The informational opacity of small businesses makes them an interesting area for the study of banks' lending practices and procedures. We use data from a survey of small businesses to analyze the micro level differences in the loan approval processes of large and small banks. We provide evidence that large banks ($1 billion or more in assets) employ standard criteria obtained from financial statements in the loan decision process, whereas small banks rely to a greater extent on information about the character of the borrower. These cookie-cutter and character approaches are compatible with the incentives and environments facing large and small banks.

This article develops a theory of multiunit auctions where short squeezes can occur in the secondary market. Both uniform and discriminatory auctions are studied and bidders can submit multiple bids. We show that bidders with short and long preauction positions have different valuations in an otherwise common value setting. Discriminatory auctions lead to more short squeezing and higher revenue than uniform auctions, ceteris paribus. Asymptotically, as the auction size approaches infinity, the two formats lead to equivalent outcomes. Shorts employ more aggressive equilibrium bidding strategies. Most longs strategically choose to be passive. Free riding on a squeeze by small, long players has no impact on these results, but affects revenue in discriminatory auctions.

We examine insider trading in specialist and dealer markets, using the trades of stock brokers who had advance copies of a stock analysis column in Business Week magazine. We find that increases in price and volume occur after informed trades. During informed trading, market makers decrease depth. Depth falls more on the NYSE and Amex than on the Nasdaq. Spreads increase on the NYSE and Amex, but not on the Nasdaq. We find none of these pre-release changes in a nontraded control sample of stocks mentioned in the column. Our results show that insider trading has a negative impact on market liquidity; depth is an important tool to manage asymmetric information risk; and specialist markets are better at detecting informed trades.

In econometrics, models stated as conditional moment restrictions are typically estimated by means of the generalized method of moments (GMM). The GMM estimation procedure can render inconsistent estimates since the number of arbitrarily chosen instruments is finite. In fact, consistency of the GMM estimators relies on additional assumptions that imply unclear restrictions on the data generating process. This article introduces a new, simple and consistent estimation procedure for these models that is directly based on the definition of the conditional moments. The main feature of our procedure is its simplicity, since its implementation does not require the selection of any user-chosen number, and statistical inference is straightforward since the proposed estimator is asymptotically normal. In addition, we suggest an asymptotically efficient estimator constructed by carrying out one Newton–Raphson step in the direction of the efficient GMM estimator.

Surveys on the use of agency credit ratings reveal that some investors believe that rating agencies are relatively slow in adjusting their ratings. A well-accepted explanation for this perception on the timeliness of ratings is the through-the-cycle methodology that agencies use. According to Moody’s, through-the-cycle ratings are stable because they are intended to measure default risk over long investment horizons, and because they are changed only when agencies are confident that observed changes in a company’s risk profile are likely to be permanent. To verify this explanation, we quantify the impact of the long-term default horizon and the prudent migration policy on rating stability from the perspective of an investor – with no desire for rating stability. This is done by benchmarking agency ratings with a financial ratio-based (credit-scoring) agency-rating prediction model and (credit-scoring) default-prediction models of various time horizons. We also examine rating-migration practices. The final result is a better quantitative understanding of the through-the-cycle methodology. By varying the time horizon in the estimation of default-prediction models, we search for a best match with the agency-rating prediction model. Consistent with the agencies’ stated objectives, we conclude that agency ratings are focused on the long term. In contrast to one-year default prediction models, agency ratings place less weight on short-term indicators of credit quality. We also demonstrate that the focus of agencies on long investment horizons explains only part of the relative stability of agency ratings. The other aspect of through-the-cycle methodology – agency-rating migration policy – is an even more important factor underlying the stability of agency ratings. We find that rating migrations are triggered when the difference between the actual agency rating and the model predicted rating exceeds a certain threshold level. When rating migrations are triggered, agencies adjust their ratings only partially, consistent with the known serial dependency of agency-rating migrations.

We identify a number of unintended consequences of grouping when the capital asset pricing model is true and when it is false. When the model is true, grouping may cause fundamental problems with the most basic capital asset pricing and cross-sectional regression relationships. For example, with traditional grouping, the market portfolio is super-efficient––unless securities in each group are value weighted. Yet, when the model is grossly false, grouping may cause the model to appear to be absolutely correct. Ironically, the only way this can occur is when securities in each group are value weighted. To make matters worse, when the model is false, the slope of a cross-sectional regression of expected returns on betas fitted to grouped data may be either steeper or flatter than when the regression is fitted to ungrouped data. In other words, grouping may exacerbate the very problem it was meant to alleviate.

We test how active management of bank credit risk exposure through the loan sales market affects capital structure, lending, profits, and risk. We find that banks that rebalance their loan portfolio exposures by both buying and selling loans – that is, banks that use the loan sales market for risk management purposes rather than to alter their holdings of loans – hold less capital than other banks; they also make more risky loans (loans to businesses) as a percentage of total assets than other banks. Holding size, leverage and lending activities constant, banks active in the loan sales market have lower risk and higher profits than other banks. Our results suggest that banks that improve their ability to manage credit risk may operate with greater leverage and may lend more of their assets to risky borrowers. Thus, the benefits of advances in risk management in banking may be greater credit availability, rather than reduced risk in the banking system.

This paper develops a new measure of the quality of business environment that complements existing measures of the quality of life. An annual panel of these measures is constructed and analyzed for 37 cities from 1977 to 1995. Findings indicate that many cities attractive to firms are unattractive to households, and vice versa. In addition, the size of a city's workforce increases with improvements in the quality of the business environment. In contrast, cities most likely to be dominated by retirees are those that are less attractive to firms. Additional specifications support theoretical arguments that retirees are drawn to cities in which local attributes are capitalized into lower wages rather than higher rents.

The apparent conflict between the level of resources dedicated to technical analysis by practitioners and academic theories of market efficiency is a long-standing puzzle. We explore a previously unexamined feature of technical analysis — namely its relation to liquidity provision. We demonstrate that support and resistance levels coincide with peaks in depth on the limit order book and moving average forecasts reveal information about the relative position of depth on the book. Furthermore, we show that these relationships stem from technical rules locating depth already in place on the limit order book.

Most transplanted kidneys are from cadavers, but there are also many transplants from live donors. Recently, there have started to be kidney exchanges involving two donor-patient pairs such that each donor cannot give a kidney to the intended recipient because of immunological incompatibility, but each patient can receive a kidney from the other donor. Exchanges are also made in which a donor-patient pair makes a donation to someone waiting for a cadaver kidney, in return for the patient in the pair receiving high priority for a compatible cadaver kidney when one becomes available. There are stringent legal/ethical constraints on how exchanges can be conducted. We explore how larger scale exchanges of these kinds can be arranged efficiently and incentive compatibly, within existing constraints. The problem resembles some of the “housing” problems studied in the mechanism design literature for indivisible goods, with the novel feature that while live donor kidneys can be assigned simultaneously, cadaver kidneys cannot. In addition to studying the theoretical properties of the proposed kidney exchange, we present simulation results suggesting that the welfare gains from larger scale exchange would be substantial, both in increased number of feasible live donation transplants, and in improved match quality of transplanted kidneys.