Knowledge that Transforms

Amihud and Mendelson (1986) and Constantinides (1986) provide a theoretical basis for the proposition that assets with higher transactions costs are held by investors for longer holding periods, and vice versa. We examine average holding periods and bid‐ask spreads for Nasdaq stocks from 1983 through 1991 and for New York Stock Exchange (NYSE) stocks from 1975 through 1989 and find strong evidence that, as predicted, the length of investors' holding periods is related to bid‐ask spreads. We also find that the relation between holding periods and bid‐ask spreads is much stronger on Nasdaq, where spreads are larger, than on the NYSE, where spreads are smaller.

This article compares the bid‐ask spread for New York Stock Exchange (NYSE)‐listed securities before and after a major third market broker‐dealer, Bernard L. Madoff Investment Securities (Madoff), begins to selectively purchase and execute orders in those securities. Tests reveal the quoted bid‐ask spread tightens when Madoff enters the market. Furthermore, trading costs as measured by the difference between the transaction price and the midpoint of the contemporaneous bid‐ask spread do not increase. Together, these results suggest that the adverse selection problem associated with allowing agents to selectively execute orders in exchange‐listed securities may be economically insignificant.

This article examines incentives for adopting antitakeover charter amendments (ATAs) that are associated with compensation contracts. The evidence is consistent with the hypothesis that antitakeover measures such as ATAs help managers protect above‐market levels of compensation. Chief executive officers (CEOs) of firms that adopt ATAs receive higher salaries and more valuable option grants than CEOs at similar firms that do not adopt them. Furthermore, the magnitude of this difference increases following ATA adoption. The evidence is inconsistent with the hypothesis that ATAs facilitate the writing of efficient compensation contracts.

It is well documented that expected stock returns vary with the day‐of‐the‐week (the Monday or weekend effect). In this article we show that the well‐known Monday effect occurs primarily in the last two weeks (fourth and fifth weeks) of the month. In addition, the mean Monday return of the first three weeks of the month is not significantly different from zero. This result holds for most of the subperiods during the 1962–1993 sampling period and for various stock return indexes. The monthly effect reported by Ariel (1987) and Lakonishok and Smidt (1988) cannot fully explain this phenomenon.

We examine data on analyst following for a sample of initial public offerings completed between 1975 and 1987 to see how they relate to three well-documented IPO anomalies. We find that higher underpricing leads to increased analyst following. Analysts are overoptimistic about the earnings potential and long term growth prospects of recent IPOs. More firms complete IPOs when analysts are particularly optimistic about the growth prospects of recent IPOs. In the long run, IPOs have better stock performance when analysts ascribe low growth potential rather than high growth potential. These results suggest that the anomalies may be partially driven by overoptimism.

This article examines the efficiency of the National Football League (NFL) betting market. The standard ordinary least squares (OLS) regression methodology is replaced by a probit model. This circumvents potential econometric problems, and allows us to implement more sophisticated betting strategies where bets are placed only when there is a relatively high probability of success. In‐sample tests indicate that probit‐based betting strategies generate statistically significant profits. Whereas the profitability of a number of these betting strategies is confirmed by out‐of‐sample testing, there is some inconsistency among the remaining out‐of‐sample predictions. Our results also suggest that widely documented inefficiencies in this market tend to dissipate over time.

The development of asset pricing models that rely on instrumental variables together with the increased availability of easily‐accessible economic time‐series have renewed interest in predicting security returns. Evaluating the significance of these new research findings, however, is no easy task. Because these asset pricing theory tests are not independent, classical methods of assessing goodness‐of‐fit are inappropriate. This study investigates the distribution of the maximal when k of m regressors are used to predict security returns. We provide a simple procedure that adjusts critical values to account for selecting variables by searching among potential regressors.

This article presents the first application in finance of recently developed methods for the Gaussian estimation of continuous time dynamic models. A range of one factor continuous time models of the short‐term interest rate are estimated using a discrete time model and compared to a recent discrete approximation used by Chan, Karolyi, Longstaff, and Sanders (1992a, hereafter CKLS). Whereas the volatility of short‐term rates is highly sensitive to the level of rates in the United States, it is not in the United Kingdom.

This article empirically examines the liquidity premium predicted by the Amihud and Mendelson (1986) model using Nasdaq data over the 1973–1990 period. The results support the model and are much stronger than for the New York Stock Exchange (NYSE), as reported by Chen and Kan (1989) and Eleswarapu and Reinganum (1993) . I conjecture that the stronger evidence on the Nasdaq is due to the dealers' inside spreads on the Nasdaq being a better proxy for the actual cost of transacting than the quoted spreads on the NYSE, since the Nasdaq dealers do not face competition from limit orders or floor traders.

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.