Although credit history scoring offers benefits to lenders and borrowers, failure to consider situational circumstances raises important statistical issues that may affect the ability of scoring systems to accurately quantify an individual’s credit risk. Evidence from a national sample of credit reporting agency records suggests that failure to consider measures of local economic circumstances and individual trigger events when developing credit history scores can diminish the potential effectiveness of such models. There are practical difficulties, however, associated with developing scoring models that incorporate situational data, arising largely because of inherent limitations of the credit reporting agency databases used to build scoring models.

ABSTRACT In this paper we study when it is advantageous to improve corporate transparency by allowing shareholders direct access to corporate information and when it is preferable to rely on a reporting system in which shareholders only gain access to information that management chooses to disclose. We show that in an agency model that allows for contract renegotiation, the desirability of a fully transparent reporting regime hinges on the stewardship properties of the information in question. Specifically, information that is mainly useful for predicting future events and of little use for evaluating past actions should only be made available to the public through management's self‐interested disclosures. Only if the information is useful for making inference about managerial actions can it be optimal to have full corporate transparency, so that outsiders have independent access to the same information as management.

We provide an asymptotic distribution theory for a class of generalized method of moments estimators that arise in the study of differentiated product markets when the number of observations is associated with the number of products within a given market. We allow for three sources of error: sampling error in estimating market shares, simulation error in approximating the shares predicted by the model, and the underlying model error. It is shown that the estimators are CAN provided the size of the consumer sample and the number of simulation draws grow at a large enough rate relative to the number of products. We consider the implications of the results for the Berry, Levinsohn and Pakes (1995) random coefficient logit model and the pure characteristics model analysed in Berry and Pakes (2002). The required rates differ for these two frequently used demand models. A small Monte Carlo study shows that the differences in asymptotic properties of the two models are reflected, in quite a striking way, in the models' small sample properties. Moreover the limit distributions provide a good approximation to the actual Monte Carlo distribution of the parameter estimates. The results have important implications for the computational burden of the two models.

In this paper we look at the case for bundHng in an oHgopohstic environment. We show that bundhng is a particularly effective entry-deterrent strategy. A company that has market power in two goods, A and B, can, by bundling them together, make it harder for a rival with only one of these goods to enter the market. Bundling allows an incumbent to credibly defend both products without having to price low in each. The traditional explanation for bundling that economists have given is that it serves as an effective tool of price discrimination by a monopolist. Although price discrimination provides a reason to bundle, the gains are small compared with the gains from the entry-deterrent effect.

We investigate the market liquidity effects of the introduction of index-tracking stocks for the Dow Jones Industrial Average (DIAMONDS) and the NASDAQ 100 index (Q's). Our main finding is liquidity of the underlying DJIA 30 index stocks improves after the introduction of the exchange-traded fund, largely because of a decline in the cost of informed trading. Further, we find the DIAMONDS has significantly lower liquidity costs over the first 50 days of trading as compared to the portfolio of its component stocks, again primarily because of lower adverse selection costs. Finally, we find weaker but qualitatively similar results for the Q's.

A dynamic model of a multistage investment project that captures many features of research and development (R&D) ventures and start-up companies is developed. An important feature these problems share is that firms learn about the potential profitability of the project throughout its life, but that technical uncertainty about the R&D effort is only resolved through additional investment. Consequently the risks associated with the ultimate cash flows have a systematic component even while the purely technical risks are idiosyncratic. Our model captures these different sources of risk and allows us to study their interaction in determining the value and risk premium of the venture.