We examine the importance of industry to firm-level financial and real decisions. We find that in addition to standard industry fixed effects, financial structure also depends on a firm's position within its industry. In competitive industries, a firm's financial leverage depends on its natural hedge (its proximity to the median industry capital--labor ratio), the actions of other firms in the industry, and its status as entrant, incumbent, or exiting firm. Financial leverage is higher and less dispersed in concentrated industries, where strategic debt interactions are also stronger, but a firm's natural hedge is not significant. Our results show that financial structure, technology, and risk are jointly determined within industries. These findings are consistent with recent industry equilibrium models of financial structure.

We parsimoniously characterize the severity of market frictions affecting a stock using the delay with which its price responds to information. The most delayed firms command a large return premium not explained by size, liquidity, or microstructure effects. Moreover, delay captures part of the size effect, idiosyncratic risk is priced only among the most delayed firms, and earnings drift is monotonically increasing in delay. Frictions associated with investor recognition appear most responsible for the delay effect. The very small segment of delayed firms, comprising only 0.02% of the market, generates substantial variation in average returns, highlighting the importance of frictions.

I show that investment in housing plays a crucial role in explaining the patterns of cross-sectional variation in the composition of wealth and the level of stockholdings observed in portfolio composition data. Due to investment in housing, younger and poorer investors have limited financial wealth to invest in stocks, which reduces the benefits of equity market participation. House price risk crowds out stockholdings, and this crowding out effect is larger for low financial net-worth. In the model as in the data leverage is positively correlated with stockholdings.

This article solves a realistically calibrated life cycle model of consumption and portfolio choice with non-tradable labor income and borrowing constraints. Since labor income substitutes for riskless asset holdings, the optimal share invested in equities is roughly decreasing over life. We compute a measure of the importance of human capital for investment behavior. We find that ignoring labor income generates large utility costs, while the cost of ignoring only its risk is an order of magnitude smaller, except when we allow for a disastrous labor income shock. Moreover, we study the implications of introducing endogenous borrowing constraints in this incomplete-markets setting.

In theory, the sum of squares of log returns sampled at high frequency estimates their variance. When market microstructure noise is present but unaccounted for, however, we show that the optimal sampling frequency is finite and derives its closed-form expression. But even with optimal sampling, using say 5-min returns when transactions are recorded every second, a vast amount of data is discarded, in contradiction to basic statistical principles. We demonstrate that modeling the noise and using all the data is a better solution, even if one misspecifies the noise distribution. So the answer is: sample as often as possible.

This article presents a continuous-time agency model in the presence of adverse selection and moral hazard with a risk-averse agent and a risk-neutral principal. Under the model setup, we show that the optimal controls are constant over time, and thus the optimal menu consists of contracts that are linear in the final outcome. We also show that when a moral hazard problem adds to an adverse selection problem, the monotonicity condition well known in the pure adverse selection literature needs to be modified to ensure the incentive compatibility for information revelation. The model is applied to a few managerial compensation problems involving managerial project selection and capital budgeting decisions. We argue that in the third-best world, the relationship between the volatility of the outcome and the sensitivity of the contract depends on interactions between the managerial cost and the firm's production functions. Contrary to conventional wisdom, sometimes the higher the volatility, the higher the sensitivity of the contract. The firm receiving good news sometimes chooses safer projects or invests less than it does with bad news. We also examine the effects of the observability of the volatility on corporate investment decisions.

Executives can only impact firm outcomes if they have influence over crucial decisions. On the basis of this idea, we develop and test the hypothesis that firms whose CEOs have more decision-making power should experience more variability in performance. Focusing primarily on the power the CEO has over the board and other top executives as a consequence of his formal position and titles, status as a founder, and status as the board's sole insider, we find that stock returns are more variable for firms run by powerful CEOs. Our findings suggest that the interaction between executive characteristics and organizational variables has important consequences for firm performance.

We investigate whether domestic investors have an edge over foreign investors in trading domestic stocks. Using Korean data, we show that foreign money managers pay more than domestic money managers when they buy and receive less when they sell for medium and large trades. The sample average daily trade-weighted disadvantage of foreign money managers is 21 basis points for purchases and 16 basis points for sales. There is also some evidence that domestic individual investors have an edge over foreign investors. The explanation for these results is that prices move more against foreign investors than against domestic investors before trades.

This paper explores liquidity movements in stock and Treasury bond markets over a period of more than 1800 trading days. Cross-market dynamics in liquidity are documented by estimating a vector autoregressive model for liquidity (that is, bid-ask spreads and depth), returns, volatility, and order flow in the stock and bond markets. We find that a shock to quoted spreads in one market affects the spreads in both markets, and that return volatility is an important driver of liquidity. Innovations to stock and bond market liquidity and volatility prove to be significantly correlated, suggesting that common factors drive liquidity and volatility in both markets. Monetary expansion increases equity market liquidity during periods of financial crises, and unexpected increases (decreases) in the federal funds rate lead to decreases (increases) in liquidity and increases (decreases) in stock and bond volatility. Finally, we find that flows to the stock and government bond sectors play an important role in forecasting stock and bond liquidity. The results establish a link between 'macro' liquidity, or money flows, and 'micro' or transactions liquidity