In a multivariate regression model relating individual returns to the market return, CAPM implies non-linear restrictions on the parameters. Several asymptotically valid tests of these restrictions have been suggested. The existing Monte Carlo evidence shows that some of these tests are unreliable for reasonable sample sizes, but does not indicate well which tests are reliable. This paper reports the results of an extensive Monte Carlo experiment. Shanken's CSR test and Jobson and Korkie's corrected likelihood ratio test are quite accurate in all cases we consider.

We examine the economic determinants of short-sale supply, and its consequences for future stock returns. Lendable supply increases with expected borrowing costs and decreases with financial statement constructs that indicate overvaluation. Although rising loan fees help ease supply constraints, we find shares are still least available when they are most attractive to short sellers. Using a number of firm characteristics, we derive useful instruments for real-time loan supply and demand conditions in the lending market. Further, we show that (1) when lendable supply is binding (non-binding), short-sale supply (demand) is the main predictor of future stock returns, (2) abnormal returns to the short-side of nine well-known market anomalies are attributable solely to “special” stocks, and (3) loan fees significantly reduce the profitability of the short side and several of these anomalies cease to be profitable. Overall our evidence highlights the central role played by the supply of lendable shares in equity price formation and returns prediction.

We investigate the U.S. experience with macroprudential policies by studying the interagency guidance on leveraged lending. We find that the guidance primarily impacted large, closely supervised banks, but only after supervisors issued important clarifications. It also triggered a migration of leveraged lending to nonbanks. While we do not find that nonbanks use more lax lending policies than banks, we unveil important evidence that nonbanks increased bank borrowing following the guidance, possibly to finance their growing leveraged lending. The guidance was effective at reducing banks’ leveraged lending activity, but it is less clear whether it accomplished its broader goal of reducing the risk that these loans pose for the stability of the financial system. Our findings highlight the importance of supervisory monitoring for macroprudential policy goals, and the challenge that the revolving door of risk poses to the effectiveness of macroprudential regulations.

One hesitates to write history as it happens, or to draw policy lessons from current events. The conference took place in May 2008 - after the government-assisted takeover of Bear Stearns but before a capital market downturn fueled a system-wide liquidity crisis, with successive insolvencies at IndyMac, Fannie Mae, Freddie Mac, Lehman, AIG, WaMu, and, as I write, Citigroup. But it would be odd to comment on capital market regulation without mentioning the events of the last three months. I am first to acknowledge that anything I might have written in May would not have foreseen the crisis or linked capital market regulation to financial institutions, which in the US have been conventionally treated as discrete in discourse and institutions (e.g., U.S. Treasury 2008; Leuz and Wysocki 2008).

The purpose of this paper is to investigate whether financial analysts with superior earnings forecasting ability can be distinguished on the basis of ex post forecast accuracy. I explore the question by estimating and comparing average accuracy across individuals, and by considering whether the observed distribution of analyst forecast accuracies differs from the distribution expected if their relative performances each year were purely random. Overall, I do not find systematic differences in forecast accuracy across individuals. Financial press coverage suggests there are superior financial analysts. For example, Institutional Investor's annual All American Research Team includes analysts rated by money managers as superior on a variety of criteria, including earnings forecasting, ability to pick stocks, and the quality of written reports. Clearly, financial analyst services other than forecast accuracy are valued by their clients. I focus on only one activity, earnings forecasting, for two reasons. First, forecast data are available, quantitative, and can be evaluated against observable earnings outcomes. Services such as insightful, well-written research reports are harder to evaluate quantitatively. Second, academic use of analyst forecasts as earnings expectations data in capital markets empir-

Auditing, Extrapolation, Time-series, Cost variance

Ian R. C. Eggleton, [Discussion of Patterns, Prototypes, and Predictions: An Exploratory Study]: A Reply, Journal of Accounting Research, Vol. 14, Studies on Human Information Processing in Accounting (1976), pp. 145-158

Prediction, Decision making, Sequential patters, Time-series