Principal-agent theory suggests that a manager should be paid relative to a benchmark that removes the effect of market or sector performance on the firm's own performance. Recently, it has been argued that such indexation is not observed in the data because executives can set pay in their own interests; that is, they can enjoy “pay for luck” as well as “pay for performance.” We first show that this argument is incomplete. The positive expected return on stock markets reflects compensation for bearing systematic risk. If executives’ pay is tied to market movements, they can only expect to receive the market-determined return for risk-bearing. This argument, however, assumes that executive pay is tied to bad luck as well as to good luck. If executives can truly influence the setting of their pay, they will seek to have their performance benchmarked only when it is in their interest, namely, when the benchmark has fallen. Using industry benchmarks, we find significantly less pay for luck when luck is down (in which case, pay for luck would reduce compensation) than when it is up. These empirical results are robust to a variety of alternative hypotheses and robustness checks, and they suggest that the average executive loses 25–45% less pay from bad luck than is gained from good luck.

We analyze firms’ choice of exchange to list equity and exchanges’ choice of listing standards when insiders have private information about firm value, but outsiders can produce (noisy) information at a cost. Exchanges are populated by two kinds of investors, whose numbers vary across exchanges: sophisticated (low information production cost) investors and ordinary (high–cost) investors. While firms are short-lived, exchanges are long-lived, value-maximizing agents whose listing and disclosure standards evolve over time. The listing standards chosen by exchanges affect their “reputation,” since outsiders can partially infer the rigor of these standards from the post-listing performance of firms. We show that, while exchanges use their listing standards as a tool in competing for listings with other exchanges, this will not necessarily lead to a “race to the bottom” in listing standards. Further, a merger between two exchanges may result in a higher listing standard for the combined exchange relative to that of either of the merging exchanges. We develop several other implications for firms’ listing choices and resulting valuation effects, the impact of competition and co-operation among exchanges on listing standards, and the optimal regulation of exchanges.

This paper studies whether incorporating business cycle predictors benefits a real time optimizing investor who must allocate funds across 3,123 NYSE-AMEX stocks and cash. Realized returns are positive when adjusted by the Fama-French and momentum factors as well as by the size, book-to-market, and past return characteristics. The investor optimally holds small-cap, growth, and momentum stocks and loads less (more) heavily on momentum (small-cap) stocks during recessions. Returns on individual stocks are predictable out-of-sample due to alpha variation, whereas the equity premium predictability, the major focus of previous work, is questionable.

We document that gold mining firms have consistently realized economically significant cash flow gains from their derivatives transactions. We conclude that these cash flows have increased shareholder value since there is no evidence of an offsetting adjustment in firms’ systematic risk. This finding contradicts a central assumption in the risk management literature that derivatives transactions have zero net present value, and highlights an important motive for firms to use derivatives that the literature has hitherto ignored. Although we find considerable evidence of selective hedging in our sample, the cash flow gains from selective hedging appear to be small at best.

If investors are myopic mean-variance optimizers, a stock's expected return is linearly related to its beta in the cross-section. The slope of the relation is the cross-sectional price of risk, which should equal the expected equity premium. We use this simple observation to forecast the equity-premium time series with the cross-sectional price of risk. We also introduce novel statistical methods for testing stock-return predictability based on endogenous variables whose shocks are potentially correlated with return shocks. Our empirical tests show that the cross-sectional price of risk (1) is strongly correlated with the market's yield measures and (2) predicts equity-premium realizations, especially in the first half of our 1927–2002 sample.