Are market experts prone to heuristics, and do these heuristics transfer across buying and selling domains? We investigate this question using a unique data set of institutional investors with portfolios averaging $573 million. A striking finding emerges: While there is evidence of skill in buying, selling decisions underperform substantially, even relative to random‐selling strategies. This holds despite the similarity between the two decisions in frequency, substance, and consequences for performance. Evidence suggests an asymmetric allocation of cognitive resources such as attention can explain the discrepancy: We document a systematic, costly heuristic process for selling but not for buying.

We examine the relation between insiders’ investment horizon and the information content of their trades with respect to future stock returns. We conjecture that an insider's investment horizon establishes a benchmark for expected patterns of continued trading behavior and thus helps identify unexpected insider trades, which should be more informative in efficient markets. Consistent with this conjecture, the trades of short‐horizon insiders are both more unexpected and more informed, on average, than those of long‐horizon insiders. Short‐horizon insiders and their firms also tend to display characteristics that are associated with a greater focus on short‐termism.

We present a simple rational model to highlight the effect of investors' participation costs on the response of mutual fund flows to past fund performance. By incorporating participation costs into a model in which investors learn about managers' ability from past returns, we show that mutual funds with lower participation costs have a higher flow sensitivity to medium performance and a lower flow sensitivity to high performance than their higher‐cost peers. Using various fund characteristics as proxies for the reduction in participation costs, we provide empirical evidence supporting the model's implications for the asymmetric flow‐performance relationship.

This paper offers a model in which asset prices reflect both covariance risk and misperceptions of firms' prospects, and in which arbitrageurs trade against mispricing. In equilibrium, expected returns are linearly related to both risk and mispricing measures (e.g., fundamental/price ratios). With many securities, mispricing of idiosyncratic value components diminishes but systematic mispricing does not. The theory offers untested empirical implications about volume, volatility, fundamental/price ratios, and mean returns, and is consistent with several empirical findings. These include the ability of fundamental/price ratios and market value to forecast returns, and the domination of beta by these variables in some studies.

We propose a new method to estimate the marginal value of capital under minimal assumptions. By combining asset prices with fundamentals, our method provides a quasi‐model‐free marginal q together with a simple correction for measurement error in (average) Tobin's Q using linear regressions. Marginal q yields plausible and robust estimates of adjustment costs and investment sensitivities to fundamentals. The widening gap between marginal q and Tobin's Q is driven primarily by market power and intangible capital. Our novel findings strongly support the neoclassical theory of investment and challenge the widespread use of Tobin's Q as proxy for investment opportunities.

Using a comprehensive hedge fund database, we examine the role of managerial incentives and discretion in hedge fund performance. Hedge funds with greater managerial incentives, proxied by the delta of the option‐like incentive fee contracts, higher levels of managerial ownership, and the inclusion of high‐water mark provisions in the incentive contracts, are associated with superior performance. The incentive fee percentage rate by itself does not explain performance. We also find that funds with a higher degree of managerial discretion, proxied by longer lockup, notice, and redemption periods, deliver superior performance. These results are robust to using alternative performance measures and controlling for different data‐related biases.

I. FOUNDATIONS. 1. Introduction and Overview. 2. The Financial Environment: Concepts and Principles. 3. Accounting, Cash Flows, and Taxes. II. VALUE AND CAPITAL BUDGETING. 4. The Time Value of Money. 5. Valuing Bonds and Stocks. 6. Business Investment Rules. 7. Capital Budgeting Cash Flows. 8. Capital Budgeting in Practice. III. RISK AND RETURN. 9. Risk and Return: Stocks. 10. Risk and Return: Asset Pricing Models. 11. Risk, Return, and Capital Budgeting. 12. Risk, Return, and Contingent Outcomes. 13. Risk, Return, and Agency Theory. IV. CAPITAL STRUCTURE AND DIVIDEND POLICY. 14. Capital Market Efficiency: Explanation & Implications. 15. Capital Structure Policy. 16. Managing Capital Structure. 17. Dividend Policy. V. LONG-TERM FINANCING. 18. Issuing Securities and the Role of Investment Banking. 19. Long-Term Debt. 20. Leasing and Other Asset-Based Financing. 21. Derivatives and Hedging. VI. WORKING CAPITAL MANAGEMENT. 22. Cash and Working Capital Management. 23. Accounts Receivable and Inventory. 24. Financial Planning. VII. SPECIAL TOPICS. 25. Mergers and Acquisitions. 26. Financial Distress. 27. International Corporate Finance.

When the underlying price process is a one-dimensional diffusion, as well as in certain restricted stochastic volatility settings, a contingent claim's delta is bounded by the infimum and supremum of its delta at maturity. Further, if the claim's payoff is convex (concave), the claim's price is a convex (concave) function of the underlying asset's value. However, when volatility is less specialized, or when the underlying process is discontinuous or non-Markovian, a call's price can be a decreasing, concave function of the underlying price over some range, increasing with the passage of time, and decreasing in the level of interest rates.