Paper Versus Practice: A Field Investigation of Integrity Hotlines

In an effort to motivate firms to more rapidly detect potential misconduct, legislators, regulators, and enforcement agencies incentivize firms to have integrity or “whistleblowing” hotlines. These hotlines provide individuals an opportunity to report alleged misconduct and seek guidance about how to appropriately respond. Beyond some isolated examples, little is known about the responsiveness of hotlines to actual claims of alleged misconduct. I undertake a field study to investigate how hotlines function in practice by making four different inquiries involving alleged misconduct to nearly 250 firms. I find that one-fifth of firms have impediments (e.g., phone line disconnected, email bounce back, direct to incorrect website) that hinder reporting and approximately 10% of firms do not respond in a timely manner. Overall, this investigation illuminates several differences between integrity hotlines “on paper” and how they actually perform in practice.     

Liquidity Cycles and Make/Take Fees in Electronic Markets

We develop a model in which the speed of reaction to trading opportunities is endogenous. Traders face a trade‐off between the benefit of being first to seize a profit opportunity and the cost of attention required to be first to seize this opportunity. The model provides an explanation for maker/taker pricing, and has implications for the effects of algorithmic trading on liquidity, volume, and welfare. Liquidity suppliers’ and liquidity demanders’ trading intensities reinforce each other, highlighting a new form of liquidity externalities. Data on durations between trades and quotes could be used to identify these externalities.     

Business Cycles and the Behavior of Metals Prices

The theory of storage says that the marginal convenience yield on inventory falls at a decreasing rate as inventory increases. The authors test this hypothesis by examining the relative variation of spot and futures prices for metals. As the hypothesis implies, futures prices are less variable than spot prices when inventory is low, but spot and futures prices have similar variability when inventory is high. The theory of storage also explains inversions of “normal” futures-spot price relations around business-cycle peaks. Positive demand shocks around peaks reduce metal inventories and, as the theory predicts, generate large convenience yields and price inversions.     

Private Interaction Between Firm Management and Sell‐Side Analysts

Although sell‐side analysts often privately interact with managers of publicly traded firms, the private nature of this contact has historically obscured direction examination. By examining a set of proprietary records compiled by a large‐cap NYSE‐traded firm, I offer insights into which analysts privately meet with management, when analysts privately interact with management, and why these interactions occur. I also compare private interaction to public interaction between analysts and managers on conference calls. The evidence suggests that private interaction with management is an important communication channel for analysts for reasons other than firm‐specific forecasting news.     

Stock Returns,Expected Returns,and Real Activity

Measuring the total return variation explained by shocks to expected cash flows, time-varying expected returns, and shocks to expected returns is one way to judge the rationality of stock prices. Variables that proxy for expected returns and expected-return shocks capture 30% of the variance of annual NYSE value-weighted returns. Growth rates of production, used to proxy for shocks to expected cash flows, explain 43% of the return variance. Whether the combined explanatory power of the variables—about 58% of the variance of annual returns—is good or bad news about market efficiency is left for the reader to judge.     

The Cross-Section of Expected Stock Returns

Two easily measured variables, size and book-to-market equity, combine to capture the cross-sectional variation in average stock returns associated with market β, size, leverage, book-to-market equity, and earnings-price ratios. Moreover, when the tests allow for variation in β that is unrelated to size, the relation between market β and average return is flat, even when β is the only explanatory variable.     

Gathering Data for Archival,Field, Survey,and Experimental Accounting Research

In the published proceedings of the first Journal of Accounting Research Conference, Vatter [1966] lamented that “Gathering direct and original facts is a tedious and difficult task, and it is not surprising that such work is avoided.” For the fiftieth JAR Conference, we introduce a framework to help researchers understand the complementary value of seven empirical methods that gather data in different ways: prestructured archives, unstructured (“hand‐collected”) archives, field studies, field experiments, surveys, laboratory studies, and laboratory experiments. The framework spells out five goals of an empirical literature and defines the seven methods according to researchers’ choices with respect to five data gathering tasks. We use the framework and examples of successful research studies in the financial reporting literature to clarify how data gathering choices affect a study's ability to achieve its goals, and conclude by showing how the complementary nature of different methods allows researchers to build a literature more effectively than they could with less diverse approaches to gathering data.     

The CAPM is Wanted,Dead or Alive

Kothari, Shanken, and Sloan (1995) claim that βs from annual returns produce a stronger positive relation between β and average return than βs from monthly returns. They also contend that the relation between average return and book-to-market equity (BE/ME) is seriously exaggerated by survivor bias. We argue that survivor bias does not explain the relation between BE/ME and average return. We also show that annual and monthly βs produce the same inferences about the β premium. Our main point on the β premium is, however, more basic. It cannot save the Capital asset pricing model (CAPM), given the evidence that β alone cannot explain expected return.     

Luck versus Skill in the Cross‐Section of Mutual Fund Returns

The aggregate portfolio of actively managed U.S. equity mutual funds is close to the market portfolio, but the high costs of active management show up intact as lower returns to investors. Bootstrap simulations suggest that few funds produce benchmark‐adjusted expected returns sufficient to cover their costs. If we add back the costs in fund expense ratios, there is evidence of inferior and superior performance (nonzero true α) in the extreme tails of the cross‐section of mutual fund α estimates.