This paper examines the capital structure implications of market timing. I isolate timing attempts in a single major financing event, the initial public offering, by identifying market timers as firms that go public in hot issue markets. I find that hot‐market IPO firms issue substantially more equity, and lower their leverage ratios by more, than cold‐market firms do. However, immediately after going public, hot‐market firms increase their leverage ratios by issuing more debt and less equity relative to cold‐market firms. At the end of the second year following the IPO, the impact of market timing on leverage completely vanishes.

I analyze the sensitivity of a firm's investment to its own cash flow in the benchmark case where financing is frictionless. This sensitivity has been proposed as a measure of financing constraints in earlier studies. I find that the investment–cash flow sensitivities that obtain in the frictionless benchmark are very similar, both in magnitude and in patterns they exhibit, to those observed in the data. In particular, the sensitivity is higher for firms with high growth rates and low dividend payout ratios. Tobin's q is shown to be a more noisy measure of near‐term investment plans for these firms.

We structurally estimate a model in which agents’ information processing biases can cause predictability in firms’ asset returns and investment inefficiencies. We generalize the neoclassical investment model by allowing for two biases—overconfidence and overextrapolation of trends—that distort agents’ expectations of firm productivity. Our model's predictions closely match empirical data on asset pricing and firm behavior. The estimated bias parameters are well identified and exhibit plausible magnitudes. Alternative models without either bias or with efficient investment fail to match observed return predictability and firm behavior. These results suggest that biases affect firm behavior, which in turn affects return anomalies.

We present a dynamic model that links characteristic‐based return predictability to systematic factors that determine the evolution of firm fundamentals. In the model, an economy‐wide disruption process reallocates profits from existing businesses to new projects and thus generates a source of systematic risk for portfolios of firms sorted on value, profitability, and asset growth. If investors are overconfident about their ability to evaluate the disruption climate, these characteristic‐sorted portfolios exhibit persistent mispricing. The model generates predictions about the conditional predictability of characteristic‐sorted portfolio returns and illustrates how return persistence increases the likelihood of observing characteristic‐based anomalies.