advertising markets and investigate the resulting winner-determination problem and the computation of core payments. For realistic instances of the respective winner-determination problems, very good solutions with a small integrality gap can be found quickly, although closing the integrality gap to find marginally better solutions or prove optimality can take a prohibitively large amount of time. Our subsequent adaptation of a constraint-generation technique for the computation of bidder-optimal core payments to this environment is a practically viable paradigm by which core-selecting auction designs can be applied to large markets with potentially hundreds of items. Such auction designs allow bidders to express their preferences with a low number of parameters, while at the same time providing incentives for truthful bidding. We complement our computational experiments in the context of TV advertising markets with additional results for volume discount auctions in procurement to illustrate the applicability of the approach in different types of large markets. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2014.2076 .

Financially constrained borrowers have the incentive to influence the appraisal process in order to increase borrowing or reduce the interest rate. We document that the average valuation bias for residential refinance transactions is above 5%. The bias is larger for highly leveraged transactions, around critical leverage thresholds, and for transactions mediated through a broker. Mortgages with inflated valuations default more often. Lenders account for 60%–90% of the bias through pricing.