Knowledge of the one‐month interest rate is useful in forecasting the sign as well as the variance of the excess return on stocks. The services of a portfolio manager who makes use of the forecasting model to shift funds between bills and stocks would be worth an annual management fee of 2% of the value of the assets managed. During 1954:4 to 1986:12, the variance of monthly returns on the managed portfolio was about 60% of the variance of the returns on the value weighted index, whereas the average return was two basis points higher.

Studies relating accounting and price data often use the COMPUSTAT or related PDE data base as the source for the accounting data. This practice may introduce a lookahead bias and an ex-post-selection bias into the study. We examine this problem by comparing results from the standard COMPUSTAT data base with those from a data base which suffers from neither bias. We find that rates of return from portfolios chosen on the basis of accounting data from the two data bases differ significantly. Further, we find that these differences imply different conclusions when we test a specific hypothesis relating accounting and price data. Finally, we propose a number of remedies which may reduce the bias when the standard COMPUSTAT data base is used. THE RELATIONS AMONGTHE economic activities of the firm, the accounting measures of these activities, and the market returns on the debt and the equity of the firm are of central interest to financial economists. Recently, there has been a renewed interest in the empirical relation between market return to equity and basic characteristics of the firm, such as the size and earnings yield of the firm.' Some researchers use the so-called merged COMPUSTAT file for the PDE (price-dividendearnings) file which includes all firms which were on the file at any time during the sample period. This file is obviously not subject to the survivor bias. 779 780 The Journal of Finance The look-ahead bias is due to a dating problem. Data reported for a particular point in time, say at the end of the year, typically are not actually available to the investor until sometime later in the next year. Computing earnings yields with year-end prices and earnings may imply the ability of the investor to forecast future reported earnings without error. For example, the annual COMPUSTAT file reports earnings of $1.24 per share for Zenith for year end 1978. The 12month earnings per share actually observed by the investor as of December 31, 1978 was $0.85 per share. At a December 31, 1978 price of $12.87, the earnings yield computed using the COMPUSTAT data file was 9.6%, whereas the earnings yield using observed data was 6.6%. As might be expected, the price of Zenith stock went from the year-end price of $12.87 to a March ending price (when the new earnings were known to investors) of $15.00.Empirical researchers have long been aware of these potential problems. Until now, there has been no practical way of measuring the size of the biases introduced. Some studies have ignored the problems, others have used various measures designed to reduce the biases, while some have claimed that the biases are of a negligible magnitude.4The purpose of this paper is to examine the effect of the described idiosyncracies of the COMPUSTAT data base using two empirical relations, the "P/E effect" and the "small firm effect" as examples. We show that there are significant differences in returns to portfolios formed using the COMPUSTAT data base and returns to portfolios formed using a data source which does not have the look-...

We hypothesized that PET myocardial perfusion imaging with 82 Rb (PET MPI), would reduce downstream utilization of diagnostic arteriography, compared with SPECT, in patients matched for pretest likelihood of coronary disease (pCAD). PET MPI is more accurate for assessment of impaired coronary flow reserve compared with SPECT MPI, potentially reducing the demand for subsequent arteriography, percutaneous transcoronary intervention, and coronary artery bypass grafting (CABG), with attendant cost savings, while avoiding a negative impact on coronary events. Methods: The frequency of diagnostic arteriography, revascularization, costs, and 1-y clinical outcomes in 2,159 patients studied with PET MPI was compared with 2 control groups studied with SPECT MPI matched to the PET group by pCAD: an internal control group of 102 patients and an external SPECT control group of 5,826 patients. CAD management costs were approximated with realistic global fee estimates. Results: Arteriography rates were 0.34 and 0.31 for the external and internal control SPECT groups and 0.13 for the patients studied with PET (P , 0.0001). pCAD averaged 0.39 in patients studied with PET MPI, and in the external SPECT control group, and 0.37 in the internal SPECT controls. Revascularization rates were 0.13 and 0.11 for external and internal SPECT patients and 0.06 for the PET group (P , 0.0001; P , 0.01), with a cost savings of 30% noted for PET patients, with no significant difference in cardiac death or myocardial infaction at 1-y follow-up. Conclusion: PET MPI in patients with intermediate pCAD results in a .50% reduction in invasive coronary arteriography and CABG, a 30% cost savings, and excellent clinical outcomes at 1 y compared with SPECT.

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In this paper we develop a measure of liquidity, price impact, which quantifies the change in a firm's stock price associated with its observed net trading volume. For a large set of institutional trades we compare out-of-sample, characteristic-based estimates of price impact to actual price impacts. Predictive predetermined firm characteristics, chosen to proxy for the severity of adverse selection in the equity market, the non-information-based costs of making a market in the stock, and the extent of shareholder heterogeneity, include relative size, historical relative trading volume, institutional holdings, and the inverse of the stock price. We find numerous aspects of trade execution which are significantly related to the price impact forecast error in economically plausible ways: For example, the predicted price impact overestimates the actual price impact for very large trades, for trades executed in a more patient manner, and for trades where the institution pays higher commissions.Liquidity, Price Impact, Transactions Costs