Introduction The accuracy of estimates of future loss costs plays a fundamental role in determining the underwriting profits of property-liability insurers. A standard measure of loss costs used by regulators, auditors, policyholders, and security analysts is the loss ratio. The well-known cyclical pattern in loss ratios over time (e.g., Witt, 1977, 1978) has led to a series of analyses of underwriting profit cycles. The underwriting cycle in the United States is about six years (see Cummins and Outreville, 1987; Venezian, 1985; Smith and Gahin, 1983). Explanations for the cycles fall into two categories. The first type suggests that the insurance markets are unstable such that prices fail to converge on an equilibrium due to periods of destructive competition followed by cutbacks in supply (Berger, 1988) or ratemaking with limited information (Venezian, 1985). For example, Brockett and Witt (1982) point out that an explanation for the autoregressive behavior of loss ratios is that premiums are based in part on past losses. A second type of model explains the cycle in terms of the insurance market response to external events, such as the liability insurance crisis (Harrington, 1988), interest rate changes (Doherty and Kang, 1988; Doherty and Garven, 1991), and institutional and regulatory rigidities (Cummins and Outreville, 1987). This article contributes to the analysis of underwriting profits and cycles by exploring the short-term forecasting of measures of underwriting profits. Specifically, the empirical Bayes model is proposed as a methodology for estimating loss ratios--the ratio of incurred losses to earned premiums.(1) The position taken is that of an outsider (such as a security analyst, rating agency, auditor, or insurance regulator) attempting to gauge the financial performance of an insurer. We use the loss ratio rather than the profit ratio because loss costs are the primary source of uncertainty in the determination of insurance profits. Although this analysis focuses on the loss ratio, any financial measure could be estimated using the empirical Bayes method. Hence, this analysis is intended to be an example of a technique appropriate for predicting financial measures when evaluating the financial performance of the property-liability insurer. The empirical Bayes approach is particularly appropriate for the case of loss ratios reported by the property-liability insurance industry because of data availability and comparability. The by-line loss ratio can be derived for a large number of insurers across time using the A. M. Best tapes. The empirical Bayes methodology was developed for just such a multiparameter estimation problem (Efron and Morris, 1973, 1975, 1977; Morris, 1983) and is designed for situations in which many similar parameters are to be estimated but the information on each may be weak. The procedure borrows (Tukey, 1963) from the whole set of data for estimating each parameter. Thus, empirical Bayes estimators gain their advantage over frequentist estimators by using information about all parameters in estimating each individual parameter, which is intuitively reasonable if the parameters are similar. In the case of loss ratios, although there is information across many firms, the information on each firm's by-line loss ratios may be weak. The information within the firm is considered weak when the historic trend is short or if the business is not widely underwritten such that the historic trend has high variance. An empirical Bayes model suggests that the estimate of a firm's by-line loss ratio may borrow strength from information provided by the experience reported across all firms with business in that line. The empirical Bayes model has been previously applied for actuarial purposes. For example, Morris and Van Slyke (1978) developed an empirical Bayes method for pricing insurance claims. Similarly, some applications of credibility theory have employed the empirical Bayes methodology (see, for example, Neuhaus, 1984; Norberg, 1980). …